diff --git "a/data/dataset_Inhibition.csv" "b/data/dataset_Inhibition.csv" new file mode 100644--- /dev/null +++ "b/data/dataset_Inhibition.csv" @@ -0,0 +1,34876 @@ +"keyword","repo_name","file_path","file_extension","file_size","line_count","content","language" +"Inhibition","simonetacannodelas/BioVL-Library","Rx_Fermentation_Ecoli_Glucose_Aerobic_Fedbatch.py",".py","11040","292","# -*- coding: utf-8 -*- +"""""" +Created on Friday Jan 24 13:34:32 2020 + +@author: simoca +"""""" +from scipy.integrate import odeint +#Package for plotting +import math +#Package for the use of vectors and matrix +import numpy as np +import pandas as pd +import array as arr +from matplotlib.backends.backend_qt5agg import FigureCanvasQTAgg as FigureCanvas +from matplotlib.figure import Figure +import sys +import os +import matplotlib.pyplot as plt +from matplotlib.ticker import FormatStrFormatter +import glob +from random import sample +import random +import time +import plotly +import plotly.graph_objs as go +import json +from plotly.subplots import make_subplots + + +class Ecoli_Aero: + def __init__(self, Control = False): + self.Kap=0.5088 #g/L + self.Ksa=0.0128 + self.Kia=1.2602 #g/L + self.Ks= 0.0381 #g/L + self.Kis = 1.8383 #g/L affinity constant + self.Ko = 0.0001 + self.qAcmax= 0.1148 #g/gh + self.qm=0.0133 #g/gh + self.qOmax= 13.4*31.9988/1000 #g/gh + self.qSmax= 0.635 #g/gh + self.Yas= 0.8938 #g/g + self.Yoa= 0.5221 #g/g + self.Yos= 1.5722 #g/g + self.Yxa= 0.5794 #g/g + self.Yem = 0.5321 #g/g + self.Yxsof = 0.229 # g/g + self.pAmax= 0.2286 #gA/gXh + self.kla= 220 + self.H= 1400 #Henry constant + + self.G0 = 4.94 + self.A0 = 0.0129 + self.O0 = 98 + self.X0 = 0.17 + self.tau= 35 #response time + self.t_start = 0 + self.V0 = 2 + self.F0 = 0 + self.SFR = 1.5 + self.t_expfb_start = 0 + self.t_constfb_start = 1.5 + self.t_end = 15 + + #parameters for control, default every 1/24 hours: + self.Control = Control + self.coolingOn = True + self.Contamination=False + self.steps = (self.t_end - self.t_start)*24 + self.T0 = 35 + self.K_p = 2.31e+01 + self.K_i = 1 + self.K_d = 0 + self.Tset = 30 + self.u_max = 150 + self.u_min = 0 + + def update_param_value(self, param, new_value): + + class_name = str(self.__class__).split('.')[1].split(""'"")[0] + param_current_value = self.__dict__.get(param, None) + + if param_current_value is None: + print(""Class {} does not contain param with name {}"".format(class_name, param)) + return + + self.__dict__[param] = new_value + print(""Value of param {} in class {} updated to {}"".format(param, class_name, new_value)) + + + def rxn(self, C,t , u, fc): + #when there is no control, k has no effect + k=1 + #when cooling is off than u = 0 + if self.coolingOn == False: + u = 0 + if self.Contamination == True: + fc=np.random.randint(0,10) + fc=fc/17 + + if self.Control == True : + #Cardinal temperature model with inflection: Salvado et al 2011 ""Temperature Adaptation Markedly Determines Evolution within the Genus Saccharomyces"" + #Strain E.coli W310 + Topt = 35 + Tmax = 45.48 + Tmin = 10 + T = C[5] + if T < Tmin or T > Tmax: + k = 0 + else: + D = (T-Tmax)*(T-Tmin)**2 + E = (Topt-Tmin)*((Topt-Tmin)*(T-Topt)-(Topt-Tmax)*(Topt+Tmin-2*T)) + k = D/E + # Volume balance + if (t >= self.t_expfb_start): + if (t < self.t_constfb_start): + Fin = self.F0 * math.exp(self.SFR * (t - self.t_expfb_start)) + Fout = 0 + else: + Fin = self.F0 * math.exp(self.SFR * (self.t_constfb_start - self.t_expfb_start)) + Fout = 0 + else: + Fin = 0 + Fout = 0 + + F = Fin - Fout + + qS = (self.qSmax/(1+C[1]/self.Kia))*(C[0]/(C[0]+self.Ks)) + qSof = self.pAmax*(qS/(qS+self.Kap)) + pA = qSof*self.Yas + qSox = (qS-qSof)*(C[2]/(C[2]+self.Ko)) + qSan = (qSox-self.qm)*self.Yem*(0.488/0.391) + qsA = (self.qAcmax/(1+(qS/self.Kis)))*(C[1]/(C[1]+self.Ksa)) + qA = pA - qsA + mu = (qSox - self.qm)*self.Yem+ qsA*self.Yxa + qSof*self.Yxsof + qO = self.Yos*(qSox-qSan)+qsA*self.Yoa + + #Solving the mass balances + dGdt = (F/C[4])*(self.G0-C[0])-(qS*C[3]) + dAdt = qA*C[3]-((F/C[4])*C[1]) + dOdt = self.kla*(self.O0-C[2])-qO*C[3]*self.H + dXdt = (mu - (F/C[4]))*C[3] + dVdt = F + if self.Control == True : + ''' + dHrxn heat produced by cells estimated by yeast heat combustion coeficcient dhc0 = -21.2 kJ/g + dHrxn = dGdt*V*dhc0(G)-dEdt*V*dhc0(E)-dXdt*V*dhc0(X) + (when cooling is working) Q = - dHrxn -W , + dT = V[L] * 1000 g/L / 4.1868 [J/gK]*dE [kJ]*1000 J/KJ + dhc0(EtOH) = -1366.8 kJ/gmol/46 g/gmol [KJ/g] + dhc0(Glc) = -2805 kJ/gmol/180g/gmol [KJ/g] + + ''' + #Metabolic heat: [W]=[J/s], dhc0 from book ""Bioprocess Engineering Principles"" (Pauline M. Doran) : Appendix Table C.8 + dHrxndt = dXdt*C[4]*(-21200) #[J/s] + dGdt*C[4]*(15580)- dEdt*C[4]*(29710) + #Shaft work 1 W/L1 + W = 1*C[4] #[J/S] negative because exothermic + #Cooling just an initial value (constant cooling to see what happens) + #dQdt = -0.03*C[4]*(-21200) #[J/S] + #velocity of cooling water: u [m3/h] -->controlled by PID + + #Mass flow cooling water + M=u/3600*1000 #[kg/s] + #Define Tin = 5 C, Tout=TReactor + #heat capacity water = 4190 J/kgK + Tin = 5 + #Estimate water at outlet same as Temp in reactor + Tout = C[5] + cpc = 4190 + #Calculate Q from Eq 9.47 + Q=-M*cpc*(Tout-Tin) # J/s + #Calculate Temperature change + dTdt = -1*(dHrxndt - Q + W)/(C[4]*1000*4.1868) #[K/s] + else: + dTdt = 0 + return [dGdt,dAdt,dOdt,dXdt,dVdt, dTdt] + + def solve(self): + #solve normal: + t = np.linspace(self.t_start, self.t_end, self.steps) + if self.Control == False : + u = 0 + fc= 1 + C0 = [self.G0, self.A0, self.O0, self.X0,self.V0,self.T0] + C = odeint(self.rxn, C0, t, rtol = 1e-7, mxstep= 500000, args=(u,fc,)) + + #solve for Control + else: + fc=0 + """""" + PID Temperature Control: + """""" + # storage for recording values + C = np.ones([len(t), 6]) + C0 = [self.G0, self.A0, self.O0, self.X0,self.V0,self.T0] + self.ctrl_output = np.zeros(len(t)) # controller output + e = np.zeros(len(t)) # error + ie = np.zeros(len(t)) # integral of the error + dpv = np.zeros(len(t)) # derivative of the pv + P = np.zeros(len(t)) # proportional + I = np.zeros(len(t)) # integral + D = np.zeros(len(t)) # derivative + + for i in range(len(t)-1): + #print(t[i]) + #PID control of cooling water + dt = t[i+1]-t[i] + #Error + e[i] = C[i,5] - self.Tset + #print(e[i]) + if i >= 1: + dpv[i] = (C[i,5]-C[i-1,5])/dt + ie[i] = ie[i-1] + e[i]*dt + P[i]=self.K_p*e[i] + I[i]=self.K_i*ie[i] + D[i]=self.K_d*dpv[i] + + self.ctrl_output[i]=P[i]+I[i]+D[i] + u=self.ctrl_output[i] + if u>self.u_max: + u=self.u_max + ie[i] = ie[i] - e[i]*dt # anti-reset windup + if u < self.u_min: + u =self.u_min + ie[i] = ie[i] - e[i]*dt # anti-reset windup + #time for solving ODE + ts = [t[i],t[i+1]] + #disturbance + #if self.t[i] > 5 and self.t[i] < 10: + # u = 0 + #solve ODE from last timepoint to new timepoint with old values + + y = odeint(self.rxn, C0, ts, rtol = 1e-7, mxstep= 500000, args=(u,fc,)) + #update C0 + C0 = y[-1] + #merge y to C + C[i+1]=y[-1] + return t, C + def create_plot(self, t, C): + figure = make_subplots(rows=2, cols=1) + [self.G0, self.A0, self.O0, self.X0, self.V0, self.T0] + G = C[:, 0] + A = C[:, 1] + B = C[:, 3] + O = C[:, 2] + V = C[:, 4] + df = pd.DataFrame({'t': t, 'G': G, 'B': B, 'A': A, 'O': O, 'V': V}) + figure.append_trace(go.Scatter(x=df['t'], y=df['G'], name='Glucose'), row=1, col=1) + figure.append_trace(go.Scatter(x=df['t'], y=df['O'], name='Oxygen'), row=1, col=1) + figure.append_trace(go.Scatter(x=df['t'], y=df['B'], name='Biomass'), row=1, col=1) + figure.append_trace(go.Scatter(x=df['t'], y=df['A'], name='Acetate'), row=1, col=1) + # fig.update_layout(title=('Simulation of the model for the Scerevisiae'), + # xaxis_title='time (h)', + # yaxis_title='Concentration (g/L)') + + + Tset = self.T0 + df2 = pd.DataFrame({'t': t, 'T': T, 'Tset': Tset}) + figure.append_trace(go.Scatter(x=df2['t'], y=df2['T'], name='Temperature'), row=2, col=1) + figure.append_trace(go.Scatter(x=df2['t'], y=df2['Tset'], name='Set Value Temperature'), row=2, col=1) + figure.update_layout(title=('Simulation of the model for the Saccharomyces cerevisiae'), + xaxis_title='time (h)', + yaxis_title='Concentration (g/L)') + + S = C[:, 0] + A = C[:, 1] + B = C[:, 3] + df = pd.DataFrame({'t': t, 'Substrate': S, 'Biomass': B, 'Acetate': A}) + fig = go.Figure() + fig.add_trace(go.Scatter(x=df['t'], y=df['Substrate'], name='Glucose')) + fig.add_trace(go.Scatter(x=df['t'], y=df['Biomass'], name='Biomass')) + fig.add_trace(go.Scatter(x=df['t'], y=df['Acetate'], name='Acetate')) + fig.update_layout(title=('Simulation of aerobic batch growth of Escherichia coli by acetate cycling'), + xaxis_title='time (h)', + yaxis_title='Concentration (g/L)') + print('print') + graphJson = json.dumps(fig, cls=plotly.utils.PlotlyJSONEncoder) + return graphJson + + + +f= Ecoli_Aero() +f.solve() +C= f.solve()[1] +print(C) + +plt.plot(f.solve()[0], f.solve()[1][:,0]) +plt.plot(f.solve()[0], f.solve()[1][:,1]) +plt.plot(f.solve()[0], f.solve()[1][:,2]) +plt.plot(f.solve()[0], f.solve()[1][:,3]) +plt.show() +","Python" +"Inhibition","simonetacannodelas/BioVL-Library","Rx_Fermentation_Monod-Herbert_Aerobic.py",".py","7444","219","# -*- coding: utf-8 -*- +"""""" +Created on Thu Sep 6 13:34:32 2018 + +@author: simoca +"""""" + + +from scipy.integrate import odeint +#Package for plotting +import math +#Package for the use of vectors and matrix +import numpy as np +import array as arr +from matplotlib.backends.backend_qt5agg import FigureCanvasQTAgg as FigureCanvas +from matplotlib.figure import Figure +import sys +import os +import matplotlib.pyplot as plt +from matplotlib.ticker import FormatStrFormatter +import glob +from random import sample +import random +import time + + + +class Monod_Herbert: + def __init__(self, Control=False): + self.Y_XS = 0.8 + self.Y_OX = 1.05 + self.y_x = 0.5 + self.mu_max = 2.1 + self.Ks = 0.17 + self.kd = 0.21 + self.kla = 1000 + self.O_sat = 0.0755 + + self.S0 = 18 + self.O0 = 0.0755 + self.X0 = 0.01 + self.V0 = 10 +# #parameters for control, default every 1/24 hours: + self.t_end = 30 + self.t_start = 0 + self.Control = Control + self.coolingOn = True + self.steps = (self.t_end - self.t_start)*24 + self.T0 = 30 + self.K_p = 2.31e+01 + self.K_i = 3.03e-01 + self.K_d = -3.58e-03 + self.Tset = 30 + self.u_max = 150 + self.u_min = 0 + def rxn(self, C,t, u): + #when there is no control, k has no effect + k=1 + #when cooling is off than u = 0 + if self.coolingOn == False: + u = 0 + + if self.Control == True : + #Cardinal temperature model with inflection: Salvado et al 2011 ""Temperature Adaptation Markedly Determines Evolution within the Genus Saccharomyces"" + #Strain S. cerevisiae PE35 M + Topt = 30 + Tmax = 45.48 + Tmin = 5.04 + T = C[5] + if T < Tmin or T > Tmax: + k = 0 + else: + D = (T-Tmax)*(T-Tmin)**2 + E = (Topt-Tmin)*((Topt-Tmin)*(T-Topt)-(Topt-Tmax)*(Topt+Tmin-2*T)) + k = D/E + + #number of components + n = 3 + m = 3 + #initialize the stoichiometric matrix, s + s = np.zeros((m,n)) + s[0,0] = -1/self.Y_XS + s[0,1] = -1/self.Y_OX + s[0,2] = 1 + + + s[1,0] = 0 + s[1,1] = 1/self.y_x + s[1,2] = -1 + + s[2,0] = 0 + s[2,1] = self.kla + s[2,2] = 0 + #initialize the rate vector + rho = np.zeros((m,1)) + ##initialize the overall conversion vector + r=np.zeros((n,1)) + rho[0,0] = self.mu_max*(C[0]/(C[0]+self.Ks))*C[2] + rho[1,0] = self.kd*C[2] + rho[2,0] = self.kla*(self.O_sat - C[1]) + + #Developing the matrix, the overall conversion rate is stoichiometric *rates + r[0,0] = (s[0,0]*rho[0,0])+(s[1,0]*rho[1,0])+(s[2,0]*rho[2,0]) + r[1,0] = (s[0,1]*rho[0,0])+(s[1,1]*rho[1,0])+(s[2,1]*rho[2,0]) + r[2,0] = (s[0,2]*rho[0,0])+(s[1,2]*rho[1,0])+(s[2,2]*rho[2,0]) + + + #Solving the mass balances + dSdt = r[0,0] + dOdt = r[1,0] + dXdt = r[2,0] + dVdt = 0 + if self.Control == True : + ''' + dHrxn heat produced by cells estimated by yeast heat combustion coeficcient dhc0 = -21.2 kJ/g + dHrxn = dGdt*V*dhc0(G)-dEdt*V*dhc0(E)-dXdt*V*dhc0(X) + (when cooling is working) Q = - dHrxn -W , + dT = V[L] * 1000 g/L / 4.1868 [J/gK]*dE [kJ]*1000 J/KJ + dhc0(EtOH) = -1366.8 kJ/gmol/46 g/gmol [KJ/g] + dhc0(Glc) = -2805 kJ/gmol/180g/gmol [KJ/g] + + ''' + #Metabolic heat: [W]=[J/s], dhc0 from book ""Bioprocess Engineering Principles"" (Pauline M. Doran) : Appendix Table C.8 + dHrxndt = dXdt*C[4]*(-21200) #[J/s] + dGdt*C[4]*(15580)- dEdt*C[4]*(29710) + #Shaft work 1 W/L1 + W = -1*C[4] #[J/S] negative because exothermic + #Cooling just an initial value (constant cooling to see what happens) + #dQdt = -0.03*C[4]*(-21200) #[J/S] + #velocity of cooling water: u [m3/h] -->controlled by PID + + #Mass flow cooling water + M=u/3600*1000 #[kg/s] + #Define Tin = 5 C, Tout=TReactor + #heat capacity water = 4190 J/kgK + Tin = 5 + #Estimate water at outlet same as Temp in reactor + Tout = C[5] + cpc = 4190 + #Calculate Q from Eq 9.47 + Q=-M*cpc*(Tout-Tin) # J/s + #Calculate Temperature change + dTdt = -1*(dHrxndt - Q + W)/(C[4]*1000*4.1868) #[K/s] + else: + dTdt = 0 + return [dSdt, dOdt, dXdt, dVdt, dTdt] + + def solve(self): + #solve normal: + t = np.linspace(self.t_start, self.t_end, self.steps) + if self.Control == False : + u = 0 +# fc = 1 + C0 = [self.S0, self.O0, self.X0,self.V0, self.T0] + C = odeint(self.rxn, C0, t, rtol = 1e-7, mxstep= 500000, args=(u,)) + + #solve for Control + else: + """""" + PID Temperature Control: + """""" + # storage for recording values + C = np.ones([len(t), 6]) + C0 = [self.S0, self.O0, self.X0,self.V0,self.T0] + C[0] = C0 + self.ctrl_output = np.zeros(len(t)) # controller output + e = np.zeros(len(t)) # error + ie = np.zeros(len(t)) # integral of the error + dpv = np.zeros(len(t)) # derivative of the pv + P = np.zeros(len(t)) # proportional + I = np.zeros(len(t)) # integral + D = np.zeros(len(t)) # derivative + + for i in range(len(t)-1): + #print(t[i]) + #PID control of cooling water + dt = t[i+1]-t[i] + #Error + e[i] = C[i,5] - self.Tset + #print(e[i]) + if i >= 1: + dpv[i] = (C[i,5]-C[i-1,5])/dt + ie[i] = ie[i-1] + e[i]*dt + P[i]=self.K_p*e[i] + I[i]=self.K_i*ie[i] + D[i]=self.K_d*dpv[i] + + self.ctrl_output[i]=P[i]+I[i]+D[i] + u=self.ctrl_output[i] + if u>self.u_max: + u=self.u_max + ie[i] = ie[i] - e[i]*dt # anti-reset windup + if u < self.u_min: + u =self.u_min + ie[i] = ie[i] - e[i]*dt # anti-reset windup + #time for solving ODE + ts = [t[i],t[i+1]] + #disturbance + #if self.t[i] > 5 and self.t[i] < 10: + # u = 0 + #solve ODE from last timepoint to new timepoint with old values + + y = odeint(self.rxn, C0, ts, rtol = 1e-7, mxstep= 500000, args=(u,)) + #update C0 + C0 = y[-1] + #merge y to C + C[i+1]=y[-1] + + return t, C + + + +#f= Monod_Herbert() +#f.solve() +#plt.plot(f.solve()[0], f.solve()[1]) +#plt.show() + + + +","Python" +"Inhibition","simonetacannodelas/BioVL-Library","__init__.py",".py","93","8","# -*- coding: utf-8 -*- +"""""" +Created on Wed Sep 5 09:51:37 2018 + +@author: bjogut +"""""" + +","Python" +"Inhibition","simonetacannodelas/BioVL-Library","Sinusoidal oscillating perturbation.py",".py","952","30","#This is the function to add perturbations inside a mechanistic model with concentrations + +#import the model + +def disturbances(modelInUse): + modelInUse.solve() + t = modelInUse.solve()[0] + C= modelInUse.solve()[1] + + #Initialization of the vectors for the superposition of the perturbations + PV =[] + PV2 = [] + PV3 = [] + + #Creation of the vector to collect the data with the noise/perturbation + C_noise = np.zeros((C.shape)) + import numpy as np + + #sinusoidal oscillation of each time - Dependance of time + for i in range(len(t)): + PV.append((((math.sin(t[i]*83))/47)+(math.cos(t[i]*11))/23)) + PV2.append(0.6*(((math.sin(t[i] * 61)/31) + (math.cos(t[i] * 43)) / 61))) + PV3.append(0.33*(((math.sin(t[i] * 41))/19) + (math.cos(t[i] * 61)) / 89)) + + #stablishing the perturbation inside the concentration + for i in range(len(C[0])): + C_noise [:, i] = C[:, i] + C[:, i]* PV + C[:, i]*PV2 + C[:, i]*PV3 + + return C_noise +","Python" +"Inhibition","simonetacannodelas/BioVL-Library","Rx_Fermentation_Scerevisiae_Glucose_Aerobic_Batch.py",".py","8529","234","# -*- coding: utf-8 -*- +"""""" +Created on Thu Sep 6 13:34:32 2018 + +@author: bjogut and simoca +"""""" + + +from scipy.integrate import odeint +#Package for plotting +import math +#Package for the use of vectors and matrix +import numpy as np +import array as arr +from matplotlib.backends.backend_qt5agg import FigureCanvasQTAgg as FigureCanvas +from matplotlib.figure import Figure +import sys +import os +import matplotlib.pyplot as plt +from matplotlib.ticker import FormatStrFormatter +import glob +from random import sample +import random +import time + + +class SCerevisiae_Aero: + def __init__(self, Control = False): + self.Yox_XG = 0.8 + self.Yred_XG = 0.05 + self.Yox_XE = 0.72 + self.Y_OG = 1.067 + self.Y_EG = 0.5 + self.Y_OE = 1.5 + self.q_g = 3.5 + self.q_o = 0.37 + self.q_e = 0.32 + self.t_lag = 4.66 + self.Kg = 0.17 + self.Ke = 0.56 + self.Ko = 0.0001 + self.Ki = 0.31 + self.O_sat = 0.00755 + self.kla = 1004 + + self.G0 = 18 + self.E0 = 0.0 + self.O0 = 0.00755 + self.X0 = 0.1 + + self.t_end = 30 + self.t_start = 0 + self.V0 = 2 + + #parameters for control, default every 1/24 hours: + self.Control = Control + self.coolingOn = True + self.Contamination=False + self.steps = (self.t_end - self.t_start)*24 + self.T0 = 30 + self.K_p = 2.31e+01 + self.K_i = 3.03e-01 + self.K_d = -3.58e-03 + self.Tset = 30 + self.u_max = 150 + self.u_min = 0 + + def rxn(self, C,t , u, fc): + #when there is no control, k has no effect + k=1 + #when cooling is off than u = 0 + if self.coolingOn == False: + u = 0 + if self.Contamination == True: + fc=np.random.randint(0,10) + fc=fc/17 + + if self.Control == True : + #Cardinal temperature model with inflection: Salvado et al 2011 ""Temperature Adaptation Markedly Determines Evolution within the Genus Saccharomyces"" + #Strain S. cerevisiae PE35 M + Topt = 30 + Tmax = 45.48 + Tmin = 5.04 + T = C[5] + if T < Tmin or T > Tmax: + k = 0 + else: + D = (T-Tmax)*(T-Tmin)**2 + E = (Topt-Tmin)*((Topt-Tmin)*(T-Topt)-(Topt-Tmax)*(Topt+Tmin-2*T)) + k = D/E + + #number of components + n = 4 + m = 4 + #initialize the stoichiometric matrix, s + s = np.zeros((m,n)) + s[0,0] = -1 + s[0,1] = 0 + s[0,2] = -self.Y_OG + s[0,3] = self.Yox_XG + + s[1,0] = -1 + s[1,1] = self.Y_EG + s[1,2] = 0 + s[1,3] = self.Yred_XG + + s[2,0] = 0 + s[2,1] = -1 + s[2,2] = -self.Y_OE + s[2,3] = self.Yox_XE + + s[3,0] = 0 + s[3,1] = 0 + s[3,2] = 1 + s[3,3] = 0 + #initialize the rate vector + rho = np.zeros((4,1)) + ##initialize the overall conversion vector + r=np.zeros((4,1)) + rho[0,0] = k*((1/self.Y_OG)*min(self.q_o*(C[2]/(C[2]+self.Ko)),self.Y_OG*(self.q_g*(C[0]/(C[0]+self.Kg)))))*C[3] + rho[1,0] = k*((1-math.exp(-t/self.t_lag))*((self.q_g*(C[0]/(C[0]+self.Kg)))-(1/self.Y_OG)*min(self.q_o*(C[2]/(C[2]+self.Ko)),self.Y_OG*(self.q_g*(C[0]/(C[0]+self.Kg))))))*C[3] + rho[2,0] = k*((1/self.Y_OE)*min(self.q_o*(C[2]/(C[2]+self.Ko))-(1/self.Y_OG)*min(self.q_o*(C[2]/(C[2]+self.Ko)),self.Y_OG*(self.q_g*(C[0]/(C[0]+self.Kg)))),self.Y_OE*(self.q_e*(C[1]/(C[1]+self.Ke))*(self.Ki/(C[0]+self.Ki)))))*C[3] + rho[3,0] = self.kla*(self.O_sat - C[2]) + + #Developing the matrix, the overall conversion rate is stoichiometric *rates + r[0,0] = (s[0,0]*rho[0,0])+(s[1,0]*rho[1,0])+(s[2,0]*rho[2,0])+(s[3,0]*rho[3,0]) + r[1,0] = (s[0,1]*rho[0,0])+(s[1,1]*rho[1,0])+(s[2,1]*rho[2,0])+(s[3,1]*rho[3,0]) + r[2,0] = (s[0,2]*rho[0,0])+(s[1,2]*rho[1,0])+(s[2,2]*rho[2,0])+(s[3,2]*rho[3,0]) + r[3,0] = (s[0,3]*rho[0,0])+(s[1,3]*rho[1,0])+(s[2,3]*rho[2,0])+(s[3,3]*rho[3,0]) + + #Solving the mass balances + dGdt = r[0,0] + dEdt = r[1,0]*fc + dOdt = r[2,0] + dXdt = r[3,0] + dVdt = 0 + if self.Control == True : + ''' + dHrxn heat produced by cells estimated by yeast heat combustion coeficcient dhc0 = -21.2 kJ/g + dHrxn = dGdt*V*dhc0(G)-dEdt*V*dhc0(E)-dXdt*V*dhc0(X) + (when cooling is working) Q = - dHrxn -W , + dT = V[L] * 1000 g/L / 4.1868 [J/gK]*dE [kJ]*1000 J/KJ + dhc0(EtOH) = -1366.8 kJ/gmol/46 g/gmol [KJ/g] + dhc0(Glc) = -2805 kJ/gmol/180g/gmol [KJ/g] + + ''' + #Metabolic heat: [W]=[J/s], dhc0 from book ""Bioprocess Engineering Principles"" (Pauline M. Doran) : Appendix Table C.8 + dHrxndt = dXdt*C[4]*(-21200) #[J/s] + dGdt*C[4]*(15580)- dEdt*C[4]*(29710) + #Shaft work 1 W/L1 + W = 1*C[4] #[J/S] negative because exothermic + #Cooling just an initial value (constant cooling to see what happens) + #dQdt = -0.03*C[4]*(-21200) #[J/S] + #velocity of cooling water: u [m3/h] -->controlled by PID + + #Mass flow cooling water + M=u/3600*1000 #[kg/s] + #Define Tin = 5 C, Tout=TReactor + #heat capacity water = 4190 J/kgK + Tin = 5 + #Estimate water at outlet same as Temp in reactor + Tout = C[5] + cpc = 4190 + #Calculate Q from Eq 9.47 + Q=-M*cpc*(Tout-Tin) # J/s + #Calculate Temperature change + dTdt = -1*(dHrxndt - Q + W)/(C[4]*1000*4.1868) #[K/s] + else: + dTdt = 0 + return [dGdt,dEdt,dOdt,dXdt,dVdt, dTdt] + + def solve(self): + #solve normal: + t = np.linspace(self.t_start, self.t_end, self.steps) + if self.Control == False : + u = 0 + fc= 1 + C0 = [self.G0, self.E0, self.O0, self.X0,self.V0,self.T0] + C = odeint(self.rxn, C0, t, rtol = 1e-7, mxstep= 500000, args=(u,fc,)) + + #solve for Control + else: + fc=0 + """""" + PID Temperature Control: + """""" + # storage for recording values + C = np.ones([len(t), 6]) + C0 = [self.G0, self.E0, self.O0, self.X0,self.V0,self.T0] + C[0] = C0 + self.ctrl_output = np.zeros(len(t)) # controller output + e = np.zeros(len(t)) # error + ie = np.zeros(len(t)) # integral of the error + dpv = np.zeros(len(t)) # derivative of the pv + P = np.zeros(len(t)) # proportional + I = np.zeros(len(t)) # integral + D = np.zeros(len(t)) # derivative + + for i in range(len(t)-1): + #print(t[i]) + #PID control of cooling water + dt = t[i+1]-t[i] + #Error + e[i] = C[i,5] - self.Tset + #print(e[i]) + if i >= 1: + dpv[i] = (C[i,5]-C[i-1,5])/dt + ie[i] = ie[i-1] + e[i]*dt + P[i]=self.K_p*e[i] + I[i]=self.K_i*ie[i] + D[i]=self.K_d*dpv[i] + + self.ctrl_output[i]=P[i]+I[i]+D[i] + u=self.ctrl_output[i] + if u>self.u_max: + u=self.u_max + ie[i] = ie[i] - e[i]*dt # anti-reset windup + if u < self.u_min: + u =self.u_min + ie[i] = ie[i] - e[i]*dt # anti-reset windup + #time for solving ODE + ts = [t[i],t[i+1]] + #disturbance + #if self.t[i] > 5 and self.t[i] < 10: + # u = 0 + #solve ODE from last timepoint to new timepoint with old values + + y = odeint(self.rxn, C0, ts, rtol = 1e-7, mxstep= 500000, args=(u,fc,)) + #update C0 + C0 = y[-1] + #merge y to C + C[i+1]=y[-1] + return t, C + +","Python" +"Inhibition","simonetacannodelas/BioVL-Library","Rx_Fermentation_Monod-Herbert_Anaerobic.py",".py","8180","233","# -*- coding: utf-8 -*- +"""""" +Created on Thu Sep 6 13:34:32 2018 + +@author: simoca +"""""" + + +from scipy.integrate import odeint +#Package for plotting +import math +#Package for the use of vectors and matrix +import numpy as np +import array as arr +from matplotlib.backends.backend_qt5agg import FigureCanvasQTAgg as FigureCanvas +from matplotlib.figure import Figure +import sys +import os +import matplotlib.pyplot as plt +from matplotlib.ticker import FormatStrFormatter +import glob +from random import sample +import random +import time + + +class Monod_Herbert_Anaero: + def __init__(self, Control=False): + self.Y_XS = 0.8 + self.Y_OX = 1.05 + self.Y_PX = 2 + self.y_x = 0.5 + self.mu_max = 2.1 + self.Ks = 0.17 + self.kd = 0.21 + self.kla = 1000 + self.O_sat = 0.0755 + + self.S0 = 18 + self.X0 = 0.1 + self.V0 = 10 + self.P0=0 +# #parameters for control, default every 1/24 hours: + self.t_end = 30 + self.t_start = 0 + self.Control = Control + self.coolingOn = True + self.steps = (self.t_end - self.t_start)*24 + self.T0 = 30 + self.K_p = 2.31e+01 + self.K_i = 3.03e-01 + self.K_d = -3.58e-03 + self.Tset = 30 + self.u_max = 150 + self.u_min = 0 +# def compounds(self): +# self.compound = ['Substrate','Product','Biomass'] +# return self.compound + def rxn(self, C,t, u): + #when there is no control, k has no effect + k=1 + #when cooling is off than u = 0 + if self.coolingOn == False: + u = 0 + + if self.Control == True : + #Cardinal temperature model with inflection: Salvado et al 2011 ""Temperature Adaptation Markedly Determines Evolution within the Genus Saccharomyces"" + #Strain S. cerevisiae PE35 M + Topt = 30 + Tmax = 45.48 + Tmin = 5.04 + T = C[5] + if T < Tmin or T > Tmax: + k = 0 + else: + D = (T-Tmax)*(T-Tmin)**2 + E = (Topt-Tmin)*((Topt-Tmin)*(T-Topt)-(Topt-Tmax)*(Topt+Tmin-2*T)) + k = D/E + + #number of components + self.s = np.zeros((2,3)) + self.rho=np.zeros((2,1)) + self.s[0,2]=1 + self.s[0,0]=(-1/self.Y_XS) + self.s[0,1] = (1/self.Y_PX) + self.s[1,2]=-1 + + self.rho[0,0]=((self.mu_max*C[0])/(C[0]+self.Ks))*C[2] + self.rho[1,0]=self.kd*C[2] + +# print(self.rho) +# print(self.s) + self.r= np.zeros((3,1)) + self.r[0,0]= self.s[0,0]*self.rho[0,0]+self.s[1,0]*self.rho[1,0] + self.r[1,0]= self.s[0,1]*self.rho[0,0]+self.s[1,1]*self.rho[1,0] + self.r[2,0]= self.s[0,2]*self.rho[0,0]+self.s[1,2]*self.rho[1,0] + dSdt = self.r[0,0] + dPdt = self.r[1,0] + dXdt = self.r[2,0] + dVdt = 0 +# +# n = 3 +# m = 3 +# #initialize the stoichiometric matrix, s +# s = np.zeros((m,n)) +# s[0,0] = -1/self.Y_XS +# s[0,1] = -1/self.Y_OX +# s[0,2] = 1 +# +# +# s[1,0] = 0 +# s[1,1] = 1/self.y_x +# s[1,2] = -1 +# +# s[2,0] = 0 +# s[2,1] = self.kla +# s[2,2] = 0 +# #initialize the rate vector +# rho = np.zeros((m,1)) +# ##initialize the overall conversion vector +# r=np.zeros((n,1)) +# rho[0,0] = self.mu_max*(C[0]/(C[0]+self.Ks))*C[2] +# rho[1,0] = self.kd*C[2] +# rho[2,0] = self.kla*(self.O_sat - C[1]) +# +# #Developing the matrix, the overall conversion rate is stoichiometric *rates +# r[0,0] = (s[0,0]*rho[0,0])+(s[1,0]*rho[1,0])+(s[2,0]*rho[2,0]) +# r[1,0] = (s[0,1]*rho[0,0])+(s[1,1]*rho[1,0])+(s[2,1]*rho[2,0]) +# r[2,0] = (s[0,2]*rho[0,0])+(s[1,2]*rho[1,0])+(s[2,2]*rho[2,0]) +# +# +# #Solving the mass balances +# dSdt = r[0,0] +# dOdt = r[1,0] +# dXdt = r[2,0] +# dVdt = 0 + if self.Control == True : + ''' + dHrxn heat produced by cells estimated by yeast heat combustion coeficcient dhc0 = -21.2 kJ/g + dHrxn = dGdt*V*dhc0(G)-dEdt*V*dhc0(E)-dXdt*V*dhc0(X) + (when cooling is working) Q = - dHrxn -W , + dT = V[L] * 1000 g/L / 4.1868 [J/gK]*dE [kJ]*1000 J/KJ + dhc0(EtOH) = -1366.8 kJ/gmol/46 g/gmol [KJ/g] + dhc0(Glc) = -2805 kJ/gmol/180g/gmol [KJ/g] + + ''' + #Metabolic heat: [W]=[J/s], dhc0 from book ""Bioprocess Engineering Principles"" (Pauline M. Doran) : Appendix Table C.8 + dHrxndt = dXdt*C[4]*(-21200) #[J/s] + dGdt*C[4]*(15580)- dEdt*C[4]*(29710) + #Shaft work 1 W/L1 + W = -1*C[4] #[J/S] negative because exothermic + #Cooling just an initial value (constant cooling to see what happens) + #dQdt = -0.03*C[4]*(-21200) #[J/S] + #velocity of cooling water: u [m3/h] -->controlled by PID + + #Mass flow cooling water + M=u/3600*1000 #[kg/s] + #Define Tin = 5 C, Tout=TReactor + #heat capacity water = 4190 J/kgK + Tin = 5 + #Estimate water at outlet same as Temp in reactor + Tout = C[5] + cpc = 4190 + #Calculate Q from Eq 9.47 + Q=-M*cpc*(Tout-Tin) # J/s + #Calculate Temperature change + dTdt = -1*(dHrxndt - Q + W)/(C[4]*1000*4.1868) #[K/s] + else: + dTdt = 0 + return [dSdt, dPdt, dXdt, dVdt, dTdt] + + def solve(self): + #solve normal: + t = np.linspace(self.t_start, self.t_end, self.steps) + if self.Control == False : + u = 0 + C0 = [self.S0, self.P0, self.X0,self.V0, self.T0] + C = odeint(self.rxn, C0, t, rtol = 1e-7, mxstep= 500000, args=(u,)) + + #solve for Control + else: + """""" + PID Temperature Control: + """""" + # storage for recording values + C = np.ones([len(t), 6]) + C0 = [self.S0, self.P0, self.X0,self.V0,self.T0] + self.ctrl_output = np.zeros(len(t)) # controller output + e = np.zeros(len(t)) # error + ie = np.zeros(len(t)) # integral of the error + dpv = np.zeros(len(t)) # derivative of the pv + P = np.zeros(len(t)) # proportional + I = np.zeros(len(t)) # integral + D = np.zeros(len(t)) # derivative + + for i in range(len(t)-1): + #print(t[i]) + #PID control of cooling water + dt = t[i+1]-t[i] + #Error + e[i] = C[i,5] - self.Tset + #print(e[i]) + if i >= 1: + dpv[i] = (C[i,5]-C[i-1,5])/dt + ie[i] = ie[i-1] + e[i]*dt + P[i]=self.K_p*e[i] + I[i]=self.K_i*ie[i] + D[i]=self.K_d*dpv[i] + + self.ctrl_output[i]=P[i]+I[i]+D[i] + u=self.ctrl_output[i] + if u>self.u_max: + u=self.u_max + ie[i] = ie[i] - e[i]*dt # anti-reset windup + if u < self.u_min: + u =self.u_min + ie[i] = ie[i] - e[i]*dt # anti-reset windup + #time for solving ODE + ts = [t[i],t[i+1]] + #disturbance + #if self.t[i] > 5 and self.t[i] < 10: + # u = 0 + #solve ODE from last timepoint to new timepoint with old values + + y = odeint(self.rxn, C0, ts, rtol = 1e-7, mxstep= 500000, args=(u,)) + #update C0 + C0 = y[-1] + #merge y to C + C[i+1]=y[-1] + + return t, C + + +","Python" +"Inhibition","simonetacannodelas/BioVL-Library","Rx_Fermentation_Ecoli_Glucose_Aerobic_Batch.py",".py","10981","296","# -*- coding: utf-8 -*- +"""""" +Created on Friday Jan 24 13:34:32 2020 + +@author: simoca +"""""" +from scipy.integrate import odeint +#Package for plotting +import math +#Package for the use of vectors and matrix +import numpy as np +import pandas as pd +import array as arr +from matplotlib.backends.backend_qt5agg import FigureCanvasQTAgg as FigureCanvas +from matplotlib.figure import Figure +import sys +import os +import matplotlib.pyplot as plt +from matplotlib.ticker import FormatStrFormatter +import glob +from random import sample +import random +import time +import plotly +import plotly.graph_objs as go +import json +from plotly.subplots import make_subplots + + +class Ecoli_Aero: + def __init__(self, Control = False): + self.Kap=0.5088 #g/L + self.Ksa=0.0128 + self.Kia=1.2602 #g/L + self.Ks= 0.0381 #g/L + self.Kis = 1.8383 #g/L affinity constant + self.Ko = 0.0001 + self.qAcmax= 0.1148 #g/gh + self.qm=0.0133 #g/gh + self.qOmax= 13.4*31.9988/1000 #g/gh + self.qSmax= 0.635 #g/gh + self.Yas= 0.8938 #g/g + self.Yoa= 0.5221 #g/g + self.Yos= 1.5722 #g/g + self.Yxa= 0.5794 #g/g + self.Yem = 0.5321 #g/g + self.Yxsof = 0.229 # g/g + self.pAmax= 0.2286 #gA/gXh + self.kla= 220 + self.H= 1400 #Henry constant + + self.G0 = 4.94 + self.A0 = 0.0129 + self.O0 = 0.0977 + self.X0 = 0.17 + self.tau= 35 #response time + self.t_start = 0 + self.V0 = 2 + self.F0 = 0 + self.SFR = 1.5 + self.t_expfb_start = 0 + self.t_constfb_start = 1.5 + self.t_end = 30 + + #parameters for control, default every 1/24 hours: + self.Control = Control + self.coolingOn = True + self.Contamination=False + self.steps = (self.t_end - self.t_start)*24 + self.T0 = 35 + self.K_p = 2.31e+01 + self.K_i = 1 + self.K_d = 0 + self.Tset = 30 + self.u_max = 150 + self.u_min = 0 + + def update_param_value(self, param, new_value): + + class_name = str(self.__class__).split('.')[1].split(""'"")[0] + param_current_value = self.__dict__.get(param, None) + + if param_current_value is None: + print(""Class {} does not contain param with name {}"".format(class_name, param)) + return + + self.__dict__[param] = new_value + print(""Value of param {} in class {} updated to {}"".format(param, class_name, new_value)) + + + def rxn(self, C,t , u, fc): + #when there is no control, k has no effect + k=1 + #when cooling is off than u = 0 + if self.coolingOn == False: + u = 0 + if self.Contamination == True: + fc=np.random.randint(0,10) + fc=fc/17 + + if self.Control == True : + #Cardinal temperature model with inflection: Salvado et al 2011 ""Temperature Adaptation Markedly Determines Evolution within the Genus Saccharomyces"" + #Strain E.coli W310 + Topt = 35 + Tmax = 45.48 + Tmin = 10 + T = C[5] + if T < Tmin or T > Tmax: + k = 0 + else: + D = (T-Tmax)*(T-Tmin)**2 + E = (Topt-Tmin)*((Topt-Tmin)*(T-Topt)-(Topt-Tmax)*(Topt+Tmin-2*T)) + k = D/E + # Volume balance + if (t >= self.t_expfb_start): + if (t < self.t_constfb_start): + Fin = self.F0 * math.exp(self.SFR * (t - self.t_expfb_start)) + Fout = 0 + else: + Fin = self.F0 * math.exp(self.SFR * (self.t_constfb_start - self.t_expfb_start)) + Fout = 0 + else: + Fin = 0 + Fout = 0 + + F = Fin - Fout + + qS = (self.qSmax/(1+C[1]/self.Kia))*(C[0]/(C[0]+self.Ks)) + qSof = self.pAmax*(qS/(qS+self.Kap)) + pA = qSof*self.Yas + qSox = (qS-qSof)*(C[2]/(C[2]+self.Ko)) + qSan = (qSox-self.qm)*self.Yem*(0.488/0.391) + qsA = (self.qAcmax/(1+(qS/self.Kis)))*(C[1]/(C[1]+self.Ksa)) + qA = pA - qsA + mu = (qSox - self.qm)*self.Yem+ qsA*self.Yxa + qSof*self.Yxsof + qO = self.Yos*(qSox-qSan)+qsA*self.Yoa + + #Solving the mass balances + dGdt = -(qS*C[3]) + dAdt = qA*C[3] + dOdt = self.kla*(self.O0-C[2]) + dXdt = (mu)*C[3] + dVdt = 0 + if self.Control == True : + ''' + dHrxn heat produced by cells estimated by yeast heat combustion coeficcient dhc0 = -21.2 kJ/g + dHrxn = dGdt*V*dhc0(G)-dEdt*V*dhc0(E)-dXdt*V*dhc0(X) + (when cooling is working) Q = - dHrxn -W , + dT = V[L] * 1000 g/L / 4.1868 [J/gK]*dE [kJ]*1000 J/KJ + dhc0(EtOH) = -1366.8 kJ/gmol/46 g/gmol [KJ/g] + dhc0(Glc) = -2805 kJ/gmol/180g/gmol [KJ/g] + + ''' + #Metabolic heat: [W]=[J/s], dhc0 from book ""Bioprocess Engineering Principles"" (Pauline M. Doran) : Appendix Table C.8 + dHrxndt = dXdt*C[4]*(-21200) #[J/s] + dGdt*C[4]*(15580)- dEdt*C[4]*(29710) + #Shaft work 1 W/L1 + W = 1*C[4] #[J/S] negative because exothermic + #Cooling just an initial value (constant cooling to see what happens) + #dQdt = -0.03*C[4]*(-21200) #[J/S] + #velocity of cooling water: u [m3/h] -->controlled by PID + + #Mass flow cooling water + M=u/3600*1000 #[kg/s] + #Define Tin = 5 C, Tout=TReactor + #heat capacity water = 4190 J/kgK + Tin = 5 + #Estimate water at outlet same as Temp in reactor + Tout = C[5] + cpc = 4190 + #Calculate Q from Eq 9.47 + Q=-M*cpc*(Tout-Tin) # J/s + #Calculate Temperature change + dTdt = -1*(dHrxndt - Q + W)/(C[4]*1000*4.1868) #[K/s] + else: + dTdt = 0 + return [dGdt,dAdt,dOdt,dXdt,dVdt, dTdt] + + def solve(self): + #solve normal: + t = np.linspace(self.t_start, self.t_end, self.steps) + if self.Control == False : + u = 0 + fc= 1 + C0 = [self.G0, self.A0, self.O0, self.X0,self.V0,self.T0] + C = odeint(self.rxn, C0, t, rtol = 1e-7, mxstep= 500000, args=(u,fc,)) + + #solve for Control + else: + fc=0 + """""" + PID Temperature Control: + """""" + # storage for recording values + C = np.ones([len(t), 6]) + C0 = [self.G0, self.A0, self.O0, self.X0,self.V0,self.T0] + self.ctrl_output = np.zeros(len(t)) # controller output + e = np.zeros(len(t)) # error + ie = np.zeros(len(t)) # integral of the error + dpv = np.zeros(len(t)) # derivative of the pv + P = np.zeros(len(t)) # proportional + I = np.zeros(len(t)) # integral + D = np.zeros(len(t)) # derivative + + for i in range(len(t)-1): + #print(t[i]) + #PID control of cooling water + dt = t[i+1]-t[i] + #Error + e[i] = C[i,5] - self.Tset + #print(e[i]) + if i >= 1: + dpv[i] = (C[i,5]-C[i-1,5])/dt + ie[i] = ie[i-1] + e[i]*dt + P[i]=self.K_p*e[i] + I[i]=self.K_i*ie[i] + D[i]=self.K_d*dpv[i] + + self.ctrl_output[i]=P[i]+I[i]+D[i] + u=self.ctrl_output[i] + if u>self.u_max: + u=self.u_max + ie[i] = ie[i] - e[i]*dt # anti-reset windup + if u < self.u_min: + u =self.u_min + ie[i] = ie[i] - e[i]*dt # anti-reset windup + #time for solving ODE + ts = [t[i],t[i+1]] + #disturbance + #if self.t[i] > 5 and self.t[i] < 10: + # u = 0 + #solve ODE from last timepoint to new timepoint with old values + + y = odeint(self.rxn, C0, ts, rtol = 1e-7, mxstep= 500000, args=(u,fc,)) + #update C0 + C0 = y[-1] + #merge y to C + C[i+1]=y[-1] + return t, C + def create_plot(self, t, C): + figure = make_subplots(rows=2, cols=1) + [self.G0, self.A0, self.O0, self.X0, self.V0, self.T0] + G = C[:, 0] + A = C[:, 1] + B = C[:, 3] + O = C[:, 2] + V = C[:, 4] + df = pd.DataFrame({'t': t, 'G': G, 'B': B, 'A': A, 'O': O, 'V': V}) + figure.append_trace(go.Scatter(x=df['t'], y=df['G'], name='Glucose'), row=1, col=1) + figure.append_trace(go.Scatter(x=df['t'], y=df['O'], name='Oxygen'), row=1, col=1) + figure.append_trace(go.Scatter(x=df['t'], y=df['B'], name='Biomass'), row=1, col=1) + figure.append_trace(go.Scatter(x=df['t'], y=df['A'], name='Acetate'), row=1, col=1) + # fig.update_layout(title=('Simulation of the model for the Scerevisiae'), + # xaxis_title='time (h)', + # yaxis_title='Concentration (g/L)') + + + Tset = self.T0 + df2 = pd.DataFrame({'t': t, 'T': T, 'Tset': Tset}) + figure.append_trace(go.Scatter(x=df2['t'], y=df2['T'], name='Temperature'), row=2, col=1) + figure.append_trace(go.Scatter(x=df2['t'], y=df2['Tset'], name='Set Value Temperature'), row=2, col=1) + figure.update_layout(title=('Simulation of the model for the Saccharomyces cerevisiae'), + xaxis_title='time (h)', + yaxis_title='Concentration (g/L)') + + S = C[:, 0] + A = C[:, 1] + B = C[:, 3] + df = pd.DataFrame({'t': t, 'Substrate': S, 'Biomass': B, 'Acetate': A}) + fig = go.Figure() + fig.add_trace(go.Scatter(x=df['t'], y=df['Substrate'], name='Glucose')) + fig.add_trace(go.Scatter(x=df['t'], y=df['Biomass'], name='Biomass')) + fig.add_trace(go.Scatter(x=df['t'], y=df['Acetate'], name='Acetate')) + fig.update_layout(title=('Simulation of aerobic batch growth of Escherichia coli by acetate cycling'), + xaxis_title='time (h)', + yaxis_title='Concentration (g/L)') + print('print') + graphJson = json.dumps(fig, cls=plotly.utils.PlotlyJSONEncoder) + return graphJson + + + +f= Ecoli_Aero() +f.solve() +t =f.solve()[0] +C= f.solve()[1] +print(C) + +plt.plot(t, C[:,0]) +plt.plot(t, C[:,1]) +plt.plot(t, C[:,2]) +plt.plot(t, C[:,3]) +plt.title(""Trial"") +plt.xlabel(""Time (h)"") +plt.ylabel(""Concentration"") +plt.show() +","Python" +"Inhibition","chaninlab/estrogen-receptor-alpha-qsar","01_ER_alpha_preparation.ipynb",".ipynb","256935","5237","{ + ""cells"": [ + { + ""cell_type"": ""code"", + ""execution_count"": 1, + ""metadata"": {}, + ""outputs"": [ + { + ""name"": ""stdout"", + ""output_type"": ""stream"", + ""text"": [ + ""Entry name\tLength\tEntry\tGene names\n"", + ""ESR1_HUMAN\t595\tP03372\tESR1 ESR NR3A1\n"", + ""ANDR_HUMAN\t920\tP10275\tAR DHTR NR3C4\n"", + ""EGFR_HUMAN\t1210\tP00533\tEGFR ERBB ERBB1 HER1\n"", + ""\n"" + ] + } + ], + ""source"": [ + ""from bioservices import UniProt\n"", + ""u = UniProt(verbose=False)\n"", + ""data = u.search(\""Estrogen receptor+and+human+and+alpha\"", frmt=\""tab\"", limit=3,\n"", + "" columns=\""entry name,length,id, genes\"") # zap70 specy in human organism\n"", + ""print(data)"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 2, + ""metadata"": { + ""collapsed"": true + }, + ""outputs"": [], + ""source"": [ + ""import numpy as np\n"", + ""import pandas as pd\n"", + ""from time import time\n"", + ""\n"", + ""from sklearn.metrics import f1_score\n"", + ""from sklearn.metrics import confusion_matrix\n"", + ""import seaborn as sns\n"", + ""%matplotlib inline"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 3, + ""metadata"": {}, + ""outputs"": [ + { + ""name"": ""stdout"", + ""output_type"": ""stream"", + ""text"": [ + ""True\n"" + ] + } + ], + ""source"": [ + ""# Target object handler for API access and check connection to the db\n"", + ""from chembl_webresource_client import *\n"", + ""\n"", + ""targets = TargetResource()\n"", + ""print targets.status()"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 4, + ""metadata"": {}, + ""outputs"": [ + { + ""data"": { + ""text/html"": [ + ""
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bioactivityCountchemblIdcompoundCountdescriptiongeneNamesorganismpreferredNameproteinAccessionsynonymstargetType
010936CHEMBL2065942Estrogen receptor alphaUnspecifiedHomo sapiensEstrogen receptor alphaP03372ESR1,Estradiol receptor,ER,ER-alpha,ESR ,Estro...SINGLE PROTEIN
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activity_commentassay_chemblidassay_descriptionassay_typebioactivity_typeingredient_cmpd_chemblidname_in_referenceoperatororganismparent_cmpd_chemblidreferencetarget_chemblidtarget_confidencetarget_nameunitsvalue
0UnspecifiedCHEMBL677870Binding affinity for estrogen receptor alphaBIC50CHEMBL2197634=Homo sapiensCHEMBL219763Bioorg. Med. Chem. Lett., (2003) 13:22:4089CHEMBL2069Estrogen receptor alphanM9.57
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0UnspecifiedCHEMBL677870Binding affinity for estrogen receptor alphaBIC50CHEMBL2197634=Homo sapiensCHEMBL219763Bioorg. Med. Chem. Lett., (2003) 13:22:4089CHEMBL2069Estrogen receptor alphanM9.57active
9UnspecifiedCHEMBL873603Displacement of [3H]17-beta-estradiol from ful...BIC50CHEMBL9272019=Homo sapiensCHEMBL92720Bioorg. Med. Chem. Lett., (2004) 14:11:2741CHEMBL2068Estrogen receptor alphanM41.10active
10UnspecifiedCHEMBL679886Potency in cellular transactivation assay util...FIC50CHEMBL9272019=Homo sapiensCHEMBL92720Bioorg. Med. Chem. Lett., (2004) 14:11:2741CHEMBL2068Estrogen receptor alphanM274.00active
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activity_commentassay_chemblidassay_descriptionassay_typebioactivity_typeingredient_cmpd_chemblidname_in_referenceoperatororganismparent_cmpd_chemblidreferencetarget_chemblidtarget_confidencetarget_nameunitsvalueSTATUS
0UnspecifiedCHEMBL677870Binding affinity for estrogen receptor alphaBIC50CHEMBL2197634=Homo sapiensCHEMBL219763Bioorg. Med. Chem. Lett., (2003) 13:22:4089CHEMBL2069Estrogen receptor alphanM9.57active
9UnspecifiedCHEMBL873603Displacement of [3H]17-beta-estradiol from ful...BIC50CHEMBL9272019=Homo sapiensCHEMBL92720Bioorg. Med. Chem. Lett., (2004) 14:11:2741CHEMBL2068Estrogen receptor alphanM41.10active
10UnspecifiedCHEMBL679886Potency in cellular transactivation assay util...FIC50CHEMBL9272019=Homo sapiensCHEMBL92720Bioorg. Med. Chem. Lett., (2004) 14:11:2741CHEMBL2068Estrogen receptor alphanM274.00active
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"" + ], + ""text/plain"": [ + "" activity_comment assay_chemblid \\\n"", + ""0 Unspecified CHEMBL677870 \n"", + ""9 Unspecified CHEMBL873603 \n"", + ""10 Unspecified CHEMBL679886 \n"", + ""\n"", + "" assay_description assay_type \\\n"", + ""0 Binding affinity for estrogen receptor alpha B \n"", + ""9 Displacement of [3H]17-beta-estradiol from ful... B \n"", + ""10 Potency in cellular transactivation assay util... F \n"", + ""\n"", + "" bioactivity_type ingredient_cmpd_chemblid name_in_reference operator \\\n"", + ""0 IC50 CHEMBL219763 4 = \n"", + ""9 IC50 CHEMBL92720 19 = \n"", + ""10 IC50 CHEMBL92720 19 = \n"", + ""\n"", + "" organism parent_cmpd_chemblid \\\n"", + ""0 Homo sapiens CHEMBL219763 \n"", + ""9 Homo sapiens CHEMBL92720 \n"", + ""10 Homo sapiens CHEMBL92720 \n"", + ""\n"", + "" reference target_chemblid \\\n"", + ""0 Bioorg. Med. Chem. Lett., (2003) 13:22:4089 CHEMBL206 \n"", + ""9 Bioorg. Med. Chem. Lett., (2004) 14:11:2741 CHEMBL206 \n"", + ""10 Bioorg. Med. Chem. Lett., (2004) 14:11:2741 CHEMBL206 \n"", + ""\n"", + "" target_confidence target_name units value STATUS \n"", + ""0 9 Estrogen receptor alpha nM 9.57 active \n"", + ""9 8 Estrogen receptor alpha nM 41.10 active \n"", + ""10 8 Estrogen receptor alpha nM 274.00 active "" + ] + }, + ""execution_count"": 14, + ""metadata"": {}, + ""output_type"": ""execute_result"" + } + ], + ""source"": [ + ""bioactsDF['STATUS'] = STATUS\n"", + ""bioactsDF.head(3)"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 15, + ""metadata"": {}, + ""outputs"": [ + { + ""data"": { + ""text/plain"": [ + ""= 2237\n"", + ""> 404\n"", + ""< 29\n"", + ""Unspecified 16\n"", + ""Name: operator, dtype: int64"" + ] + }, + ""execution_count"": 15, + ""metadata"": {}, + ""output_type"": ""execute_result"" + } + ], + ""source"": [ + ""bioactsDF['operator'].value_counts()"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 16, + ""metadata"": {}, + ""outputs"": [ + { + ""data"": { + ""text/plain"": [ + ""PUBCHEM_BIOASSAY: Estrogen Receptor-alpha Coactivator Binding Inhibitors Dose Response Confirmation. (Class of assay: confirmatory) [Related pubchem assays: 629 (Primary screen preceding this dose response confirmation assay.)] 439\n"", + ""Inhibitory concentration against human ER alpha expressed in Escherichia coli was determined using [3H]17-beta-estradiol as radio ligand 107\n"", + ""Binding affinity to human ERalpha 79\n"", + ""Inhibition of [3H]17-beta-estradiol binding to human estrogen receptor alpha expressed in Escherichia coli 70\n"", + ""Binding affinity to ERalpha 67\n"", + ""Displacement of radiolabeled estrogen from estrogen receptor alpha by scintillation counting 60\n"", + ""Antagonist activity at estrogen receptor in human Ishikawa cells assessed as 17-beta-estradiol-induced alkaline phosphatase activity after 3 days by chemiluminescence assay 60\n"", + ""Antagonist activity at estrogen receptor in human MCF7 cells assessed as 17-beta-estradiol-induced cell proliferation after 24 hrs by [14C]thymidine incorporation assay 60\n"", + ""Binding affinity for human Estrogen receptor alpha 54\n"", + ""BindingDB_Patents: Binding Assay. The competition assay was performed in a 96-well plate (polystyrene*) which binds <2.0% of the total input [3H]-17beta-estradiol and each data point was gathered in triplicate. 100 uG/100 uL of the receptor preparation was aliquoted per well. A saturating dose of 2.5 nM [3H]17beta-estradiol+competitor (or buffer) in a 50 uL volume was added in the preliminary competition when 100x and 500x competitor were evaluated, only 0.8 nM [3H]17beta-estradiol was used. The plate was incubated at room temperature for 2.5 h. At the end of this incubation period 150 uL of ice-cold dextran coated charcoal (5% activated charcoal coated with 0.05% 69K dextran) was added to each well and the plate was immediately centrifuged at 99 g for 5 minutes at 4 C. 200 uL of the supernatant solution was then removed for scintillation counting. Samples were counted to 2% or 10 minutes, whichever occurs first. 54\n"", + ""Antagonist activity at ERalpha in human HEC1 cells assessed as inhibition of estrogen-induced transcriptional activity after 24 hrs by reporter gene assay 51\n"", + ""Binding affinity to human recombinant ERalpha by scintillation proximity assay 51\n"", + ""Inhibition of estrogen receptor alpha 43\n"", + ""Displacement of [3H]estrone from ER alpha 40\n"", + ""Suppression of ER alpha in human MCF7 cells after 24 hrs by immunostaining analysis 37\n"", + ""Binding affinity to ERalpha by scintillation proximity assay 37\n"", + ""Binding affinity for human estrogen receptor alpha 36\n"", + ""Binding affinity to ER alpha (unknown origin) by LanthaScreen TR-FRET competitive binding assay 36\n"", + ""Binding potency for human ER alpha 34\n"", + ""Displacement of [3H]17-beta-estradiol from full length human estrogen receptor alpha 33\n"", + ""Inhibition of binding to ER in Ad5-wt-ER-infected HAECT1 cells by NF-kappaB-mediated luciferase reporter 31\n"", + ""In vitro concentration required to inhibit [3H]estradiol binding to human estrogen receptor alpha expressed in 293T cells 30\n"", + ""Inhibition of bindign to recombinant human estrogen receptor alpha 30\n"", + ""Binding affinity against human estrogen receptor alpha in competitive binding assay 27\n"", + ""Displacement of fluormone ES2 from GST-tagged recombinant human ERalpha LBD after 1 hr by Lanthascreen TR-FRET assay 26\n"", + ""Activity at human ERalpha expressed in HAECT1 cells assessed as inhibition of NF-kappaB transcription 26\n"", + ""Inhibitory concentration against estrogen receptor alpha using radioligand binding assay. 26\n"", + ""Displacement of [3H]estradiol from full length human recombinant ERalpha after 3 hrs by scintillation proximity assay 25\n"", + ""Downregulation of ERalpha in human MCF7 cells incubated for 18 to 22 hrs by immunofluorescence assay 25\n"", + ""Inhibitory concentration against estrogen receptor alpha using estrogen response element (ERE) assay. 24\n"", + "" ... \n"", + ""Agonist activity at ERalpha (unknown origin) by cell-based assay 1\n"", + ""Antagonist activity at ERalpha receptor in human MCF7 cells in presence of 0.25 uM tamoxifen 1\n"", + ""Binding affinity to human estrogen receptor assessed as decrease in estradiol-dependent beta-galactosidase activity 1\n"", + ""Displacement of [3H]Estradiol from human recombinant estrogen receptor alpha expressed in Sf9 cells after 2 hrs 1\n"", + ""Antagonist activity at human ERalpha expressed in human MCF7 cells by luciferase reporter gene assay 1\n"", + ""Antagonist activity at ERalpha by TR-FRET assay 1\n"", + ""Displacement of fluorescent labeled ES2 from recombinant ERalpha 1\n"", + ""Antagonist activity at human ERalpha LBD in human MCF7 cells assessed as inhibition of estradiol-induced cell proliferation after up to 6 days by celltiter-glo assay 1\n"", + ""In vitro binding affinity for Estrogen receptor alpha in HEK 293 cells 1\n"", + ""Antagonist activity at human ER ligand binding domain expressed in african green monkey COS7 cells in presence of 17-beta-estradiol by Gal4 hybrid assay 1\n"", + ""Inhibition of ERalpha expressed in human tumor cells 1\n"", + ""Binding affinity to ERalpha by fluorescence polarization assay 1\n"", + ""Binding affinity to human recombinant ERalpha receptor by liquid scintillation counter 1\n"", + ""Mixed-type inhibition of ER-alpha (unknown origin) expressed in ER-negative human SKBR3 cells coexpressing ERE using estradiol as substrate 1\n"", + ""Inhibition of estrogen receptor alpha mediated transcriptional activation 1\n"", + ""Antagonist activity at estrogen receptor ligand binding domain by two hybrid assay 1\n"", + ""Binding affinity to estrogen receptor 1\n"", + ""Antagonist activity at gal-ERalpha expressed in african green monkey CV1 cells co-expressing MH100 by luciferase reporter gene assay 1\n"", + ""Displacement of fluorescent estrogen ES2 from human recombinant ERalpha by fluorescence polarization assay 1\n"", + ""Inhibition of estrogen receptor alpha in MCF-7-2a cells at 10 uM 1\n"", + ""Binding affinity to estrogen receptor (unknown origin) incubated for 4 hrs 1\n"", + ""Inhibition of estradiol-induced estrogen receptor-alpha (unknown origin)-mediated transcription expressed in HEK293T cells after 16 hrs by luciferase reporter gene assay 1\n"", + ""Antagonist activity at estrogen receptor in human MCF7 cells assessed as inhibition of estradiol-induced cell proliferation after 5 days by WST-8 assay 1\n"", + ""Antagonist activity at estrogen receptor by cellular reporter gene assay 1\n"", + ""Antagonist activity at human recombinant ERalpha by FRET assay 1\n"", + ""Displacement of [3H]E2 from ERalpha ligand binding domain 1\n"", + ""Binding affinity at ERalpha 1\n"", + ""Binding affinity to ERalpha assessed as inhibition of steroid receptor coactivator interaction in african green monkey COS7 cells by mammalian 2 hybrid assay 1\n"", + ""Inhibitory activity against Estrogen receptor alpha 1\n"", + ""Displacement of [3H]-17beta-estradiol from human recombinant ERalpha expressed in rabbit reticulocytes 1\n"", + ""Name: assay_description, Length: 174, dtype: int64"" + ] + }, + ""execution_count"": 16, + ""metadata"": {}, + ""output_type"": ""execute_result"" + } + ], + ""source"": [ + ""bioactsDF['assay_description'].value_counts()"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 17, + ""metadata"": { + ""collapsed"": true + }, + ""outputs"": [], + ""source"": [ + ""bioactsDF = bioactsDF.rename(columns={'ingredient_cmpd_chemblid': 'chemblId'})"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 18, + ""metadata"": { + ""scrolled"": false + }, + ""outputs"": [ + { + ""name"": ""stdout"", + ""output_type"": ""stream"", + ""text"": [ + ""1708 294 19\n"" + ] + } + ], + ""source"": [ + ""# Filter bioactivities\n"", + ""bioactsDF_Train = bioactsDF[(bioactsDF['operator'] == '=') & # only exact measurements\n"", + "" (bioactsDF['assay_type'] == 'B') & # only binding data\n"", + "" (bioactsDF['target_confidence'] == 9)] # only high target confidence\n"", + ""bioactsDF_Test_gra = bioactsDF[(bioactsDF['operator'] == '>') & \n"", + "" (bioactsDF['assay_type'] == 'B') & # only binding data\n"", + "" (bioactsDF['target_confidence'] == 9)] # only high target confidence\n"", + ""bioactsDF_Test_les = bioactsDF[(bioactsDF['operator'] == '<') & \n"", + "" (bioactsDF['assay_type'] == 'B') & # only binding data\n"", + "" (bioactsDF['target_confidence'] == 9)] # only high target confidence\n"", + ""\n"", + ""print len(bioactsDF_Train), len(bioactsDF_Test_gra), len(bioactsDF_Test_les)"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 19, + ""metadata"": {}, + ""outputs"": [ + { + ""data"": { + ""text/html"": [ + ""
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activity_commentassay_chemblidassay_descriptionassay_typebioactivity_typechemblIdname_in_referenceoperatororganismparent_cmpd_chemblidreferencetarget_chemblidtarget_confidencetarget_nameunitsvalueSTATUS
9601UnspecifiedCHEMBL2445311Inhibition of estrogen receptor alpha-mediated...BIC50CHEMBL2441817S58=Homo sapiensCHEMBL2441817UnspecifiedCHEMBL2069Estrogen receptor alphanM64908.26inactive
9608UnspecifiedCHEMBL2445311Inhibition of estrogen receptor alpha-mediated...BIC50CHEMBL2441818S59=Homo sapiensCHEMBL2441818UnspecifiedCHEMBL2069Estrogen receptor alphanM64908.26inactive
9609UnspecifiedCHEMBL2445311Inhibition of estrogen receptor alpha-mediated...BIC50CHEMBL2441819S60=Homo sapiensCHEMBL2441819UnspecifiedCHEMBL2069Estrogen receptor alphanM64908.26inactive
\n"", + ""
"" + ], + ""text/plain"": [ + "" activity_comment assay_chemblid \\\n"", + ""9601 Unspecified CHEMBL2445311 \n"", + ""9608 Unspecified CHEMBL2445311 \n"", + ""9609 Unspecified CHEMBL2445311 \n"", + ""\n"", + "" assay_description assay_type \\\n"", + ""9601 Inhibition of estrogen receptor alpha-mediated... B \n"", + ""9608 Inhibition of estrogen receptor alpha-mediated... B \n"", + ""9609 Inhibition of estrogen receptor alpha-mediated... B \n"", + ""\n"", + "" bioactivity_type chemblId name_in_reference operator organism \\\n"", + ""9601 IC50 CHEMBL2441817 S58 = Homo sapiens \n"", + ""9608 IC50 CHEMBL2441818 S59 = Homo sapiens \n"", + ""9609 IC50 CHEMBL2441819 S60 = Homo sapiens \n"", + ""\n"", + "" parent_cmpd_chemblid reference target_chemblid target_confidence \\\n"", + ""9601 CHEMBL2441817 Unspecified CHEMBL206 9 \n"", + ""9608 CHEMBL2441818 Unspecified CHEMBL206 9 \n"", + ""9609 CHEMBL2441819 Unspecified CHEMBL206 9 \n"", + ""\n"", + "" target_name units value STATUS \n"", + ""9601 Estrogen receptor alpha nM 64908.26 inactive \n"", + ""9608 Estrogen receptor alpha nM 64908.26 inactive \n"", + ""9609 Estrogen receptor alpha nM 64908.26 inactive "" + ] + }, + ""execution_count"": 19, + ""metadata"": {}, + ""output_type"": ""execute_result"" + } + ], + ""source"": [ + ""bioactsDF_Train[(bioactsDF_Train['assay_description'] == 'Inhibition of estrogen receptor alpha-mediated human MCF7 cell growth inhibition')]"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 20, + ""metadata"": {}, + ""outputs"": [ + { + ""data"": { + ""text/plain"": [ + ""Inhibition of estrogen receptor alpha-mediated human MCF7 cell growth inhibition 16\n"", + ""PUBCHEM_BIOASSAY: Estrogen Receptor-alpha Coactivator Binding Inhibitors Dose Response Confirmation. (Class of assay: confirmatory) [Related pubchem assays: 629 (Primary screen preceding this dose response confirmation assay.)] 2\n"", + ""Binding affinity to ER alpha (unknown origin) by LanthaScreen TR-FRET competitive binding assay 1\n"", + ""Name: assay_description, dtype: int64"" + ] + }, + ""execution_count"": 20, + ""metadata"": {}, + ""output_type"": ""execute_result"" + } + ], + ""source"": [ + ""bioactsDF_Test_les['assay_description'].value_counts()"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 21, + ""metadata"": {}, + ""outputs"": [ + { + ""data"": { + ""text/plain"": [ + ""PUBCHEM_BIOASSAY: Estrogen Receptor-alpha Coactivator Binding Inhibitors Dose Response Confirmation. (Class of assay: confirmatory) [Related pubchem assays: 629 (Primary screen preceding this dose response confirmation assay.)] 157\n"", + ""Binding affinity to human ERalpha 16\n"", + ""Binding affinity to ERalpha 11\n"", + ""Binding affinity to human recombinant ERalpha by scintillation proximity assay 11\n"", + ""Displacement of [3H]estradiol from human estrogen receptor alpha 8\n"", + ""Inhibition of estrogen receptor alpha (unknown origin)-SRC1 coactivator interaction incubated for 1 hr by receptor cofactor assay system based method 7\n"", + ""Binding affinity for human Estrogen receptor alpha 7\n"", + ""Binding affinity to human ER alpha 6\n"", + ""Binding affinity for human estrogen receptor alpha 6\n"", + ""Inhibition of human LBD of of ERalpha 5\n"", + ""Inhibition of estrogen receptor (unknown origin) by Gal4-based luciferase assay 5\n"", + ""Binding affinity to ERalpha by scintillation proximity assay 5\n"", + ""Agonist activity at ER alpha in human MCF7 cells assessed as PR gene expression after 24 hrs by laser scanning imaging cytometer analysis 5\n"", + ""Displacement of fluormone from human estrogen receptor by fluorescence binding assay 5\n"", + ""Binding affinity at human recombinant ERalpha 4\n"", + ""Inhibitory concentration against human ER alpha expressed in Escherichia coli was determined using [3H]17-beta-estradiol as radio ligand 4\n"", + ""Inhibition of human estrogen receptor alpha 4\n"", + ""Binding affinity to ER-alpha (unknown origin) 3\n"", + ""Suppression of ER alpha in human MCF7 cells after 24 hrs by immunostaining analysis 3\n"", + ""Binding affinity to human ER-alpha 2\n"", + ""Inhibition of estrogen receptor (unknown origin) 2\n"", + ""Binding affinity for human Estrogen receptor Alpha 2\n"", + ""Antagonist activity at estrogen receptor alpha (unknown origin) by NH Pro assay 2\n"", + ""Antiestrogenic activity in human T47D cells expressing estrogen receptor assessed as estrogen-dependent transcription by luciferase reporter gene assay 2\n"", + ""Displacement of fluorescein-labeled estrogen ligand from recombinant ER-alpha (unknown origin) incubated for 2 hrs by fluorescence polarization assay 2\n"", + ""Binding affinity to estrogen receptor (unknown origin) incubated for 4 hrs 1\n"", + ""Antagonist activity at human estrogen receptor alpha expressed in CHOK1 cells assessed as inhibition of beta-estradiol-induced protein interaction with steroid receptor co-activator peptide after overnight incubation by beta-galactosidase reporter gene assay 1\n"", + ""Displacement of fluorescein-labeled estradiol from human recombinant ERalpha by fluorescence polarization assay 1\n"", + ""Binding affinity to human recombinant ERalpha 1\n"", + ""Inhibition of [3H]17-beta-estradiol binding to human estrogen receptor alpha expressed in Escherichia coli 1\n"", + ""Binding affinity for Human Estrogen receptor-alpha 1\n"", + ""Displacement of [3H]17beta-estradiol from recombinant human ERalpha expressed in 293T cells 1\n"", + ""Displacement of [3H]17-beta-estradiol from human estrogen receptor alpha 1\n"", + ""PUBCHEM_BIOASSAY: Estrogen Receptor-alpha Coactivator Binding Inhibitors ELISA Secondary Assay. (Class of assay: confirmatory) [Related pubchem assays: 713 (Primary dose response assay preceding this ELISA secondary assay.), 629 (Primary screen preceding this dose response confirmation assay.)] 1\n"", + ""Displacement of fluorescein labeled estradiol from human recombinant ERalpha expressed in baculovirus infected insect cells by fluorescence polarization assay 1\n"", + ""Name: assay_description, dtype: int64"" + ] + }, + ""execution_count"": 21, + ""metadata"": {}, + ""output_type"": ""execute_result"" + } + ], + ""source"": [ + ""bioactsDF_Test_gra['assay_description'].value_counts()"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 22, + ""metadata"": {}, + ""outputs"": [ + { + ""data"": { + ""text/plain"": [ + ""(1708, 1424)"" + ] + }, + ""execution_count"": 22, + ""metadata"": {}, + ""output_type"": ""execute_result"" + } + ], + ""source"": [ + ""len(bioactsDF_Train), len(bioactsDF_Train['chemblId'].unique())"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 23, + ""metadata"": {}, + ""outputs"": [ + { + ""data"": { + ""text/html"": [ + ""
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activity_commentassay_chemblidassay_descriptionassay_typebioactivity_typechemblIdname_in_referenceoperatororganismparent_cmpd_chemblidreferencetarget_chemblidtarget_confidencetarget_nameunitsvalueSTATUS
10934UnspecifiedCHEMBL3829134Displacement of fluorescein-labeled estrogen l...BIC50CHEMBL382811720d=Homo sapiensCHEMBL3828117UnspecifiedCHEMBL2069Estrogen receptor alphanM38300.0inactive
10935UnspecifiedCHEMBL3825990Inhibition of ER-alpha (unknown origin) by Lan...BIC50CHEMBL382319286=Homo sapiensCHEMBL3823192Bioorg. Med. Chem., (2016) 24:18:4075CHEMBL2069Estrogen receptor alphanM574.0active
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"" + ], + ""text/plain"": [ + "" activity_comment assay_chemblid \\\n"", + ""10934 Unspecified CHEMBL3829134 \n"", + ""10935 Unspecified CHEMBL3825990 \n"", + ""\n"", + "" assay_description assay_type \\\n"", + ""10934 Displacement of fluorescein-labeled estrogen l... B \n"", + ""10935 Inhibition of ER-alpha (unknown origin) by Lan... B \n"", + ""\n"", + "" bioactivity_type chemblId name_in_reference operator \\\n"", + ""10934 IC50 CHEMBL3828117 20d = \n"", + ""10935 IC50 CHEMBL3823192 86 = \n"", + ""\n"", + "" organism parent_cmpd_chemblid \\\n"", + ""10934 Homo sapiens CHEMBL3828117 \n"", + ""10935 Homo sapiens CHEMBL3823192 \n"", + ""\n"", + "" reference target_chemblid \\\n"", + ""10934 Unspecified CHEMBL206 \n"", + ""10935 Bioorg. Med. Chem., (2016) 24:18:4075 CHEMBL206 \n"", + ""\n"", + "" target_confidence target_name units value STATUS \n"", + ""10934 9 Estrogen receptor alpha nM 38300.0 inactive \n"", + ""10935 9 Estrogen receptor alpha nM 574.0 active "" + ] + }, + ""execution_count"": 23, + ""metadata"": {}, + ""output_type"": ""execute_result"" + } + ], + ""source"": [ + ""bioactsDF_Train.tail(2)"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 24, + ""metadata"": {}, + ""outputs"": [ + { + ""data"": { + ""text/plain"": [ + ""(294, 290)"" + ] + }, + ""execution_count"": 24, + ""metadata"": {}, + ""output_type"": ""execute_result"" + } + ], + ""source"": [ + ""len(bioactsDF_Test_gra), len(bioactsDF_Test_gra['chemblId'].unique())"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 25, + ""metadata"": {}, + ""outputs"": [ + { + ""data"": { + ""text/plain"": [ + ""(19, 19)"" + ] + }, + ""execution_count"": 25, + ""metadata"": {}, + ""output_type"": ""execute_result"" + } + ], + ""source"": [ + ""len(bioactsDF_Test_les), len(bioactsDF_Test_les['chemblId'].unique())"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 26, + ""metadata"": {}, + ""outputs"": [ + { + ""name"": ""stdout"", + ""output_type"": ""stream"", + ""text"": [ + ""('1259', '449', '1708')\n"" + ] + } + ], + ""source"": [ + ""bioactsDF_Train_dup = pd.concat(g for _, \n"", + "" g in bioactsDF_Train.groupby(\""chemblId\"") \n"", + "" if len(g) > 1)\n"", + ""bioactsDF_Train_non = bioactsDF_Train.loc[~bioactsDF_Train.index.isin(bioactsDF_Train_dup.index)]\n"", + ""\n"", + ""print (str(len(bioactsDF_Train_non)), \n"", + "" str(len(bioactsDF_Train_dup)), \n"", + "" str(len(bioactsDF_Train_dup)+len(bioactsDF_Train_non)))"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 27, + ""metadata"": {}, + ""outputs"": [ + { + ""data"": { + ""text/plain"": [ + ""(1259, 1259)"" + ] + }, + ""execution_count"": 27, + ""metadata"": {}, + ""output_type"": ""execute_result"" + } + ], + ""source"": [ + ""len(bioactsDF_Train_non), len(bioactsDF_Train_non['chemblId'].unique())"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 28, + ""metadata"": {}, + ""outputs"": [ + { + ""data"": { + ""text/html"": [ + ""
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\n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + ""
activity_commentassay_chemblidassay_descriptionassay_typebioactivity_typechemblIdname_in_referenceoperatororganismparent_cmpd_chemblidreferencetarget_chemblidtarget_confidencetarget_nameunitsvalueSTATUS
6530UnspecifiedCHEMBL831663Inhibition of transcriptional activation by hu...BIC50CHEMBL10023113=Homo sapiensCHEMBL100231Bioorg. Med. Chem. Lett., (2005) 15:4:957CHEMBL2069Estrogen receptor alphanM5.2000active
6531UnspecifiedCHEMBL831661Inhibition of binding to human estrogen recept...BIC50CHEMBL10023113=Homo sapiensCHEMBL100231Bioorg. Med. Chem. Lett., (2005) 15:4:957CHEMBL2069Estrogen receptor alphanM70.9600active
3114UnspecifiedCHEMBL3736957Antagonist activity at ERalpha in human T47D-K...BIC50CHEMBL100414-OHT=Homo sapiensCHEMBL10041Bioorg. Med. Chem., (2015) 23:24:7597CHEMBL2069Estrogen receptor alphanM10.0000active
3115UnspecifiedCHEMBL3736956Antagonist activity at luciferase-fused ERalph...BIC50CHEMBL100414-OHT=Homo sapiensCHEMBL10041Bioorg. Med. Chem., (2015) 23:24:7597CHEMBL2069Estrogen receptor alphanM70.0000active
6798UnspecifiedCHEMBL831663Inhibition of transcriptional activation by hu...BIC50CHEMBL10059519=Homo sapiensCHEMBL100595Bioorg. Med. Chem. Lett., (2005) 15:4:957CHEMBL2069Estrogen receptor alphanM16.1100active
6799UnspecifiedCHEMBL831661Inhibition of binding to human estrogen recept...BIC50CHEMBL10059519=Homo sapiensCHEMBL100595Bioorg. Med. Chem. Lett., (2005) 15:4:957CHEMBL2069Estrogen receptor alphanM46.0300active
1739UnspecifiedCHEMBL831663Inhibition of transcriptional activation by hu...BIC50CHEMBL10061715=Homo sapiensCHEMBL100617Bioorg. Med. Chem. Lett., (2005) 15:4:957CHEMBL2069Estrogen receptor alphanM0.6998active
1740UnspecifiedCHEMBL831661Inhibition of binding to human estrogen recept...BIC50CHEMBL10061715=Homo sapiensCHEMBL100617Bioorg. Med. Chem. Lett., (2005) 15:4:957CHEMBL2069Estrogen receptor alphanM33.9600active
7559UnspecifiedCHEMBL831663Inhibition of transcriptional activation by hu...BIC50CHEMBL10076310=Homo sapiensCHEMBL100763Bioorg. Med. Chem. Lett., (2005) 15:4:957CHEMBL2069Estrogen receptor alphanM3.3040active
7560UnspecifiedCHEMBL831661Inhibition of binding to human estrogen recept...BIC50CHEMBL10076310=Homo sapiensCHEMBL100763Bioorg. Med. Chem. Lett., (2005) 15:4:957CHEMBL2069Estrogen receptor alphanM26.9800active
1282UnspecifiedCHEMBL831663Inhibition of transcriptional activation by hu...BIC50CHEMBL1010839=Homo sapiensCHEMBL101083Bioorg. Med. Chem. Lett., (2005) 15:4:957CHEMBL2069Estrogen receptor alphanM1.9010active
1283UnspecifiedCHEMBL831661Inhibition of binding to human estrogen recept...BIC50CHEMBL1010839=Homo sapiensCHEMBL101083Bioorg. Med. Chem. Lett., (2005) 15:4:957CHEMBL2069Estrogen receptor alphanM38.0200active
985UnspecifiedCHEMBL831007Inhibition of 17-beta-estradiol mediated lucif...BIC50CHEMBL10138260=Homo sapiensCHEMBL101382J. Med. Chem., (2005) 48:2:364CHEMBL2069Estrogen receptor alphanM16.5000active
986UnspecifiedCHEMBL831528Inhibition of [3H]estradiol binding to human e...BIC50CHEMBL10138260=Homo sapiensCHEMBL101382J. Med. Chem., (2005) 48:2:364CHEMBL2069Estrogen receptor alphanM19.0000active
987UnspecifiedCHEMBL831663Inhibition of transcriptional activation by hu...BIC50CHEMBL1013822=Homo sapiensCHEMBL101382Bioorg. Med. Chem. Lett., (2005) 15:4:957CHEMBL2069Estrogen receptor alphanM16.4800active
988UnspecifiedCHEMBL831661Inhibition of binding to human estrogen recept...BIC50CHEMBL1013822=Homo sapiensCHEMBL101382Bioorg. Med. Chem. Lett., (2005) 15:4:957CHEMBL2069Estrogen receptor alphanM19.0100active
1399UnspecifiedCHEMBL831663Inhibition of transcriptional activation by hu...BIC50CHEMBL1018074=Homo sapiensCHEMBL101807Bioorg. Med. Chem. Lett., (2005) 15:4:957CHEMBL2069Estrogen receptor alphanM2.6000active
1400UnspecifiedCHEMBL831661Inhibition of binding to human estrogen recept...BIC50CHEMBL1018074=Homo sapiensCHEMBL101807Bioorg. Med. Chem. Lett., (2005) 15:4:957CHEMBL2069Estrogen receptor alphanM28.9700active
2241UnspecifiedCHEMBL831663Inhibition of transcriptional activation by hu...BIC50CHEMBL1019978=Homo sapiensCHEMBL101997Bioorg. Med. Chem. Lett., (2005) 15:4:957CHEMBL2069Estrogen receptor alphanM1.6000active
2242UnspecifiedCHEMBL831661Inhibition of binding to human estrogen recept...BIC50CHEMBL1019978=Homo sapiensCHEMBL101997Bioorg. Med. Chem. Lett., (2005) 15:4:957CHEMBL2069Estrogen receptor alphanM30.9700active
7518UnspecifiedCHEMBL831663Inhibition of transcriptional activation by hu...BIC50CHEMBL10329411=Homo sapiensCHEMBL103294Bioorg. Med. Chem. Lett., (2005) 15:4:957CHEMBL2069Estrogen receptor alphanM3.0970active
7519UnspecifiedCHEMBL831661Inhibition of binding to human estrogen recept...BIC50CHEMBL10329411=Homo sapiensCHEMBL103294Bioorg. Med. Chem. Lett., (2005) 15:4:957CHEMBL2069Estrogen receptor alphanM34.9900active
1470UnspecifiedCHEMBL831663Inhibition of transcriptional activation by hu...BIC50CHEMBL10330518=Homo sapiensCHEMBL103305Bioorg. Med. Chem. Lett., (2005) 15:4:957CHEMBL2069Estrogen receptor alphanM4.4980active
1471UnspecifiedCHEMBL831661Inhibition of binding to human estrogen recept...BIC50CHEMBL10330518=Homo sapiensCHEMBL103305Bioorg. Med. Chem. Lett., (2005) 15:4:957CHEMBL2069Estrogen receptor alphanM48.9800active
6569UnspecifiedCHEMBL3736957Antagonist activity at ERalpha in human T47D-K...BIC50CHEMBL12220354; ICI-164,384=Homo sapiensCHEMBL1222035Bioorg. Med. Chem., (2015) 23:24:7597CHEMBL2069Estrogen receptor alphanM50.0000active
6570UnspecifiedCHEMBL3736956Antagonist activity at luciferase-fused ERalph...BIC50CHEMBL12220354; ICI-164,384=Homo sapiensCHEMBL1222035Bioorg. Med. Chem., (2015) 23:24:7597CHEMBL2069Estrogen receptor alphanM340.0000active
3878UnspecifiedCHEMBL3825990Inhibition of ER-alpha (unknown origin) by Lan...BIC50CHEMBL135Estradiol=Homo sapiensCHEMBL135Bioorg. Med. Chem., (2016) 24:18:4075CHEMBL2069Estrogen receptor alphanM1.7000active
3885UnspecifiedCHEMBL3599826Inhibition of estrogen receptor alpha (unknown...BIC50CHEMBL135E2=Homo sapiensCHEMBL135Bioorg. Med. Chem., (2015) 23:15:4132CHEMBL2069Estrogen receptor alphanM0.9000active
3892UnspecifiedCHEMBL3382763Displacement of fluorescein-labeled estrogen f...BIC50CHEMBL1351=Homo sapiensCHEMBL135J. Med. Chem., (2014) 57:22:9370CHEMBL2069Estrogen receptor alphanM5.7000active
3902UnspecifiedCHEMBL3102026Agonist activity at ERalpha (unknown origin) b...BIC50CHEMBL135E2=Homo sapiensCHEMBL135Bioorg. Med. Chem., (2014) 22:1:303CHEMBL2069Estrogen receptor alphanM1.3000active
......................................................
3691UnspecifiedCHEMBL863236Inhibition of binding to recombinant human ERa...BIC50CHEMBL812=Homo sapiensCHEMBL81Bioorg. Med. Chem. Lett., (2005) 15:23:5124CHEMBL2069Estrogen receptor alphanM1.8000active
3697UnspecifiedCHEMBL1909145DRUGMATRIX: Estrogen ERalpha radioligand bindi...BIC50CHEMBL81RALOXIFENE=Homo sapiensCHEMBL81UnspecifiedCHEMBL2069Estrogen receptor alphanM0.5530active
3708UnspecifiedCHEMBL904964Displacement of [3H]estradiol from human recom...BIC50CHEMBL81raloxifene=Homo sapiensCHEMBL81J. Med. Chem., (2007) 50:11:2682CHEMBL2069Estrogen receptor alphanM20.6000active
3713UnspecifiedCHEMBL867524Binding affinity to ERalphaBIC50CHEMBL81raloxifene=Homo sapiensCHEMBL81J. Med. Chem., (2006) 49:11:3056CHEMBL2069Estrogen receptor alphanM1.8000active
3715UnspecifiedCHEMBL831072Binding potency for human ER alphaBIC50CHEMBL81Raloxifene=Homo sapiensCHEMBL81Bioorg. Med. Chem. Lett., (2004) 14:15:3865CHEMBL2069Estrogen receptor alphanM1.8000active
3716UnspecifiedCHEMBL831071Binding potency for human ER alphaBIC50CHEMBL81Raloxifene=Homo sapiensCHEMBL81Bioorg. Med. Chem. Lett., (2004) 14:15:3861CHEMBL2069Estrogen receptor alphanM1.8000active
3717UnspecifiedCHEMBL829466Inhibition of [3H]17-beta-estradiol binding to...BIC50CHEMBL81Raloxifene=Homo sapiensCHEMBL81Bioorg. Med. Chem. Lett., (2005) 15:11:2891CHEMBL2069Estrogen receptor alphanM0.8900active
3718UnspecifiedCHEMBL832667Inhibition of bindign to recombinant human est...BIC50CHEMBL81Raloxifene=Homo sapiensCHEMBL81Bioorg. Med. Chem. Lett., (2005) 15:1:107CHEMBL2069Estrogen receptor alphanM1.8000active
3719UnspecifiedCHEMBL831007Inhibition of 17-beta-estradiol mediated lucif...BIC50CHEMBL817=Homo sapiensCHEMBL81J. Med. Chem., (2005) 48:2:364CHEMBL2069Estrogen receptor alphanM2.4000active
3720UnspecifiedCHEMBL831528Inhibition of [3H]estradiol binding to human e...BIC50CHEMBL817=Homo sapiensCHEMBL81J. Med. Chem., (2005) 48:2:364CHEMBL2069Estrogen receptor alphanM22.0000active
3721UnspecifiedCHEMBL833547Inhibition of estrogen receptor alphaBIC50CHEMBL811=Homo sapiensCHEMBL81Bioorg. Med. Chem. Lett., (2005) 15:5:1505CHEMBL2069Estrogen receptor alphanM0.7300active
3727UnspecifiedCHEMBL679892In vitro inhibition of transcriptional activat...BIC50CHEMBL81Raloxifene=Homo sapiensCHEMBL81J. Med. Chem., (2002) 45:25:5492CHEMBL2069Estrogen receptor alphanM0.7000active
3728UnspecifiedCHEMBL679888In vitro binding affinity for estrogen recepto...BIC50CHEMBL811 (Raloxifene)=Homo sapiensCHEMBL81Bioorg. Med. Chem. Lett., (2003) 13:11:1919CHEMBL2069Estrogen receptor alphanM7.0000active
3734UnspecifiedCHEMBL681157In vitro inhibition of [3H]17-beta-estradiol b...BIC50CHEMBL812, Raloxifene=Homo sapiensCHEMBL81J. Med. Chem., (2001) 44:11:1654CHEMBL2069Estrogen receptor alphanM4.0000active
3735UnspecifiedCHEMBL677871Binding affinity for Human Estrogen receptor-a...BIC50CHEMBL811=Homo sapiensCHEMBL81Bioorg. Med. Chem. Lett., (2003) 13:3:479CHEMBL2069Estrogen receptor alphanM1.8000active
279UnspecifiedCHEMBL3829134Displacement of fluorescein-labeled estrogen l...BIC50CHEMBL83Tamoxifen=Homo sapiensCHEMBL83UnspecifiedCHEMBL2069Estrogen receptor alphanM1000.0000active
280UnspecifiedCHEMBL3777355Displacement of [3H]-E2 from human ER-alpha in...BIC50CHEMBL83TAM=Homo sapiensCHEMBL83UnspecifiedCHEMBL2069Estrogen receptor alphanM8.0000active
281UnspecifiedCHEMBL3624759Antagonist activity at ERalpha receptor in hum...BIC50CHEMBL831a=Homo sapiensCHEMBL83J. Med. Chem., (2015) 58:20:8128CHEMBL2069Estrogen receptor alphanM10000.0000inactive
282UnspecifiedCHEMBL3624758Binding affinity to ERalpha receptor (unknown ...BIC50CHEMBL831a=Homo sapiensCHEMBL83J. Med. Chem., (2015) 58:20:8128CHEMBL2069Estrogen receptor alphanM24.5500active
283UnspecifiedCHEMBL3382767Binding affinity to human ERalphaBIC50CHEMBL832=Homo sapiensCHEMBL83J. Med. Chem., (2014) 57:22:9370CHEMBL2069Estrogen receptor alphanM60.9000active
284UnspecifiedCHEMBL3382763Displacement of fluorescein-labeled estrogen f...BIC50CHEMBL832=Homo sapiensCHEMBL83J. Med. Chem., (2014) 57:22:9370CHEMBL2069Estrogen receptor alphanM61.0000active
286UnspecifiedCHEMBL2406604Antagonist activity at Gal4 DBD-fused human ER...BIC50CHEMBL83Tamoxifen=Homo sapiensCHEMBL83J. Med. Chem., (2013) 56:14:5782CHEMBL2069Estrogen receptor alphanM39.0000active
306UnspecifiedCHEMBL1023895Antiestrogenic activity in human T47D cells ex...BIC50CHEMBL83Tamoxifen=Homo sapiensCHEMBL83Bioorg. Med. Chem. Lett., (2009) 19:4:1218CHEMBL2069Estrogen receptor alphanM100.0000active
307UnspecifiedCHEMBL940315Displacement of fluorescein labeled estradiol ...BIC50CHEMBL831a=Homo sapiensCHEMBL83Bioorg. Med. Chem., (2008) 16:21:9554CHEMBL2069Estrogen receptor alphanM70.0000active
310UnspecifiedCHEMBL831007Inhibition of 17-beta-estradiol mediated lucif...BIC50CHEMBL836=Homo sapiensCHEMBL83J. Med. Chem., (2005) 48:2:364CHEMBL2069Estrogen receptor alphanM622.0000active
313UnspecifiedCHEMBL682292Displacement of [3H]17-beta-estradiol from hum...BIC50CHEMBL831=Homo sapiensCHEMBL83J. Med. Chem., (1999) 42:16:3126CHEMBL2069Estrogen receptor alphanM42.0000active
5548UnspecifiedCHEMBL831663Inhibition of transcriptional activation by hu...BIC50CHEMBL9847012=Homo sapiensCHEMBL98470Bioorg. Med. Chem. Lett., (2005) 15:4:957CHEMBL2069Estrogen receptor alphanM2.6000active
5549UnspecifiedCHEMBL831661Inhibition of binding to human estrogen recept...BIC50CHEMBL9847012=Homo sapiensCHEMBL98470Bioorg. Med. Chem. Lett., (2005) 15:4:957CHEMBL2069Estrogen receptor alphanM43.9500active
7480UnspecifiedCHEMBL831663Inhibition of transcriptional activation by hu...BIC50CHEMBL985583=Homo sapiensCHEMBL98558Bioorg. Med. Chem. Lett., (2005) 15:4:957CHEMBL2069Estrogen receptor alphanM1.7990active
7481UnspecifiedCHEMBL831661Inhibition of binding to human estrogen recept...BIC50CHEMBL985583=Homo sapiensCHEMBL98558Bioorg. Med. Chem. Lett., (2005) 15:4:957CHEMBL2069Estrogen receptor alphanM39.8100active
\n"", + ""

449 rows × 17 columns

\n"", + ""
"" + ], + ""text/plain"": [ + "" activity_comment assay_chemblid \\\n"", + ""6530 Unspecified CHEMBL831663 \n"", + ""6531 Unspecified CHEMBL831661 \n"", + ""3114 Unspecified CHEMBL3736957 \n"", + ""3115 Unspecified CHEMBL3736956 \n"", + ""6798 Unspecified CHEMBL831663 \n"", + ""6799 Unspecified CHEMBL831661 \n"", + ""1739 Unspecified CHEMBL831663 \n"", + ""1740 Unspecified CHEMBL831661 \n"", + ""7559 Unspecified CHEMBL831663 \n"", + ""7560 Unspecified CHEMBL831661 \n"", + ""1282 Unspecified CHEMBL831663 \n"", + ""1283 Unspecified CHEMBL831661 \n"", + ""985 Unspecified CHEMBL831007 \n"", + ""986 Unspecified CHEMBL831528 \n"", + ""987 Unspecified CHEMBL831663 \n"", + ""988 Unspecified CHEMBL831661 \n"", + ""1399 Unspecified CHEMBL831663 \n"", + ""1400 Unspecified CHEMBL831661 \n"", + ""2241 Unspecified CHEMBL831663 \n"", + ""2242 Unspecified CHEMBL831661 \n"", + ""7518 Unspecified CHEMBL831663 \n"", + ""7519 Unspecified CHEMBL831661 \n"", + ""1470 Unspecified CHEMBL831663 \n"", + ""1471 Unspecified CHEMBL831661 \n"", + ""6569 Unspecified CHEMBL3736957 \n"", + ""6570 Unspecified CHEMBL3736956 \n"", + ""3878 Unspecified CHEMBL3825990 \n"", + ""3885 Unspecified CHEMBL3599826 \n"", + ""3892 Unspecified CHEMBL3382763 \n"", + ""3902 Unspecified CHEMBL3102026 \n"", + ""... ... ... \n"", + ""3691 Unspecified CHEMBL863236 \n"", + ""3697 Unspecified CHEMBL1909145 \n"", + ""3708 Unspecified CHEMBL904964 \n"", + ""3713 Unspecified CHEMBL867524 \n"", + ""3715 Unspecified CHEMBL831072 \n"", + ""3716 Unspecified CHEMBL831071 \n"", + ""3717 Unspecified CHEMBL829466 \n"", + ""3718 Unspecified CHEMBL832667 \n"", + ""3719 Unspecified CHEMBL831007 \n"", + ""3720 Unspecified CHEMBL831528 \n"", + ""3721 Unspecified CHEMBL833547 \n"", + ""3727 Unspecified CHEMBL679892 \n"", + ""3728 Unspecified CHEMBL679888 \n"", + ""3734 Unspecified CHEMBL681157 \n"", + ""3735 Unspecified CHEMBL677871 \n"", + ""279 Unspecified CHEMBL3829134 \n"", + ""280 Unspecified CHEMBL3777355 \n"", + ""281 Unspecified CHEMBL3624759 \n"", + ""282 Unspecified CHEMBL3624758 \n"", + ""283 Unspecified CHEMBL3382767 \n"", + ""284 Unspecified CHEMBL3382763 \n"", + ""286 Unspecified CHEMBL2406604 \n"", + ""306 Unspecified CHEMBL1023895 \n"", + ""307 Unspecified CHEMBL940315 \n"", + ""310 Unspecified CHEMBL831007 \n"", + ""313 Unspecified CHEMBL682292 \n"", + ""5548 Unspecified CHEMBL831663 \n"", + ""5549 Unspecified CHEMBL831661 \n"", + ""7480 Unspecified CHEMBL831663 \n"", + ""7481 Unspecified CHEMBL831661 \n"", + ""\n"", + "" assay_description assay_type \\\n"", + ""6530 Inhibition of transcriptional activation by hu... B \n"", + ""6531 Inhibition of binding to human estrogen recept... B \n"", + ""3114 Antagonist activity at ERalpha in human T47D-K... B \n"", + ""3115 Antagonist activity at luciferase-fused ERalph... B \n"", + ""6798 Inhibition of transcriptional activation by hu... B \n"", + ""6799 Inhibition of binding to human estrogen recept... B \n"", + ""1739 Inhibition of transcriptional activation by hu... B \n"", + ""1740 Inhibition of binding to human estrogen recept... B \n"", + ""7559 Inhibition of transcriptional activation by hu... B \n"", + ""7560 Inhibition of binding to human estrogen recept... B \n"", + ""1282 Inhibition of transcriptional activation by hu... B \n"", + ""1283 Inhibition of binding to human estrogen recept... B \n"", + ""985 Inhibition of 17-beta-estradiol mediated lucif... B \n"", + ""986 Inhibition of [3H]estradiol binding to human e... B \n"", + ""987 Inhibition of transcriptional activation by hu... B \n"", + ""988 Inhibition of binding to human estrogen recept... B \n"", + ""1399 Inhibition of transcriptional activation by hu... B \n"", + ""1400 Inhibition of binding to human estrogen recept... B \n"", + ""2241 Inhibition of transcriptional activation by hu... B \n"", + ""2242 Inhibition of binding to human estrogen recept... B \n"", + ""7518 Inhibition of transcriptional activation by hu... B \n"", + ""7519 Inhibition of binding to human estrogen recept... B \n"", + ""1470 Inhibition of transcriptional activation by hu... B \n"", + ""1471 Inhibition of binding to human estrogen recept... B \n"", + ""6569 Antagonist activity at ERalpha in human T47D-K... B \n"", + ""6570 Antagonist activity at luciferase-fused ERalph... B \n"", + ""3878 Inhibition of ER-alpha (unknown origin) by Lan... B \n"", + ""3885 Inhibition of estrogen receptor alpha (unknown... B \n"", + ""3892 Displacement of fluorescein-labeled estrogen f... B \n"", + ""3902 Agonist activity at ERalpha (unknown origin) b... B \n"", + ""... ... ... \n"", + ""3691 Inhibition of binding to recombinant human ERa... B \n"", + ""3697 DRUGMATRIX: Estrogen ERalpha radioligand bindi... B \n"", + ""3708 Displacement of [3H]estradiol from human recom... B \n"", + ""3713 Binding affinity to ERalpha B \n"", + ""3715 Binding potency for human ER alpha B \n"", + ""3716 Binding potency for human ER alpha B \n"", + ""3717 Inhibition of [3H]17-beta-estradiol binding to... B \n"", + ""3718 Inhibition of bindign to recombinant human est... B \n"", + ""3719 Inhibition of 17-beta-estradiol mediated lucif... B \n"", + ""3720 Inhibition of [3H]estradiol binding to human e... B \n"", + ""3721 Inhibition of estrogen receptor alpha B \n"", + ""3727 In vitro inhibition of transcriptional activat... B \n"", + ""3728 In vitro binding affinity for estrogen recepto... B \n"", + ""3734 In vitro inhibition of [3H]17-beta-estradiol b... B \n"", + ""3735 Binding affinity for Human Estrogen receptor-a... B \n"", + ""279 Displacement of fluorescein-labeled estrogen l... B \n"", + ""280 Displacement of [3H]-E2 from human ER-alpha in... B \n"", + ""281 Antagonist activity at ERalpha receptor in hum... B \n"", + ""282 Binding affinity to ERalpha receptor (unknown ... B \n"", + ""283 Binding affinity to human ERalpha B \n"", + ""284 Displacement of fluorescein-labeled estrogen f... B \n"", + ""286 Antagonist activity at Gal4 DBD-fused human ER... B \n"", + ""306 Antiestrogenic activity in human T47D cells ex... B \n"", + ""307 Displacement of fluorescein labeled estradiol ... B \n"", + ""310 Inhibition of 17-beta-estradiol mediated lucif... B \n"", + ""313 Displacement of [3H]17-beta-estradiol from hum... B \n"", + ""5548 Inhibition of transcriptional activation by hu... B \n"", + ""5549 Inhibition of binding to human estrogen recept... B \n"", + ""7480 Inhibition of transcriptional activation by hu... B \n"", + ""7481 Inhibition of binding to human estrogen recept... B \n"", + ""\n"", + "" bioactivity_type chemblId name_in_reference operator organism \\\n"", + ""6530 IC50 CHEMBL100231 13 = Homo sapiens \n"", + ""6531 IC50 CHEMBL100231 13 = Homo sapiens \n"", + ""3114 IC50 CHEMBL10041 4-OHT = Homo sapiens \n"", + ""3115 IC50 CHEMBL10041 4-OHT = Homo sapiens \n"", + ""6798 IC50 CHEMBL100595 19 = Homo sapiens \n"", + ""6799 IC50 CHEMBL100595 19 = Homo sapiens \n"", + ""1739 IC50 CHEMBL100617 15 = Homo sapiens \n"", + ""1740 IC50 CHEMBL100617 15 = Homo sapiens \n"", + ""7559 IC50 CHEMBL100763 10 = Homo sapiens \n"", + ""7560 IC50 CHEMBL100763 10 = Homo sapiens \n"", + ""1282 IC50 CHEMBL101083 9 = Homo sapiens \n"", + ""1283 IC50 CHEMBL101083 9 = Homo sapiens \n"", + ""985 IC50 CHEMBL101382 60 = Homo sapiens \n"", + ""986 IC50 CHEMBL101382 60 = Homo sapiens \n"", + ""987 IC50 CHEMBL101382 2 = Homo sapiens \n"", + ""988 IC50 CHEMBL101382 2 = Homo sapiens \n"", + ""1399 IC50 CHEMBL101807 4 = Homo sapiens \n"", + ""1400 IC50 CHEMBL101807 4 = Homo sapiens \n"", + ""2241 IC50 CHEMBL101997 8 = Homo sapiens \n"", + ""2242 IC50 CHEMBL101997 8 = Homo sapiens \n"", + ""7518 IC50 CHEMBL103294 11 = Homo sapiens \n"", + ""7519 IC50 CHEMBL103294 11 = Homo sapiens \n"", + ""1470 IC50 CHEMBL103305 18 = Homo sapiens \n"", + ""1471 IC50 CHEMBL103305 18 = Homo sapiens \n"", + ""6569 IC50 CHEMBL1222035 4; ICI-164,384 = Homo sapiens \n"", + ""6570 IC50 CHEMBL1222035 4; ICI-164,384 = Homo sapiens \n"", + ""3878 IC50 CHEMBL135 Estradiol = Homo sapiens \n"", + ""3885 IC50 CHEMBL135 E2 = Homo sapiens \n"", + ""3892 IC50 CHEMBL135 1 = Homo sapiens \n"", + ""3902 IC50 CHEMBL135 E2 = Homo sapiens \n"", + ""... ... ... ... ... ... \n"", + ""3691 IC50 CHEMBL81 2 = Homo sapiens \n"", + ""3697 IC50 CHEMBL81 RALOXIFENE = Homo sapiens \n"", + ""3708 IC50 CHEMBL81 raloxifene = Homo sapiens \n"", + ""3713 IC50 CHEMBL81 raloxifene = Homo sapiens \n"", + ""3715 IC50 CHEMBL81 Raloxifene = Homo sapiens \n"", + ""3716 IC50 CHEMBL81 Raloxifene = Homo sapiens \n"", + ""3717 IC50 CHEMBL81 Raloxifene = Homo sapiens \n"", + ""3718 IC50 CHEMBL81 Raloxifene = Homo sapiens \n"", + ""3719 IC50 CHEMBL81 7 = Homo sapiens \n"", + ""3720 IC50 CHEMBL81 7 = Homo sapiens \n"", + ""3721 IC50 CHEMBL81 1 = Homo sapiens \n"", + ""3727 IC50 CHEMBL81 Raloxifene = Homo sapiens \n"", + ""3728 IC50 CHEMBL81 1 (Raloxifene) = Homo sapiens \n"", + ""3734 IC50 CHEMBL81 2, Raloxifene = Homo sapiens \n"", + ""3735 IC50 CHEMBL81 1 = Homo sapiens \n"", + ""279 IC50 CHEMBL83 Tamoxifen = Homo sapiens \n"", + ""280 IC50 CHEMBL83 TAM = Homo sapiens \n"", + ""281 IC50 CHEMBL83 1a = Homo sapiens \n"", + ""282 IC50 CHEMBL83 1a = Homo sapiens \n"", + ""283 IC50 CHEMBL83 2 = Homo sapiens \n"", + ""284 IC50 CHEMBL83 2 = Homo sapiens \n"", + ""286 IC50 CHEMBL83 Tamoxifen = Homo sapiens \n"", + ""306 IC50 CHEMBL83 Tamoxifen = Homo sapiens \n"", + ""307 IC50 CHEMBL83 1a = Homo sapiens \n"", + ""310 IC50 CHEMBL83 6 = Homo sapiens \n"", + ""313 IC50 CHEMBL83 1 = Homo sapiens \n"", + ""5548 IC50 CHEMBL98470 12 = Homo sapiens \n"", + ""5549 IC50 CHEMBL98470 12 = Homo sapiens \n"", + ""7480 IC50 CHEMBL98558 3 = Homo sapiens \n"", + ""7481 IC50 CHEMBL98558 3 = Homo sapiens \n"", + ""\n"", + "" parent_cmpd_chemblid reference \\\n"", + ""6530 CHEMBL100231 Bioorg. Med. Chem. Lett., (2005) 15:4:957 \n"", + ""6531 CHEMBL100231 Bioorg. Med. Chem. Lett., (2005) 15:4:957 \n"", + ""3114 CHEMBL10041 Bioorg. Med. Chem., (2015) 23:24:7597 \n"", + ""3115 CHEMBL10041 Bioorg. Med. Chem., (2015) 23:24:7597 \n"", + ""6798 CHEMBL100595 Bioorg. Med. Chem. Lett., (2005) 15:4:957 \n"", + ""6799 CHEMBL100595 Bioorg. Med. Chem. Lett., (2005) 15:4:957 \n"", + ""1739 CHEMBL100617 Bioorg. Med. Chem. Lett., (2005) 15:4:957 \n"", + ""1740 CHEMBL100617 Bioorg. Med. Chem. Lett., (2005) 15:4:957 \n"", + ""7559 CHEMBL100763 Bioorg. Med. Chem. Lett., (2005) 15:4:957 \n"", + ""7560 CHEMBL100763 Bioorg. Med. Chem. Lett., (2005) 15:4:957 \n"", + ""1282 CHEMBL101083 Bioorg. Med. Chem. Lett., (2005) 15:4:957 \n"", + ""1283 CHEMBL101083 Bioorg. Med. Chem. Lett., (2005) 15:4:957 \n"", + ""985 CHEMBL101382 J. Med. Chem., (2005) 48:2:364 \n"", + ""986 CHEMBL101382 J. Med. Chem., (2005) 48:2:364 \n"", + ""987 CHEMBL101382 Bioorg. Med. Chem. Lett., (2005) 15:4:957 \n"", + ""988 CHEMBL101382 Bioorg. Med. Chem. Lett., (2005) 15:4:957 \n"", + ""1399 CHEMBL101807 Bioorg. Med. Chem. Lett., (2005) 15:4:957 \n"", + ""1400 CHEMBL101807 Bioorg. Med. Chem. Lett., (2005) 15:4:957 \n"", + ""2241 CHEMBL101997 Bioorg. Med. Chem. Lett., (2005) 15:4:957 \n"", + ""2242 CHEMBL101997 Bioorg. Med. Chem. Lett., (2005) 15:4:957 \n"", + ""7518 CHEMBL103294 Bioorg. Med. Chem. Lett., (2005) 15:4:957 \n"", + ""7519 CHEMBL103294 Bioorg. Med. Chem. Lett., (2005) 15:4:957 \n"", + ""1470 CHEMBL103305 Bioorg. Med. Chem. Lett., (2005) 15:4:957 \n"", + ""1471 CHEMBL103305 Bioorg. Med. Chem. Lett., (2005) 15:4:957 \n"", + ""6569 CHEMBL1222035 Bioorg. Med. Chem., (2015) 23:24:7597 \n"", + ""6570 CHEMBL1222035 Bioorg. Med. Chem., (2015) 23:24:7597 \n"", + ""3878 CHEMBL135 Bioorg. Med. Chem., (2016) 24:18:4075 \n"", + ""3885 CHEMBL135 Bioorg. Med. Chem., (2015) 23:15:4132 \n"", + ""3892 CHEMBL135 J. Med. Chem., (2014) 57:22:9370 \n"", + ""3902 CHEMBL135 Bioorg. Med. Chem., (2014) 22:1:303 \n"", + ""... ... ... \n"", + ""3691 CHEMBL81 Bioorg. Med. Chem. Lett., (2005) 15:23:5124 \n"", + ""3697 CHEMBL81 Unspecified \n"", + ""3708 CHEMBL81 J. Med. Chem., (2007) 50:11:2682 \n"", + ""3713 CHEMBL81 J. Med. Chem., (2006) 49:11:3056 \n"", + ""3715 CHEMBL81 Bioorg. Med. Chem. Lett., (2004) 14:15:3865 \n"", + ""3716 CHEMBL81 Bioorg. Med. Chem. Lett., (2004) 14:15:3861 \n"", + ""3717 CHEMBL81 Bioorg. Med. Chem. Lett., (2005) 15:11:2891 \n"", + ""3718 CHEMBL81 Bioorg. Med. Chem. Lett., (2005) 15:1:107 \n"", + ""3719 CHEMBL81 J. Med. Chem., (2005) 48:2:364 \n"", + ""3720 CHEMBL81 J. Med. Chem., (2005) 48:2:364 \n"", + ""3721 CHEMBL81 Bioorg. Med. Chem. Lett., (2005) 15:5:1505 \n"", + ""3727 CHEMBL81 J. Med. Chem., (2002) 45:25:5492 \n"", + ""3728 CHEMBL81 Bioorg. Med. Chem. Lett., (2003) 13:11:1919 \n"", + ""3734 CHEMBL81 J. Med. Chem., (2001) 44:11:1654 \n"", + ""3735 CHEMBL81 Bioorg. Med. Chem. Lett., (2003) 13:3:479 \n"", + ""279 CHEMBL83 Unspecified \n"", + ""280 CHEMBL83 Unspecified \n"", + ""281 CHEMBL83 J. Med. Chem., (2015) 58:20:8128 \n"", + ""282 CHEMBL83 J. Med. Chem., (2015) 58:20:8128 \n"", + ""283 CHEMBL83 J. Med. Chem., (2014) 57:22:9370 \n"", + ""284 CHEMBL83 J. Med. Chem., (2014) 57:22:9370 \n"", + ""286 CHEMBL83 J. Med. Chem., (2013) 56:14:5782 \n"", + ""306 CHEMBL83 Bioorg. Med. Chem. Lett., (2009) 19:4:1218 \n"", + ""307 CHEMBL83 Bioorg. Med. Chem., (2008) 16:21:9554 \n"", + ""310 CHEMBL83 J. Med. Chem., (2005) 48:2:364 \n"", + ""313 CHEMBL83 J. Med. Chem., (1999) 42:16:3126 \n"", + ""5548 CHEMBL98470 Bioorg. Med. Chem. Lett., (2005) 15:4:957 \n"", + ""5549 CHEMBL98470 Bioorg. Med. Chem. Lett., (2005) 15:4:957 \n"", + ""7480 CHEMBL98558 Bioorg. Med. Chem. Lett., (2005) 15:4:957 \n"", + ""7481 CHEMBL98558 Bioorg. Med. Chem. Lett., (2005) 15:4:957 \n"", + ""\n"", + "" target_chemblid target_confidence target_name units \\\n"", + ""6530 CHEMBL206 9 Estrogen receptor alpha nM \n"", + ""6531 CHEMBL206 9 Estrogen receptor alpha nM \n"", + ""3114 CHEMBL206 9 Estrogen receptor alpha nM \n"", + ""3115 CHEMBL206 9 Estrogen receptor alpha nM \n"", + ""6798 CHEMBL206 9 Estrogen receptor alpha nM \n"", + ""6799 CHEMBL206 9 Estrogen receptor alpha nM \n"", + ""1739 CHEMBL206 9 Estrogen receptor alpha nM \n"", + ""1740 CHEMBL206 9 Estrogen receptor alpha nM \n"", + ""7559 CHEMBL206 9 Estrogen receptor alpha nM \n"", + ""7560 CHEMBL206 9 Estrogen receptor alpha nM \n"", + ""1282 CHEMBL206 9 Estrogen receptor alpha nM \n"", + ""1283 CHEMBL206 9 Estrogen receptor alpha nM \n"", + ""985 CHEMBL206 9 Estrogen receptor alpha nM \n"", + ""986 CHEMBL206 9 Estrogen receptor alpha nM \n"", + ""987 CHEMBL206 9 Estrogen receptor alpha nM \n"", + ""988 CHEMBL206 9 Estrogen receptor alpha nM \n"", + ""1399 CHEMBL206 9 Estrogen receptor alpha nM \n"", + ""1400 CHEMBL206 9 Estrogen receptor alpha nM \n"", + ""2241 CHEMBL206 9 Estrogen receptor alpha nM \n"", + ""2242 CHEMBL206 9 Estrogen receptor alpha nM \n"", + ""7518 CHEMBL206 9 Estrogen receptor alpha nM \n"", + ""7519 CHEMBL206 9 Estrogen receptor alpha nM \n"", + ""1470 CHEMBL206 9 Estrogen receptor alpha nM \n"", + ""1471 CHEMBL206 9 Estrogen receptor alpha nM \n"", + ""6569 CHEMBL206 9 Estrogen receptor alpha nM \n"", + ""6570 CHEMBL206 9 Estrogen receptor alpha nM \n"", + ""3878 CHEMBL206 9 Estrogen receptor alpha nM \n"", + ""3885 CHEMBL206 9 Estrogen receptor alpha nM \n"", + ""3892 CHEMBL206 9 Estrogen receptor alpha nM \n"", + ""3902 CHEMBL206 9 Estrogen receptor alpha nM \n"", + ""... ... ... ... ... \n"", + ""3691 CHEMBL206 9 Estrogen receptor alpha nM \n"", + ""3697 CHEMBL206 9 Estrogen receptor alpha nM \n"", + ""3708 CHEMBL206 9 Estrogen receptor alpha nM \n"", + ""3713 CHEMBL206 9 Estrogen receptor alpha nM \n"", + ""3715 CHEMBL206 9 Estrogen receptor alpha nM \n"", + ""3716 CHEMBL206 9 Estrogen receptor alpha nM \n"", + ""3717 CHEMBL206 9 Estrogen receptor alpha nM \n"", + ""3718 CHEMBL206 9 Estrogen receptor alpha nM \n"", + ""3719 CHEMBL206 9 Estrogen receptor alpha nM \n"", + ""3720 CHEMBL206 9 Estrogen receptor alpha nM \n"", + ""3721 CHEMBL206 9 Estrogen receptor alpha nM \n"", + ""3727 CHEMBL206 9 Estrogen receptor alpha nM \n"", + ""3728 CHEMBL206 9 Estrogen receptor alpha nM \n"", + ""3734 CHEMBL206 9 Estrogen receptor alpha nM \n"", + ""3735 CHEMBL206 9 Estrogen receptor alpha nM \n"", + ""279 CHEMBL206 9 Estrogen receptor alpha nM \n"", + ""280 CHEMBL206 9 Estrogen receptor alpha nM \n"", + ""281 CHEMBL206 9 Estrogen receptor alpha nM \n"", + ""282 CHEMBL206 9 Estrogen receptor alpha nM \n"", + ""283 CHEMBL206 9 Estrogen receptor alpha nM \n"", + ""284 CHEMBL206 9 Estrogen receptor alpha nM \n"", + ""286 CHEMBL206 9 Estrogen receptor alpha nM \n"", + ""306 CHEMBL206 9 Estrogen receptor alpha nM \n"", + ""307 CHEMBL206 9 Estrogen receptor alpha nM \n"", + ""310 CHEMBL206 9 Estrogen receptor alpha nM \n"", + ""313 CHEMBL206 9 Estrogen receptor alpha nM \n"", + ""5548 CHEMBL206 9 Estrogen receptor alpha nM \n"", + ""5549 CHEMBL206 9 Estrogen receptor alpha nM \n"", + ""7480 CHEMBL206 9 Estrogen receptor alpha nM \n"", + ""7481 CHEMBL206 9 Estrogen receptor alpha nM \n"", + ""\n"", + "" value STATUS \n"", + ""6530 5.2000 active \n"", + ""6531 70.9600 active \n"", + ""3114 10.0000 active \n"", + ""3115 70.0000 active \n"", + ""6798 16.1100 active \n"", + ""6799 46.0300 active \n"", + ""1739 0.6998 active \n"", + ""1740 33.9600 active \n"", + ""7559 3.3040 active \n"", + ""7560 26.9800 active \n"", + ""1282 1.9010 active \n"", + ""1283 38.0200 active \n"", + ""985 16.5000 active \n"", + ""986 19.0000 active \n"", + ""987 16.4800 active \n"", + ""988 19.0100 active \n"", + ""1399 2.6000 active \n"", + ""1400 28.9700 active \n"", + ""2241 1.6000 active \n"", + ""2242 30.9700 active \n"", + ""7518 3.0970 active \n"", + ""7519 34.9900 active \n"", + ""1470 4.4980 active \n"", + ""1471 48.9800 active \n"", + ""6569 50.0000 active \n"", + ""6570 340.0000 active \n"", + ""3878 1.7000 active \n"", + ""3885 0.9000 active \n"", + ""3892 5.7000 active \n"", + ""3902 1.3000 active \n"", + ""... ... ... \n"", + ""3691 1.8000 active \n"", + ""3697 0.5530 active \n"", + ""3708 20.6000 active \n"", + ""3713 1.8000 active \n"", + ""3715 1.8000 active \n"", + ""3716 1.8000 active \n"", + ""3717 0.8900 active \n"", + ""3718 1.8000 active \n"", + ""3719 2.4000 active \n"", + ""3720 22.0000 active \n"", + ""3721 0.7300 active \n"", + ""3727 0.7000 active \n"", + ""3728 7.0000 active \n"", + ""3734 4.0000 active \n"", + ""3735 1.8000 active \n"", + ""279 1000.0000 active \n"", + ""280 8.0000 active \n"", + ""281 10000.0000 inactive \n"", + ""282 24.5500 active \n"", + ""283 60.9000 active \n"", + ""284 61.0000 active \n"", + ""286 39.0000 active \n"", + ""306 100.0000 active \n"", + ""307 70.0000 active \n"", + ""310 622.0000 active \n"", + ""313 42.0000 active \n"", + ""5548 2.6000 active \n"", + ""5549 43.9500 active \n"", + ""7480 1.7990 active \n"", + ""7481 39.8100 active \n"", + ""\n"", + ""[449 rows x 17 columns]"" + ] + }, + ""execution_count"": 28, + ""metadata"": {}, + ""output_type"": ""execute_result"" + } + ], + ""source"": [ + ""bioactsDF_Train_dup"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 29, + ""metadata"": {}, + ""outputs"": [ + { + ""data"": { + ""text/html"": [ + ""
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chemblIdvalue
meanstd
0CHEMBL10023138.0846.499342
1CHEMBL1004140.0042.426407
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"" + ], + ""text/plain"": [ + "" chemblId value \n"", + "" mean std\n"", + ""0 CHEMBL100231 38.08 46.499342\n"", + ""1 CHEMBL10041 40.00 42.426407"" + ] + }, + ""execution_count"": 29, + ""metadata"": {}, + ""output_type"": ""execute_result"" + } + ], + ""source"": [ + ""# mean and std of all duplicae\n"", + ""\n"", + ""mean_std = bioactsDF_Train_dup.groupby(['chemblId'], as_index=False).agg(\n"", + "" {'value':['mean','std']})\n"", + ""mean_std.head(2)"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 30, + ""metadata"": {}, + ""outputs"": [ + { + ""data"": { + ""text/plain"": [ + ""165"" + ] + }, + ""execution_count"": 30, + ""metadata"": {}, + ""output_type"": ""execute_result"" + } + ], + ""source"": [ + ""len(mean_std)"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 31, + ""metadata"": {}, + ""outputs"": [ + { + ""name"": ""stderr"", + ""output_type"": ""stream"", + ""text"": [ + ""/Users/zeromtmu/anaconda2/envs/ER_alpha/lib/python2.7/site-packages/pandas/core/reshape/merge.py:551: UserWarning: merging between different levels can give an unintended result (1 levels on the left, 2 on the right)\n"", + "" warnings.warn(msg, UserWarning)\n"", + ""/Users/zeromtmu/anaconda2/envs/ER_alpha/lib/python2.7/site-packages/pandas/core/reshape/merge.py:862: PerformanceWarning: dropping on a non-lexsorted multi-index without a level parameter may impact performance.\n"", + "" self.right = self.right.drop(right_drop, axis=1)\n"" + ] + }, + { + ""data"": { + ""text/plain"": [ + ""449"" + ] + }, + ""execution_count"": 31, + ""metadata"": {}, + ""output_type"": ""execute_result"" + } + ], + ""source"": [ + ""bioactsDF_Train_dup = bioactsDF_Train_dup.merge(mean_std, on='chemblId', how='inner')\n"", + ""len(bioactsDF_Train_dup)"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 32, + ""metadata"": {}, + ""outputs"": [ + { + ""data"": { + ""text/html"": [ + ""
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activity_commentassay_chemblidassay_descriptionassay_typebioactivity_typechemblIdname_in_referenceoperatororganismparent_cmpd_chemblidreferencetarget_chemblidtarget_confidencetarget_nameunitsvalueSTATUS(value, mean)(value, std)
0UnspecifiedCHEMBL831663Inhibition of transcriptional activation by hu...BIC50CHEMBL10023113=Homo sapiensCHEMBL100231Bioorg. Med. Chem. Lett., (2005) 15:4:957CHEMBL2069Estrogen receptor alphanM5.20active38.0846.499342
1UnspecifiedCHEMBL831661Inhibition of binding to human estrogen recept...BIC50CHEMBL10023113=Homo sapiensCHEMBL100231Bioorg. Med. Chem. Lett., (2005) 15:4:957CHEMBL2069Estrogen receptor alphanM70.96active38.0846.499342
2UnspecifiedCHEMBL3736957Antagonist activity at ERalpha in human T47D-K...BIC50CHEMBL100414-OHT=Homo sapiensCHEMBL10041Bioorg. Med. Chem., (2015) 23:24:7597CHEMBL2069Estrogen receptor alphanM10.00active40.0042.426407
\n"", + ""
"" + ], + ""text/plain"": [ + "" activity_comment assay_chemblid \\\n"", + ""0 Unspecified CHEMBL831663 \n"", + ""1 Unspecified CHEMBL831661 \n"", + ""2 Unspecified CHEMBL3736957 \n"", + ""\n"", + "" assay_description assay_type \\\n"", + ""0 Inhibition of transcriptional activation by hu... B \n"", + ""1 Inhibition of binding to human estrogen recept... B \n"", + ""2 Antagonist activity at ERalpha in human T47D-K... B \n"", + ""\n"", + "" bioactivity_type chemblId name_in_reference operator organism \\\n"", + ""0 IC50 CHEMBL100231 13 = Homo sapiens \n"", + ""1 IC50 CHEMBL100231 13 = Homo sapiens \n"", + ""2 IC50 CHEMBL10041 4-OHT = Homo sapiens \n"", + ""\n"", + "" parent_cmpd_chemblid reference \\\n"", + ""0 CHEMBL100231 Bioorg. Med. Chem. Lett., (2005) 15:4:957 \n"", + ""1 CHEMBL100231 Bioorg. Med. Chem. Lett., (2005) 15:4:957 \n"", + ""2 CHEMBL10041 Bioorg. Med. Chem., (2015) 23:24:7597 \n"", + ""\n"", + "" target_chemblid target_confidence target_name units value \\\n"", + ""0 CHEMBL206 9 Estrogen receptor alpha nM 5.20 \n"", + ""1 CHEMBL206 9 Estrogen receptor alpha nM 70.96 \n"", + ""2 CHEMBL206 9 Estrogen receptor alpha nM 10.00 \n"", + ""\n"", + "" STATUS (value, mean) (value, std) \n"", + ""0 active 38.08 46.499342 \n"", + ""1 active 38.08 46.499342 \n"", + ""2 active 40.00 42.426407 "" + ] + }, + ""execution_count"": 32, + ""metadata"": {}, + ""output_type"": ""execute_result"" + } + ], + ""source"": [ + ""bioactsDF_Train_dup.head(3)"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 34, + ""metadata"": {}, + ""outputs"": [ + { + ""data"": { + ""text/plain"": [ + ""(80, 31)"" + ] + }, + ""execution_count"": 34, + ""metadata"": {}, + ""output_type"": ""execute_result"" + } + ], + ""source"": [ + ""#keep only SD less thab 2SD\n"", + ""\n"", + ""bioactsDF_Train_dup = bioactsDF_Train_dup[(bioactsDF_Train_dup[('value', 'std')] < 2)]\n"", + ""len(bioactsDF_Train_dup), len(bioactsDF_Train_dup['chemblId'].unique())"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 35, + ""metadata"": {}, + ""outputs"": [ + { + ""data"": { + ""text/html"": [ + ""
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activity_commentassay_chemblidassay_descriptionassay_typebioactivity_typechemblIdname_in_referenceoperatororganismparent_cmpd_chemblidreferencetarget_chemblidtarget_confidencetarget_nameunitsvalueSTATUS(value, mean)(value, std)select
0UnspecifiedCHEMBL831007Inhibition of 17-beta-estradiol mediated lucif...BIC50CHEMBL10138260=Homo sapiensCHEMBL101382J. Med. Chem., (2005) 48:2:364CHEMBL2069Estrogen receptor alphanM16.50active17.74751.4520651.2475
1UnspecifiedCHEMBL831528Inhibition of [3H]estradiol binding to human e...BIC50CHEMBL10138260=Homo sapiensCHEMBL101382J. Med. Chem., (2005) 48:2:364CHEMBL2069Estrogen receptor alphanM19.00active17.74751.4520651.2525
2UnspecifiedCHEMBL831661Inhibition of binding to human estrogen recept...BIC50CHEMBL1013822=Homo sapiensCHEMBL101382Bioorg. Med. Chem. Lett., (2005) 15:4:957CHEMBL2069Estrogen receptor alphanM19.01active17.74751.4520651.2625
3UnspecifiedCHEMBL831663Inhibition of transcriptional activation by hu...BIC50CHEMBL1013822=Homo sapiensCHEMBL101382Bioorg. Med. Chem. Lett., (2005) 15:4:957CHEMBL2069Estrogen receptor alphanM16.48active17.74751.4520651.2675
4UnspecifiedCHEMBL832667Inhibition of bindign to recombinant human est...BIC50CHEMBL18079219=Homo sapiensCHEMBL180792Bioorg. Med. Chem. Lett., (2005) 15:1:107CHEMBL2069Estrogen receptor alphanM1.70active1.50000.2828430.2000
\n"", + ""
"" + ], + ""text/plain"": [ + "" activity_comment assay_chemblid \\\n"", + ""0 Unspecified CHEMBL831007 \n"", + ""1 Unspecified CHEMBL831528 \n"", + ""2 Unspecified CHEMBL831661 \n"", + ""3 Unspecified CHEMBL831663 \n"", + ""4 Unspecified CHEMBL832667 \n"", + ""\n"", + "" assay_description assay_type \\\n"", + ""0 Inhibition of 17-beta-estradiol mediated lucif... B \n"", + ""1 Inhibition of [3H]estradiol binding to human e... B \n"", + ""2 Inhibition of binding to human estrogen recept... B \n"", + ""3 Inhibition of transcriptional activation by hu... B \n"", + ""4 Inhibition of bindign to recombinant human est... B \n"", + ""\n"", + "" bioactivity_type chemblId name_in_reference operator organism \\\n"", + ""0 IC50 CHEMBL101382 60 = Homo sapiens \n"", + ""1 IC50 CHEMBL101382 60 = Homo sapiens \n"", + ""2 IC50 CHEMBL101382 2 = Homo sapiens \n"", + ""3 IC50 CHEMBL101382 2 = Homo sapiens \n"", + ""4 IC50 CHEMBL180792 19 = Homo sapiens \n"", + ""\n"", + "" parent_cmpd_chemblid reference \\\n"", + ""0 CHEMBL101382 J. Med. Chem., (2005) 48:2:364 \n"", + ""1 CHEMBL101382 J. Med. Chem., (2005) 48:2:364 \n"", + ""2 CHEMBL101382 Bioorg. Med. Chem. Lett., (2005) 15:4:957 \n"", + ""3 CHEMBL101382 Bioorg. Med. Chem. Lett., (2005) 15:4:957 \n"", + ""4 CHEMBL180792 Bioorg. Med. Chem. Lett., (2005) 15:1:107 \n"", + ""\n"", + "" target_chemblid target_confidence target_name units value \\\n"", + ""0 CHEMBL206 9 Estrogen receptor alpha nM 16.50 \n"", + ""1 CHEMBL206 9 Estrogen receptor alpha nM 19.00 \n"", + ""2 CHEMBL206 9 Estrogen receptor alpha nM 19.01 \n"", + ""3 CHEMBL206 9 Estrogen receptor alpha nM 16.48 \n"", + ""4 CHEMBL206 9 Estrogen receptor alpha nM 1.70 \n"", + ""\n"", + "" STATUS (value, mean) (value, std) select \n"", + ""0 active 17.7475 1.452065 1.2475 \n"", + ""1 active 17.7475 1.452065 1.2525 \n"", + ""2 active 17.7475 1.452065 1.2625 \n"", + ""3 active 17.7475 1.452065 1.2675 \n"", + ""4 active 1.5000 0.282843 0.2000 "" + ] + }, + ""execution_count"": 35, + ""metadata"": {}, + ""output_type"": ""execute_result"" + } + ], + ""source"": [ + ""bioactsDF_Train_dup['select'] = (bioactsDF_Train_dup['value']- bioactsDF_Train_dup[('value', 'mean')]).abs()\n"", + ""#value_median = keep.groupby('chemblId')['value'].transform('median')\n"", + ""\n"", + ""bioactsDF_Train_dup = bioactsDF_Train_dup.groupby([\""chemblId\""]).apply(lambda x: x.sort_values([\""select\""], ascending = True)).reset_index(drop=True)\n"", + ""bioactsDF_Train_dup.head(5)"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 36, + ""metadata"": {}, + ""outputs"": [ + { + ""data"": { + ""text/html"": [ + ""
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activity_commentassay_chemblidassay_descriptionassay_typebioactivity_typechemblIdname_in_referenceoperatororganismparent_cmpd_chemblidreferencetarget_chemblidtarget_confidencetarget_nameunitsvalueSTATUS(value, mean)(value, std)select
0UnspecifiedCHEMBL831007Inhibition of 17-beta-estradiol mediated lucif...BIC50CHEMBL10138260=Homo sapiensCHEMBL101382J. Med. Chem., (2005) 48:2:364CHEMBL2069Estrogen receptor alphanM16.5active17.74751.4520651.2475
4UnspecifiedCHEMBL832667Inhibition of bindign to recombinant human est...BIC50CHEMBL18079219=Homo sapiensCHEMBL180792Bioorg. Med. Chem. Lett., (2005) 15:1:107CHEMBL2069Estrogen receptor alphanM1.7active1.50000.2828430.2000
\n"", + ""
"" + ], + ""text/plain"": [ + "" activity_comment assay_chemblid \\\n"", + ""0 Unspecified CHEMBL831007 \n"", + ""4 Unspecified CHEMBL832667 \n"", + ""\n"", + "" assay_description assay_type \\\n"", + ""0 Inhibition of 17-beta-estradiol mediated lucif... B \n"", + ""4 Inhibition of bindign to recombinant human est... B \n"", + ""\n"", + "" bioactivity_type chemblId name_in_reference operator organism \\\n"", + ""0 IC50 CHEMBL101382 60 = Homo sapiens \n"", + ""4 IC50 CHEMBL180792 19 = Homo sapiens \n"", + ""\n"", + "" parent_cmpd_chemblid reference \\\n"", + ""0 CHEMBL101382 J. Med. Chem., (2005) 48:2:364 \n"", + ""4 CHEMBL180792 Bioorg. Med. Chem. Lett., (2005) 15:1:107 \n"", + ""\n"", + "" target_chemblid target_confidence target_name units value \\\n"", + ""0 CHEMBL206 9 Estrogen receptor alpha nM 16.5 \n"", + ""4 CHEMBL206 9 Estrogen receptor alpha nM 1.7 \n"", + ""\n"", + "" STATUS (value, mean) (value, std) select \n"", + ""0 active 17.7475 1.452065 1.2475 \n"", + ""4 active 1.5000 0.282843 0.2000 "" + ] + }, + ""execution_count"": 36, + ""metadata"": {}, + ""output_type"": ""execute_result"" + } + ], + ""source"": [ + ""bioactsDF_Train_dup = bioactsDF_Train_dup.drop_duplicates(subset='chemblId', keep='first')\n"", + ""bioactsDF_Train_dup.head(2)"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 37, + ""metadata"": {}, + ""outputs"": [ + { + ""data"": { + ""text/plain"": [ + ""(31, 31)"" + ] + }, + ""execution_count"": 37, + ""metadata"": {}, + ""output_type"": ""execute_result"" + } + ], + ""source"": [ + ""len(bioactsDF_Train_dup), len(bioactsDF_Train_dup['chemblId'].unique())"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 38, + ""metadata"": {}, + ""outputs"": [ + { + ""data"": { + ""text/html"": [ + ""
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activity_commentassay_chemblidassay_descriptionassay_typebioactivity_typechemblIdname_in_referenceoperatororganismparent_cmpd_chemblidreferencetarget_chemblidtarget_confidencetarget_nameunitsvalueSTATUS(value, mean)(value, std)select
76UnspecifiedCHEMBL3776153Downregulation of ERalpha in human MCF7 cells ...BIC50CHEMBL377590822=Homo sapiensCHEMBL3775908ACS Med. Chem. Lett., (2016) 7:1:94CHEMBL2069Estrogen receptor alphanM0.370active0.8350.6576090.465
78UnspecifiedCHEMBL1909145DRUGMATRIX: Estrogen ERalpha radioligand bindi...BIC50CHEMBL691ETHINYLESTRADIOL=Homo sapiensCHEMBL691UnspecifiedCHEMBL2069Estrogen receptor alphanM0.448active1.2241.0974300.776
\n"", + ""
"" + ], + ""text/plain"": [ + "" activity_comment assay_chemblid \\\n"", + ""76 Unspecified CHEMBL3776153 \n"", + ""78 Unspecified CHEMBL1909145 \n"", + ""\n"", + "" assay_description assay_type \\\n"", + ""76 Downregulation of ERalpha in human MCF7 cells ... B \n"", + ""78 DRUGMATRIX: Estrogen ERalpha radioligand bindi... B \n"", + ""\n"", + "" bioactivity_type chemblId name_in_reference operator organism \\\n"", + ""76 IC50 CHEMBL3775908 22 = Homo sapiens \n"", + ""78 IC50 CHEMBL691 ETHINYLESTRADIOL = Homo sapiens \n"", + ""\n"", + "" parent_cmpd_chemblid reference target_chemblid \\\n"", + ""76 CHEMBL3775908 ACS Med. Chem. Lett., (2016) 7:1:94 CHEMBL206 \n"", + ""78 CHEMBL691 Unspecified CHEMBL206 \n"", + ""\n"", + "" target_confidence target_name units value STATUS \\\n"", + ""76 9 Estrogen receptor alpha nM 0.370 active \n"", + ""78 9 Estrogen receptor alpha nM 0.448 active \n"", + ""\n"", + "" (value, mean) (value, std) select \n"", + ""76 0.835 0.657609 0.465 \n"", + ""78 1.224 1.097430 0.776 "" + ] + }, + ""execution_count"": 38, + ""metadata"": {}, + ""output_type"": ""execute_result"" + } + ], + ""source"": [ + ""bioactsDF_Train_dup.tail(2)"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 39, + ""metadata"": { + ""collapsed"": true + }, + ""outputs"": [], + ""source"": [ + ""bioactsDF_Train_dup = bioactsDF_Train_dup.drop('select', 1)\n"", + ""bioactsDF_Train_dup = bioactsDF_Train_dup.drop(('value', 'mean'), 1)\n"", + ""bioactsDF_Train_dup = bioactsDF_Train_dup.drop(('value', 'std'), 1)"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 40, + ""metadata"": {}, + ""outputs"": [ + { + ""data"": { + ""text/plain"": [ + ""(31, 1259, 1290)"" + ] + }, + ""execution_count"": 40, + ""metadata"": {}, + ""output_type"": ""execute_result"" + } + ], + ""source"": [ + ""bioactsDF_Train_final = pd.concat([bioactsDF_Train_non, bioactsDF_Train_dup])\n"", + ""len(bioactsDF_Train_dup), len(bioactsDF_Train_non), len(bioactsDF_Train_final)"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 41, + ""metadata"": {}, + ""outputs"": [ + { + ""data"": { + ""text/html"": [ + ""
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activity_commentassay_chemblidassay_descriptionassay_typebioactivity_typechemblIdname_in_referenceoperatororganismparent_cmpd_chemblidreferencetarget_chemblidtarget_confidencetarget_nameunitsvalueSTATUS
76UnspecifiedCHEMBL3776153Downregulation of ERalpha in human MCF7 cells ...BIC50CHEMBL377590822=Homo sapiensCHEMBL3775908ACS Med. Chem. Lett., (2016) 7:1:94CHEMBL2069Estrogen receptor alphanM0.370active
78UnspecifiedCHEMBL1909145DRUGMATRIX: Estrogen ERalpha radioligand bindi...BIC50CHEMBL691ETHINYLESTRADIOL=Homo sapiensCHEMBL691UnspecifiedCHEMBL2069Estrogen receptor alphanM0.448active
\n"", + ""
"" + ], + ""text/plain"": [ + "" activity_comment assay_chemblid \\\n"", + ""76 Unspecified CHEMBL3776153 \n"", + ""78 Unspecified CHEMBL1909145 \n"", + ""\n"", + "" assay_description assay_type \\\n"", + ""76 Downregulation of ERalpha in human MCF7 cells ... B \n"", + ""78 DRUGMATRIX: Estrogen ERalpha radioligand bindi... B \n"", + ""\n"", + "" bioactivity_type chemblId name_in_reference operator organism \\\n"", + ""76 IC50 CHEMBL3775908 22 = Homo sapiens \n"", + ""78 IC50 CHEMBL691 ETHINYLESTRADIOL = Homo sapiens \n"", + ""\n"", + "" parent_cmpd_chemblid reference target_chemblid \\\n"", + ""76 CHEMBL3775908 ACS Med. Chem. Lett., (2016) 7:1:94 CHEMBL206 \n"", + ""78 CHEMBL691 Unspecified CHEMBL206 \n"", + ""\n"", + "" target_confidence target_name units value STATUS \n"", + ""76 9 Estrogen receptor alpha nM 0.370 active \n"", + ""78 9 Estrogen receptor alpha nM 0.448 active "" + ] + }, + ""execution_count"": 41, + ""metadata"": {}, + ""output_type"": ""execute_result"" + } + ], + ""source"": [ + ""bioactsDF_Train_final.tail(2)"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 42, + ""metadata"": {}, + ""outputs"": [ + { + ""name"": ""stdout"", + ""output_type"": ""stream"", + ""text"": [ + ""('286', '8', '294')\n"" + ] + } + ], + ""source"": [ + ""bioactsDF_Test_gra_dup = pd.concat(g for _, \n"", + "" g in bioactsDF_Test_gra.groupby(\""chemblId\"") \n"", + "" if len(g) > 1)\n"", + ""bioactsDF_Test_gra_non = bioactsDF_Train.loc[~bioactsDF_Test_gra.index.isin(bioactsDF_Test_gra_dup.index)]\n"", + ""\n"", + ""print (str(len(bioactsDF_Test_gra_non)), \n"", + "" str(len(bioactsDF_Test_gra_dup)), \n"", + "" str(len(bioactsDF_Test_gra_dup)+len(bioactsDF_Test_gra_non)))"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 43, + ""metadata"": {}, + ""outputs"": [ + { + ""data"": { + ""text/plain"": [ + ""(8, 286)"" + ] + }, + ""execution_count"": 43, + ""metadata"": {}, + ""output_type"": ""execute_result"" + } + ], + ""source"": [ + ""len(bioactsDF_Test_gra_dup), len(bioactsDF_Test_gra_non)"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 44, + ""metadata"": {}, + ""outputs"": [ + { + ""data"": { + ""text/html"": [ + ""
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chemblIdvalue
7577CHEMBL1031000.0
7578CHEMBL1031000.0
8294CHEMBL24614010000.0
8297CHEMBL24614010000.0
5597CHEMBL33899100000.0
5598CHEMBL338991270.0
4200CHEMBL38663010000.0
4201CHEMBL38663010000.0
\n"", + ""
"" + ], + ""text/plain"": [ + "" chemblId value\n"", + ""7577 CHEMBL103 1000.0\n"", + ""7578 CHEMBL103 1000.0\n"", + ""8294 CHEMBL246140 10000.0\n"", + ""8297 CHEMBL246140 10000.0\n"", + ""5597 CHEMBL33899 100000.0\n"", + ""5598 CHEMBL33899 1270.0\n"", + ""4200 CHEMBL386630 10000.0\n"", + ""4201 CHEMBL386630 10000.0"" + ] + }, + ""execution_count"": 44, + ""metadata"": {}, + ""output_type"": ""execute_result"" + } + ], + ""source"": [ + ""check = bioactsDF_Test_gra_dup[['chemblId','value']]\n"", + ""check"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 45, + ""metadata"": {}, + ""outputs"": [ + { + ""data"": { + ""text/plain"": [ + ""292"" + ] + }, + ""execution_count"": 45, + ""metadata"": {}, + ""output_type"": ""execute_result"" + } + ], + ""source"": [ + ""# drop variable ID \n"", + ""bioactsDF_Test_gra = bioactsDF_Test_gra[~bioactsDF_Test_gra['chemblId'].isin(['CHEMBL33899'])]\n"", + ""len(bioactsDF_Test_gra)"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 46, + ""metadata"": {}, + ""outputs"": [ + { + ""name"": ""stdout"", + ""output_type"": ""stream"", + ""text"": [ + ""('1290', '292', '19')\n"" + ] + } + ], + ""source"": [ + ""print (str(len(bioactsDF_Train_final)), \n"", + "" str(len(bioactsDF_Test_gra)), \n"", + "" str(len(bioactsDF_Test_les)))"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 47, + ""metadata"": {}, + ""outputs"": [ + { + ""name"": ""stdout"", + ""output_type"": ""stream"", + ""text"": [ + ""('1290', '289', '19')\n"" + ] + } + ], + ""source"": [ + ""# Load the compounds in the dataframe\n"", + ""\n"", + ""compounds = CompoundResource()\n"", + ""\n"", + ""cpdsDF_Train_final = pd.DataFrame.from_dict(compounds.get(\n"", + "" list(bioactsDF_Train_final['chemblId'])))\n"", + ""\n"", + ""cpdsDF_Test_gra = pd.DataFrame.from_dict(compounds.get(\n"", + "" list(bioactsDF_Test_gra['chemblId'].unique())))\n"", + ""\n"", + ""cpdsDF_Test_les = pd.DataFrame.from_dict(compounds.get(\n"", + "" list(bioactsDF_Test_les['chemblId'])))\n"", + ""\n"", + ""print (str(len(cpdsDF_Train_final)), \n"", + "" str(len(cpdsDF_Test_gra)), \n"", + "" str(len(cpdsDF_Test_les)))"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 48, + ""metadata"": {}, + ""outputs"": [ + { + ""name"": ""stderr"", + ""output_type"": ""stream"", + ""text"": [ + ""/Users/zeromtmu/anaconda2/envs/ER_alpha/lib/python2.7/site-packages/ipykernel_launcher.py:5: SettingWithCopyWarning: \n"", + ""A value is trying to be set on a copy of a slice from a DataFrame.\n"", + ""Try using .loc[row_indexer,col_indexer] = value instead\n"", + ""\n"", + ""See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n"", + "" \""\""\""\n"" + ] + } + ], + ""source"": [ + ""# making sure everything is float\n"", + ""\n"", + ""bioactsDF_Train_final['value'] = bioactsDF_Train_non['value'].astype(float)\n"", + ""bioactsDF_Test_gra ['value'] = bioactsDF_Test_gra ['value'].astype(float)\n"", + ""bioactsDF_Test_les ['value'] = bioactsDF_Test_les ['value'].astype(float)"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 49, + ""metadata"": {}, + ""outputs"": [ + { + ""data"": { + ""text/html"": [ + ""
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activity_commentassay_chemblidassay_descriptionassay_typebioactivity_typechemblIdname_in_referenceoperatororganismparent_cmpd_chemblidreferencetarget_chemblidtarget_confidencetarget_nameunitsvalueSTATUS
76UnspecifiedCHEMBL3776153Downregulation of ERalpha in human MCF7 cells ...BIC50CHEMBL377590822=Homo sapiensCHEMBL3775908ACS Med. Chem. Lett., (2016) 7:1:94CHEMBL2069Estrogen receptor alphanMNaNactive
78UnspecifiedCHEMBL1909145DRUGMATRIX: Estrogen ERalpha radioligand bindi...BIC50CHEMBL691ETHINYLESTRADIOL=Homo sapiensCHEMBL691UnspecifiedCHEMBL2069Estrogen receptor alphanMNaNactive
\n"", + ""
"" + ], + ""text/plain"": [ + "" activity_comment assay_chemblid \\\n"", + ""76 Unspecified CHEMBL3776153 \n"", + ""78 Unspecified CHEMBL1909145 \n"", + ""\n"", + "" assay_description assay_type \\\n"", + ""76 Downregulation of ERalpha in human MCF7 cells ... B \n"", + ""78 DRUGMATRIX: Estrogen ERalpha radioligand bindi... B \n"", + ""\n"", + "" bioactivity_type chemblId name_in_reference operator organism \\\n"", + ""76 IC50 CHEMBL3775908 22 = Homo sapiens \n"", + ""78 IC50 CHEMBL691 ETHINYLESTRADIOL = Homo sapiens \n"", + ""\n"", + "" parent_cmpd_chemblid reference target_chemblid \\\n"", + ""76 CHEMBL3775908 ACS Med. Chem. Lett., (2016) 7:1:94 CHEMBL206 \n"", + ""78 CHEMBL691 Unspecified CHEMBL206 \n"", + ""\n"", + "" target_confidence target_name units value STATUS \n"", + ""76 9 Estrogen receptor alpha nM NaN active \n"", + ""78 9 Estrogen receptor alpha nM NaN active "" + ] + }, + ""execution_count"": 49, + ""metadata"": {}, + ""output_type"": ""execute_result"" + } + ], + ""source"": [ + ""bioactsDF_Train_final.tail(2)"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 50, + ""metadata"": {}, + ""outputs"": [ + { + ""data"": { + ""text/html"": [ + ""
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acdAcidicPkaacdBasicPkaacdLogdacdLogpalogpchemblIdknownDrugmolecularFormulamolecularWeightnumRo5ViolationspassesRuleOfThreepreferredCompoundNamerotatableBondssmilesspeciesstdInChiKeysynonyms
0NaNNaNNaNNaNNaNCHEMBL219763NoC8H5B10ONaNNaNNoNaNNaNNaNNaNNaNNaN
17.69NaN0.330.53.31CHEMBL370037NoC16H12O3252.260.0NoNaN1.0CC1=C(C(=O)c2ccc(O)cc12)c3ccc(O)cc3NEUTRALIOAPPLGKQYAVIJ-UHFFFAOYSA-NNaN
\n"", + ""
"" + ], + ""text/plain"": [ + "" acdAcidicPka acdBasicPka acdLogd acdLogp alogp chemblId knownDrug \\\n"", + ""0 NaN NaN NaN NaN NaN CHEMBL219763 No \n"", + ""1 7.69 NaN 0.33 0.5 3.31 CHEMBL370037 No \n"", + ""\n"", + "" molecularFormula molecularWeight numRo5Violations passesRuleOfThree \\\n"", + ""0 C8H5B10O NaN NaN No \n"", + ""1 C16H12O3 252.26 0.0 No \n"", + ""\n"", + "" preferredCompoundName rotatableBonds smiles \\\n"", + ""0 NaN NaN NaN \n"", + ""1 NaN 1.0 CC1=C(C(=O)c2ccc(O)cc12)c3ccc(O)cc3 \n"", + ""\n"", + "" species stdInChiKey synonyms \n"", + ""0 NaN NaN NaN \n"", + ""1 NEUTRAL IOAPPLGKQYAVIJ-UHFFFAOYSA-N NaN "" + ] + }, + ""execution_count"": 50, + ""metadata"": {}, + ""output_type"": ""execute_result"" + } + ], + ""source"": [ + ""cpdsDF_Train_final.head(2)"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 52, + ""metadata"": {}, + ""outputs"": [ + { + ""name"": ""stdout"", + ""output_type"": ""stream"", + ""text"": [ + ""('1290', '1290')\n"", + ""('292', '289')\n"", + ""('19', '19')\n"" + ] + } + ], + ""source"": [ + ""print (str(len(bioactsDF_Train_final)), str(len(cpdsDF_Train_final)))\n"", + ""print (str(len(bioactsDF_Test_gra)), str(len(cpdsDF_Test_gra)))\n"", + ""print (str(len(bioactsDF_Test_les)), str(len(cpdsDF_Test_les)))"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 53, + ""metadata"": {}, + ""outputs"": [ + { + ""name"": ""stdout"", + ""output_type"": ""stream"", + ""text"": [ + ""1290\n"", + ""292\n"", + ""19\n"" + ] + } + ], + ""source"": [ + ""# merge cpd to bio activity\n"", + ""\n"", + ""Train_ER_alpha_RAW = cpdsDF_Train_final.merge(bioactsDF_Train_final, on='chemblId', how='inner')\n"", + ""Test_gra_ER_alpha_RAW = cpdsDF_Test_gra.merge (bioactsDF_Test_gra , on='chemblId', how='inner')\n"", + ""Test_les_ER_alpha_RAW = cpdsDF_Test_les.merge (bioactsDF_Test_les , on='chemblId', how='inner')\n"", + ""\n"", + ""print len(Train_ER_alpha_RAW)\n"", + ""print len(Test_gra_ER_alpha_RAW)\n"", + ""print len(Test_les_ER_alpha_RAW)"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 54, + ""metadata"": {}, + ""outputs"": [ + { + ""data"": { + ""text/html"": [ + ""
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acdAcidicPkaacdBasicPkaacdLogdacdLogpalogpchemblIdknownDrugmolecularFormulamolecularWeightnumRo5Violations...operatororganismparent_cmpd_chemblidreferencetarget_chemblidtarget_confidencetarget_nameunitsvalueSTATUS
12884.066.57-0.542.452.11CHEMBL3775908NoC23H24F3NO3419.440.0...=Homo sapiensCHEMBL3775908ACS Med. Chem. Lett., (2016) 7:1:94CHEMBL2069Estrogen receptor alphanMNaNactive
128910.24NaN4.114.114.89CHEMBL691YesC20H24O2296.400.0...=Homo sapiensCHEMBL691UnspecifiedCHEMBL2069Estrogen receptor alphanMNaNactive
\n"", + ""

2 rows × 33 columns

\n"", + ""
"" + ], + ""text/plain"": [ + "" acdAcidicPka acdBasicPka acdLogd acdLogp alogp chemblId \\\n"", + ""1288 4.06 6.57 -0.54 2.45 2.11 CHEMBL3775908 \n"", + ""1289 10.24 NaN 4.11 4.11 4.89 CHEMBL691 \n"", + ""\n"", + "" knownDrug molecularFormula molecularWeight numRo5Violations ... \\\n"", + ""1288 No C23H24F3NO3 419.44 0.0 ... \n"", + ""1289 Yes C20H24O2 296.40 0.0 ... \n"", + ""\n"", + "" operator organism parent_cmpd_chemblid \\\n"", + ""1288 = Homo sapiens CHEMBL3775908 \n"", + ""1289 = Homo sapiens CHEMBL691 \n"", + ""\n"", + "" reference target_chemblid target_confidence \\\n"", + ""1288 ACS Med. Chem. Lett., (2016) 7:1:94 CHEMBL206 9 \n"", + ""1289 Unspecified CHEMBL206 9 \n"", + ""\n"", + "" target_name units value STATUS \n"", + ""1288 Estrogen receptor alpha nM NaN active \n"", + ""1289 Estrogen receptor alpha nM NaN active \n"", + ""\n"", + ""[2 rows x 33 columns]"" + ] + }, + ""execution_count"": 54, + ""metadata"": {}, + ""output_type"": ""execute_result"" + } + ], + ""source"": [ + ""Train_ER_alpha_RAW.tail(2)"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 55, + ""metadata"": {}, + ""outputs"": [ + { + ""name"": ""stdout"", + ""output_type"": ""stream"", + ""text"": [ + ""Train_ER_alpha of 1290 after drop duplicate SMILEs are reduce to 1282\n"", + ""Test_gra_ER_alpha of 292 after drop duplicate SMILEs are reduce to 292\n"", + ""Test_les_ER_alpha of 19 after drop duplicate SMILEs are reduce to 19\n"" + ] + } + ], + ""source"": [ + ""# drop Nan in SMILES\n"", + ""\n"", + ""Train_ER_alpha_RAW1 = Train_ER_alpha_RAW [pd.notnull(Train_ER_alpha_RAW ['smiles'])]\n"", + ""Test_gra_ER_alpha_RAW1 = Test_gra_ER_alpha_RAW[pd.notnull(Test_gra_ER_alpha_RAW['smiles'])]\n"", + ""Test_les_ER_alpha_RAW1 = Test_les_ER_alpha_RAW[pd.notnull(Test_les_ER_alpha_RAW['smiles'])]\n"", + ""\n"", + ""print 'Train_ER_alpha of '+str(len(Train_ER_alpha_RAW))+' after drop duplicate SMILEs are reduce to '+ str(len(Train_ER_alpha_RAW1)) \n"", + ""print 'Test_gra_ER_alpha of '+str(len(Test_gra_ER_alpha_RAW))+' after drop duplicate SMILEs are reduce to '+ str(len(Test_gra_ER_alpha_RAW1)) \n"", + ""print 'Test_les_ER_alpha of '+str(len(Test_les_ER_alpha_RAW))+' after drop duplicate SMILEs are reduce to '+ str(len(Test_les_ER_alpha_RAW1))"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 56, + ""metadata"": {}, + ""outputs"": [ + { + ""data"": { + ""text/html"": [ + ""
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acdAcidicPkaacdBasicPkaacdLogdacdLogpalogpchemblIdknownDrugmolecularFormulamolecularWeightnumRo5Violations...operatororganismparent_cmpd_chemblidreferencetarget_chemblidtarget_confidencetarget_nameunitsvalueSTATUS
12884.066.57-0.542.452.11CHEMBL3775908NoC23H24F3NO3419.440.0...=Homo sapiensCHEMBL3775908ACS Med. Chem. Lett., (2016) 7:1:94CHEMBL2069Estrogen receptor alphanMNaNactive
128910.24NaN4.114.114.89CHEMBL691YesC20H24O2296.400.0...=Homo sapiensCHEMBL691UnspecifiedCHEMBL2069Estrogen receptor alphanMNaNactive
\n"", + ""

2 rows × 33 columns

\n"", + ""
"" + ], + ""text/plain"": [ + "" acdAcidicPka acdBasicPka acdLogd acdLogp alogp chemblId \\\n"", + ""1288 4.06 6.57 -0.54 2.45 2.11 CHEMBL3775908 \n"", + ""1289 10.24 NaN 4.11 4.11 4.89 CHEMBL691 \n"", + ""\n"", + "" knownDrug molecularFormula molecularWeight numRo5Violations ... \\\n"", + ""1288 No C23H24F3NO3 419.44 0.0 ... \n"", + ""1289 Yes C20H24O2 296.40 0.0 ... \n"", + ""\n"", + "" operator organism parent_cmpd_chemblid \\\n"", + ""1288 = Homo sapiens CHEMBL3775908 \n"", + ""1289 = Homo sapiens CHEMBL691 \n"", + ""\n"", + "" reference target_chemblid target_confidence \\\n"", + ""1288 ACS Med. Chem. Lett., (2016) 7:1:94 CHEMBL206 9 \n"", + ""1289 Unspecified CHEMBL206 9 \n"", + ""\n"", + "" target_name units value STATUS \n"", + ""1288 Estrogen receptor alpha nM NaN active \n"", + ""1289 Estrogen receptor alpha nM NaN active \n"", + ""\n"", + ""[2 rows x 33 columns]"" + ] + }, + ""execution_count"": 56, + ""metadata"": {}, + ""output_type"": ""execute_result"" + } + ], + ""source"": [ + ""Train_ER_alpha_RAW1.tail(2)"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 57, + ""metadata"": {}, + ""outputs"": [ + { + ""data"": { + ""text/plain"": [ + ""(292, 289)"" + ] + }, + ""execution_count"": 57, + ""metadata"": {}, + ""output_type"": ""execute_result"" + } + ], + ""source"": [ + ""len(Test_gra_ER_alpha_RAW1), len(Test_gra_ER_alpha_RAW1['chemblId'].unique())"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 58, + ""metadata"": {}, + ""outputs"": [ + { + ""data"": { + ""text/plain"": [ + ""(292, 289)"" + ] + }, + ""execution_count"": 58, + ""metadata"": {}, + ""output_type"": ""execute_result"" + } + ], + ""source"": [ + ""Test_gra_ER_alpha_RAW2 = Test_gra_ER_alpha_RAW1.drop_duplicates(subset='chemblId', keep='last')\n"", + ""\n"", + ""len(Test_gra_ER_alpha_RAW1), len(Test_gra_ER_alpha_RAW2)"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 59, + ""metadata"": {}, + ""outputs"": [ + { + ""name"": ""stdout"", + ""output_type"": ""stream"", + ""text"": [ + ""RAW data of 1282 Train compounds has been reduced to 1282 Compounds.\n"", + ""RAW data of 289 Test_gracompounds has been reduced to 289 Compounds.\n"", + ""RAW data of 19 Test_les compounds has been reduced to 19 Compounds.\n"" + ] + } + ], + ""source"": [ + ""# only smiles exist\n"", + ""\n"", + ""Train_ER_alpha = Train_ER_alpha_RAW1[pd.notnull(\n"", + "" Train_ER_alpha_RAW1['smiles'])]\n"", + ""\n"", + ""Test_gra_ER_alpha = Test_gra_ER_alpha_RAW2[pd.notnull(\n"", + "" Test_gra_ER_alpha_RAW2['smiles'])]\n"", + ""\n"", + ""\n"", + ""Test_les_ER_alpha = Test_les_ER_alpha_RAW1[pd.notnull(\n"", + "" Test_les_ER_alpha_RAW1['smiles'])]\n"", + ""\n"", + ""\n"", + ""print \""RAW data of \"" + str(len(Train_ER_alpha_RAW1)) + \\\n"", + "" \"" Train compounds has been reduced to \"" \\\n"", + "" + str(len(Train_ER_alpha)) + \"" Compounds.\""\n"", + ""\n"", + ""print \""RAW data of \"" + str(len(Test_gra_ER_alpha_RAW2)) + \\\n"", + "" \"" Test_gracompounds has been reduced to \"" \\\n"", + "" + str(len(Test_gra_ER_alpha)) + \"" Compounds.\""\n"", + "" \n"", + ""print \""RAW data of \"" + str(len(Test_les_ER_alpha_RAW1)) + \\\n"", + "" \"" Test_les compounds has been reduced to \"" \\\n"", + "" + str(len(Test_les_ER_alpha)) + \"" Compounds.\"""" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 60, + ""metadata"": { + ""collapsed"": true + }, + ""outputs"": [], + ""source"": [ + ""# clean smiles\n"", + ""\n"", + ""from rdkit.Chem.SaltRemover import SaltRemover\n"", + ""from rdkit.Chem import MolFromSmiles,MolToSmiles\n"", + ""\n"", + ""def clean_smiles (ListSMILEs):\n"", + "" remover = SaltRemover()\n"", + "" len(remover.salts)\n"", + ""\n"", + "" SMILES_desalt = []\n"", + ""\n"", + "" for i in ListSMILEs:\n"", + "" mol = MolFromSmiles(i) \n"", + "" mol_desalt = remover.StripMol(mol)\n"", + "" mol_SMILES = MolToSmiles(mol_desalt)\n"", + "" SMILES_desalt.append(mol_SMILES)\n"", + "" return SMILES_desalt"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 61, + ""metadata"": { + ""collapsed"": true + }, + ""outputs"": [], + ""source"": [ + ""Train_ER_alpha['SMILES_desalt'] = clean_smiles(Train_ER_alpha.smiles)"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 62, + ""metadata"": { + ""collapsed"": true + }, + ""outputs"": [], + ""source"": [ + ""Test_gra_ER_alpha['SMILES_desalt'] = clean_smiles(Test_gra_ER_alpha.smiles)"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 63, + ""metadata"": { + ""collapsed"": true + }, + ""outputs"": [], + ""source"": [ + ""Test_les_ER_alpha['SMILES_desalt'] = clean_smiles(Test_les_ER_alpha.smiles)"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 64, + ""metadata"": {}, + ""outputs"": [ + { + ""name"": ""stdout"", + ""output_type"": ""stream"", + ""text"": [ + ""RAW data of 1282 SMILES has been reduced to 1238 SMILES.\n"" + ] + } + ], + ""source"": [ + ""Train_ER_alpha2 = Train_ER_alpha.drop_duplicates(\\\n"", + "" subset='SMILES_desalt', keep='last')\n"", + ""\n"", + ""print \""RAW data of \"" + str(len(Train_ER_alpha)) + \\\n"", + "" \"" SMILES has been reduced to \"" \\\n"", + "" + str(len(Train_ER_alpha2)) + \"" SMILES.\"""" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 65, + ""metadata"": {}, + ""outputs"": [ + { + ""name"": ""stdout"", + ""output_type"": ""stream"", + ""text"": [ + ""RAW data of 289 SMILES has been reduced to 283 SMILES.\n"" + ] + } + ], + ""source"": [ + ""Test_gra_ER_alpha2 = Test_gra_ER_alpha.drop_duplicates(\\\n"", + "" subset='SMILES_desalt', keep='last')\n"", + ""\n"", + ""print \""RAW data of \"" + str(len(Test_gra_ER_alpha)) + \\\n"", + "" \"" SMILES has been reduced to \"" \\\n"", + "" + str(len(Test_gra_ER_alpha2)) + \"" SMILES.\"""" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 66, + ""metadata"": {}, + ""outputs"": [ + { + ""name"": ""stdout"", + ""output_type"": ""stream"", + ""text"": [ + ""RAW data of 19 SMILES has been reduced to 19 SMILES.\n"" + ] + } + ], + ""source"": [ + ""Test_les_ER_alpha2 = Test_les_ER_alpha.drop_duplicates(\\\n"", + "" subset='SMILES_desalt', keep='last')\n"", + ""\n"", + ""print \""RAW data of \"" + str(len(Test_les_ER_alpha)) + \\\n"", + "" \"" SMILES has been reduced to \"" \\\n"", + "" + str(len(Test_les_ER_alpha2)) + \"" SMILES.\"""" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 67, + ""metadata"": { + ""collapsed"": true + }, + ""outputs"": [], + ""source"": [ + ""# save model\n"", + ""Train_ER_alpha2.to_csv ('model/Train_ER_alpha.csv' , sep=',' ,index=False)\n"", + ""Test_gra_ER_alpha2.to_csv('model/Test_gra_ER_alpha.csv', sep=',' ,index=False)\n"", + ""Test_les_ER_alpha2.to_csv('model/Test_les_ER_alpha.csv', sep=',' ,index=False)"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 68, + ""metadata"": { + ""collapsed"": true + }, + ""outputs"": [], + ""source"": [ + ""Train_smiles = Train_ER_alpha2[['SMILES_desalt','chemblId']]\n"", + ""Train_smiles.to_csv('smiles/Train_ER_alpha.smi', sep='\\t' ,header=False ,index=False)"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 69, + ""metadata"": { + ""collapsed"": true + }, + ""outputs"": [], + ""source"": [ + ""Test_gra = Test_gra_ER_alpha2[['SMILES_desalt','chemblId']]\n"", + ""Test_gra.to_csv('smiles/Test_gra_ER_alpha.smi', sep='\\t' ,header=False ,index=False)"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 70, + ""metadata"": { + ""collapsed"": true + }, + ""outputs"": [], + ""source"": [ + ""Test_les = Test_les_ER_alpha2[['SMILES_desalt','chemblId']]\n"", + ""Test_les.to_csv('smiles/Test_les_ER_alpha.smi', sep='\\t' ,header=False ,index=False)"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 71, + ""metadata"": { + ""collapsed"": true + }, + ""outputs"": [], + ""source"": [ + ""Train_QSAR = Train_ER_alpha2[['chemblId','value']]\n"", + ""\n"", + ""Train_QSAR.to_csv('Train_QSAR.csv', sep=',' ,index=False)"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 72, + ""metadata"": {}, + ""outputs"": [ + { + ""data"": { + ""text/html"": [ + ""
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acdAcidicPkaacdBasicPkaacdLogdacdLogpalogpchemblIdknownDrugmolecularFormulamolecularWeightnumRo5Violations...organismparent_cmpd_chemblidreferencetarget_chemblidtarget_confidencetarget_nameunitsvalueSTATUSSMILES_desalt
12874.067.140.313.112.60CHEMBL3775766NoC24H27F2NO3415.470.0...Homo sapiensCHEMBL3775766ACS Med. Chem. Lett., (2016) 7:1:94CHEMBL2069Estrogen receptor alphanMNaNactiveCC(C)CN1C(c2c(F)cc(C=CC(=O)O)cc2F)c2ccc(O)cc2C...
12884.066.57-0.542.452.11CHEMBL3775908NoC23H24F3NO3419.440.0...Homo sapiensCHEMBL3775908ACS Med. Chem. Lett., (2016) 7:1:94CHEMBL2069Estrogen receptor alphanMNaNactiveCC(CF)CN1C(C)Cc2cc(O)ccc2C1c1c(F)cc(C=CC(=O)O)...
128910.24NaN4.114.114.89CHEMBL691YesC20H24O2296.400.0...Homo sapiensCHEMBL691UnspecifiedCHEMBL2069Estrogen receptor alphanMNaNactiveC#CC1(O)CCC2C3CCc4cc(O)ccc4C3CCC21C
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3 rows × 34 columns

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"" + ], + ""text/plain"": [ + "" acdAcidicPka acdBasicPka acdLogd acdLogp alogp chemblId \\\n"", + ""1287 4.06 7.14 0.31 3.11 2.60 CHEMBL3775766 \n"", + ""1288 4.06 6.57 -0.54 2.45 2.11 CHEMBL3775908 \n"", + ""1289 10.24 NaN 4.11 4.11 4.89 CHEMBL691 \n"", + ""\n"", + "" knownDrug molecularFormula molecularWeight numRo5Violations \\\n"", + ""1287 No C24H27F2NO3 415.47 0.0 \n"", + ""1288 No C23H24F3NO3 419.44 0.0 \n"", + ""1289 Yes C20H24O2 296.40 0.0 \n"", + ""\n"", + "" ... organism \\\n"", + ""1287 ... Homo sapiens \n"", + ""1288 ... Homo sapiens \n"", + ""1289 ... Homo sapiens \n"", + ""\n"", + "" parent_cmpd_chemblid reference \\\n"", + ""1287 CHEMBL3775766 ACS Med. Chem. Lett., (2016) 7:1:94 \n"", + ""1288 CHEMBL3775908 ACS Med. Chem. Lett., (2016) 7:1:94 \n"", + ""1289 CHEMBL691 Unspecified \n"", + ""\n"", + "" target_chemblid target_confidence target_name units value \\\n"", + ""1287 CHEMBL206 9 Estrogen receptor alpha nM NaN \n"", + ""1288 CHEMBL206 9 Estrogen receptor alpha nM NaN \n"", + ""1289 CHEMBL206 9 Estrogen receptor alpha nM NaN \n"", + ""\n"", + "" STATUS SMILES_desalt \n"", + ""1287 active CC(C)CN1C(c2c(F)cc(C=CC(=O)O)cc2F)c2ccc(O)cc2C... \n"", + ""1288 active CC(CF)CN1C(C)Cc2cc(O)ccc2C1c1c(F)cc(C=CC(=O)O)... \n"", + ""1289 active C#CC1(O)CCC2C3CCc4cc(O)ccc4C3CCC21C \n"", + ""\n"", + ""[3 rows x 34 columns]"" + ] + }, + ""execution_count"": 72, + ""metadata"": {}, + ""output_type"": ""execute_result"" + } + ], + ""source"": [ + ""Train_ER_alpha2.tail(3)"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": null, + ""metadata"": { + ""collapsed"": true + }, + ""outputs"": [], + ""source"": [] + }, + { + ""cell_type"": ""code"", + ""execution_count"": null, + ""metadata"": { + ""collapsed"": true + }, + ""outputs"": [], + ""source"": [] + } + ], + ""metadata"": { + ""kernelspec"": { + ""display_name"": ""Python 2"", + ""language"": ""python"", + ""name"": ""python2"" + }, + ""language_info"": { + ""codemirror_mode"": { + ""name"": ""ipython"", + ""version"": 2 + }, + ""file_extension"": "".py"", + ""mimetype"": ""text/x-python"", + ""name"": ""python"", + ""nbconvert_exporter"": ""python"", + ""pygments_lexer"": ""ipython2"", + ""version"": ""2.7.13"" + } + }, + ""nbformat"": 4, + ""nbformat_minor"": 2 +} +","Unknown" +"Inhibition","chaninlab/estrogen-receptor-alpha-qsar","04_Regression.ipynb",".ipynb","3634077","2282","{ + ""cells"": [ + { + ""cell_type"": ""code"", + ""execution_count"": 1, + ""metadata"": { + ""collapsed"": true + }, + ""outputs"": [], + ""source"": [ + ""import pandas as pd\n"", + ""import numpy as np\n"", + ""\n"", + ""df0 = pd.read_csv('model/ER_alpha_Train_final.csv', header = 0)"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 2, + ""metadata"": {}, + ""outputs"": [ + { + ""data"": { + ""text/plain"": [ + ""1231"" + ] + }, + ""execution_count"": 2, + ""metadata"": {}, + ""output_type"": ""execute_result"" + } + ], + ""source"": [ + ""len(df0)"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 3, + ""metadata"": { + ""scrolled"": true + }, + ""outputs"": [ + { + ""data"": { + ""text/html"": [ + ""
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STATUSMWLogPnHAccnHDonacdAcidicPkaacdBasicPkaacdLogdacdLogpalogp...referencetarget_chemblidtarget_confidencetarget_nameunitsvalueSTATUS.1SMILES_desaltpIC50chemblId
1229active419.4434.7098324.066.57-0.542.452.11...ACS Med. Chem. Lett., (2016) 7:1:94CHEMBL2069Estrogen receptor alphanMNaNactiveCC(CF)CN1C(C)Cc2cc(O)ccc2C1c1c(F)cc(C=CC(=O)O)...NaNCHEMBL3775908
1230active296.4103.61262210.24NaN4.114.114.89...UnspecifiedCHEMBL2069Estrogen receptor alphanMNaNactiveC#CC1(O)CCC2C3CCc4cc(O)ccc4C3CCC21CNaNCHEMBL691
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2 rows × 40 columns

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"" + ], + ""text/plain"": [ + "" STATUS MW LogP nHAcc nHDon acdAcidicPka acdBasicPka \\\n"", + ""1229 active 419.443 4.7098 3 2 4.06 6.57 \n"", + ""1230 active 296.410 3.6126 2 2 10.24 NaN \n"", + ""\n"", + "" acdLogd acdLogp alogp ... \\\n"", + ""1229 -0.54 2.45 2.11 ... \n"", + ""1230 4.11 4.11 4.89 ... \n"", + ""\n"", + "" reference target_chemblid target_confidence \\\n"", + ""1229 ACS Med. Chem. Lett., (2016) 7:1:94 CHEMBL206 9 \n"", + ""1230 Unspecified CHEMBL206 9 \n"", + ""\n"", + "" target_name units value STATUS.1 \\\n"", + ""1229 Estrogen receptor alpha nM NaN active \n"", + ""1230 Estrogen receptor alpha nM NaN active \n"", + ""\n"", + "" SMILES_desalt pIC50 chemblId \n"", + ""1229 CC(CF)CN1C(C)Cc2cc(O)ccc2C1c1c(F)cc(C=CC(=O)O)... NaN CHEMBL3775908 \n"", + ""1230 C#CC1(O)CCC2C3CCc4cc(O)ccc4C3CCC21C NaN CHEMBL691 \n"", + ""\n"", + ""[2 rows x 40 columns]"" + ] + }, + ""execution_count"": 3, + ""metadata"": {}, + ""output_type"": ""execute_result"" + } + ], + ""source"": [ + ""df0.tail(2)"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 4, + ""metadata"": { + ""collapsed"": true + }, + ""outputs"": [], + ""source"": [ + ""df = df0.drop(['STATUS.1'], axis=1)"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 5, + ""metadata"": { + ""collapsed"": true + }, + ""outputs"": [], + ""source"": [ + ""df = df[['chemblId','pIC50']]"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 6, + ""metadata"": {}, + ""outputs"": [ + { + ""data"": { + ""text/html"": [ + ""
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chemblIdpIC50
0CHEMBL3700378.221849
1CHEMBL1890735.762708
2CHEMBL3652906.943095
3CHEMBL1916486.278189
4CHEMBL1844318.657577
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"" + ], + ""text/plain"": [ + "" chemblId pIC50\n"", + ""0 CHEMBL370037 8.221849\n"", + ""1 CHEMBL189073 5.762708\n"", + ""2 CHEMBL365290 6.943095\n"", + ""3 CHEMBL191648 6.278189\n"", + ""4 CHEMBL184431 8.657577"" + ] + }, + ""execution_count"": 6, + ""metadata"": {}, + ""output_type"": ""execute_result"" + } + ], + ""source"": [ + ""df.head()"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 7, + ""metadata"": { + ""collapsed"": true + }, + ""outputs"": [], + ""source"": [ + ""path = r'Fingerprint/'\n"", + ""\n"", + ""Fp1 = pd.read_csv(path + 'Train_ER_alpha-Fingerprinter.csv' , header = 0)\n"", + ""Fp2 = pd.read_csv(path + 'Train_ER_alpha-ExtendedFingerprinter.csv' , header = 0)\n"", + ""Fp3 = pd.read_csv(path + 'Train_ER_alpha-EStateFingerprinter.csv' , header = 0)\n"", + ""Fp4 = pd.read_csv(path + 'Train_ER_alpha-GraphOnlyFingerprinter.csv' , header = 0)\n"", + ""Fp5 = pd.read_csv(path + 'Train_ER_alpha-MACCSFingerprinter.csv' , header = 0)\n"", + ""Fp6 = pd.read_csv(path + 'Train_ER_alpha-PubchemFingerprinter.csv' , header = 0)\n"", + ""Fp7 = pd.read_csv(path + 'Train_ER_alpha-SubstructureFingerprinter.csv' , header = 0)\n"", + ""Fp8 = pd.read_csv(path + 'Train_ER_alpha-SubstructureFingerprintCount.csv', header = 0)\n"", + ""Fp9 = pd.read_csv(path + 'Train_ER_alpha-KlekotaRothFingerprinter.csv' , header = 0)\n"", + ""Fp10 = pd.read_csv(path + 'Train_ER_alpha-KlekotaRothFingerprintCount.csv' , header = 0)\n"", + ""Fp11 = pd.read_csv(path + 'Train_ER_alpha-AtomPairs2DFingerprinter.csv' , header = 0)\n"", + ""Fp12 = pd.read_csv(path + 'Train_ER_alpha-AtomPairs2DFingerprintCount.csv' , header = 0)"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 8, + ""metadata"": { + ""collapsed"": true + }, + ""outputs"": [], + ""source"": [ + ""Fp1 = Fp1.rename(columns = {'Name':'chemblId'})\n"", + ""Fp2 = Fp2.rename(columns = {'Name':'chemblId'})\n"", + ""Fp3 = Fp3.rename(columns = {'Name':'chemblId'})\n"", + ""Fp4 = Fp4.rename(columns = {'Name':'chemblId'})\n"", + ""Fp5 = Fp5.rename(columns = {'Name':'chemblId'})\n"", + ""Fp6 = Fp6.rename(columns = {'Name':'chemblId'})\n"", + ""Fp7 = Fp7.rename(columns = {'Name':'chemblId'})\n"", + ""Fp8 = Fp8.rename(columns = {'Name':'chemblId'})\n"", + ""Fp9 = Fp9.rename(columns = {'Name':'chemblId'})\n"", + ""Fp10= Fp10.rename(columns = {'Name':'chemblId'})\n"", + ""Fp11= Fp11.rename(columns = {'Name':'chemblId'})\n"", + ""Fp12= Fp12.rename(columns = {'Name':'chemblId'})"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 9, + ""metadata"": { + ""collapsed"": true + }, + ""outputs"": [], + ""source"": [ + ""from sklearn import preprocessing\n"", + ""\n"", + ""def normalized (Fp):\n"", + "" \n"", + "" chemblId = Fp.chemblId\n"", + "" Fp_ix = Fp.ix[:,1:]\n"", + "" min_max_scaler = preprocessing.MinMaxScaler()\n"", + "" np_scaled = min_max_scaler.fit_transform(Fp_ix)\n"", + "" Fp_normalized = pd.DataFrame(np_scaled)\n"", + "" Fp_normalized\n"", + "" Fp_normalized = pd.DataFrame(np_scaled, columns=Fp_ix.columns)\n"", + "" Fp_normalized['chemblId'] = chemblId\n"", + "" \n"", + "" return Fp_normalized"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 10, + ""metadata"": {}, + ""outputs"": [ + { + ""name"": ""stderr"", + ""output_type"": ""stream"", + ""text"": [ + ""/Users/zeromtmu/anaconda2/envs/ER_alpha/lib/python2.7/site-packages/ipykernel_launcher.py:6: DeprecationWarning: \n"", + "".ix is deprecated. Please use\n"", + "".loc for label based indexing or\n"", + "".iloc for positional indexing\n"", + ""\n"", + ""See the documentation here:\n"", + ""http://pandas.pydata.org/pandas-docs/stable/indexing.html#deprecate_ix\n"", + "" \n"" + ] + } + ], + ""source"": [ + ""Fp1_n = normalized (Fp1 )\n"", + ""Fp2_n = normalized (Fp2 )\n"", + ""Fp3_n = normalized (Fp3 )\n"", + ""Fp4_n = normalized (Fp4 )\n"", + ""Fp5_n = normalized (Fp5 )\n"", + ""Fp6_n = normalized (Fp6 )\n"", + ""Fp7_n = normalized (Fp7 )\n"", + ""Fp8_n = normalized (Fp8 )\n"", + ""Fp9_n = normalized (Fp9 )\n"", + ""Fp10_n = normalized (Fp10)\n"", + ""Fp11_n = normalized (Fp11)\n"", + ""Fp12_n = normalized (Fp12)"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 13, + ""metadata"": {}, + ""outputs"": [], + ""source"": [ + ""Fp1_n .to_csv('Train_Fp_normalized/Fingerprinter.csv' , sep=',' ,index=False)\n"", + ""Fp2_n .to_csv('Train_Fp_normalized/ExtendedFingerprinter.csv' , sep=',' ,index=False)\n"", + ""Fp3_n .to_csv('Train_Fp_normalized/EStateFingerprinter.csv' , sep=',' ,index=False)\n"", + ""Fp4_n .to_csv('Train_Fp_normalized/GraphOnlyFingerprinter.csv' , sep=',' ,index=False)\n"", + ""Fp5_n .to_csv('Train_Fp_normalized/MACCSFingerprinter.csv' , sep=',' ,index=False)\n"", + ""Fp6_n .to_csv('Train_Fp_normalized/PubchemFingerprinter.csv' , sep=',' ,index=False)\n"", + ""Fp7_n .to_csv('Train_Fp_normalized/SubstructureFingerprinter.csv' , sep=',' ,index=False)\n"", + ""Fp8_n .to_csv('Train_Fp_normalized/SubstructureFingerprintCount.csv', sep=',' ,index=False)\n"", + ""Fp9_n .to_csv('Train_Fp_normalized/KlekotaRothFingerprinter.csv' , sep=',' ,index=False)\n"", + ""Fp10_n.to_csv('Train_Fp_normalized/KlekotaRothFingerprintCount.csv' , sep=',' ,index=False)\n"", + ""Fp11_n.to_csv('Train_Fp_normalized/AtomPairs2DFingerprinter.csv' , sep=',' ,index=False)\n"", + ""Fp12_n.to_csv('Train_Fp_normalized/AtomPairs2DFingerprintCount.csv' , sep=',' ,index=False)"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 14, + ""metadata"": { + ""collapsed"": true + }, + ""outputs"": [], + ""source"": [ + ""raw1 = df.merge(Fp1_n , on='chemblId', how='outer')\n"", + ""raw2 = df.merge(Fp2_n , on='chemblId', how='outer')\n"", + ""raw3 = df.merge(Fp3_n , on='chemblId', how='outer')\n"", + ""raw4 = df.merge(Fp4_n , on='chemblId', how='outer')\n"", + ""raw5 = df.merge(Fp5_n , on='chemblId', how='outer')\n"", + ""raw6 = df.merge(Fp6_n , on='chemblId', how='outer')\n"", + ""raw7 = df.merge(Fp7_n , on='chemblId', how='outer')\n"", + ""raw8 = df.merge(Fp8_n , on='chemblId', how='outer')\n"", + ""raw9 = df.merge(Fp9_n , on='chemblId', how='outer')\n"", + ""raw10 = df.merge(Fp10_n, on='chemblId', how='outer')\n"", + ""raw11 = df.merge(Fp11_n, on='chemblId', how='outer')\n"", + ""raw12 = df.merge(Fp12_n, on='chemblId', how='outer')"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 15, + ""metadata"": { + ""collapsed"": true + }, + ""outputs"": [], + ""source"": [ + ""raw1 .set_index('chemblId', inplace=True) \n"", + ""raw2 .set_index('chemblId', inplace=True) \n"", + ""raw3 .set_index('chemblId', inplace=True) \n"", + ""raw4 .set_index('chemblId', inplace=True) \n"", + ""raw5 .set_index('chemblId', inplace=True) \n"", + ""raw6 .set_index('chemblId', inplace=True) \n"", + ""raw7 .set_index('chemblId', inplace=True)\n"", + ""raw8 .set_index('chemblId', inplace=True)\n"", + ""raw9 .set_index('chemblId', inplace=True)\n"", + ""raw10.set_index('chemblId', inplace=True)\n"", + ""raw11.set_index('chemblId', inplace=True)\n"", + ""raw12.set_index('chemblId', inplace=True)"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 16, + ""metadata"": { + ""collapsed"": true + }, + ""outputs"": [], + ""source"": [ + ""raw1 .to_csv('QSAR/ER_alpha_Fingerprinter.csv' , sep=',' ,index=True)\n"", + ""raw2 .to_csv('QSAR/ER_alpha_ExtendedFingerprinter.csv' , sep=',' ,index=True)\n"", + ""raw3 .to_csv('QSAR/ER_alpha_EStateFingerprinter.csv' , sep=',' ,index=True)\n"", + ""raw4 .to_csv('QSAR/ER_alpha_GraphOnlyFingerprinter.csv' , sep=',' ,index=True)\n"", + ""raw5 .to_csv('QSAR/ER_alpha_MACCSFingerprinter.csv' , sep=',' ,index=True)\n"", + ""raw6 .to_csv('QSAR/ER_alpha_PubchemFingerprinter.csv' , sep=',' ,index=True)\n"", + ""raw7 .to_csv('QSAR/ER_alpha_SubstructureFingerprinter.csv' , sep=',' ,index=True)\n"", + ""raw8 .to_csv('QSAR/ER_alpha_SubstructureFingerprintCount.csv', sep=',' ,index=True)\n"", + ""raw9 .to_csv('QSAR/ER_alpha_KlekotaRothFingerprinter.csv' , sep=',' ,index=True)\n"", + ""raw10.to_csv('QSAR/ER_alpha_KlekotaRothFingerprintCount.csv' , sep=',' ,index=True)\n"", + ""raw11.to_csv('QSAR/ER_alpha_AtomPairs2DFingerprinter.csv' , sep=',' ,index=True)\n"", + ""raw12.to_csv('QSAR/ER_alpha_AtomPairs2DFingerprintCount.csv' , sep=',' ,index=True)"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 17, + ""metadata"": {}, + ""outputs"": [ + { + ""name"": ""stdout"", + ""output_type"": ""stream"", + ""text"": [ + ""(1231, 1231, 1231, 1231, 1231, 1231, 1231, 1231, 1231, 1231, 1231, 1231)\n"" + ] + } + ], + ""source"": [ + ""print (len(raw1 ),len(raw2 ),len(raw3 ),len(raw4 ),len(raw5 )\n"", + "" ,len(raw6 ),len(raw7 ),len(raw8 ),len(raw9 ),len(raw10)\n"", + "" ,len(raw11),len(raw12))"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 18, + ""metadata"": {}, + ""outputs"": [ + { + ""name"": ""stdout"", + ""output_type"": ""stream"", + ""text"": [ + ""(1025, 1025, 80, 1025, 1231, 882, 308, 308, 4861, 1231, 781, 781)\n"" + ] + } + ], + ""source"": [ + ""print (len(raw1 .columns),len(raw2 .columns),len(raw3 .columns),len(raw4 .columns),len(raw5 )\n"", + "" ,len(raw6 .columns),len(raw7 .columns),len(raw8 .columns),len(raw9 .columns),len(raw10)\n"", + "" ,len(raw11.columns),len(raw12.columns))"" + ] + }, + { + ""cell_type"": ""markdown"", + ""metadata"": {}, + ""source"": [ + ""# Build function"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 19, + ""metadata"": { + ""collapsed"": true + }, + ""outputs"": [], + ""source"": [ + ""import numpy as np\n"", + ""\n"", + ""def Remove_useless_descriptor(df, threshold):\n"", + "" \n"", + "" des1 = len(df.columns) \n"", + "" \n"", + "" h = df.columns.tolist()\n"", + "" df = df.as_matrix().astype(np.float)\n"", + "" df = np.array(df)\n"", + ""\n"", + "" STDEV = np.std(df, axis=0)\n"", + "" idx = [idx for idx, val in enumerate(STDEV) if val > threshold]\n"", + "" df2 = df[:,idx]\n"", + "" hx = np.array(h)[idx]\n"", + "" \n"", + "" df = pd.DataFrame(df2, columns=[hx])\n"", + "" \n"", + "" des2 = len(df.columns)\n"", + "" \n"", + "" print 'from Remove useless descriptor'\n"", + "" print \""The initial set of \"" + str(des1) + \\\n"", + "" \"" descriptors has been reduced to \"" + str(des2) + \"" descriptors.\""\n"", + "" \n"", + "" return df, des1, des2"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 20, + ""metadata"": { + ""collapsed"": true + }, + ""outputs"": [], + ""source"": [ + ""from scipy import stats\n"", + ""\n"", + ""def correlation(df, threshold):\n"", + ""\n"", + "" des3 = len(df.columns) \n"", + "" corr = stats.pearsonr\n"", + "" col_corr = set() # Set of all the names of deleted columns\n"", + "" corr_matrix = df.corr()\n"", + "" for i in range(len(corr_matrix.columns)):\n"", + "" for j in range(i):\n"", + "" if corr_matrix.iloc[i, j] >= threshold:\n"", + "" colname = corr_matrix.columns[i] # getting the name of column\n"", + "" col_corr.add(colname)\n"", + "" if colname in df.columns:\n"", + "" del df[colname] # deleting the column from the dataset\n"", + "" des4 = len(df.columns) \n"", + ""\n"", + "" print 'from Remove correlation'\n"", + "" print (\""The initial set of \"" + str(des3) + ' descriptors'+ \n"", + "" \"" has been reduced to \"" + str(des4) + \"" descriptors.\"")\n"", + ""\n"", + "" return df, des3, des4"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 21, + ""metadata"": {}, + ""outputs"": [ + { + ""name"": ""stderr"", + ""output_type"": ""stream"", + ""text"": [ + ""/Users/zeromtmu/anaconda2/envs/ER_alpha/lib/python2.7/site-packages/sklearn/cross_validation.py:44: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.\n"", + "" \""This module will be removed in 0.20.\"", DeprecationWarning)\n"" + ] + } + ], + ""source"": [ + ""from sklearn.cross_validation import train_test_split\n"", + ""from sklearn.ensemble import RandomForestRegressor\n"", + ""from sklearn.cross_validation import StratifiedShuffleSplit\n"", + ""from sklearn.metrics import mean_squared_error\n"", + ""from sklearn.metrics import mean_absolute_error\n"", + ""from sklearn.metrics import r2_score\n"", + ""from sklearn import cross_validation\n"", + ""from collections import defaultdict\n"", + ""\n"", + ""def build_model(X, Y, seed, hx, f):\n"", + "" \n"", + "" \n"", + "" #Data split using 70/30 ratio\n"", + "" X_internal, X_external, Y_internal, Y_external = train_test_split(X,\n"", + "" Y, test_size=0.3,\n"", + "" random_state=seed)\n"", + ""\n"", + "" # Training set\n"", + "" rf = RandomForestRegressor(n_estimators=400, \n"", + "" max_features='sqrt', \n"", + "" min_samples_leaf = 1,\n"", + "" random_state = 13,\n"", + "" n_jobs=-1)\n"", + "" \n"", + "" rf.fit(X_internal,Y_internal)\n"", + "" prediction = rf.predict(X_internal)\n"", + "" \n"", + "" # Cross-validation\n"", + "" cv = cross_validation.cross_val_predict(rf, X_internal, Y_internal, cv=10, n_jobs=-1)\n"", + "" \n"", + "" # External set \n"", + "" prediction_external = rf.predict(X_external)\n"", + "" \n"", + "" #print result from each seed \n"", + "" R2_train.append(r2_score(Y_internal, prediction))\n"", + "" RMSE_train.append(np.sqrt(mean_absolute_error(Y_internal, prediction)))\n"", + "" Q2_CV.append(r2_score(Y_internal, cv))\n"", + "" RMSE_CV.append(np.sqrt(mean_absolute_error(Y_internal, cv)))\n"", + "" Q2_External.append(r2_score(Y_external, prediction_external))\n"", + "" RMSE_External.append((mean_absolute_error(Y_external, prediction_external)))\n"", + "" \n"", + "" #Feature Importance\n"", + "" Feature = hx[:]\n"", + "" feature_importance = rf.feature_importances_\n"", + "" importances = 100.0 * (feature_importance / feature_importance.max()) #index\n"", + "" \n"", + "" for i, fx in enumerate(Feature):\n"", + "" importances_dict[fx].append(importances[i])\n"", + "" \n"", + "" return R2_train, RMSE_train, Q2_CV, RMSE_CV, Q2_External, RMSE_External, Feature, \\\n"", + "" X_internal, X_external, Y_internal, Y_external, rf, prediction, importances_dict"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 22, + ""metadata"": { + ""collapsed"": true + }, + ""outputs"": [], + ""source"": [ + ""from copy import deepcopy\n"", + ""\n"", + ""def Y_scrambling(X_internal, X_external, Y_internal, Y_external):\n"", + "" # Do the Y-scrambling. Loop over the actual learning for 100 times.\n"", + "" for randomseedcounter in range(1,101):\n"", + "" y_train_scrambled = deepcopy(Y_internal)\n"", + "" X_train_scrambled = deepcopy(X_internal)\n"", + "" np.random.shuffle(y_train_scrambled)\n"", + "" np.random.shuffle(X_train_scrambled)\n"", + ""\n"", + "" # training was done on \""scrambled\"" data - prediction on test set\n"", + "" RF_scrambled = RandomForestRegressor()\n"", + "" RF_scrambled = RF_scrambled.fit(X_internal,y_train_scrambled)\n"", + "" y_predict_scrambled = RF_scrambled.predict(X_external)\n"", + "" \n"", + "" acclist_predictionOnTest_scrambledtrain.append((RF_scrambled.score(X_external,Y_external))**2)\n"", + "" \n"", + "" # training was done on \""scrambled\"" data - prediction on train set\n"", + "" y_predict_scrambled_predictTrain = RF_scrambled.predict(X_internal)\n"", + "" \n"", + "" acclist_predictionOnTrain_scrambledtrain.append((RF_scrambled.score(X_internal,Y_internal))**2)\n"", + "" \n"", + "" r2 = pd.DataFrame(acclist_predictionOnTrain_scrambledtrain, columns=['R2'])\n"", + "" q2 = pd.DataFrame(acclist_predictionOnTest_scrambledtrain, columns=['Q2'])\n"", + ""\n"", + "" #result = pd.concat([r2, q2], axis=1, join='inner').to_csv(f+\""_Y_scrambling.csv\"", header=False, index=False)\n"", + "" \n"", + "" \n"", + "" return acclist_predictionOnTest_scrambledtrain,acclist_predictionOnTrain_scrambledtrain"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 23, + ""metadata"": { + ""collapsed"": true + }, + ""outputs"": [], + ""source"": [ + ""def mean(R2_train, RMSE_train, Q2_CV, RMSE_CV, Q2_External, RMSE_External, importances_dict):\n"", + "" R2_train_mean = np.mean(R2_train)\n"", + "" RMSE_train_mean = np.mean(RMSE_train)\n"", + "" Q2_CV_mean = np.mean(Q2_CV)\n"", + "" RMSE_CV_mean = np.mean(RMSE_CV)\n"", + "" Q2_External_mean = np.mean(Q2_External)\n"", + "" RMSE_External_mean = np.mean(RMSE_External)\n"", + "" importances_mean0 = {}\n"", + "" for fx in importances_dict:\n"", + "" importances_mean0[fx] = np.mean(importances_dict[fx])\n"", + "" importances_mean = sorted([(k,v) for k,v in importances_mean0.iteritems()],\n"", + "" key=lambda x: x[1], reverse=True)\n"", + "" \n"", + "" #predictionOnTest_mean = np.mean(acclist_predictionOnTest_scrambledtrain)\n"", + "" #predictionOnTrain_mean = np.mean(acclist_predictionOnTrain_scrambledtrain)\n"", + "" \n"", + "" return R2_train_mean, RMSE_train_mean, Q2_CV_mean, RMSE_CV_mean, Q2_External_mean, \\\n"", + "" RMSE_External_mean, importances_mean"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 24, + ""metadata"": { + ""collapsed"": true + }, + ""outputs"": [], + ""source"": [ + ""def std(R2_train, RMSE_train, Q2_CV, RMSE_CV, Q2_External, RMSE_External, importances_dict):\n"", + "" R2_train_std = np.std(R2_train)\n"", + "" RMSE_train_std = np.std(RMSE_train)\n"", + "" Q2_CV_std = np.std(Q2_CV)\n"", + "" RMSE_CV_std = np.std(RMSE_CV)\n"", + "" Q2_External_std = np.std(Q2_External)\n"", + "" RMSE_External_std = np.std(RMSE_External)\n"", + "" importances_std0 = {}\n"", + "" for fx in importances_dict:\n"", + "" importances_std0[fx] = np.std(importances_dict[fx])\n"", + "" importances_std = sorted([(k,v) for k,v in importances_std0.iteritems()],\n"", + "" key=lambda x: x[1], reverse=True)\n"", + "" #predictionOnTest_std = np.std(acclist_predictionOnTest_scrambledtrain)\n"", + "" #predictionOnTrain_std = np.std(acclist_predictionOnTrain_scrambledtrain)\n"", + "" \n"", + "" return R2_train_std, RMSE_train_std, Q2_CV_std, RMSE_CV_std, Q2_External_std,\\\n"", + "" RMSE_External_std, importances_std"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 25, + ""metadata"": { + ""collapsed"": true + }, + ""outputs"": [], + ""source"": [ + ""def print_output(R2_train_mean, RMSE_train_mean, Q2_CV_mean, RMSE_CV_mean, Q2_External_mean, \n"", + "" RMSE_External_mean, R2_train_std, RMSE_train_std, Q2_CV_std, RMSE_CV_std, Q2_External_std, \n"", + "" RMSE_External_std, X_internal, X_external, X, f, des1, des2 ):\n"", + "" #outfile = open('all_output.csv', 'a')\n"", + "" name = [os.path.basename(f)]\n"", + "" print_out = name\n"", + "" print_out = [name.replace('.smi','') for name in print_out]\n"", + "" \n"", + "" print >> outfile, '%s,%d,%d,%d,%d,%0.4f,%0.4f,%0.4f,%0.4f,%d,%0.4f,%0.4f,%0.4f,%0.4f,%d,%0.4f,%0.4f,%0.4f,%0.4f' \\\n"", + "" % (print_out, len(X_internal),\n"", + "" des1, des2, des4,\n"", + "" R2_train_mean, R2_train_std,\n"", + "" RMSE_train_mean, RMSE_train_std,\n"", + "" len(X_internal),\n"", + "" Q2_CV_mean, Q2_CV_std,\n"", + "" RMSE_CV_mean, RMSE_CV_std,\n"", + "" len(X_external),\n"", + "" Q2_External_mean, Q2_External_std,\n"", + "" RMSE_External_mean, RMSE_External_std,)\n"", + "" \n"", + "" print '\\nTraining set\\n------------'\n"", + "" print 'N: ' + (str(len(X_internal)))\n"", + "" print 'R2: %0.4f'%(R2_train_mean)\n"", + "" print 'std_R2: %0.4f'%(R2_train_std)\n"", + "" print 'RMSE: %0.4f'%(RMSE_train_mean)\n"", + "" print 'std_RMSE: %0.4f'%(RMSE_train_std)\n"", + ""\n"", + "" print '\\nCross-validation set\\n------------'\n"", + "" print 'N: ' + (str(len(X_internal)))\n"", + "" print 'Q2: %0.4f'%(Q2_CV_mean)\n"", + "" print 'std_Q2: %0.4f'%(Q2_CV_std)\n"", + "" print 'RMSE: %0.4f'%(RMSE_CV_mean)\n"", + "" print 'std_RMSE: %0.4f'%(RMSE_CV_std)\n"", + ""\n"", + "" print '\\nExternal set\\n------------'\n"", + "" print 'N: ' + (str(len(X_external)))\n"", + "" print 'Q2_EXt: %0.4f'%(Q2_External_mean)\n"", + "" print 'std_Q2_EXt: %0.4f'%(Q2_External_std)\n"", + "" print 'RMSE: %0.4f'%(RMSE_External_mean)\n"", + "" print 'std_RMSE: %0.4f'%(RMSE_External_std)"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 26, + ""metadata"": {}, + ""outputs"": [ + { + ""name"": ""stderr"", + ""output_type"": ""stream"", + ""text"": [ + ""/Users/zeromtmu/anaconda2/envs/ER_alpha/lib/python2.7/site-packages/statsmodels/compat/pandas.py:56: FutureWarning: The pandas.core.datetools module is deprecated and will be removed in a future version. Please use the pandas.tseries module instead.\n"", + "" from pandas.core import datetools\n"" + ] + } + ], + ""source"": [ + ""import matplotlib.pyplot as plt\n"", + ""from matplotlib.ticker import MultipleLocator\n"", + ""import pylab as py \n"", + ""import statsmodels.api as sm\n"", + ""from statsmodels.stats.outliers_influence import summary_table\n"", + ""\n"", + ""def plot_model (f, X_internal, X_external, Y_internal, Y_external,\n"", + "" R2_train_mean, Q2_External_mean,\n"", + "" importances_mean, importances_std, Feature, prediction, \n"", + "" acclist_predictionOnTest_scrambledtrain, acclist_predictionOnTrain_scrambledtrain):\n"", + "" \n"", + "" # Prepare plot\n"", + "" m = rf.fit(X_internal,Y_internal)\n"", + "" cm = plt.cm.RdBu\n"", + "" cv = cross_validation.cross_val_predict(rf, X_internal, Y_internal, cv=10, n_jobs=-1)\n"", + ""\n"", + "" fig_size = plt.rcParams[\""figure.figsize\""]\n"", + "" fig_size[0]=5\n"", + "" fig_size[1]=5\n"", + ""\n"", + "" # Train Set\n"", + "" x_train = np.array(Y_internal)\n"", + "" y_train = m.predict(X_internal).flatten()\n"", + "" py.scatter(x_train, y_train, s=50, marker='.', alpha=0.3,\n"", + "" c='g', cmap=cm ,edgecolors='g')\n"", + "" #label=r\""$R^{2}_{Tr}$ = %.4f\"" % R2_train_mean)\n"", + "" \n"", + "" \n"", + "" # CV Set\n"", + "" np.array(cv)\n"", + "" x_test = np.array(Y_internal)\n"", + "" y_test = cv\n"", + "" py.scatter(x_test, y_test, s=50, marker='.', alpha=0.3,\n"", + "" c='b', cmap=cm ,edgecolors='b') \n"", + "" #label=r\""$Q^{2}_{Ext}$ = %.4f\"" % Q2_External_mean)\n"", + "" #2SD line\n"", + "" X = sm.add_constant(x_test)\n"", + "" res = sm.OLS(y_test, X).fit()\n"", + ""\n"", + "" st, data, ss2 = summary_table(res, alpha=0.05)\n"", + "" fittedvalues = data[:,2]\n"", + "" #predict_mean_se  = data[:,3]\n"", + "" predict_mean_ci_low, predict_mean_ci_upp = data[:,4:6].T\n"", + "" predict_ci_low, predict_ci_upp = data[:,6:8].T\n"", + "" #2SD line\n"", + "" plt.plot(X, predict_ci_low, '--b', linewidth=0.5, alpha=0.5)\n"", + "" plt.plot(X, predict_ci_upp, '--b', linewidth=0.5, alpha=0.5)\n"", + "" \n"", + "" # External Set\n"", + "" x_test = np.array(Y_external)\n"", + "" y_test = m.predict(X_external).flatten()\n"", + "" py.scatter(x_test, y_test, s=50, marker='.', alpha=0.3,\n"", + "" c='r', cmap=cm ,edgecolors='r') \n"", + "" #label=r\""$Q^{2}_{Ext}$ = %.4f\"" % Q2_External_mean)\n"", + "" \n"", + "" \n"", + "" #py.plot(x_train, np.polyval(np.polyfit(x_train,y_train,1), x_train), '--r') #mean line\n"", + "" plt.legend(loc=2,prop={'size':6})\n"", + "" plt.xlabel(\""Experimental $pIC_{50}$ values\"", fontsize=10)\n"", + "" plt.ylabel(\""Predicted $pIC_{50}$ values\"", fontsize=10)\n"", + "" \n"", + "" min_axis = np.min(np.concatenate([Y_internal, prediction], axis=0))\n"", + "" max_axis = np.max(np.concatenate([Y_internal, prediction], axis=0))\n"", + "" plt.xlim([(min_axis*0.9),(max_axis*1.05)])\n"", + "" plt.ylim([(min_axis*0.9),(max_axis*1.05)])\n"", + "" plt.tick_params(axis='both', which='major', labelsize=14)\n"", + ""\n"", + "" \n"", + "" # Save plot to file\n"", + "" plt.savefig(f+'_compair_Fp.pdf', dpi=300)\n"", + "" plt.show()\n"", + "" \n"", + "" # Y-scrambling plot\n"", + "" py.scatter(Q2_CV_mean, R2_train_mean, s=100, marker='.', alpha=0.3,\n"", + "" c='b', cmap=cm ,edgecolors='b') \n"", + "" \n"", + "" py.scatter(acclist_predictionOnTest_scrambledtrain,acclist_predictionOnTrain_scrambledtrain, \\\n"", + "" s=100, marker='.', alpha=0.3, c='r', cmap=cm ,edgecolors='r')\n"", + ""\n"", + "" plt.legend(loc=2,prop={'size':6})\n"", + "" plt.xlabel(\""$Q^{2}$\"", fontsize=10)\n"", + "" plt.ylabel(\""$R^{2}$\"", fontsize=10)\n"", + "" plt.tick_params(axis='both', which='major', labelsize=14)\n"", + "" \n"", + "" plt.ylim([0, 1])\n"", + "" plt.xlim([0, 1])\n"", + "" plt.axhline(0.5, color='gray',linestyle='--', dashes=(5, 10), linewidth=0.5)\n"", + "" plt.axvline(0.5, color='gray',linestyle='--', dashes=(5, 10), linewidth=0.5)\n"", + "" \n"", + "" plt.savefig(f+'_Y_scrambling.pdf', dpi=300)\n"", + ""\n"", + "" plt.show()\n"", + ""\n"", + "" #Feature Importance \n"", + "" fig_size = plt.rcParams[\""figure.figsize\""]\n"", + "" fig_size[0]=5\n"", + "" fig_size[1]=10\n"", + ""\n"", + "" barlist = plt.barh(range(20), [x[1] for x in importances_mean[:20]], \n"", + "" color=\""g\"", xerr=[x[1] for x in importances_std[:20]], align=\""center\"", \\\n"", + "" error_kw=dict(ecolor='gray', lw=2, capsize=5, capthick=2))\n"", + ""\n"", + "" print (str(\""top10\""), [x[0] for x in importances_mean[:10]])\n"", + "" print ('')\n"", + "" print (str(\""top20\""), [x[0] for x in importances_mean[:20]])\n"", + "" print ('')\n"", + "" print (str(\""top30\""), [x[0] for x in importances_mean[:30]])\n"", + "" print ('')\n"", + "" print (str(\""top40\""), [x[0] for x in importances_mean[:40]])\n"", + "" print ('')\n"", + "" print (str(\""top50\""), [x[0] for x in importances_mean[:50]])\n"", + "" \n"", + "" plt.yticks(range(20), [x[0] for x in importances_mean[:20]])\n"", + "" plt.ylim([-1, 20])\n"", + "" \n"", + "" plt.xlabel(r\""$\\bf{Gini}$\"" + \"" \""+ r\""$\\bf{index}$\"", fontsize=12)\n"", + "" ax = plt.gca()\n"", + "" ax.invert_yaxis()\n"", + "" plt.tight_layout(pad=2.0, w_pad=0.7, h_pad=2.0)\n"", + "" plt.savefig(f+'_Feature_importances.pdf', dpi=300)\n"", + "" plt.show()"" + ] + }, + { + ""cell_type"": ""markdown"", + ""metadata"": {}, + ""source"": [ + ""# Constructing the QSAR model\n"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 28, + ""metadata"": {}, + ""outputs"": [ + { + ""name"": ""stderr"", + ""output_type"": ""stream"", + ""text"": [ + ""/Users/zeromtmu/anaconda2/envs/ER_alpha/lib/python2.7/site-packages/ipykernel_launcher.py:21: DeprecationWarning: \n"", + "".ix is deprecated. Please use\n"", + "".loc for label based indexing or\n"", + "".iloc for positional indexing\n"", + ""\n"", + ""See the documentation here:\n"", + ""http://pandas.pydata.org/pandas-docs/stable/indexing.html#deprecate_ix\n"" + ] + }, + { + ""name"": ""stdout"", + ""output_type"": ""stream"", + ""text"": [ + ""\n"", + ""\n"", + ""************************************************************************************\n"", + ""\n"", + ""QSAR/ER_alpha_GraphOnlyFingerprinter.csv\n"", + ""\n"", + ""from Remove useless descriptor\n"", + ""The initial set of 1024 descriptors has been reduced to 982 descriptors.\n"", + ""from Remove correlation\n"", + ""The initial set of 982 descriptors has been reduced to 599 descriptors.\n"", + ""\n"", + ""Training set\n"", + ""------------\n"", + ""N: 861\n"", + ""R2: 0.9163\n"", + ""std_R2: 0.0042\n"", + ""RMSE: 0.5238\n"", + ""std_RMSE: 0.0035\n"", + ""\n"", + ""Cross-validation set\n"", + ""------------\n"", + ""N: 861\n"", + ""Q2: 0.6914\n"", + ""std_Q2: 0.0105\n"", + ""RMSE: 0.7600\n"", + ""std_RMSE: 0.0063\n"", + ""\n"", + ""External set\n"", + ""------------\n"", + ""N: 370\n"", + ""Q2_EXt: 0.7138\n"", + ""std_Q2_EXt: 0.0097\n"", + ""RMSE: 0.5663\n"", + ""std_RMSE: 0.0153\n"" + ] + }, + { + ""data"": { + ""image/png"": 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l4yL93jKvqZc7+Zz+L4bu7ACrTXu6OhE8hhDjk7meRycNkP4dPV+dx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\n8RvuhvFz3CVPBU/S7P4K8J86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Txk+62afzwr2q1D0ve/BBx/0R2TsmwJza0j5veyTd1lR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3sBFEQplhfTL65xdcq3UzdKYSFjFzuZZXv+EHoiiyOCnBbx7Bj6vh31xll1xmc7sGk4Exgdp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PIBQAAQOIQcgEAAJA4hFwAAAAkDiEXAAAAiUPIBQAAQOIQcgEAAJA4hFwAaDGZTKbRQwCA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\npwWrRO3szIz3Nd/obK7ZHQAAAJYIQu5SV2hasErUzra0eJ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+ ""text/plain"": [ + """" + ] + }, + ""metadata"": { + ""image/png"": { + ""height"": 328, + ""width"": 348 + } + }, + ""output_type"": ""display_data"" + }, + { + ""name"": ""stdout"", + ""output_type"": ""stream"", + ""text"": [ + ""('top10', ['GraphFP60', 'GraphFP382', 'GraphFP83', 'GraphFP21', 'GraphFP342', 'GraphFP16', 'GraphFP140', 'GraphFP228', 'GraphFP224', 'GraphFP101'])\n"", + ""\n"", + ""('top20', ['GraphFP60', 'GraphFP382', 'GraphFP83', 'GraphFP21', 'GraphFP342', 'GraphFP16', 'GraphFP140', 'GraphFP228', 'GraphFP224', 'GraphFP101', 'GraphFP394', 'GraphFP3', 'GraphFP163', 'GraphFP329', 'GraphFP72', 'GraphFP59', 'GraphFP270', 'GraphFP156', 'GraphFP462', 'GraphFP499'])\n"", + ""\n"", + ""('top30', ['GraphFP60', 'GraphFP382', 'GraphFP83', 'GraphFP21', 'GraphFP342', 'GraphFP16', 'GraphFP140', 'GraphFP228', 'GraphFP224', 'GraphFP101', 'GraphFP394', 'GraphFP3', 'GraphFP163', 'GraphFP329', 'GraphFP72', 'GraphFP59', 'GraphFP270', 'GraphFP156', 'GraphFP462', 'GraphFP499', 'GraphFP89', 'GraphFP197', 'GraphFP387', 'GraphFP834', 'GraphFP298', 'GraphFP576', 'GraphFP918', 'GraphFP10', 'GraphFP529', 'GraphFP54'])\n"", + ""\n"", + ""('top40', ['GraphFP60', 'GraphFP382', 'GraphFP83', 'GraphFP21', 'GraphFP342', 'GraphFP16', 'GraphFP140', 'GraphFP228', 'GraphFP224', 'GraphFP101', 'GraphFP394', 'GraphFP3', 'GraphFP163', 'GraphFP329', 'GraphFP72', 'GraphFP59', 'GraphFP270', 'GraphFP156', 'GraphFP462', 'GraphFP499', 'GraphFP89', 'GraphFP197', 'GraphFP387', 'GraphFP834', 'GraphFP298', 'GraphFP576', 'GraphFP918', 'GraphFP10', 'GraphFP529', 'GraphFP54', 'GraphFP859', 'GraphFP674', 'GraphFP14', 'GraphFP245', 'GraphFP137', 'GraphFP293', 'GraphFP13', 'GraphFP397', 'GraphFP380', 'GraphFP363'])\n"", + ""\n"", + ""('top50', ['GraphFP60', 'GraphFP382', 'GraphFP83', 'GraphFP21', 'GraphFP342', 'GraphFP16', 'GraphFP140', 'GraphFP228', 'GraphFP224', 'GraphFP101', 'GraphFP394', 'GraphFP3', 'GraphFP163', 'GraphFP329', 'GraphFP72', 'GraphFP59', 'GraphFP270', 'GraphFP156', 'GraphFP462', 'GraphFP499', 'GraphFP89', 'GraphFP197', 'GraphFP387', 'GraphFP834', 'GraphFP298', 'GraphFP576', 'GraphFP918', 'GraphFP10', 'GraphFP529', 'GraphFP54', 'GraphFP859', 'GraphFP674', 'GraphFP14', 'GraphFP245', 'GraphFP137', 'GraphFP293', 'GraphFP13', 'GraphFP397', 'GraphFP380', 'GraphFP363', 'GraphFP786', 'GraphFP752', 'GraphFP519', 'GraphFP166', 'GraphFP157', 'GraphFP255', 'GraphFP189', 'GraphFP1002', 'GraphFP40', 'GraphFP465'])\n"" + ] + }, + { + ""data"": { + ""image/png"": 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LJjvKpwWnxUZIspXkuE5rnL4fPHwnsCewA7Aa8mObZyeMH61XV+iR/CpwGXJ3k72mezfwt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\nWXwk8MmqeumE+vO+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+ ""text/plain"": [ + """" + ] + }, + ""metadata"": { + ""image/png"": { + ""height"": 690, + ""width"": 332 + } + }, + ""output_type"": ""display_data"" + }, + { + ""name"": ""stdout"", + ""output_type"": ""stream"", + ""text"": [ + ""\n"", + ""\n"", + ""************************************************************************************\n"", + ""\n"", + ""QSAR/ER_alpha_Fingerprinter.csv\n"", + ""\n"", + ""from Remove useless descriptor\n"", + ""The initial set of 1024 descriptors has been reduced to 1021 descriptors.\n"", + ""from Remove correlation\n"", + ""The initial set of 1021 descriptors has been reduced to 934 descriptors.\n"", + ""\n"", + ""Training set\n"", + ""------------\n"", + ""N: 861\n"", + ""R2: 0.9478\n"", + ""std_R2: 0.0029\n"", + ""RMSE: 0.4697\n"", + ""std_RMSE: 0.0031\n"", + ""\n"", + ""Cross-validation set\n"", + ""------------\n"", + ""N: 861\n"", + ""Q2: 0.7002\n"", + ""std_Q2: 0.0083\n"", + ""RMSE: 0.7561\n"", + ""std_RMSE: 0.0050\n"", + ""\n"", + ""External set\n"", + ""------------\n"", + ""N: 370\n"", + ""Q2_EXt: 0.7245\n"", + ""std_Q2_EXt: 0.0134\n"", + ""RMSE: 0.5585\n"", + ""std_RMSE: 0.0175\n"" + ] + }, + { + ""data"": { + ""image/png"": 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rVGl2BLqK+tXvsK5zBXJxnpmhz/MwibSE32VyD62N1YrHnOanCQKxGzazh1t10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vlhWeFq46Wlz0qL/ouu6rVSmbWIe+F2yXpUyGEr81zCurlzBmv4Pb2Xh9wI4mE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\npPdJukTSlZLuSoq9VtI/mVnh31cAAIAiK0NP7mclzVEEs7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+ ""text/plain"": [ + """" + ] + }, + ""metadata"": { + ""image/png"": { + ""height"": 328, + ""width"": 348 + } + }, + ""output_type"": ""display_data"" + }, + { + ""name"": ""stdout"", + ""output_type"": ""stream"", + ""text"": [ + ""('top10', ['FP135', 'FP97', 'FP933', 'FP639', 'FP28', 'FP75', 'FP239', 'FP243', 'FP137', 'FP404'])\n"", + ""\n"", + ""('top20', ['FP135', 'FP97', 'FP933', 'FP639', 'FP28', 'FP75', 'FP239', 'FP243', 'FP137', 'FP404', 'FP373', 'FP133', 'FP571', 'FP362', 'FP957', 'FP676', 'FP343', 'FP206', 'FP259', 'FP14'])\n"", + ""\n"", + ""('top30', ['FP135', 'FP97', 'FP933', 'FP639', 'FP28', 'FP75', 'FP239', 'FP243', 'FP137', 'FP404', 'FP373', 'FP133', 'FP571', 'FP362', 'FP957', 'FP676', 'FP343', 'FP206', 'FP259', 'FP14', 'FP92', 'FP37', 'FP237', 'FP385', 'FP280', 'FP78', 'FP398', 'FP978', 'FP600', 'FP130'])\n"", + ""\n"", + ""('top40', ['FP135', 'FP97', 'FP933', 'FP639', 'FP28', 'FP75', 'FP239', 'FP243', 'FP137', 'FP404', 'FP373', 'FP133', 'FP571', 'FP362', 'FP957', 'FP676', 'FP343', 'FP206', 'FP259', 'FP14', 'FP92', 'FP37', 'FP237', 'FP385', 'FP280', 'FP78', 'FP398', 'FP978', 'FP600', 'FP130', 'FP998', 'FP920', 'FP367', 'FP515', 'FP171', 'FP427', 'FP529', 'FP630', 'FP314', 'FP72'])\n"", + ""\n"", + ""('top50', ['FP135', 'FP97', 'FP933', 'FP639', 'FP28', 'FP75', 'FP239', 'FP243', 'FP137', 'FP404', 'FP373', 'FP133', 'FP571', 'FP362', 'FP957', 'FP676', 'FP343', 'FP206', 'FP259', 'FP14', 'FP92', 'FP37', 'FP237', 'FP385', 'FP280', 'FP78', 'FP398', 'FP978', 'FP600', 'FP130', 'FP998', 'FP920', 'FP367', 'FP515', 'FP171', 'FP427', 'FP529', 'FP630', 'FP314', 'FP72', 'FP438', 'FP112', 'FP581', 'FP559', 'FP87', 'FP319', 'FP737', 'FP285', 'FP447', 'FP178'])\n"" + ] + }, + { + ""data"": { + ""image/png"": 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FW+aT+nJXkrKo6LMPI5kySD1TVixYbZW2tPShJquo7kjw5w0jmh6vqGa21ZR+5t9aOn1Q+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+ ""text/plain"": [ + """" + ] + }, + ""metadata"": { + ""image/png"": { + ""height"": 690, + ""width"": 333 + } + }, + ""output_type"": ""display_data"" + }, + { + ""name"": ""stdout"", + ""output_type"": ""stream"", + ""text"": [ + ""\n"", + ""\n"", + ""************************************************************************************\n"", + ""\n"", + ""QSAR/ER_alpha_ExtendedFingerprinter.csv\n"", + ""\n"", + ""from Remove useless descriptor\n"", + ""The initial set of 1024 descriptors has been reduced to 1003 descriptors.\n"", + ""from Remove correlation\n"", + ""The initial set of 1003 descriptors has been reduced to 951 descriptors.\n"", + ""\n"", + ""Training set\n"", + ""------------\n"", + ""N: 861\n"", + ""R2: 0.9484\n"", + ""std_R2: 0.0030\n"", + ""RMSE: 0.4691\n"", + ""std_RMSE: 0.0039\n"", + ""\n"", + ""Cross-validation set\n"", + ""------------\n"", + ""N: 861\n"", + ""Q2: 0.7055\n"", + ""std_Q2: 0.0078\n"", + ""RMSE: 0.7555\n"", + ""std_RMSE: 0.0050\n"", + ""\n"", + ""External set\n"", + ""------------\n"", + ""N: 370\n"", + ""Q2_EXt: 0.7270\n"", + ""std_Q2_EXt: 0.0124\n"", + ""RMSE: 0.5576\n"", + ""std_RMSE: 0.0176\n"" + ] + }, + { + ""data"": { + ""image/png"": 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kcBgmfe/fDKz+LwJ8fZUOEEEI8WHstD2bb0GC0EVad+ypefthmZuBf/IuNj60P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e5HnuRx84xueAE0mveuyGz1/kNXpx30uRfGQfLgP8jAfYgaGjxNdh3/1r/Ze/5N/cnjpqD7JHHaS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\n6w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bSQQJzLZ47u0LV32Z8PsHTN1bR8huM2XvIKYtSU+dae5exQZnqsXPU2hqNhghomZoR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\nxbPE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sInRRCeJQYYhE+J0KGCLEse5DI2n8rTEy1izs9hzr1KffZ9eVUY4KTGZzHLKErXx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IuAAAAEoeQCwAAgMQh5AIAACBxCLkAAABIHEIuAAAAEoeQCwBNJp1Oxz0EAKg5Qi4ANJlM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\nS6C+9HKLu7O1eZN0hqRXJHmVrbSgxHDr+YLCIhjV6nlQ0s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+ ""text/plain"": [ + """" + ] + }, + ""metadata"": { + ""image/png"": { + ""height"": 328, + ""width"": 348 + } + }, + ""output_type"": ""display_data"" + }, + { + ""name"": ""stdout"", + ""output_type"": ""stream"", + ""text"": [ + ""('top10', ['ExtFP286', 'ExtFP914', 'ExtFP315', 'ExtFP767', 'ExtFP343', 'ExtFP338', 'ExtFP106', 'ExtFP726', 'ExtFP111', 'ExtFP711'])\n"", + ""\n"", + ""('top20', ['ExtFP286', 'ExtFP914', 'ExtFP315', 'ExtFP767', 'ExtFP343', 'ExtFP338', 'ExtFP106', 'ExtFP726', 'ExtFP111', 'ExtFP711', 'ExtFP868', 'ExtFP811', 'ExtFP212', 'ExtFP783', 'ExtFP840', 'ExtFP718', 'ExtFP242', 'ExtFP445', 'ExtFP423', 'ExtFP905'])\n"", + ""\n"", + ""('top30', ['ExtFP286', 'ExtFP914', 'ExtFP315', 'ExtFP767', 'ExtFP343', 'ExtFP338', 'ExtFP106', 'ExtFP726', 'ExtFP111', 'ExtFP711', 'ExtFP868', 'ExtFP811', 'ExtFP212', 'ExtFP783', 'ExtFP840', 'ExtFP718', 'ExtFP242', 'ExtFP445', 'ExtFP423', 'ExtFP905', 'ExtFP467', 'ExtFP560', 'ExtFP959', 'ExtFP499', 'ExtFP176', 'ExtFP31', 'ExtFP30', 'ExtFP719', 'ExtFP915', 'ExtFP706'])\n"", + ""\n"", + ""('top40', ['ExtFP286', 'ExtFP914', 'ExtFP315', 'ExtFP767', 'ExtFP343', 'ExtFP338', 'ExtFP106', 'ExtFP726', 'ExtFP111', 'ExtFP711', 'ExtFP868', 'ExtFP811', 'ExtFP212', 'ExtFP783', 'ExtFP840', 'ExtFP718', 'ExtFP242', 'ExtFP445', 'ExtFP423', 'ExtFP905', 'ExtFP467', 'ExtFP560', 'ExtFP959', 'ExtFP499', 'ExtFP176', 'ExtFP31', 'ExtFP30', 'ExtFP719', 'ExtFP915', 'ExtFP706', 'ExtFP48', 'ExtFP140', 'ExtFP756', 'ExtFP470', 'ExtFP390', 'ExtFP775', 'ExtFP290', 'ExtFP972', 'ExtFP520', 'ExtFP984'])\n"", + ""\n"", + ""('top50', ['ExtFP286', 'ExtFP914', 'ExtFP315', 'ExtFP767', 'ExtFP343', 'ExtFP338', 'ExtFP106', 'ExtFP726', 'ExtFP111', 'ExtFP711', 'ExtFP868', 'ExtFP811', 'ExtFP212', 'ExtFP783', 'ExtFP840', 'ExtFP718', 'ExtFP242', 'ExtFP445', 'ExtFP423', 'ExtFP905', 'ExtFP467', 'ExtFP560', 'ExtFP959', 'ExtFP499', 'ExtFP176', 'ExtFP31', 'ExtFP30', 'ExtFP719', 'ExtFP915', 'ExtFP706', 'ExtFP48', 'ExtFP140', 'ExtFP756', 'ExtFP470', 'ExtFP390', 'ExtFP775', 'ExtFP290', 'ExtFP972', 'ExtFP520', 'ExtFP984', 'ExtFP216', 'ExtFP980', 'ExtFP377', 'ExtFP572', 'ExtFP220', 'ExtFP6', 'ExtFP325', 'ExtFP790', 'ExtFP699', 'ExtFP434'])\n"" + ] + }, + { + ""data"": { + ""image/png"": 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rOkClJkqTmdp50AVo8y5+9nLVTayddhiRJehxwJFOSJEnNGTIlSZLUnCFTkiRJ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+ ""text/plain"": [ + """" + ] + }, + ""metadata"": { + ""image/png"": { + ""height"": 690, + ""width"": 332 + } + }, + ""output_type"": ""display_data"" + }, + { + ""name"": ""stdout"", + ""output_type"": ""stream"", + ""text"": [ + ""\n"", + ""\n"", + ""************************************************************************************\n"", + ""\n"", + ""QSAR/ER_alpha_PubchemFingerprinter.csv\n"", + ""\n"", + ""from Remove useless descriptor\n"", + ""The initial set of 881 descriptors has been reduced to 529 descriptors.\n"", + ""from Remove correlation\n"", + ""The initial set of 529 descriptors has been reduced to 196 descriptors.\n"", + ""\n"", + ""Training set\n"", + ""------------\n"", + ""N: 861\n"", + ""R2: 0.9412\n"", + ""std_R2: 0.0037\n"", + ""RMSE: 0.4828\n"", + ""std_RMSE: 0.0046\n"", + ""\n"", + ""Cross-validation set\n"", + ""------------\n"", + ""N: 861\n"", + ""Q2: 0.7120\n"", + ""std_Q2: 0.0089\n"", + ""RMSE: 0.7405\n"", + ""std_RMSE: 0.0048\n"", + ""\n"", + ""External set\n"", + ""------------\n"", + ""N: 370\n"", + ""Q2_EXt: 0.7317\n"", + ""std_Q2_EXt: 0.0090\n"", + ""RMSE: 0.5379\n"", + ""std_RMSE: 0.0115\n"" + ] + }, + { + ""data"": { + ""image/png"": 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qjf2EOBodIDITpTYGthpkMRDGzk1APYLQ/j2LrGKrBTGkJpWkx8WQO2+5/yN/Q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\nQSwRwTQ92DYsFyxsrYJmtlErtniXU4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fZ+GHs0/YI4+7f7zXNvSkoT0RP0B/o57319yhq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ieO61E2Ruqyx1xNphFwU0URSFYoqlGIC/oJE+OQ8gVdeJVm+ia+5xgd6j353gRO9KFN7PnRb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DYIeQCAAAgdgi5AAAAiB1CLgAAAGKHkAsAAIDYIeQCAAAgdgi5AAAAiB1CLgA0mGQyWesh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\n/STrdWvtp9iUkZGR8aeffvrZSe4H2Tg3un2hYiu8GbXFud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+ ""text/plain"": [ + """" + ] + }, + ""metadata"": { + ""image/png"": { + ""height"": 328, + ""width"": 348 + } + }, + ""output_type"": ""display_data"" + }, + { + ""name"": ""stdout"", + ""output_type"": ""stream"", + ""text"": [ + ""('top10', ['PubchemFP193', 'PubchemFP308', 'PubchemFP375', 'PubchemFP439', 'PubchemFP696', 'PubchemFP420', 'PubchemFP199', 'PubchemFP534', 'PubchemFP391', 'PubchemFP493'])\n"", + ""\n"", + ""('top20', ['PubchemFP193', 'PubchemFP308', 'PubchemFP375', 'PubchemFP439', 'PubchemFP696', 'PubchemFP420', 'PubchemFP199', 'PubchemFP534', 'PubchemFP391', 'PubchemFP493', 'PubchemFP186', 'PubchemFP345', 'PubchemFP566', 'PubchemFP580', 'PubchemFP258', 'PubchemFP757', 'PubchemFP346', 'PubchemFP681', 'PubchemFP405', 'PubchemFP33'])\n"", + ""\n"", + ""('top30', ['PubchemFP193', 'PubchemFP308', 'PubchemFP375', 'PubchemFP439', 'PubchemFP696', 'PubchemFP420', 'PubchemFP199', 'PubchemFP534', 'PubchemFP391', 'PubchemFP493', 'PubchemFP186', 'PubchemFP345', 'PubchemFP566', 'PubchemFP580', 'PubchemFP258', 'PubchemFP757', 'PubchemFP346', 'PubchemFP681', 'PubchemFP405', 'PubchemFP33', 'PubchemFP15', 'PubchemFP299', 'PubchemFP256', 'PubchemFP206', 'PubchemFP20', 'PubchemFP694', 'PubchemFP3', 'PubchemFP381', 'PubchemFP185', 'PubchemFP380'])\n"", + ""\n"", + ""('top40', ['PubchemFP193', 'PubchemFP308', 'PubchemFP375', 'PubchemFP439', 'PubchemFP696', 'PubchemFP420', 'PubchemFP199', 'PubchemFP534', 'PubchemFP391', 'PubchemFP493', 'PubchemFP186', 'PubchemFP345', 'PubchemFP566', 'PubchemFP580', 'PubchemFP258', 'PubchemFP757', 'PubchemFP346', 'PubchemFP681', 'PubchemFP405', 'PubchemFP33', 'PubchemFP15', 'PubchemFP299', 'PubchemFP256', 'PubchemFP206', 'PubchemFP20', 'PubchemFP694', 'PubchemFP3', 'PubchemFP381', 'PubchemFP185', 'PubchemFP380', 'PubchemFP2', 'PubchemFP192', 'PubchemFP374', 'PubchemFP259', 'PubchemFP385', 'PubchemFP23', 'PubchemFP755', 'PubchemFP339', 'PubchemFP396', 'PubchemFP12'])\n"", + ""\n"", + ""('top50', ['PubchemFP193', 'PubchemFP308', 'PubchemFP375', 'PubchemFP439', 'PubchemFP696', 'PubchemFP420', 'PubchemFP199', 'PubchemFP534', 'PubchemFP391', 'PubchemFP493', 'PubchemFP186', 'PubchemFP345', 'PubchemFP566', 'PubchemFP580', 'PubchemFP258', 'PubchemFP757', 'PubchemFP346', 'PubchemFP681', 'PubchemFP405', 'PubchemFP33', 'PubchemFP15', 'PubchemFP299', 'PubchemFP256', 'PubchemFP206', 'PubchemFP20', 'PubchemFP694', 'PubchemFP3', 'PubchemFP381', 'PubchemFP185', 'PubchemFP380', 'PubchemFP2', 'PubchemFP192', 'PubchemFP374', 'PubchemFP259', 'PubchemFP385', 'PubchemFP23', 'PubchemFP755', 'PubchemFP339', 'PubchemFP396', 'PubchemFP12', 'PubchemFP695', 'PubchemFP682', 'PubchemFP690', 'PubchemFP699', 'PubchemFP180', 'PubchemFP338', 'PubchemFP143', 'PubchemFP712', 'PubchemFP432', 'PubchemFP335'])\n"" + ] + }, + { + ""data"": { + ""image/png"": 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BpwBuARUnOp62juRNtrcyX0NaxXNdnOIy29NB5SS6lzQi/ndZSuzttIsy23b75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1aVDbT++u9czkuxRVXeNnOMFwMKqurqrvz2wEnhNd38HD9pL8lu0EPuiJM+sqnHjLo/u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\n5UuTLO4WDruNPY5Xt/PLq+qG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SR4BfBzYZ5wqW7bTLsCLgY8mOaiqTp+lJkrSOjFoStII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\nYII=\n"", + ""text/plain"": [ + """" + ] + }, + ""metadata"": { + ""image/png"": { + ""height"": 690, + ""width"": 333 + } + }, + ""output_type"": ""display_data"" + }, + { + ""name"": ""stdout"", + ""output_type"": ""stream"", + ""text"": [ + ""\n"", + ""\n"", + ""************************************************************************************\n"", + ""\n"", + ""QSAR/ER_alpha_EStateFingerprinter.csv\n"", + ""\n"", + ""from Remove useless descriptor\n"", + ""The initial set of 79 descriptors has been reduced to 33 descriptors.\n"", + ""from Remove correlation\n"", + ""The initial set of 33 descriptors has been reduced to 31 descriptors.\n"", + ""\n"", + ""Training set\n"", + ""------------\n"", + ""N: 861\n"", + ""R2: 0.8806\n"", + ""std_R2: 0.0041\n"", + ""RMSE: 0.5796\n"", + ""std_RMSE: 0.0051\n"", + ""\n"", + ""Cross-validation set\n"", + ""------------\n"", + ""N: 861\n"", + ""Q2: 0.6747\n"", + ""std_Q2: 0.0113\n"", + ""RMSE: 0.7653\n"", + ""std_RMSE: 0.0064\n"", + ""\n"", + ""External set\n"", + ""------------\n"", + ""N: 370\n"", + ""Q2_EXt: 0.6916\n"", + ""std_Q2_EXt: 0.0199\n"", + ""RMSE: 0.5709\n"", + ""std_RMSE: 0.0190\n"" + ] + }, + { + ""data"": { + ""image/png"": 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e+93qhs5NMdQTnm+NcTGbwDha5HuzFwk/IWyVVLnFTD/JI6W0i+hLTAYt3B1uw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\nSBXywz2GkoQbMzC9kEAOyTgygRqQcSRKrZBqZ5UrSzGsMYlQ6dDv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HdTBBkG/jitJ5Nwa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CQOIRcAAAAJA4hFwAAAIlDyAUAAEDiEHIBAACQOIRcAAAAJA4hFwCaTDqdrvcQAKDmCLkA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\ndWnAlfz1jTd6mHz1VX9ky1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tv3eqtvGZnpWee8fOdPu3TJGZm4kpwtMLZHXdcu+BEuehZ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\nTkSuQmCC\n"", + ""text/plain"": [ + """" + ] + }, + ""metadata"": { + ""image/png"": { + ""height"": 328, + ""width"": 348 + } + }, + ""output_type"": ""display_data"" + }, + { + ""name"": ""stdout"", + ""output_type"": ""stream"", + ""text"": [ + ""('top10', ['EStateFP34', 'EStateFP30', 'EStateFP29', 'EStateFP35', 'EStateFP36', 'EStateFP24', 'EStateFP37', 'EStateFP50', 'EStateFP16', 'EStateFP13'])\n"", + ""\n"", + ""('top20', ['EStateFP34', 'EStateFP30', 'EStateFP29', 'EStateFP35', 'EStateFP36', 'EStateFP24', 'EStateFP37', 'EStateFP50', 'EStateFP16', 'EStateFP13', 'EStateFP51', 'EStateFP18', 'EStateFP9', 'EStateFP7', 'EStateFP32', 'EStateFP25', 'EStateFP19', 'EStateFP38', 'EStateFP11', 'EStateFP54'])\n"", + ""\n"", + ""('top30', ['EStateFP34', 'EStateFP30', 'EStateFP29', 'EStateFP35', 'EStateFP36', 'EStateFP24', 'EStateFP37', 'EStateFP50', 'EStateFP16', 'EStateFP13', 'EStateFP51', 'EStateFP18', 'EStateFP9', 'EStateFP7', 'EStateFP32', 'EStateFP25', 'EStateFP19', 'EStateFP38', 'EStateFP11', 'EStateFP54', 'EStateFP53', 'EStateFP15', 'EStateFP28', 'EStateFP70', 'EStateFP49', 'EStateFP21', 'EStateFP12', 'EStateFP8', 'EStateFP31', 'EStateFP10'])\n"", + ""\n"", + ""('top40', ['EStateFP34', 'EStateFP30', 'EStateFP29', 'EStateFP35', 'EStateFP36', 'EStateFP24', 'EStateFP37', 'EStateFP50', 'EStateFP16', 'EStateFP13', 'EStateFP51', 'EStateFP18', 'EStateFP9', 'EStateFP7', 'EStateFP32', 'EStateFP25', 'EStateFP19', 'EStateFP38', 'EStateFP11', 'EStateFP54', 'EStateFP53', 'EStateFP15', 'EStateFP28', 'EStateFP70', 'EStateFP49', 'EStateFP21', 'EStateFP12', 'EStateFP8', 'EStateFP31', 'EStateFP10', 'EStateFP23'])\n"", + ""\n"", + ""('top50', ['EStateFP34', 'EStateFP30', 'EStateFP29', 'EStateFP35', 'EStateFP36', 'EStateFP24', 'EStateFP37', 'EStateFP50', 'EStateFP16', 'EStateFP13', 'EStateFP51', 'EStateFP18', 'EStateFP9', 'EStateFP7', 'EStateFP32', 'EStateFP25', 'EStateFP19', 'EStateFP38', 'EStateFP11', 'EStateFP54', 'EStateFP53', 'EStateFP15', 'EStateFP28', 'EStateFP70', 'EStateFP49', 'EStateFP21', 'EStateFP12', 'EStateFP8', 'EStateFP31', 'EStateFP10', 'EStateFP23'])\n"" + ] + }, + { + ""data"": { + ""image/png"": 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PPAmu7vx8PvAQ4CTgqycFVdUtXdgRwEPBt4KfA3cBTgJcBRyc5rqrOnnDdSf+X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\nAtsBuwDn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+ ""text/plain"": [ + """" + ] + }, + ""metadata"": { + ""image/png"": { + ""height"": 690, + ""width"": 333 + } + }, + ""output_type"": ""display_data"" + }, + { + ""name"": ""stdout"", + ""output_type"": ""stream"", + ""text"": [ + ""\n"", + ""\n"", + ""************************************************************************************\n"", + ""\n"", + ""QSAR/ER_alpha_AtomPairs2DFingerprintCount.csv\n"", + ""\n"", + ""from Remove useless descriptor\n"", + ""The initial set of 780 descriptors has been reduced to 216 descriptors.\n"", + ""from Remove correlation\n"", + ""The initial set of 216 descriptors has been reduced to 120 descriptors.\n"", + ""\n"", + ""Training set\n"", + ""------------\n"", + ""N: 861\n"", + ""R2: 0.9433\n"", + ""std_R2: 0.0023\n"", + ""RMSE: 0.4866\n"", + ""std_RMSE: 0.0043\n"", + ""\n"", + ""Cross-validation set\n"", + ""------------\n"", + ""N: 861\n"", + ""Q2: 0.6886\n"", + ""std_Q2: 0.0091\n"", + ""RMSE: 0.7597\n"", + ""std_RMSE: 0.0068\n"", + ""\n"", + ""External set\n"", + ""------------\n"", + ""N: 370\n"", + ""Q2_EXt: 0.7076\n"", + ""std_Q2_EXt: 0.0146\n"", + ""RMSE: 0.5659\n"", + ""std_RMSE: 0.0148\n"" + ] + }, + { + ""data"": { + ""image/png"": 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WCyzRpDndg5z0oU1ME8dAQ7sQbX8aab2bp3UssWQEsu0C2WiIaiJDzNGOqBRLq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\n+gOJfKur8Oqrfj3XQACeu6bYt7Xld9Rg0Ld6JpM3XRGfwGz3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Q1K9IscDFt2dFFeKGoPo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oPuXfknw+FEGYW2ymEcFrS+LxjymZmb5DX9OYk/dtKzwMAAIB41e2NZ2bWLml9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\ngNT5fzwyfndgJb95AAAAAElFTkSuQmCC\n"", + ""text/plain"": [ + """" + ] + }, + ""metadata"": { + ""image/png"": { + ""height"": 328, + ""width"": 348 + } + }, + ""output_type"": ""display_data"" + }, + { + ""name"": ""stdout"", + ""output_type"": ""stream"", + ""text"": [ + ""('top10', ['APC2D1_C_C', 'APC2D2_N_O', 'APC2D10_O_O', 'APC2D1_C_N', 'APC2D1_C_O', 'APC2D4_N_O', 'APC2D2_N_N', 'APC2D3_N_O', 'APC2D7_O_O', 'APC2D9_N_O'])\n"", + ""\n"", + ""('top20', ['APC2D1_C_C', 'APC2D2_N_O', 'APC2D10_O_O', 'APC2D1_C_N', 'APC2D1_C_O', 'APC2D4_N_O', 'APC2D2_N_N', 'APC2D3_N_O', 'APC2D7_O_O', 'APC2D9_N_O', 'APC2D6_O_S', 'APC2D3_O_O', 'APC2D5_N_O', 'APC2D7_N_O', 'APC2D5_O_O', 'APC2D10_N_S', 'APC2D6_O_O', 'APC2D1_C_S', 'APC2D4_O_O', 'APC2D2_N_S'])\n"", + ""\n"", + ""('top30', ['APC2D1_C_C', 'APC2D2_N_O', 'APC2D10_O_O', 'APC2D1_C_N', 'APC2D1_C_O', 'APC2D4_N_O', 'APC2D2_N_N', 'APC2D3_N_O', 'APC2D7_O_O', 'APC2D9_N_O', 'APC2D6_O_S', 'APC2D3_O_O', 'APC2D5_N_O', 'APC2D7_N_O', 'APC2D5_O_O', 'APC2D10_N_S', 'APC2D6_O_O', 'APC2D1_C_S', 'APC2D4_O_O', 'APC2D2_N_S', 'APC2D9_O_O', 'APC2D4_N_N', 'APC2D6_N_O', 'APC2D8_O_O', 'APC2D6_N_N', 'APC2D7_O_S', 'APC2D3_N_N', 'APC2D10_C_S', 'APC2D1_C_F', 'APC2D3_O_S'])\n"", + ""\n"", + ""('top40', ['APC2D1_C_C', 'APC2D2_N_O', 'APC2D10_O_O', 'APC2D1_C_N', 'APC2D1_C_O', 'APC2D4_N_O', 'APC2D2_N_N', 'APC2D3_N_O', 'APC2D7_O_O', 'APC2D9_N_O', 'APC2D6_O_S', 'APC2D3_O_O', 'APC2D5_N_O', 'APC2D7_N_O', 'APC2D5_O_O', 'APC2D10_N_S', 'APC2D6_O_O', 'APC2D1_C_S', 'APC2D4_O_O', 'APC2D2_N_S', 'APC2D9_O_O', 'APC2D4_N_N', 'APC2D6_N_O', 'APC2D8_O_O', 'APC2D6_N_N', 'APC2D7_O_S', 'APC2D3_N_N', 'APC2D10_C_S', 'APC2D1_C_F', 'APC2D3_O_S', 'APC2D3_N_S', 'APC2D8_N_O', 'APC2D10_N_O', 'APC2D9_N_N', 'APC2D5_N_N', 'APC2D1_N_N', 'APC2D4_N_S', 'APC2D7_N_N', 'APC2D4_O_F', 'APC2D8_N_S'])\n"", + ""\n"", + ""('top50', ['APC2D1_C_C', 'APC2D2_N_O', 'APC2D10_O_O', 'APC2D1_C_N', 'APC2D1_C_O', 'APC2D4_N_O', 'APC2D2_N_N', 'APC2D3_N_O', 'APC2D7_O_O', 'APC2D9_N_O', 'APC2D6_O_S', 'APC2D3_O_O', 'APC2D5_N_O', 'APC2D7_N_O', 'APC2D5_O_O', 'APC2D10_N_S', 'APC2D6_O_O', 'APC2D1_C_S', 'APC2D4_O_O', 'APC2D2_N_S', 'APC2D9_O_O', 'APC2D4_N_N', 'APC2D6_N_O', 'APC2D8_O_O', 'APC2D6_N_N', 'APC2D7_O_S', 'APC2D3_N_N', 'APC2D10_C_S', 'APC2D1_C_F', 'APC2D3_O_S', 'APC2D3_N_S', 'APC2D8_N_O', 'APC2D10_N_O', 'APC2D9_N_N', 'APC2D5_N_N', 'APC2D1_N_N', 'APC2D4_N_S', 'APC2D7_N_N', 'APC2D4_O_F', 'APC2D8_N_S', 'APC2D1_C_Cl', 'APC2D1_N_O', 'APC2D4_N_F', 'APC2D1_O_S', 'APC2D5_N_S', 'APC2D5_O_X', 'APC2D7_O_F', 'APC2D8_O_F', 'APC2D3_O_F', 'APC2D8_N_N'])\n"" + ] + }, + { + ""data"": { + ""image/png"": 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QlSZLUCYOmJEmSOmHQlCRJUicMmpIkSeqEQVOSJEmdMGhKkiSpEwZNSZIkdcKg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\ngsvJbJvksxBvAJ5L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+ ""text/plain"": [ + """" + ] + }, + ""metadata"": { + ""image/png"": { + ""height"": 690, + ""width"": 333 + } + }, + ""output_type"": ""display_data"" + }, + { + ""name"": ""stdout"", + ""output_type"": ""stream"", + ""text"": [ + ""\n"", + ""\n"", + ""************************************************************************************\n"", + ""\n"", + ""QSAR/ER_alpha_KlekotaRothFingerprintCount.csv\n"", + ""\n"", + ""from Remove useless descriptor\n"", + ""The initial set of 4860 descriptors has been reduced to 786 descriptors.\n"", + ""from Remove correlation\n"", + ""The initial set of 786 descriptors has been reduced to 405 descriptors.\n"", + ""\n"", + ""Training set\n"", + ""------------\n"", + ""N: 861\n"", + ""R2: 0.9617\n"", + ""std_R2: 0.0014\n"", + ""RMSE: 0.4446\n"", + ""std_RMSE: 0.0035\n"", + ""\n"", + ""Cross-validation set\n"", + ""------------\n"", + ""N: 861\n"", + ""Q2: 0.7259\n"", + ""std_Q2: 0.0078\n"", + ""RMSE: 0.7339\n"", + ""std_RMSE: 0.0056\n"", + ""\n"", + ""External set\n"", + ""------------\n"", + ""N: 370\n"", + ""Q2_EXt: 0.7380\n"", + ""std_Q2_EXt: 0.0199\n"", + ""RMSE: 0.5274\n"", + ""std_RMSE: 0.0213\n"" + ] + }, + { + ""data"": { + ""image/png"": 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N7dYDBcQ9dMQW92MSlZfB3GOTS4+jlNs8/G6LVoVO+1kOppPLBks5NpUFnMMDQ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\nNB4ldjb2L3zBUW0Viw4B2BtrsR/aKoc0KuRy83S1eT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P+nNi5eo58M3+TeO70a6Xo5fqq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jLgxT1Q2uZjYYGayQ6J5jPiqhLRaIHb2HP/DK0X2dh6sxFixMEeKQ306Iqkl8TyPo8ERzXET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dqSZ+X1CppRtLvSvqa7Lc9KOkXJe2S9GXn3FHv/eUi3fcWSR+W5CUNS9pajOti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\n3h93zsVlM7U/45z7S+/9NzKPcc79pKS3pt9+yn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AAyB1CLgAAAHKHkAsAAIDcIeQCQIPp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\n8gtfgnzMyllFynwxcZw3Z/2+1MuWUvudL5/LMUialPSWrH/uvGxptF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+ ""text/plain"": [ + """" + ] + }, + ""metadata"": { + ""image/png"": { + ""height"": 328, + ""width"": 348 + } + }, + ""output_type"": ""display_data"" + }, + { + ""name"": ""stdout"", + ""output_type"": ""stream"", + ""text"": [ + ""('top10', ['KRFPC1148', 'KRFPC1642', 'KRFPC297', 'KRFPC2711', 'KRFPC1193', 'KRFPC45', 'KRFPC3560', 'KRFPC444', 'KRFPC1645', 'KRFPC433'])\n"", + ""\n"", + ""('top20', ['KRFPC1148', 'KRFPC1642', 'KRFPC297', 'KRFPC2711', 'KRFPC1193', 'KRFPC45', 'KRFPC3560', 'KRFPC444', 'KRFPC1645', 'KRFPC433', 'KRFPC2981', 'KRFPC4331', 'KRFPC1', 'KRFPC677', 'KRFPC2262', 'KRFPC2547', 'KRFPC2856', 'KRFPC669', 'KRFPC2979', 'KRFPC2548'])\n"", + ""\n"", + ""('top30', ['KRFPC1148', 'KRFPC1642', 'KRFPC297', 'KRFPC2711', 'KRFPC1193', 'KRFPC45', 'KRFPC3560', 'KRFPC444', 'KRFPC1645', 'KRFPC433', 'KRFPC2981', 'KRFPC4331', 'KRFPC1', 'KRFPC677', 'KRFPC2262', 'KRFPC2547', 'KRFPC2856', 'KRFPC669', 'KRFPC2979', 'KRFPC2548', 'KRFPC2265', 'KRFPC3729', 'KRFPC2438', 'KRFPC2667', 'KRFPC2855', 'KRFPC2242', 'KRFPC3602', 'KRFPC2986', 'KRFPC2712', 'KRFPC4813'])\n"", + ""\n"", + ""('top40', ['KRFPC1148', 'KRFPC1642', 'KRFPC297', 'KRFPC2711', 'KRFPC1193', 'KRFPC45', 'KRFPC3560', 'KRFPC444', 'KRFPC1645', 'KRFPC433', 'KRFPC2981', 'KRFPC4331', 'KRFPC1', 'KRFPC677', 'KRFPC2262', 'KRFPC2547', 'KRFPC2856', 'KRFPC669', 'KRFPC2979', 'KRFPC2548', 'KRFPC2265', 'KRFPC3729', 'KRFPC2438', 'KRFPC2667', 'KRFPC2855', 'KRFPC2242', 'KRFPC3602', 'KRFPC2986', 'KRFPC2712', 'KRFPC4813', 'KRFPC4287', 'KRFPC3295', 'KRFPC3591', 'KRFPC1154', 'KRFPC1452', 'KRFPC466', 'KRFPC14', 'KRFPC2673', 'KRFPC3561', 'KRFPC1637'])\n"", + ""\n"", + ""('top50', ['KRFPC1148', 'KRFPC1642', 'KRFPC297', 'KRFPC2711', 'KRFPC1193', 'KRFPC45', 'KRFPC3560', 'KRFPC444', 'KRFPC1645', 'KRFPC433', 'KRFPC2981', 'KRFPC4331', 'KRFPC1', 'KRFPC677', 'KRFPC2262', 'KRFPC2547', 'KRFPC2856', 'KRFPC669', 'KRFPC2979', 'KRFPC2548', 'KRFPC2265', 'KRFPC3729', 'KRFPC2438', 'KRFPC2667', 'KRFPC2855', 'KRFPC2242', 'KRFPC3602', 'KRFPC2986', 'KRFPC2712', 'KRFPC4813', 'KRFPC4287', 'KRFPC3295', 'KRFPC3591', 'KRFPC1154', 'KRFPC1452', 'KRFPC466', 'KRFPC14', 'KRFPC2673', 'KRFPC3561', 'KRFPC1637', 'KRFPC4260', 'KRFPC3892', 'KRFPC2414', 'KRFPC4254', 'KRFPC401', 'KRFPC1564', 'KRFPC582', 'KRFPC4067', 'KRFPC639', 'KRFPC3139'])\n"" + ] + }, + { + ""data"": { + ""image/png"": 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5viF/t3/enWyT0crr5kw8E3gC8GfhCkl2q6vqBdscDTwKeMepW/jSSbAX8OV2g/m9V9ZP1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\nddIE11hC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+ ""text/plain"": [ + """" + ] + }, + ""metadata"": { + ""image/png"": { + ""height"": 690, + ""width"": 332 + } + }, + ""output_type"": ""display_data"" + }, + { + ""name"": ""stdout"", + ""output_type"": ""stream"", + ""text"": [ + ""\n"", + ""\n"", + ""************************************************************************************\n"", + ""\n"", + ""QSAR/ER_alpha_AtomPairs2DFingerprinter.csv\n"", + ""\n"", + ""from Remove useless descriptor\n"", + ""The initial set of 780 descriptors has been reduced to 247 descriptors.\n"", + ""from Remove correlation\n"", + ""The initial set of 247 descriptors has been reduced to 154 descriptors.\n"", + ""\n"", + ""Training set\n"", + ""------------\n"", + ""N: 861\n"", + ""R2: 0.8997\n"", + ""std_R2: 0.0065\n"", + ""RMSE: 0.5491\n"", + ""std_RMSE: 0.0067\n"", + ""\n"", + ""Cross-validation set\n"", + ""------------\n"", + ""N: 861\n"", + ""Q2: 0.6702\n"", + ""std_Q2: 0.0059\n"", + ""RMSE: 0.7765\n"", + ""std_RMSE: 0.0057\n"", + ""\n"", + ""External set\n"", + ""------------\n"", + ""N: 370\n"", + ""Q2_EXt: 0.6864\n"", + ""std_Q2_EXt: 0.0081\n"", + ""RMSE: 0.5954\n"", + ""std_RMSE: 0.0131\n"" + ] + }, + { + ""data"": { + ""image/png"": 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ug2CE0NU57ey2xpjbqI0+CPwvlKIHseZ6+fIaz5/3Qc8Vb1Z5MQrkMfv89+3w+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RhZl1Mg6fuM462si09Pg8Rsoxii15Si+Xpbjly9S6g6YaPaZFwYYMej2BEYaCcbjXTh69LZ96lvT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\neWQoPocM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EBVjjIlCjrmtKiX9HF1/k2zo80hH5om/cX/HwB2xp2kSUSlFaiJ1Y+u2VjcR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+oh0MI7fOc5aZMyvPnOS62+9RnOgMhFajLspRM2DEqvgmWlhdxboV9PY6iJWXGf12JgdXx27\nNmYwVphHJKCN2RFrNEMxnulUODdcYBB6iUjw5/jHM03G1Y8YtPuYH2VxumkKfpFJyp3FvjizCsk7\nnallzWb+qk18a8ixfJHZYY/AaA9F7DEa+fGYDYYzPryRddLBNGszs4xDd+borv3tdfjb67cnHD2U\nJ+hTdLdO9flN8tUal28MCcWHFJ0hVxuJe1pAPZSntFQfZZbOzLgDFY6Cu0Xmz5TonDJlyheCo24y\n/w2g5zjOh8DvHOW2p/zs8IkjWocEgLh1fwGgKPDKK+6i2+u5i2sw6ArSVst1PWUZRMnievMalUmF\nrhUF0eTdKxNa4xbddJevBRWGgsp8dIBpBJlM3F2dmOuTMVVWn1cIvyzev+jYMLB//CaBixukimUU\nW8fsqCRmSwyadYoLFxBFhUTCdUCLRVd4lv/oTYS3bjB+axO5NSHiKIjhNKaQY3DsW7zxp++w0H6d\nfjaO2DNJWSk3PH3I+RLX1/HKXlRZZaAPCKpBGqMG6laRpT0TtW/RW0ti+FT6NfB/WCKoXaR8ucTH\nMQfN1DBsA5+TJOmXOb/4LC/MyzyfMil7K/R0P4YeQwrvIakGhm0xGYpohol97RxWP85StI7l6/Jm\noMoPz2i8HAkwV9fJDCZkC3sIYYk31HUmcpa1SIPoXoFeuUKnOaDRb1LMz9EqRJn5z3MsZTLk4rnb\nn+t+/FgYDTlnljBFE3vQImBtU5wJ01cjiNUIy0YV09Gob2v0vBEu1i6SDCQZj+fQNOnOhv2PEp7w\nRH2KDuvUfN5io1Gib+/hBMs4gQZltcpPdmfvaQH1UJ5ymMH9zNL9zbz22oEL/0nC8HeLzL//992c\n5n2mkewpU6Y8LkftfP4Q+He4rueUrxBHmm/2mAJgX4BKkrvwrq66i1+77T5tdhYcb53KoEK1OWRp\nPkXY70UOC2wUxlS2DBzCXDgzx/LQta8MTwjvpM1C/T0iUQliBco//h5v/ThLK57DE1QOXkY+j7i5\nQaBfoTCzRlsPMtQHnNILKG1oSilkeZ1w2C0yGg7h/T/MM/7hDUqXbnJxtEBL9DITrHNM+RjEMm+/\n1mUiFOkPt9nwb8IeRD1RVmOrvLz4Mp5Dzlc2kqXUL1FoF1iKLKFbOoFKE09jxHBxlmQsiQ/ox1Lc\nTFRJFa4j3jzN6FQUQ5kg6hHKdQ/BpQ8xgjaieIKVmTlWZkQub4HoeLCkAbalYDbmMUchbGMMdpSx\nIHP99SRte4ah7wrOJMxfWibrdplTPoNMzGQrXGVkREj3PTzb30Bp19n2vMhmQGA06TDTLKPvWoQ+\nmIXkBViWwb4zftxul4iNavzcaIQoObweOcUED6pgIiX87PkUVsfv0qzYXIzFSfgSlPa62JqJJC8i\nik/xJ+4xcwMPn6Y/vliiEb6BYO5x5liYtbUUE8dHoe1aj/stoB6LJxxmcD+z1DTh2jXXCd2/JiXp\nAWH4R6jGh4XYP4880ylTpvzsc9Ticw8YH/E2p3zB+VTyze4jAGzbvevwMnl3lLRadbXZcOg6nzVt\nRK2rcmyg80zkDWLmkF49iFfLcMVr01iLs7iwxpIJcr2APRwjtqsg2VgDid13y+wN27SdEnvhOtXV\nC5RKCvU6vDwoIpfL+M+sES4G2bkMEzFIzb9Col2AUZH46XX8fvc1NZsQuFrE3CqwE4ozsSETsjC1\nOWqyl2T1XQJzE661giQkP55Jm77ssDXZYmAMkYcTXpYzSLecr8PV71vdLbZbm6yMh/hsCWJp4t4Y\nAK1REy3VRigOkJQZzL1TeIRTqB4Bz0yTQWCTiqcLnOD5k0l+cPE6o0t+JvoYNBFrGEbozCEDoZkR\nmtlBERVaWys49iqW9gyIFo4gcsOv018csRyDbuAioulhuXwTYVJlN7jGXi+IYEByNozpjbAubdGq\nTtjekslnYJ0748c7PYvd3T2e8w8YtDvEwl488SVEyaLeL2OMQoglD+JEIq5GiAgL3NjUWZjfxYnY\nwNonOLN5pJW3f5oWlatUdt/hQmKNoOqeqEGCrERXKHQKFLvFxxefTzii6H5maaXi/lSrblTg1Cn3\nabczN2IG68qjVePDQuyfdZ7plClTvjwctfj8EXDhiLc55QvOkfY7vAvDgKtXRd56y13UwK1ef/HF\ngwrywybp8eNua8RaDapVm73rfk71djkWLBHdqRKPDPCYPkRPG8HrwV79eXayL5FNpaBSRMznQRuD\nbVOZfZ4b5SgDbcAqBRYjUAqk+LDiCuJVbcKipjF7OkhLc8P9GxtwpRjia5ZOdFVjELYZj0UWFsAy\nbLrlIfPaBrF2lEjXjzKQaMsJ6vIsaUFmoO+Q5wVmRYFjpTZCJobqizJsVuiXxtTOzjN3y/lSJIUL\nixduV7/HvDHkGxOEdpUZ20tr3GZv2KA2quHXdJyZFumFNVZmu3gEh2jAhy8xpCD8lNpIxnZs5OQW\nmeyI0GyHfj+DUH6eVa3EovE2IV8bue+nqMbZkGfxKR6GfQlRGiJINgHVTyQOqahIpwnmJIs31Cbi\n79MtTajaQfp9t9XVaATBeIS4peNNaPzNrk2xKLLOQfzYDvjROzpDRabtX0Adtkg0e/Q8IRBgMmmi\ndnVENcSgl2L7egYSMVYWe1ixGwjxMZ9YfD4GtmMzMSeY9kH+7T4hTwjd1NFMDdux75k/f1+eYkTR\n3WZpo+EKT3AHK+z3kV1Zga2bBv3mm5B4sGr87n935z6+8x149tk79/lpXvdfJabpClO+ihy1+Pyv\ngbcEQfhvgf/GcRzjiLc/5QvIp9Xv0DDg9dfhL/8Sbt50xZ0gQDTqLny//MvwjW/caZJevuxOQ7xy\nBbxeEWP7IjObHxNp62wFl9mWvKwsNljqFnAEDzuFUxQWPaTn11l/9ZbL2m7D2hr1G0FaLZjNBjFZ\nwV8tkEgUWV3J0Xk3z7DzMZ12gdY1HTOyRNifYXZWdkOkTZUJHiTFFZ4rK9BpWHjredRGi8VOF0sP\nYGsyHrWLz24xDAZpjoZcCfhYCPjwBUySOxUSjkJf0NhJB5hJqsytrt7+jBRJYT25znpynV8wf4GL\nvRBD7a9pXX6PfNShp1qENZH5lkE5rmCcNvn6sz1EZ4QgOgz1IULVvL29yqhI5NhlXna8vEGP9fwe\nS2OJGb2NX64j6CJz1hxpc0D3TJzC9QzaWESc/4CIz0YezzMZ1xATBcy9DKGUTWxBxzf00isPaHTd\nEyQUckcb+mUVK+xBN0W0sY3NBPFW/FgEJMdDe3eW6xOZZW2b4LZMp+1nKIcQLZN56SbNeJa9WIz5\nY3WOz/lJzo0pyQUsIfr4gu8TIAriPfm3+/S1Pqqs4pE9T/Y6nrAt0J35p+75X6+7gjGTOZjsFQpB\nsJpHkTewtco9BW3f/ZNz8IcdV63e4kEFRZ9an9OvANN0hSlfdY5afP4L4GPgvwL+kSAIH+FOM3Lu\nepzjOM4/OuJ9T/kc+DT7Hebz8JOfuMJTlt2JQOD+wb55093+3NydC9zurrvovvii+3ocq4HQq/C+\nuEy9GWEhMUYPiGyZfhYHPZKRFtdKtxbKEza2piPqOrY/iK67uXM+H1iEkEwdcTJkaefHeG7eRCtf\no9/MIw4/Bs8cxE4SO/4MC6EK6uk54sezGLmDReWt38/TGetoExkHgWYohW1JzE5KxPQ6H5nHuLn7\nKuNjPSrn0sz4cuj1LqNBGx0vEdXB6fex/+IvwONDXL5ztfLIHs6+8pu8Uymh9yssV2skxCCBcJwr\niwFK8g43vA1O9Er4ZT+NUYNyvwyCK6AMy2BiTrCYcP75KN7OHxPVmiTNVTaNdfr+GaK+FmvdCqLm\nYaD1mITSiAh441F6Wpv+MALjMXMBh2FZJhta5sw3TCKhOmdeKyAKKwRnQmRCfeb1TYzEHHVvFtUA\nj09E5M74sdOfxe6p7PQ7JIIxBkqAWW0bz0TH9Jo0wyEq3jDDbIrzxx2+dq5BX+vT6shPLvg+AYfz\nb1ei7vSpvtZns+NW8mcjn2B842NcOHebpbZ9IGjW1g420e+77cUClBEv3Kkav/v+b0C/7c6bTSYf\nWsX+qfY5/ZIzTVeYMuXoxedvHfp99tbP/XCAqfj8EvCUxbmPxb7IlCR3EfW5Y8jJZg/uO+yuHF4Q\n/X73hqTsx8SLEIgyLpt09BbKuEMgkEStWfTqAS6VbapVka0tkdO7XlJFFX94gCQFkWW3MX2QPpas\n4uk3cRp7pLbfpj72YOsRQhhkBjeITcpUukW2n3mVc8+t8cx/lgPl4L1nKdJzLK6JzxEMVAgPqggT\nkbEhIuPQnwTZ0J9B1i7R7YfYWYlRyHjodBxO5Nssdyykn1QpGO+jCyp2ukTobJ2537yA4lewHRvF\n62fwwrO0nQonhucIomArCmpQp6d/SLdf4P3K+/hkH0NziGVZJANJHBx+uvtTZFFGlVVCaohzeoCZ\n8TUMpcnq4Ab2QEWLeaiJq2TNLrvVPrEZgWAwie1dQ7YatBQQJAvBDJKdSbOc8JL7eg5FfAeA4LsF\nJk2dICo17xw399Z4o5FjJuMuykY2izJ3ED8WRim83TFL6k0+jp3iai+FHRiiWAbDcZRWVKCdA+9w\nDrHvp69tH43ge0Lunj6lmzqqrDIXmmMttnZQyf8pctgsXViAN95wIwF7e+41ZFkwGdn8rcCEmHOg\nGr/7f90qWVcBy+JXz5b5ud9d5c7s6jv5NK/7LzvTdIUpU45efK4c8fam/AzwhMW5j4Vtu6JvPHYX\nzn3hCe7vougKzfH4wF2xLNc52N52/5hHIiJf6wdYCc8x2/LT9HqIqnEyQT92xYM2cSjseLg5Etkt\nu4vBppXl65SYf7PAOL1CJBKiXewTZRNtdg5tZMHlj9ENEWc8ZjdymoR/QnS0S6RTRBQcrnTmuLZ9\ngfM/Ug5Cags2y+EJQa/FDd95gqMtPOYetmVjiyInhOvE5A5/1/PniOMRO5c1CopFaLVDriMyW5mQ\naKxSTJ+lIGYRRwNmLhbYa1p82DORf9HCFMaokkqhv0V5PsTCwksMb304AdvkhbqP8vUGPa2Hg0PC\nlyAbybIUWUJAYKO9QcqfYi40x25jg1+uCMRKFmN9iNjroRgiTk9lIHYQbIUto8+CX8fyxbhZm0dU\nn2F+uU88HQNDJXc8yKvPJ1G8ri2XjKXY8xRpbmm8k/dwcy9LQcyhBhTUnusC/cSX48JSHUxo/rTA\n8EOdSEtmMP8MDe8cNwIvEF7qIDg2rVISX7rE2vwOpeserte2kFubLEQ+O8G3z935t5qp4ZE9ZCPZ\nJ+vzeUSsrMD3v++2Ibt8+cBdW1wUGXu8RNIq3/39ZffGfXSd7377DXjm/GOpxk/juv8qME1XmDLl\n6CccbR/l9qb8bPCExbmPhSi6ItPvtRmORcbjAwG6Lzh9vgMhuh/KqtXcBbdYdKt8ZU8WY1Ai061g\npVeQPBki7S6BrfcYjCTicoFXPd+jKWYZiDkmmRxju06lBulygQg6pqRSdOZodFYIGi3O2U1MS6Cm\nzJKI+9DUILXwDIgSkuCn1pDY+UDBltywvRtSEzGaXpSgSlgx6Idz7PmW0IcTnuUDwiOdNSrECGBN\nJsxcm6fUCHLjxTlCvassNmZpeJ8hY8+TyYDPF0RrZmlc/JBNs8d22E90oYIqq7TGLXqTHp1Jh7An\njAjIokw2kiUTyhD2hnk29SwRb4SkP0kmlGFsjCl0CqQDadZia0QKZeTNbfwjA8ejMEk62H0IDSYk\ntS1sFELjMTO7HSrqMzjSEqHQGruRl1gOKmQXRdbWYP3ErQOqKCjPrrO+vk7xL2x2PSL9OpxbcEVK\nKOQeM1lWiD53Ac2boiUXuSJpVCQPOlmKgRyeicSJsDs5qKLZrC0vEEosIc8MWZlVeHFh5nMTfIfz\nbz+LXNOHsb3tXhuRCKTTbtqKabpf2IxYln/xWooQbYjFQFX57q+/56rGzOOrxk/juv+yM01XmDLF\n5cjEpyAIWeA8bkj9Hcdxdo5q21O+2DxFce7DuZWNv769xa9YOtfLXtr9LNp6DltypwaZpjujfX+d\n3A9l2TY884xbdFStwg93coydOufT8JxZwGOO6b9XZdi38fkkkpTxaW18MyXaUp232he4mbvAudUU\n+Y+LHFvSSC96GJOFRI7M5e8T70K/PqSn+RDboKgQFMdM5CDm2MbWDIZ9dypSMOhWdhcKcNPJIksl\nVpwCjeAKAyFEzNjirLaJLnnY9D7LthRhcXSR8/U8z/RkTptxfL4Yvn4cY+UUmYyEz+dWWPe8BqpS\np12PERic5fz8AgN9QG1Qozlu8u+v/ntWo6tEvBH8ip+RMSIbyWLaJt9c/uYd4mi/KtuyLV5aeIna\nWzdQTS9qKIpk6vgsHdHj4PH48bVNLMdCCk2w0haplE5ieJ2Ed5dYtsIkmyN1PMb557Modx18RXFH\nqMbj8PzzEA4f3CdJ7uf0lqUgSetUEut4fsVmsitSrcKwCzhuMU04DLOzIqEg6M15vnkGXnzJ5nTu\n012xH1cUfJ7CE9zr8HDu8/7r/v3fh3/34XFWlR1CfvjF2Pv8/NwGFJ5MNdr2p3DdfwWYpitMmeJy\nJOJTEIT/AfhngHDrJkcQhH/tOM7vHcX2p3zxecLi3AczGsEf/RF8+CHz1RrfqDnMTZJcGp3merXO\n5cgFQnGF48fh618/WCf3Q1nHjrl/wGs1SCSgt6hQqb2EfjzF154r0nwnj9YeMxRsuqefpzGOYnQG\nLJoFPCNIWCk0ex3r+DrX+usEv2bz/G+IZEX3fVm5ZaqX0iiTi/j1NvVGDK8zRrBqtCU/I8fHyOch\nEhPJ5w/cIL8fLvdyzEfqOFHI2Ru0qgbhzja6BOX4OkV9nmz/I+JGG6/QJmtUCHd99LpzhHSVPf+A\n3pJBuTXAtA36rR1CQg9FjoMQwrZHeGUvITVEc9TEtE26ky4ODnFvnGPxY8yH59FMjZExemBVtkdU\nyHqSEJ3Htrz0WmWkWh/J60U2TEDA8XmQlmaIypv0QmPss8tkNt/DCF1nZ22dun+Od6pr90z2Oez8\nHBaecOD8lEru+d0bqMQAACAASURBVHPs2K2OBdbBc/N597P0+dy6mEQCFhddzXT82KezYv+sVSbf\nz137gz9w/1VVMB2JQXyRf/kvx4i7s6DFHks1Puxz+MTX/VeIabrClClHID4FQfhPgd/FdTyv4QrQ\nE8DvCoLwvuM4//cn3ceUny2eegEyDFd4/vCHsLODFA6TmvWjxMbMVN9mPgbHsik8Z9fv6PNp266L\nUCq5eZ+6Dl7J4Dh5Fv1Fto0JC7YXNZdlXtQZ7rSpjNaYWQqil6E8OWgMn1aLTJR1hsNbLoRPPBjP\nKMJ1ctTDZ1EibY4Nr9HUQvTtAE3bjyTavCee5oaQxduG69fd1ybLEI/DoA1xX4zFhEV0YiFYArYT\npGd4KcROMlPdYWbcZkbWaERTqE6Ppk/AY7XR+3GiOz/maiKOGPHhGfaZLV5hYkAscJ3o9QnhJS/X\nYxYDY4AgCJyYOcFicJ6RNWFvtEfQE+Rk4iStSevhVdn7OQ+BANg22ngPx6ugxKMI7R6iZmCGAwjB\nEHqvxHDUosMMp6UQgegKamSFQncLuHeyz6OcH0Vxj6FpHtx38qQbPk4k3FZbqZTbQiiZdF/ipykE\nn7Yy+fMUYnd/xn/6pwf36Tp8+9tw/ryEeHodTj9aNdq2e0ymFdpHwzRdYcqUo3E+/zFgAq86jvND\nAEEQfgn4C9yK9qn4nPJ45PPw4Yews+MqjlgMaTxmplZj5pSPE97L8CtLiL+yfsd6aRk2hYIbmm02\nQcFgvf0mtrPB2Cyz4OgkvCriT3agViPmGRMO+6nVXMMnFIJSO4S3pRNd0eh5bTY3xfu6EMWKwruz\n3+G5Z2E8+hBxr44s2NiKj8viaTY5wTUzRzDvLiirq64QqO4YrNXfZDG2wYJSYzSwmcvKaKoHo6kx\navWIjZrErCHDmTheqYeEiD86x3YoyPFOD0MYIeU1UqEJGXPIaKxhDmXCcp65aoXYB7N4A31q0T0W\nSwNSlz/AxzWQbCLpCFcmAxzbYjm2SjKQJN/OY1rm/auys1k4dgzx/feRTRgEfZh+FaUn4cgSZtDP\nRAZdsBlhsCjFkH0itqIQ9IZZER482edhzs/8vCt0arUDcSrLrrsZjbri88UX4dVXPxuB9ySVyV8k\nhzSbhX/7b92BC7fSOsnl3NeUydx1Xt/nQ7z7vdTr7jGxbdeRnlZoPz3TdIUpU45GfD4L/Mm+8ARw\nHOf7giD8CfALR7D9KV8VtrbcFS4UcldMcB24dBqqVZzegHJB48pf2Ggji3grT5Yi1mjC/MdeFvpZ\n9qI5ngvlWepuYJcqXPKtMX8iyNozAyjfhI0N4p0OJ7wT9iZhKmaS0SCD2R7THKps7HqoXhY5fhyW\nlg5ciNHE4PvvFPnDP7PZvOnjNfkb+IKnmZOqBD0WpujhUjfLVSOHMVLo9VwNvR9yn+3lSfc3SEcr\n+M6s0eoEaVQGpPWfIosGL8pv0QlYhA0N0+sQMooYoRiTcJw2HlqhCbtele24wtKeD2skIuDlYjbC\nINHh5eyYuUYTtVpmsX0ZryMT72pIuoks2EQkmbQkoh4rI8yu0okrWIsZRFUlHUjzQuYF1pPrByHy\nXM7Na+j38TQriHsNnGYR0x/ASkTQRQe9vYcRCWH7fcSrPbRknPGc25z8YZN9HuX8xGLw7rv3F6cL\nCwfC6bNwFh+3MvmL1Lvxu991Bfz+JdRuwyuvuK8nk3m0u3a/97K97R6DM2dcAQvTCu1PwpGlKU2Z\n8jPKUYjPGG64/W6uAX/3CLY/5auAbburHLix1MPl7T4f1mBIYxjgyk2F9/YsZgtvIvQ28AlltJ5O\nylL5VrDEjlNH2xrg7JWpBtaYiEEUBWaXvfCRAaUS0njMvNIj4k0SU2YJUaGuKFTiC+ipLIJw50sb\nTQz+t//nMu9f6XP5RphWVcIxJSaDVYL+43zttJd2R2HHBFkDr+0u9I7jCqZgEL7mL5KhjLC2xtpz\nfkI1aCSCOPPPk7r6GoFAn0F1h1hZQ+gGaUQCWOE4FTmGVLEZiiLbUT9/NbPGr+sd1nzQWPAiBooo\nnk227Dgev4Tn3Zsc18Z0AzK9+RR9AbJXTFZ3NQRHYlAcMZrL4503kBstxj93DkmUaE/ad75pRXHH\nRyWTeJcW6f/gP6A1KkxMDce2ESTwiR5mdAHL9tCN+7CzGYZZd5TOwyb7PMr5AVcwwecblnySyuQv\nSu/G/cbwkuS6xf/8nz+5u3b3e/H7YTh0t9PpuLcvLrqPnVZoPx4P+2ymn9mUryJHIT5F4H5jNA0O\nCpCmTHk4+4lqiYS74tdqruPp82E3W4xrffZCK2w6yzwXyDMf2UAYV9hw1tjz+JG0ES+FC0SwGfs1\nfBEdJxuk3YZMykT88AO4dNEtgY/HkebniGgaUvESGFGsxDmiL6+x9FyOoe6Khu1tuHoV3rhU4c/+\nXKZWjxAL+YgsKlR2VXo6DByT7YqGPlQQRXcxtm13oZ+ddXMV+10bnzjC722xbftx6k1USSW5liTp\nT+ENzCKH5mh3fZTeKGM3DXaFBcrjZeR+n8V+nx05Rd6OIw3SeOmgOgYTsgQ0mf5eCKMXJXh8lUD/\nPUTdoZiLMVEk/NfChPYczImGZdsUvXN0jVlWCpskogLqIMiHSgVw8zNPzJw4EIuKAs8+i/zss8z8\n1m9RfPev6ecv4QyGeHtDNMeh7NEoTGrsxurMLK6yKsH4MSb7PMr5+bzDkrZjI4riY1cmf969G++e\nRnT6NPy9v+f+/qTu2v3eSzjs5thWKgdFXjCt0H4YX6Q0jClTvmgcVaulu8dnTpny5GSzblzv7bex\nPD7616tMmkOEYZ+KtMjHobMEz+ZIFH+At7mLFfST691AKupYooqW9jNHGScu4URVPLEBEl4S9WuI\nrY/clcBxXFtIECAQoBPy42yVmUvW6DBAquURMzlWVhRu3nRzSN/42ObKhxH8sorZFrFtEcGBgN+h\n3VTZ2hRYmHU3qyjuYhwIwPKy69Z9fNmhUp8w5+0x0j/meqNHc9xkYk4IGwKnBj68K6+g/Z3/GG3x\nNcpvbqBebbPU6GE7M+ypS5S8AXYWygTjN1B2DZqXE3RGfoYeD44cQrHTeEpJJC2DIg5QYwkaOwKJ\nukhooNGY8ZIYVlC9fkzrebZEh2ShTuzkgMWlVd6tvEulX+HUzBn8qvegV6YNxvWrVC6/RbddwpBB\nX13gcsJPsRSgkNcojpvo4x4+vURm8Qa51BKLkcXHbvR+j2ixbRRF/MzDkoZlkG/lKXaLTMwJXtmL\nEcyRml2iUJAfWJn8afVufJzH328E5v1ue9z9Pui9JJPul6lLl9w+urbtuqHTCu3780VKw5gy5YvI\nUYnP7wqC8N373SEIgnWfmx3HcY56utKUn3VuJQOaFlT/+mMm9SGjiZ+ed4VL4lm+L/0mL5QlcsMh\ngcoGRiCC0m+hjUwcWUbPx4mqXQYnn2fk+NGvF1iOqiSEMjQa7oqZy8HMDLamwWiMbXrBsgiO9rA3\n3sfTqeHp1uHkBapVBQSbzRs+TM1EDYiYuog2llE8JuGozdgYocpehkMbXXedsvl51x3SNLcnZb01\nZibupx+CM3T5oNuiYTYYtep42hZvJiIMxkFSnQivfPs/otwr0NZv0m+YtFpJtowc5ZCINAkTlPfY\nlhxkqU28UUde9KNme6wqMaRiiZaUYaQMiFkKzUkajzbEp3bpKxKWKGOrNv74kOZeCrvbRtA0is0S\nFz82EHsyH6l9fL4hx1YGXFivcKFssPXeDxgWbzIctNAlgWpQ5upwhbc955kRX2DG8tK39uj1rjM0\nh6SWAnx94etP1uj9ITaR+Jir9CcRqYZl8ObOm2y0Nyj3y7fHY6a8ZcbBcyQ5Q+H/Z+/NYiRL0/O8\n5+yx7xGZkftaVVnV1Ut1dfd0zQw5IjkkaNmEIFuCBduwANuQfOFL34gQBY0kQIDuDNi+kADpxoRF\nCLZAyhyPaA7JmZ6e6emleqk99yUyMiJjj7Ovvojeqruruqq7qrc5DxDIqojMyHP+P5DnPd/7LTvy\nJ6YAPMrejQ8TLfuoyLzfLPYH5V7nUq9Pop6FwmRc5+uvxxXa9+OrkoYRE/NV5VEJwIe112M7Pubj\nvJsMuD+usbWwyDDhUJ1VYHGJw901hlcVdvfhW/opkjlGcC0G2QU6xSSCY1HtHtB1fe6g4K2tszon\nMt/8Kwr2PsgyQT7PWAroahbSsD2Z4uPkGaUX0KfP49XXSLUmV4eeXMM0NxgORRRVBERkzaZYFrFM\niW4rgZYxmDvbZXUux0w6x9HRJKB6+fLEpmw0Jo968RR/dcRMbYajOy3knQOWLB0lmeWoHNKsJXgl\nPCTxV9d5ffQMucHfRk/A9Hdb6C0LYydJXtHJRXkSzj6niSyZ1DGid8yivc1i36OUH8DyBrf3X8RU\nFPIHb1IKlpEkCSHcozLs0U1nGKfymHTJudOIySwntsErr5Rp76xS8S8ippaxJIs3Tg9I3rhDIbpJ\n2DrksCxTOneZvBsyeukmamdAtWiTfa7NbKWIZaY42HsOt7eP3C99rML9vnyOMNGjsja3elts97dp\njpusFlfJqBl0V2env0N1BWa8LIvzq/dMAXgUvRsfdBk+KjLrdfh7f+/Bz/XT+KRzsazJ73722Uk2\nzNRUXKF9P77sNIyYmK86n1t8RlEUZ/rEPDoUhS1lg1dLG6w+G6LnJh+vhcBD2r2J9+oBKm+jWSdY\nSo7GIMCVICNDGAFECLJM9+wVXjxToXb1AOmOTVAuc9rYZGx36fSHpAYD1F6bkWqyn6vS6QVkaw7z\n1QXUo32GgwNy+Q26XchoGuWqzXDsIykiqRQg+py2RS4+L/Lf/Q8B3788EQ6bm5NZ2jduTIRotRoS\nTfdIPrmLt/AMv7DeZORETMmzGILPG0qH3aKAdfsS4501bh1myXsmc1Mp/GAK2xujaQbJrEi7lyVl\nThMg8Kr2LOu1ClqxRGU5Il9exZ1appGbIav/r0TSCaW3DsjYIYQhqhRhJVLohTLaKGDRaRPU5rhq\nBmzvQ9pZZe0szJYHWKbEwd4C+u4bHIe7OBsaQibD8fgYL/TYi3IUui5zlZu0pApQJJkKWFiE12+k\naBxJDzde8jOGiR6ltXkwPOB4fPy+8ATIqBmWC5OWUYszW/zOt1fvGV19FL0bP20Z/uiPJvb3h3kU\n0c6Pcq9zmZubHNeVK5MUkzjH85OJR2jGxHw6sfUd85Xirj/cORHfDWkdB6ivvcz80TbBaYPQb2OH\nFrqaYia8hqUVKddVhLlpGAzZmS5TrEiEGxeQ0kegiDQrGidii7CpUxuHaCcjZMfltGxxkB/TTI1I\n7PfZF3I8q1vULjrMz4acnoqMT1IQ6phG+G4VtoskRuTSMkuzKb73zDyaNuk/eXj4QUrp5HxEBF+j\nfWeJ05rBzrTGDSnJfKbC9nCXvu0itc/j3/keTnMKz4wQQp2hFdF8Q8ALPYSEiyACgkamKiKEMnJU\nx10oE156AWm9RE/RGI9BK8C57/xP5NQiN1M3ObylYhz3KGYsVtJJpromp/0M7WSRO3qOH19bwBpm\nuHAZpkuTkUPJVMDifIhzE+wg5MDvkDFtxs4YPwhpB9Osej6u18LzbEJCRERQdaJAhUCDSHxwf+Mz\nhokelbUZRiG2b+P67l1Tn+ATWkbdQy08it6N91uGf/WvJkL2PfH5OETne8R9KD8f8QjNmJhPJxaf\nMV8pRBGSskd9sEXyJwf0T2zyjTZSt4XrhNxJrlOUQ8pihOIYOIFKfrmEU6vTd1I4UpmBk+bln4oE\nEay9sIAy18C89hOOcjCbP0f/sEG2EWEUk5jLM1grIlVnRGA0KHgtkmmfpYsK5csiv3wtYO/IBk0g\nDMF3FAJXRUsGJCo+TyzOor17Nd7fn0zmKZcn9mQqNRFHP3ttjhECvcYudn0OSdvhYHxEz+4hIuIf\nXsZqzyGIFkquA06CnunhjnNYtkImUySVDhgbIZtv21QXO6TKPYIgjS40eb21j28lcTp1zq+UWD+b\nZWPjf+T578G//T9drr8lsL+9RXG8B76DXfbYlpMcqSm6DRXbUBm1TYIpAUmGIAroRE00J6QdWgxO\njxnm0szn5knICUJZxpUs+i4MPR0REcuzODjtU8rMM1uoPPiF9XOEiR6VtSkKIgk5gSqr6K5+z7Gj\nnxbJ/Ty9G++1DH/4h5OvQTB5RBH843/84O/7WYn7UH4+4hGaMTH3JxafMV8tPI+z3ZdJjrcZvXZM\n0nbJDfbJRiP2MheZnk8g+lUCYQZ5cIJoQ1Cd5k60jnSySzOa57a9wH5vknf5s6U1riycMDq6ivDm\nKaabo2kX0Asb1JMuSkZAdi0KUzrLqMhX36KSr7PgLuG/8iNm7DyytMDYkNCELLKmYPkCkedD5LHd\nGPLyyzWuXLlbDCUScOvWJCon+Vl6jRksQyEymxjqkG7lT/BxyCp5xv0Kjp5Cm99Gi6o4JxbuqIgq\nekhRGkHwMF0HPwRJsTBzV4kyPoJY5q/eKpAUCiQSFvOzI6xMlsXlC3iewquvguereMBpboNbwgaW\n6TAUmhSf/BmRehPJWMLtLXN720ZMjHjybIm9/h47J22m0mlqoUKp1aMTeXSVNItiibXI4molx5F/\nFqPvsJnYJHBSBL151hfSvHCh/uD7/RnDRI/a2lzIL9AYN+4/dvQheFix9tFl+NM/ndzIwORcJAn+\n7t+dTHb6onlcwvObLGrjEZoxMfcnFp8xXy22tpi1twmlJj/Nr7JppLgk6pTHBxRzA5KpJtpindOb\nQzIaVPtvY98SkBIiTWGOXmmVI20NdzARfy9NKeS+/x3eSrQ5kitoTp525LCbEDmfNZiztphyr1Pv\nnTBvD+joJqIwJjxu4DcGXBxHFFMBrWgVpydiRhk62WkamXnkQp+BlWB7u0alcrcYOjycCM9+H5aW\nJEQxR7oS4MrfRvYl9NEOdvYXjJ0xYRigiDIpJYWqGFj4EIa4noxvKwREJMsOStYkKWlIYYba5T/G\n7FSpuU+ymNYoZFJYqeskVzT2xxp0NtjenhT5f+tbk2P6yU8C/vSnHbzcDuNuh8KUS2lqjGecMmiW\n2UodoCdfxjIkzM400oZCWl7GOt1mrmujtHZwEi3segWhto4ZVshaTxAez5NKiKyvZ3jxyRobZx/y\nz8pnCBM9amtzrbRG25iohZ3BzvvV7h8bO/oYeW8Z/uW//GAkpuvCpUufMBLza8qvSu/LOHUhJub+\nxOIz5qvFwQFy+5i5X1ul9LMMeROy+TxRp8KMdIKfLKPn59kqnmMxLeMy4Ja5wjXlBfq5BW55azQa\nyvvC4403YDhUyNS/y9XyGnapg24K9PoOm90Ca4k8LywrTKdMzJMRPim6zz0DmSu8faPP1NZ/4Ix4\njOD+HD1IECY0jtVF3hCe50alRGUmT+M45OhIRFU/EEOnp9DrTXojAqiqxNJUhfX1CtdvLzHSb2FL\nVye5ksUuUcdC1lcIs4eIqRHRWIBQRFJ80sUBWqWJQprBcZXI20Ct7fLt3xijB68xVRhyefYSYyf3\n/jx1DjbusqTDEEZ+D0fsousBC/4ai7k+dsLDtYZ4wwpmr0x/W0LRAp5YzvDkRgqrNoV+6wxCx2Ss\nd8hkyuTWnyA/u8C3Gir1sMiF0hmSCfGzX1g/Y5joUVqbiqRwZf4KtXSNg+EBju+gydoH/U4ftGXU\n5+AP/3By05LNTm5aggC+//0HG4n5deBXrfdlnLoQE3NvYvEZ89XhQ16qUswwMzNpZJ2Tq6TydbJ7\n7zC0R1hjl+pwh/ngOkzlsQZ13ukucGivYfkK09OTVjDlMrz5Jty5AzPGFEXBQNw7Qjjex7fGWCh0\nkqv8bP48l2b/jJP+mwRnnkQcnaXbALMxpKwPqHvHjOQqI2GGZGSy3r+GKJsM21dIJksc74r8/N1c\nz15vciEVxQ9s01YLSqVJsUgy5XPQa5FNlUlKadzQJph9maCzyLi3iuZNIZhlxCBFFEmoGZ1Q8NBP\nK0hREtcLoD/N+PazmEsD3OkfE0QeYRS+XxxjuQ5Yk76j70cEhRAjGODLQ0QrgyrYRNGAhKowMxPg\nDLpouSHrGzaKFvHdi2XqCyOaVo3e2VWuL/Qx7DwbUxeYr2wwGO5z6ckkL86lOVsW37+whuFn2PfP\nGCZ61NamIilsVDfYqG48XLX+5+Rf/+tJvvB7IzHTafi93/vmRct+lXtfxsIzJuZuYvEZ89XhI15q\ntZqh24X9bp2k1CSRLiJ2WuT3/hjNHRNoEaIfkbdOeMr8OdXghM6571CaUigWJ2KkXIbBqYfQu8l/\nFvwQpbVNWugwFn2O5RKbHR/9WpYDechlIUmfs3j9POH+Lr/W+7/JeLcRPRvbS9JVcjikqflHTHGH\nlcM8f/7Dy6SiEYaRxvMkRqNJxOr4eKKjg2ASuXrvsXXSYhScYoVDcokMdiBjTm3jLv0ZkaPhnDwH\nxhShmYUQJCXE8Dx8X0NWQpJ5g0AaYw4yvHX9iKKf49yUiBjB2J0UxyRVDZIishxw+7iJQRsncOgJ\nXUJJJLTncFwTQYhwTBW7WyQ5d4uZjX0uPpVHFKFY0pCVDHWpztAeYvs2Tb3Jbm+fpJK8y44OArh9\n+3NaqZ8hTPQ4rc0vSnh+tGr9Bz/4cKeEb5ZoiXtfxsTEvEcsPmO+WnzIS63PLzOsZ1GMMdb2gBMr\nixS4yPaIQBDZnHmRbmEFrbnPs+ZPWB5cRZzZoj31HY6sNVodhbkpj7XGy6yd/JRZ51WmwyaBojEW\ncqR9g4y3y07zHJnqLLUzGp3jiPFbL7E+epNC9wYJd4wbyZTpc8a5xa4/z3E2y1PeIYVRn4M7KfKl\nEWefbnP5uSX0sczrr0+Eg+9PBND8/EQMWRZcv2NgqJtU6xZyoogXeqjJNJ3MDCdBmogQVZLxBAEf\nEddSiMQUgjbEi3xwPOSsgZ/dp9lwWPRtZpx3SGdG6P6AjeU1FtbqeLMehrbNm2/rSKVDJM1kGI3x\ng3nkUouR4XNwYwpFjbCT24yTb6Jn9tnuzxBEAZvdTS5OXWSttMZUYo6bTcidPktGnUPQp6it5Xlu\nYwlC5dFbqQ+huL6u1uaDjMX8upzLgxD3voyJifkwsfiM+eK53xXmQ16qfLDDhmEx6Lc4FQMMUcY1\nHZK6Qb+8SrXgsZC4TZRu045M1p09+jdG9EcSuXwb98wVVsItioltZvw7KOYII1dEtAyy1gFRmCHU\nIDu6jtm/zDs9UG6/RKnRIxEc4QceoSxiSnkCX6EcdgkkEKkhiwoKSVxHwtR2OJJvc7XZ45n6JZ5/\nXmZzE5JJqFQmouzqVVDUkFRxhOG8g5p7B0VQQAC9OY178BTYZbTiKSVNwDz1GPY0HFNDkCKU/JhI\n1nHMDELWJVtocPlA56x1SFI/xFUbnCnOkQ7GrBVPubEKlI+gFyAMViBIkvVbVM++hemPSUslxqMy\nI32Im26g5VoM7AHiKMDxHURRZOSMePv4Jkrj15H636E6muKc6TB17RjtjQZbv9hBW19gp71G81T5\nUqzUD3+Uvg6i5d//+0kqyId5nD07vyrEvS9jYmI+TCw+Y74YHrTM9SNeanR7C3Pbwk8F7E9fgtt3\nqAYusmMhbd0imQdRFBiuL6DfERnIFRaVY2ZS0EnWSLQPWMscYWsiWt9A78GAKlBlWmkzLffQI5tX\nDkccWDIvHPU5O9pGk3yQFZwohCjA1MpooUVdOKWgeHRTGU6TSULfRi012R/vIbYjkkqKc5VzBIHM\n2hqcPQtHR5OojiSH/Lj3JsH4p5w6fYqJImEAXreOfTpPUpWQiyc4egZb6COVPUS/Tuhp+HYCMTtG\nUSIy2ZAnoyHnQ511QWE0WyU5vcwTpaeot02kvX1G7g6plVOuVJ/COLWwHQtFFTkRIt7ZNbA7GZzQ\nwA8ignEZv/EMYdQnPHMLP/KRQom20cbX1yn2Fqg6K/xO6jZTwR5C/5jRgYvXUOlfayDJbVa/d4V0\nSgLEx26lfl0rpj8qMv/RP/rAYv9VIO59GRMT8x6x+Ix5/HykzDW0XcTEfbzZD3mpx3shp36fg9Iq\npfkMfkclGKWwpByl7k1EC6yNS5QiOE3LpCs5rOkVct0dku09ShmX6MQnnwxQcWh4VXRUFAW8QMR0\nNVKCwZJ5jVca5xBtCTEI8UKJIJtBkUwcR0L2TLLhAEmwsQOHfklgmIkouibVVI1QTTC0hxzrx8h+\nEVWdJZ2GjfMhFy6IhCHc7m5y9cYJ0m0Jt7mK5zyN6KUwd2s4wzyJlE8iEWKNLCQ5R7Eg0jYtzFFI\nGIjgagiE+K7MzP4U09YJnep5wk6EH9YJcutEiwbhwTZiGGDNBQT2iN1eH8uMSKYEUtIicj9P0Z9h\n6WyPPWsbUxcZnFToRhFjRUGu9QijkJnsDL2TDH5P5r9Y7THV2EPrNzEXVgkXMuzt6xQPNqlHx1ST\nd3Dzk3WwqgsI9TVcV3nkVurXsWL6QSz2XwXi3pcxMTHvEYvPmMfP1hb+7W16N5o0E6tYUoakrlN/\ne4eSD/K9vNkwpNN0MXoupcsZkkk4zVbxU10KYY9gbGI4Mu3jkIxxipMqEZarBKks4omL0ffo91Xm\n+jI5XSIpa+RDE8dXED0BLwgRFQdPCNA0k6LXZy97jlRkkwp1okBCEdO4kkXR05FDG8QAg4gBCjXX\n5deVl7lmrCI7BSrlDCddg6E+5sziMQfRDn9yu0tCTrCQX2Cnv4PnQa37Nxg2Qg5ONaQwhdubxrfS\nmJ5HupIhVwDds3FGZUTVRlRDItkmMAoghJinAbYg47oyzXIWzdZQ9Sx3BJFxPcuG5SHa8M6rKfRT\nl0Enie9IyFpAYCRwjSX++m+nMUSLI0dGSVpo5SZhb5Fw0EKd2ccPfFQxQdtw8MY6QmcXrXeMOb1K\nkMyQBBwhgRh4TLevkwiz+DOLBLJKotsgaLZJyFfQNOWRWqlfp4rpv/zLyePD/CqKzveIe1/GxMS8\nRyw+Yx47MW9AHAAAIABJREFU/s4Bh788Zk9cpd3J4Psgyxl6qWWWfrnD/OwB8icohhARmwSuoFJE\nJyCDODON0x1yemiTtRywLezNQw7FOnq2juvWmdbH9AyVJho3jQXO9ppc8bYwSeMIKjPSKVlGjKM0\nJ0GJkjLCshR2Ems4woCZTANtZJEQXSRxhCxaqJJLGEUcJWv8WXaDLa3MeXEL2zc56DocjJ7H0wqQ\n6jC7us0wHRGob3N4bKPKKoejQ1p6i5ODDDP+t+n4bRJztxmPhwSjKqGbJNIrjHezLG10sWQfs6Ni\nDTOEoTepfC+cIOdbqGQYHRXRvQLuqc3s+RTzVY1+H2R7TC2p0umrDFplWicBy8tjSnmN/sDj7ZdL\nKF4WW5ewkhZu4JJSUuiJNoYbUhbyJKU0Pa+DG9qUsmn07ph+t4fku3haBpFJ8VTBalKRhxTosx+s\n4M4+S0YwkQ92sI7g7KUaCwuPVgl+XSqmf9Ut9nvxdS0Qi4mJebTE4jPm8RKGNPdsBi2X00KG6elJ\nIY5lQauVpTBwkfcc5j/hSiSKEM4uYOf2SV9/BTGlkRNkjhwHw7B5J3oCVQoQI5VjZZ4dZ42pI4uV\nyi6Dygw9aYFGuEYu1abhHpIdHrEQ7REKEpaYoR9V6EQV+lRQRB+tojJIaOzqK8iWw4p/g4LbRAhD\nREHCVFN4gsCU1eFIlXknkWDFv8O0VWZXipAkERsHK+qTWmxwprpMRs2guzo7/R26VpfWcQmlpXJ5\no8beYZkTAyRJIchEWN0kdjfNnZemcTyBMIAwjEDyEHMt5HwLLRHijQV2omlqYZ3F02PsoyKelmc2\nNybY3KX95Ax7JBicQrp2QNMd0jyFlJKkOl2ju61yeqJSPptCk1QgInIyBIJJJNm4oY0oiDiBw5mF\niKORw/atPFPOmKGtY0kZPA+e4ZRZqYlbr6Llshy0RHw/QypaZoEdFO2AtbVHpwS/yIrpz/oescX+\n4MTCMybmV5dYfMY8XkSR1iDB2FKZXdRRkhPVkEzCbG7MoKkSDDTm73ElKl9aRP7xf8Q+6FMf3Uay\ndJSRSMfJcCLOcZo/Qz2rs2ofkTMH+OMkW4kZxqlV1PNr1A4V3tSfY87bZi5RZuyZaIFJGESYvsSA\nNCMxT1ExKKgjxJLIHXkaZ2iRFXaRNAEZlTDQ6ItpUp7DGfMU0ZfY1NZIBDMkZ12y1RbjoI3tCITD\nPo1DmalCn4ScIKNmmE+vcOM6nNxcRj+WGXYSuEYKKfApTzVZWpS584pIp63gehCFIgI+KAah4CAK\nIV5nCS+QIZTYSg6pSHmQWswfvE27/RrMZpGyz0NhkVfCFo4L6ZSL6U3WUhBEqnUT61jgtJlgca1O\nXivgWwm8rguZHYzETXzbRJRhOjPNXCGgd61CQ0hwrRuRO9zhNL1MZjoN4ZBc0IGLFwnLVZAmOZmK\nkmX+xKW46iBLIfBoVMbjrpj+PIVMb745qWT/MLHojImJiflkYvEZ81gJQxjmFxglG5wb7mAmlgmS\nWSRrTHa0y63kDFFh4Z6RpiVpi7dyYyLnmF0jgWJCyhlR9ZuU5D6euY8tVnAiDUde4Gfy8wyVM/j5\nNdbTEtUqCLf28RzYSV/kqvM8GVFHsAZkgg6jKM1N1pmXjjgT3GTnZIaBOcOiB5aQ5Hptioqhkh5A\nU6zhJl0qUZcZb4BlOfiKxmhQZx8NTZWQNBv99hLbtVuk6rcYOkPW8hs0rs0z2FZxu3W8cYHbXZ/A\nDylMGyxOp/BHFWRBRU3ZhE6IqtlIhSNGx7Ng5Qn7MqIUEroaaAZ+lOVnPE9LyLAg7pEwFOSuhVSI\n+O5CDaOxRSQpFMQ5lqsqTuAwtIdE6gg1P6BcSeH351FPv43uNlmst+n5ZZzRWRhdpJLPUUlmGFs5\naoUs4eIa2apIZQSLox2isUtW6mKkC+TKBerP1KnL70YMjTFIKqQffe+cx1Ux/XkKmWKLPSYmJubh\niMVnzGNFFCFYXmNcazOUIH+yg+S7BLLKMDXDWF0lvbT2iRrFCzw23/oRqeFf0s6EuLqGTY58lGHW\nPqIojLC8CNvOouGQEwfkvC4GHuc7Pyb1cxvZTjA1OKDqN7kZrdO2MiiqR2a6j2jqTA+6aAo4gUJw\n4PJs8DpS9Abp0MRUZU4HcwiGQN+XUH0HQ8wSEGCGSZb8BpvSE3QzcySnGiQpoNmLDC2BoG8wHL3F\n1EEP62BIbqfExQE8PatyVK/y2ttZhoZMOkogmzLHxwIjy0VJ2HihgBf4BIGPKDuEZCFIIqgDIkEm\nCgC7gC9m2Myq7CjnCOwcka4z5d5BMv6EVCVibmaO9lEKZd4hk9WQ/QI7hxHLZw/5/gtzLBRLTB23\naZkqh80S0SBi1F3C9yXcoUwvVFCdOlmxyl/7XoViukyyUSPdPcAZOWxuzSMWWuTkcJJHkc1OhOdj\n7J3zuCqmP0shU2yxx8TExHw2YvEZ89iZX1FovHCFN6/XOFs6IC07GL7GbWsB7cIa8yufHFLa6tyh\n1TtgunNKiSrHlVnmggNS+8d4okIQCgiCQE+somsl6sObfFf4f+mZx2CCNXDBk6lxSp4Rb3ERWQY1\n5eBHPlouRzXYRfLfohVmEaMQUQxBAkm1MUOfrjWN5ni4QpKS3KHiDcmEJhZpLDKM1RonmXlUyWYq\nW0BxVUZdBbMf8vSeTmb/BHVTJ99JczZlUQ7vMOBtVuWneVVcYq+5yq41Zuw3sYM8SS1EURUCP8Sz\nEkRCgKgaRKGMZ6u82+UeiBBEAVn1IdAIxQhFEjD8MVu9bX7zuRmSrsSu6HJ0qL5b7a6SKTVZXtP4\nb/7mFJmkzG95Z/mzXyb58U8sNGON9FMtFC3APJmisVlmoOcwlDTjdYlMGqLFDczFSbXI64mAYvpl\nVmvbiF9Q75zHVTH9MIVMe3vwb/7N3T8fi86YmJiYBycWnzGPnUm0SmFb3uBnxxt4ToiSEZk5Ayv3\n0SgH4yO63pAVOcNwFNH10xQjhVTk4QsOuphGiiRcO6CnFpgRReaCA6Jugj+Xfpe+l6Gk6ZzxdygI\nY85E29yS1knlPHzRYTZjM+X3Sfst3M4cbygvYJdHFNU2z3u3SJjwhH6EHEj0oxISHhIeh+IUx+Ei\ntpDlLfEiAx+SokxSKOI7BQqJMd8fvMGlH75NutVFD8aobpWCpFIeD5jSWmhii2LqPLutlxgJHr7S\nQA+X2OuvsJstESkB6DnwNATVRIxUwCWUBuDLQIhcaSGndQKzgJw+JlM/wVMMrF4BTRO58iLUayK7\n+y6OE4HsopZ0/tPvrJNJagCIKDRvrNK9BfVySMZ5gnEHBCekpIjcasFImuQ0Dodw7hzIMowNETkp\nYjx1BXHli+2d86grph+mkOkHP7j79dhij4mJiXl4YvEZ89A87AX/49Eq8VM1ShiF2L5Nu5Slwxzq\n6Ag1NNAjDSGISIUGA7FKECZxkREdC0HwUMMRB06OtiCRSDqEySS7/gUujF9m1bvGrjiDa8rkUwY1\n4wQ5FaE6Jke5Cp4oo5VOcJIGrdQ0T/2ii+ZDIRgxTwvDTdGSyvSkEkdRnSNW2JKm8D2BUbNGMxNR\nSXf5HeUa/3nzF8z1B0ROgGo1kYM2op7EnFkjTKYplzWe2vwJ9dCiNwwZKQquN6binVIwS7xaqWMb\nCqGdRxA15OwAqXYDJJ2wX4f+GmEAgmojpG+QKLRIzjQID8+SkUrM5+ocG/uc21jmuafz9M0hh+MT\n6tk6Z2rLwCTP8aWX4O23P9gXw5g46KIocuHCJJ8yDD+wn/N5KBQ+cNbnV77c3jmP4tc9SCHTH/0R\nvPHG3T8XRztjYmJiPhux+Ix5ID7vSMOHjVaJgkhCTrAnbpBLtZlPBpzRbxMGgBAiRRFq6NJQF0BV\nKLgNDF9ADDXGskooOYQyDA2Nm/IyM/41BkGGBfcO06fH5FM9xqKKoNpIEki1IUpkkCoPESQP7ySD\n76j0/Bx7rJFnTJYBmdAkK9hEYoJ9eZktZYZk7Q08QacTiCwMHJ4Ofsa02UZzQ/xikWQUII11fDMk\n0TlCcQx0KcPIG1Nnl065zPV6FrU7oN5q44d1jrsZbvoSaGMiySMsXyea+RmhMgLj9yB9QqC2sDwH\nSQTBVRg2K+Q1jUvzT7BeLiCKsDPYwfVdVFllJjvDanGVtdIk1Ly1NRGR4/FkBv3c3MRSPjiA6WkY\nDKBUmogvRZkIUMOY7OEnOutf49459ypkunYNrl+frM17xKIzJiYm5vMRi8+YT+VRjzR8UI2ykF9A\ntODf5Vb5209H+Dtp1G4H2dZJISOEAhVlhFpK0hgpeEORfnIKOZdBc0V0PUSKDOb8PVzfRVJspr0D\nUqGNbypEsopuuBTTLS7xOjdKiwR6DSF0mdvZpmb32GGdbWEdSXIJgaxoUhM7jNI5fimeQyjuISZG\nCK6InLAoui+R6b6BH/YRFJWkJ6Dl0viej+gFKL0WoWESWCCoPuPsiMyiRWEOOvUhO6U8i4dlFtQu\nN4t74KcQVJNQCImMCoJURMyfENgF0GcILIdAs/D1ApqUIrVo8l++8Gu8OJ+hlq5xMDzA8R00WWMh\nv8BaaQ1FmmzWe3mOTzwx+XezOdnrZHIiMg8O4MKFicCU5UlUcGkJnn128vWbNJXmkwqZXnppIkKL\nxYkI/4M/+Frr65iYmJivDLH4jLkvYRSytSV+YSMNPxwVXSmsUVYDJMXjL545R6meI9ewcPd+h7PN\nO1SCEZWcTySMGYcZjnLPkZNMtMAiE55ScEyeC37BsndMiIQe5IgUFVFQOChe4Fb6CcqjO3xPG/Bt\n7Zhcusg1P0nh9QGrgyOSoU1aNFmN9uj5OU6kGa5Fl3lGfIWThESQ3SExfYvU0hYVMY8oOkwfNUiP\nOuBKaGoKRAFHHyKGFmLgI0QRoS+QdI6oYdOsBZhiho6jY4UWYW2I4u6g1F9FPisjiQn8zhJRbwl5\ncIaw9QTRaBrBrBF5SQTFQsFDkwJSCYeFfIWe00ORimxUN9iobhBGIaIgfmyd38tzvHBhIjphsseG\nAb4/2eupKZifn1jx589PhOfv/u6j2euvEh9ODfln/wyCYBLdzecnwvOf/JMv+whjYmJivjnE4jPm\nY3iBx1Zvi4PhAbZvc+0Xs7Q3F7h8oUQmM/nIPMqRhve29BWenj1Hu9IjmRIRzq7TqWTYCev80Euy\n7O9yIb2DG7U4tFTs/Dw5q8eqcp1vjV5i1j2mGnQQImiJ04SyQEHsM5JqTCdGJCp93g6f5CThMh9c\n56m+zzINRkGfEJm2VKItlEh7Fhc5YjFoUAwGGGhYko+U67H2ZJf6pV26Rp98MstcKkGwL+ELYNpj\nQtclMXYR/RAAW1GxEyDLYyqBjyXneVPzcX2XMAopBxqeHOApk2p2RI+otIloTCE4BSIjTzSaBUDJ\ntxElgayWYXmqRnWhjyWdcn0H/savf7C+HxWecHeeo21PCony+YkovXMHTk8n1vv58xPh+V6O59LS\nZ9/nrzqGAf/2306ioFE0KSSKLfaYmJiYR08sPmPuwgs8Xj58me3+NsfjYxzXY6dpM+pITNttMplz\nyNLkY/MoRhp+2NI/OppE3D5s6c/PyTy1XuPoqIbjhtAXaexCswVtZYMtZQMr6DFSHVKhiiJHJORj\nagmBuhnRiUrsSkvoYoFneBstGDGQZ8iMjxk5SYZRjXeyl5j2ujipadKZEBJ7vJ3cYC7Y5Ky3SSCp\nyFFINepQjk75hfAcbekp1MoxQmGXntXDCW32hz2uJ9IkiiJn9keEug+eDD6EkUCEgB+A5cvYgoai\nmGQdGGsCQRSQdWBhFHCUl2gWBTRZwwkclNMznDusMrtvINuvEGjXOPQvsC3XCFQbtTKChEQ+JzEe\ngGkF+EGILN1/Qz6a5zg/P7mpsCyYnYVUCq5e/VD3pOWQtbVvpu/8SY3iY4s9JiYm5vEQi893EQQh\nesBv3Y+iaOlxHsuXyVZvi+3+Ns1xk9XiKhk1g7eV4bWdAbttyCfyzOfngc8+0vDDNvDNm/DTn04i\nn+Uy5HKTiNzR0eR7L1+e2L/Hx3DzhkijMRG62ezk+7JZUHwV044Y9EUKJR/djWi7RSwxy6zQ4iRR\nRIwU2l6OZXeAKozxQwk3NLHlEaF6SDOvc7MU4HdSlIMiB+IMM+4ehCIIER4SQhQiEiJJHtm0gKA6\ntJMvoxiT6OLIGfGXcp9i2qObiMgMRZLeZLykrkhIkoAjShwrBVqlRWbFY7pTHguDMVVLwJEkjjMh\nR2WNw4pAQIDoB3xnO8dKw2LKfYekm8aXNGY9h5pc4lXhCpHvYNo+g6GEokakktKnCk+4d8P2F16Y\nrG25DKHjUexusRAcMNO3kX/8kJVmX3HiRvGPgS+h60FMTMzXi1h8xtzFwfCA4/Hx+8ITJlqj10qy\ns2NRTPSYz88/9EjDj1r5CTlBPbXAn/5wnddfl1GUif3bak1y7PJ5ODyE+kzIb/2myJ07k6hcLjf5\nvmJxcn2zbbDtJAk1QlZsvKgPUQdFGWKlCuCJZASfkady6ldZZZsNfxtdKGIZKRYyb5I4afEKcKPc\noSyMkLwkZ6x3yPpDxlEWWQgIIgFdyDASsixqB/wnzo8JVZdN10CSk0SuiHeyRv+0xG6ny9vaMd1M\nwGqokw8sLCmJLqUJZJmjTJlr2acJMkXcsz1usMdoaGKJ0CgK7JRFAiGCKGClA4sDm5plspcpYCtr\n5ASL2cExmCGDqEvDq+CIPs12wNMvyDx1pvhAe/2pDdvxCF96GXHwbqXZ4eesNPsKYRjwL/7F3c/F\novNz8HnbYcTExPxKEYvPj/O/A//bfV53v6gD+aJ5r7em67vvC0+A+oLBsKvRHA9oHEzzSj8koYkP\nPMjmo1b+e61/orbO1RtZ+v06ly9LJJMTy7fZDOiZA/qGjlttEi4NMeXzBNEMqioRhpNrm+dNrmue\nJ/HM+RSmF2AXTiiOd0ju6dieiKerrIT7HLoZinoHxfZJBQ5CCCv4pIwu22KZXfcJGsEafvYveMLx\n2fBvko90epQRopAqAxxBI5IFZsNDBKvHtztpvns7wbHsYRxVsYwKO94M0jDJqS3zo9xZnnZGXAxu\nk/cC+kKJjGvTd6fIHI8wn1+ivVHhr06KHIxCbEdA7gWkxS5aeZcgGnLOSrDs+2xmCliqgCJYmG6O\nZkZjyRjQNrvcPDmLPDPgiRWBpzey/Mazd98N3C8Qdd8WWDe3EHe/oEqzL5CPisx/+A9Bkr6UQ/lm\n8KjbYcTExHzjicXnx2lHUXTtyz6IL4P3emuqsoru6u8LUFmJmD2/z3I0YCqY52L505vEf5hPsvJ1\nV+dHv3Q56ZlkczqQB0DVAvzUMdu7LoGsUxjfIdk+YHMnzUErQWSWyOUkarXJe5+eQjoNxYLExfkc\nbbKovbN4rRHpfhdFaZEKTnjR6FL0LXwpzY50jmOpxkCTqapNDC2kZa1zsr2ONraJvNcIBYlIAFmw\nUSOPRGQBISMhQV+ocZoest65Sc7yWA4K9EwF3TWZKrxFVusTiiqJfpbXvQvoUYUZ4YRpb4ASBWQG\nIt1kgdb2LjfGJ6yPSwj+LHeUGo7iEww6JIZ5ps5uMiXr1LIRt8IQxSggaQ6q4oM9TWpkkdJMZle6\nXHwa/uu/WeW3X1gglVA+UyDqYwL1YWZOfg2ILfbHxNYWX1g7jJiYmG8EsfiMuYuF/AKNcYOd/g7L\nhWWyWpaxM+bQ2OXpi3VenEtztvxwKV2fZOWn5AwlJYcvjkjkNVqtPFNTYIQ9BnafXjfL2obGixfm\nKRcVXrOHGJ5OWkmQVrMMBpOCGJgEXra3YTQSObVrjK3f4en+Fs8Zf0U5jMjbEllPJApVulqFbXWd\nN6YX0JUxij7LrNGgGnZ5Y/QdSqMk4zDLK+plzgg3yPgGQSgREeGFCmVvwI1EmbHrkJVC8h2Dg8wM\nbyTPoeZ3qOsdiCCQbFb9JneCRW5GT+LKAtnI5kY0jR3mcGwZ/yjJmu4yHxjUCm9Ry4i8lJsjGK4R\nSApBL0RKbmGmT5kRZyBbx9VzDPqQilrIVYPzGx7f++9F/qvffIZUQnl/PT53IOphZk5+xfP7bBv+\n+T+/+7lYdD5CvmE3KTExMY+fWHzG3MVaaY22MalCudd0nIctLvokK18UIZ/WSBaHaMkMBTnk5ETk\nsO/RMSVKJYnZ2ZDZJQNRyrBYz7KnjihURZx2FsOAXm8ioobDiW3q+2AHeUjbtJQUw7DIqTXDjegC\nU0KDqtSm5U3hBJC1TxmkTXQqLFlJIkcj1FySWo9EaPJa4klGochUeMgFd5+EY6IIKm2hSFsqkIhG\naKMcPTFCVyMiROych23Cxb6OKMp0gyEveG8yllQsX+Mn8nPIikoSn6w54rZ7BtudppTqMNttEow1\n2nWJ26UGgrGO3RuzVbxBOhdw3j1Aq68wdEWs3inZ7i7J5Rq//d+usPKdZ95vHA+PKBD1IDMnH7bS\n7EvgoyLz938/doAfKd+gm5SYmJgvjlh8xtyFIilcmb/yqdNxHpR7WfkAqUqH0lREwpFYmBUpl0P0\nfZ3BscO5DZFL320jK5MmBKWcSr7eoVyTmF8OOT2dzCEfjydiQhAmfSnn55M0xirZvTGOAz+MfgNb\nSvBi7iqaHzIwiuT8HslRlrDmkgkcnDCFrUYESogrhXiCQ0ruccs5Rz8VkJdPkXwXNRCRNJtq6TZr\n4yOmDR9dFZkLHTrZBtm2TtGxmTcNeok0UgiuIGFJCa6npkEU+TYHrHo7bIk1zpkJBN9GUU0Er8yT\n2JwYK9ycL+MoHqoDVzMG1VqKM0GJJ6URitzHn5W5taQy9eQG6y9+H/Eje/LIAlH3mjn5MJVmXxKx\nxf4F8Q25SYmJifliicXnx/lbgiD8LWAJiIAW8Arwf0RR9P98mQf2RaFIyn2n4zws97Lyrdw+Z9af\nIquncXQIQ5FcHlZqDVbOJZlbsd9/j/eEKp7KxtMily9PKpZ/9KPJ68XipFgpnZZYUafxVRHFA0Ms\nIEUCbXeBGdWiJDVRPAtZr6PtVljwT2gIC+wzRyDo7Ms1Zp1Z5kZ9toMiu/ZlEn6BOe/HiOIIVdR5\netxh2hiRd31GfgpDdnjWuE3SkfGlgLaW4Fa2xE54gXnLp0OestYkKQUsG7eYsdvkgjapwEHzHUxX\nxBFUxAhO+zX+P+G7hDkD9exNBFVm7/wMSv7XGOsJRM/DiFxamRKZ555CVLW71vrzBKI+9ty9ejE9\naKXZl4Dvwz/9p3c/F4vOx8zX+CYlJibmyyEWnx/n/Ef+v/Lu4+8IgvAXwN+Joqj1oG8mCMLr93jp\n3Gc8vi+Uzys84d5W/lxxhsVfz1B1qjQbE1E0byVoyS5h8RpWuEiWjwrVzPs6SJYnrZdg0iB9Z2ci\nSE9PFfJuCTdKkY0sfDlPX5unGXVYEnZYiA4peyPm+/NsikvsK0sc5TQ2vFusWiNqpkcpMCl71xhF\nSXKMMIQsiD6hYJG0QIjAlEXcSGaciFgIdTKOQisscVOephVWMXIGh16VZ4a38Ic+Q7XAXlgnJZhM\nhW0q0QBbUInCNLJokMPgSa5zdvjXuZnIoUoqlWyddKqAsLqOdWrD7j6nwwYr0RSrveiDkv/39ush\nA1H3L0z6tF5MXy3/+qMi8x/8g8n5xjxmvoY3KTExMV8usfj8ABP4E+DPgVvAGCgBV4C/D8wCfw34\nM0EQvh1F0fjLOtCvGw9i5T/5xCTyFkR1Xj6ssN0ffqpQ1bSJJmo2J8VHxeJESFlWQNurEUpVlqMt\nusl5PDGDZOlkvRGWpGKGGpEQIkg+smjy7HCLFWmXWdclJYhIQkSoRdiRgh2lIKxzmF7iYrCJFjoc\nZZIkQouirlAby2iRRN40OdDqtOQibbWEnBhj1wKq1iGBk+IN9RwlZcglr0816mKh4QsCtgghKnoo\nUAoMvqX8kk3ht8ipeS5NX2IpPUv48ksMDk/JD0zWhRSFfJI5tQH+y4TfuoKofSAEHzQQdb/CpJMT\nuPLtEE29Xy+mrwaxxf4l8zW7SYmJifnyicXnB8xGUTT4hOd/LAjC/wL8X8BvAheBPwD+5wd50yiK\nnv2k59+NiF76jMf6teNBrHxRBJH7CNXCCorygVAVRXj7bfjjP4arV0O6XZG9vYB2zwN/CUFqEkUu\n8/Ym8zTJG11GUYGfy9/idc6j4rIWbnPJugNWABJsyWsYUZmcNmCZTVqaScKWmPFNUl4KVfAQQpmT\nqMBQKPKU3GAU5Wn5MnJ0QEcV2JsVULM7iFYVTTpASPWQVR0jY+CGApHjIogefpQmE1kEkcC+PI2u\nqNSDLkvKPvmCzUpxlRfnz5Lc3CHc3sPrdNmp10gW01SCiOOf/ILezxuMfx6Qf/LXWFmWWVt78EDU\nRwuTEgm4venzx/9RJ1IG/LuX+jz9nMELT5bZmHr4fN/HTRjCD35w93Ox6PySuG/D2JiYmJi7icXn\nu9xDeL732ujdPNAtJtHQvy8Iwu9HUfSNbTj/OPk0K/8uoeo6sLmD+MYB2Hfe94XFtTVMF17bPOb1\nbY+3riUYdjWMYZIgEFGTETcrlwmkEqZfg3ZEGARck87x/7N350GS3ned59+/58j7qqz77jq6W926\nJUu2hGzA+JjBImA8y7VeBhzEwLAsMcAamBkPA5iAgFmWsINgGO8cXLHMDLNesI0XDGNjS7ZsWWrJ\nbam7pe46urLuK+/7OX77R/ZRXeqjqruu7vq+IhSVT+ZTmd/KSvXzqd85RR+OrXG8KJP+CO9zvgJK\n83njndQJobVLOaA57ycZ4wwDXpV28qS9PN26TNL3GCk1KNhZcmaUyUScarONoJXH0AHi9RSBdALX\nu0jnkkOu3cUOZelOfJ2yslmbrDCwYlD2QnhugHWVYNHqpW7a9PtlAhGHnm6TdDRJZtLH/cwa3edi\nTNpjVOc8AvEilqdINi2OFOaYWTzHwtoIT943yMqKxdNPb60hauPEpFAIzpzxePXCEvPrTdbWFBdX\nNAsU2UQUAAAgAElEQVTlHBNzRd737au8a/SpAxNAN4fMX/olCIf3pRSxmQRPIcQtSPjcIq11Tin1\n34CfAmLA48DX9reqe5fjwMQ5h9Lnv46dmSRaXKAt0iTdG8Ccn6c+t8DHJxN89gsO5y8o6tUA2rbQ\nbQpVbSPcVifYpql19jJDLxFq6EWYMEfR4TWUamLU01SaCcKqilaKqmmjtELZTbSqUbICdNfLhHUT\nVBNszZSZps/V9DmrtCuTC+kAVauDcF2TSXZQNuIMFou0U6USy3PO6mUpVaGjf52j5deZSRmUc0GK\neZNIrcpSoI9Za5yGCjJQXyRvplg1hxk52sSodfHC84scPTtM57JHLvAwDbeJ9g0sE8pBmxPJF0mk\nMyynpjg7FcUyuq4spXSzhqjNE5NmZ+GNi3kW1moE29foMfro7k9gVUwuXMgQb1ulLzXBic79Xa/x\nQHSxS8ueEELcEQmf23Nmw+2BfaviLraV6/blsYhrz08QODVJOL/IdMcY8WSMfrfMeGaKl06t8Or5\nPt6cv59m3cBthrATa3i6gg6uU6n3EvTL1L0ow4MmnXWNKkCsWaQSzoMTRmlN1ChQM4Lgm8T9Gklr\nnW6vgF2voCjT26hStkxeDx4hZa7R4RcImlkaRpOoCz1Ogwm7znxwkG+kushFInSvhulP1Wm2xXnR\njDB7pM53JTyGNQxkm6hqkjoWVrhK0qzjqSyOA4blMR04yuTQOEawQbbkkZ0dptRYpqmjBBsNqo0k\n2rXwtEU0ssI6cZrlYUqsk+pYZGGh6y1LKV3v/d48MWl1FRZW6xjxVZKBNipBg0jUo2/YJXNxiAvT\nZ8g8nNm38Kk1/NqvXXvfnoZO2btcCCF2jITP7dH7XcDdaLvX7ctjEcMTGcbtBZqPjmEbMZaXAWLU\ngiMsnfkiwVUDgsMEI01UrgftBvHMKtos41Ijl3cpF5vUXU3fkENkKsnRlQtUqw6RpiLmXiDNOnkj\nQc7v4DucF/FUjaijiaoiMV1CoTDcMN9KjNBl2nTHCswGbKJ1myPrNmdTEc51dDPTOMKkug/PrnKm\nLcpIt0EjkGfK/jqaIM/Xv4sF16Ff5zBjCSbSDsOhi9xnx2l3PapemIXEMBPqHdRPhohGi6xN9NIo\nRpmsHafD9xiortDQQUo6RcooccTLkHEGKPlj5FabjN5Xp57zaTSMLYX8yxOTJiehUPBpOhov4FPN\nx4gnHZJtDcJRD8OPU69ras3Gjiy9tV2bQ+ZHPvLWZaR2lexdLoQQO0rC5/bcv+H2wr5VcRe5net2\nJgOzMz4PUie/3CRDDMtqjVtcW4OsGaWer2OHloiGXMpV0GYdbZUxG3GcwDJ+dAHXbmAEFI1UnvMn\nv8mD6920fz3Pw5UpUl4F7ZtkibOiuwirBpZuMOwsUSJGxQywbHUw7C/gozmiZzifMJmLtaPa10m4\nsGKmOBt9kAv3n6CR68RbdKgtD+GpMpncMpH+aQK1FH7VorLaxet+mLOWgx+Oo9Mhnhh8gWfTPfRY\nCdxAkMVgH8uVI7QfOY/nhqhl26hXAiz7x+gNF3Fcn3H/PGY1QNMIsBjopZjoZcYZwSot4tRCxIPG\nltf03jgx6cIFg+J6GL/WRrK3QluHJtXRoFYx8Y0a4ZAiHAjuafA8EF3sIHuXCyHEDpPwuUVKqRTw\nQ5cOq8DL+1jOXWO7123fv/T4RYPOfAg3b5NtlnGDMSJRj7VCjURgEZoe9c51CBZwiylcVYZGCK8R\nQntt6PQEZriJ6rtA4uFVQoMlnBMe/oRL2Q0w0Rik5PdQcltjPtvVCoZd4RXjJA4GjuGzGrGY8jp4\nR/1NHqgusxA4TtlwiXsjjOh5VjqarCWDqMIwzWqTplHA7VjED63gDJ6naUWxnU6CjTHomca18uhm\nFBpvx3OjZOrfwws9DqOdabSbIDNj4SYy3D8WxM8NMK0T1MsKM+rzgvsgA3YX/SRQToimF2fRHaRE\nB0bOY7g9TX21m+NHt76m98YVcjwPXBumFwNU/FXKlRjnXmkjmzUIdM3wzsEQQ8m9WSx837vYN5O9\ny4UQYkdJ+ASUUt8D/LXW2r3B4wngv9Oa6Q7wH7XWjb2q7yDa6pyL7V63DQNWV13mV6t8rRzmybhJ\nj/sy69Y4maUoqpqj0zjPTCTKfEpT9y+iE2nclSGoR/FrMYg0MQ0Lu/t1Ah0Z0v11OiN9BJov4SQD\nfL7xOPnVk+hmGyiTuC5xXL2BoRXfiN6PV7fxgxWU2cCwajzgzlBUAca8WQL5Ok2vnZXoMHOxBoUj\nMezQGdzSGpa/TiA5R7hrkaH2blZPP0ZusYeOwRLpZB9a94IC1V5j+ewROuIBdN7kpbkKGDnaumoc\nO2ry9gc6aas9xDefX2dxyoLgKqVKlDOM8y33OMrWeH4KE4dIziVYsigEfe57Z3Lba3pfXiFnfBwG\nBpP8yV+UefN8L6++Cb7WROMNhtvSxN00w/HdXyx8c8j86Z+Gzs5df9kbk73LhRBix0n4bPk9IKCU\n+n9pzWCfptW62QY8A/wkrUXmobUA/a/uQ437brtjN2/nuu14DpO5KbK1AKsqQcyKU/WLpJZeZaAE\nTUJ4o72E+2Nk623YkTeomototY5ePY5OOqjOM0RGzxDuWsRsn6bk9pItB7DKS/QSohqroAoNNAXw\nTSpeCNNz8Q1IqBI5swMVLEEzTkS7TMRDLNpJZmuPEjTKqHCQFR5m1jFpb04SbV8n2PsloIht2rRH\n2hmMDROMjlPRMSrqIscjj3G88zg1p8ZqZZVoKcJDR7oIRQwmZio0HZ9Q3SCUi5L7VhfNiMVwRw/Z\ngSrruR60rSiWA+BYmKYmnm6STPmEQwYmQY4fCTI4YPKOp3xse/shyLahv8/ivv5+aoUyI6M5ApEm\n0YgiTC+xWpqZaWvXGvgOTBf7Zvfw3uWSl4UQ+0XC51W9wE9f+u9Gvgj8iNY6tzclHRy3M3bTMFrn\nbOe6fX5tgoo1jw500dMR4s38P2Sp/gZB/zXsxDrQRWCsTn0sRGLOp7Z0H/lSFiNYgPs+i9lxkeCR\nV+lNpmnqJoVGmUwhw0JpgW83XBpOilg5RjXSxPGD+B5EdY5VlcBViqH6Mi5hCsonriscaS4y3xbj\n6/ZJ3mi+B6wy7aMZurxRgpEL2JVhlmcbFNLtGN1rhO0wju9Qcgsoq0EwGINGjNXqKmpVUffruLUw\nDV1kpRRiNJ5itG2U6WlFedngWxm4+DqMjkIqYTE+mKC/A4zjPmfOGKyu+hiGQSIRZXDIZ3zMIBpz\nKbnrnF7N4E3NE7JC1+wetVWLi2AaFj/4vSkikRSo1uSiUml3e5c3h8wDETo3uof2LpdJ+0KIg0DC\nZ8uPAt8OvB0YAzqAJFAB5oGvA3+mtf7CvlW4z7YzdnPjBW5qCrLZ1lI+jz/e2gLzZtftuVKGilpn\nbKyTaNRhPrjCbNxiudpJwasTIEhHbJ6ezudJWyfprVV5tDyD4ZVwzRxzsWWmlM98eR7bsFFKUWqU\n0Gjmk0kSfi/DpSpzwQr5iE+o4nFErfCaPklTB7G8JsNqmmA5T8M0WAynmbS7uODdh3YtjGQOz3Mp\nuVki4TrR7gLluV7MwAjJ4WXS4TQoyOQz+OY3MBKPYZeO09kXI2S7lPOK/FIKWzXJZJfJV6p0J6sk\nkiM06q33IJmEaBSqVWhvb2UdzzOo1cA0Dfr6Wnvaj4wYdHa6FOw3eP2UQ25pkvrcWYIBm/nSPCuV\nFZ4efHpLAfT6rdStvwp2q3d5c8j8iZ9ofSYOnHtk73KZtC+EOCgkfAJa6y8DX97vOg6yrY7d3HyB\nq9WgWGxNaPnyl6Gnp7UTzfWu2772qbt1Iu1rJBuai3MVzI5pYsY6jWKDwnQYM7VEuH2VVCDAA6VX\nCHizRIwiYRTlmsPYsk17tcFzfT5V2yFmx0Ab2KbBdKciYPXhqnYGeJ0RF2pGjEVzjElG+YbzbYxY\nbzIYsgnY6zQjJTKhFBPufbjVPlAOBEo0i200k0tYoQXqRh3L72Ykfh95Xma1vI5tmTieQyH0NQLx\nIH3xPoKl+1hfqJMvOKQiCZrFKHXHxRqcYm7VgFyJ40dSQGtf9fZ2OHoULlyA7u5WSNe6FUoffLD1\nvgcCMFtYJDOzSk3D/W1dPDkYodwsM5Vr/VXQFe3a0tqce9m7fGC72G/kHtm7XCbtCyEOCgmf4pa2\nM3bzehe4fB5OnWoFl76+1vX6etdtQxmErBA9w2U81lCFCktzYSI8SK05h5l4nVD3Kj1DZRJzK0Qz\necLZGit9SSpBhS7W6M2EGdNjLCx2Mh06jq8CGKFljHCDSnKR59pjjKweYVBp7PAqTR0h46aZbA6j\nnQaTPTXOda2jPAOTXoz8UbxqO1TT4Ibx10dophdww0uEklnW8ga+l6ejPoR74TvJlasYVhM7vYjZ\ndp7E+OuMRR4mUp5m+WyYtnAvhg5Qr5oUc2Ei8QEy600SZpVwuBU+XbcV4qPRVmjv7ob3vhcGBuDF\nF1vvbaPRCoQzK1mmpmF0KMzQUA2AWCDGSGqEqfwUmcLWF4bfi97lA9/FfiP3wN7lMmlfCHFQSPgU\nt7SdVrHrXeBSKXjyydYFbnQU3v/+G7/WUHKI+bZ5MvpFUo5JyvZJ2J2s575FMPQ6sYEc81Wf7swq\n4XWX1a44xKMkdZDFUopZN0bvqsPAzDu4YD6LjwWBMqp7gnrHKWpOiLOpAGeNMVTzJNoPQtjBCJwj\nYmWwTvwV/tCnIDtOuHw/esHEp4mnQdcT0Ixj6ijRQJSQ18HinAbXxVgATz9Mwg2hrTpmY4Wge4wn\n3+Ey3F5lbWaacHKAVLSX7sE8CxejzFyIUc3HqZULBIKKas1HYWBZraxTqVz73h471lrnFFrvZb3h\nM1eyCafyjIwE6R2qXHkf48E4TbdJw936wvC72bu8OWR++MMwPHz7z7ev7sLgKZP2hRAHiYRPsSVb\naRXbygXOcW5+gRtPj7NSaSWg6eKXwb5IyTeJ9uTwGkUsK4brOoQ8RcDVVIIKy2tSz3egS914dBGs\nX8R2DDwdxDcCKFz8Qopm9WGITUNiFpUfRft2a88qLwCuAUMvU4l/C99oYHedxwzE0JU+gsqnMfYF\n3GwfdnkMu3SS3EScamEBFVqk6bqsV0uk+tY40t1J0GtncXacTvcIQ56B1ucorCSp5ZMMH80Sjtgk\n003SnU1Wlk1818aKGMxmWm9KTw9EIm9tcXxr769BeL3KsrnEwP0NLDt65X0sNUoErABBa+sLw+9G\n7/Jd18V+j7qHJ+0LIe5CEj7FlmylVWwnLnC2afP04NN0RbvwfA/Xd7lYuEg0EKUz2km1WaVhNqgZ\nOYq6QTm7RDVoYKz2ocpR0kaehpPGcToxuhewbGgWE3jlOF5qEVXpxcDEN3yotYMbBKuOb89Rrbv4\n8QnAp+k3Yb0TlW/DTE+jzTx0FbATTQIVj/JaGu3WSLnDqGoMO7hAqZBnxp6jI1amZ2AYnT1Cbd0h\n3j1J0uqioFIU/AuEnG7aOiGb8zGqTYz1AbxaFMdpvQeFQmvM5+DgW1scN/f+Hl2P8rW5IJnSJKY1\nQjwYp9QoMZ2fpi/et+2F4Xeyd/mu7WK/R91Dk/aFEHc5CZ9iS7baKrYTFzjbtDnReYLx9Di5Wo5K\ns4JlWqxV1yg0CqxWVnGDeeygQ98aZNpsyg2PSLlIullgiSdZDncTCns03DoeETw7D80wutGGl5iB\nQB365lGqidYBcMNg1QmVjtNsP432FU7DhKYiYBewsQmYEImu00g8j7v2DwgThGoXRqmTUChKUKep\nOjNYR5uMdnewkE+xWHiT4/E+OnuP0lhQGLqTpcoSrudidtkMGEdIonj8RJgjl7qh29tb4z1v1eJo\nGNe2FE/lp2i6TQJWgL54H2NtY4ynb7+v/HaD5+aQ+aEPtSZPif11j0zaF0LcAyR8ii3bSqvY+Dgs\nLfuAcUcXOMdzOL9+nlKzRNWtEvAD1JwadbdO1anyRptLW1lhmybHixZGbo1Kw2be6GbK6OaNYBqn\nXsLT4NMEuwpOFJwQVjPIeM9fMOSsEXQ1DVuRCbczWXoEVRom0TeNaZgU7Sae1cB0kqQSFrZp4/gO\n9eVhVDOO5Sri3TmidojOjjjlQop8TeEU1lkMVKj4WcYSSY62d9P24CBmRXF2KkpbRxo7XMephWhE\nejn5D9N821Mm99/f+tm30+K4saU4U8jQcBsEreBtrfN5p6SL/WC7RybtCyHuARI+xW3ZHI4cz2Ei\nO0GmkKHc3sCrp+mODtEe7CMathgYaE2Y2coFzvEcXph9gcncJBfzF1mprOB4DjWnRtWt4vs+vmXy\n0rCi1haiXo1gJSOsLMSYyD7EROQIrqqhy0m09iGUb43tbMSwrDxPN88wVpijt9Yg6Ps0DIP+cJ2u\neg8v9xkkAil649280bmOU80RLp8kGF0nHPaoVy1Kqw9iKINI3ySdXd04JhiNNOlUhdpqL+FqB9FK\nmtExj3c/EubpwSHwbXLrYBldLCx0Uc/5xIMGx8dbofzYsRu/t7dyuaX4ROeJLU8u2mnSxX53uAcm\n7Qsh7gESPsUd2xgWF0oLra7fSICudD/F/AME3UeZmLCYm9taK8tEdoLJ3CSLpUV6oj0shheZyE3g\n+i5hM0xRFXF9l4AdIdMXZcGwsLwiy292U52swKIL+T60aoLhQSMJTgiSs4xzhrHaBD1ln8lkhIpt\nEXVcRgsueMvkKzVyeJiGSVv/KsovEyp5WOUHidc7iKoqus0grzSpvnWO9AziBaOsLlVZWnWpLY3Q\ncMOkUhF6LYOhOOBff7LQbrQ67XXw3Bwyv+/74JFH9rQEcZskeAoh9ouET3HHNobFsbYxYoEY+UqF\nL3/FwVuvk2hmSdpdW9pNxdf+le0wx9rGOOedQ6FoC7WxXl6loitorTGUgae91vfgU/RzNPu/BNY8\ndlCjs0s4hS7wDAiWIL4EPacZqn+V3ukkk4xSIQfUqRBjmjbGmGNdFVl06mQKGfoT/cQeWKGyqhjU\nbRxNDuMpg3OTZdaWA/SlHsZTTZqp01heG6H8UaLBAPFgGO21dih6+WXI5a7+vDdqddqpFsu9as2S\nLnYhhBC3S8KnuGMbw2Is0JriXlrqxsiFuThf420nF3niaNeNt+Pc0GVfdaqcXjrNcmWZkx0n0c0m\nD6ybxJcDZHMGq36TC1E4l9I4pkOhXsA2bWpODU85qJ7X0J2T6OwoFHpas9nNJqRmUG0XCJ7XBEMD\nVIJpKPdg+EEwHaqxLKHGOmZwkXItgJ1/iHD2PXhuiHjQI9C3hB6eIxYxeaLrBPmJMRr5NiKpFexU\nnaVqEleneDw9y7tH5hlqr1OphXjz9BBT7jhdXfY1C3gbxrU/d92t3/ae7Hu9X7d0sQshhLgTEj7F\nHbm8JWbTbV4JngCrC2GquSSJ7lmscARf+8Rixlu347xOl/1MYYZSo8S35k4xcm4RdzJPW67BynqF\nbl0nHdOkS4rnBlxq2sfxHTzHwM8eg8IwvhNC2Q1IXoS2SbAcLGWBUjQCHo22WaK2QyXSjeGHsQOK\nhL2C11zGCEcw5p/GKT9IllFsHSMcUig/RDhk8uiTdQbHB1ltO8rMRYuFhV7qOR9/weOJygs8nprk\nWHUBe7JJ3AoQjsxz/sUVZvuf5sSJq0nwukMVrMC292Tfy/26N4fM97wHnnlmZ55bCCHE4SHhU9yR\ny1tiBqwA5WaZWCCG70OzaVCpukS7fWzDRmsN6jrbcV6nyz4RTPDC7Ausv/4iJ+cswmXFS9EKF4M+\ngZrJeNHEKDqs5hVn2zWea+DPvgOyI1DqAy+INutQ7IVKFwy+gDY1QTPIYgoWiy7j5SUy7SvUQgF6\nVZRjpQALbSkqoRMkio8SbB4hMbRGNLZKnB4q2U6KS1EGvXEe6rNxOmGitxWiqzXoyU6QMCc5llik\n3jtGJRzDrJVJLk8Rz4N1sQvfP4FhtAL79X7u29mT/ab7dfs+XV3GdbdM3E43/yc+0Ro6sJG0dh4g\nMnNICHGXkfB5iO3UNWsoOcR8aZ6p3BQjqdZC556qkHeLVPM1io1TvL76OiEzRI89RsR8kGAw0NqO\n8zpd9mPpMQqNAtbpL6CWqsx2x1mr1vF8Dx2LsmjDkXWDQt1mIQTZ2W50dgTKfZCewghW8RthyI20\nCoyuEutfoD3STn6gSrZZZ7AS48miC6UGXkCxkjKYS9tMOuNEGmN0DZbpTicwlIGvy1RiZzg/c4IX\nzyzy0ANDYDjQMQF2Bpp12p/PEPcXyCbvJxhu/RxeOEY2MUJqcYpIdpo317nSxf76yussV5Z5ou+J\nKz/37ezJvnk7U+U69OQm6CllWDlbp7Icgg+2+uAdg21380sX+wG112MthBBiB0n4PGR245p1vYXO\nC6EOKsEA81Mh7PYVCJTAiRMsGhw/kuV7e57B18HrdtlbhsVj3Y+wZL5Ep2nRM/YUK9NVPN+jO9ZN\nJVShLVvmeHyA6TaTb5xpxy/1QXoCFaihNRAoQ2oacqOowjAdx5o81PUQxUYRp8vArLcTzWvW84ss\nOlkuROvk+jpJXhgk5waJRcusVdfw/NbM96AdZGZtidmsouH08vX5q13mTrNBv7dKhBrfXBzj/lCM\nWNSkVoPlYpyxUI2CPseZzAILlSXqbp3p3DTFRpHeWC+xQAzLaP2vuJ092TdvZ6pch/QbLxBZnCSY\nXUDNNomqAP5X5/EXF/jaIEyUZ7bUzb85ZD75JHz3d9/e50PssL0cayGEELtAwuchslvXrOstdH7K\n/xbB6TpWM0W8+hB2JYajyhTDb5IPu2TMJo+pD1zpsi/WyyRCVwNozWuQTHXTU/aJWh2kginWzSyV\nZoVw3SMYTWCGwuQa6/hOT2t/9kAFpUwsZbVaSUM1fC+E5UeJW0liwRgP9zxMPBgnFoiRKS2yXExR\n8WoMBeJ8W3KQV/KdvDrrUK0adKbaCJgBml6T1VydOgXWHZjIRd7SZR4ef4HihTUsNctEJkjUSmFZ\n0BUpYfgN6qkFFiv1K+e7nsvLiy8znZ8mGUwymBwEtrcn++btTHtyE63gmVskmxpjmRhtA2WM5SmW\nygus5mCxU920m/9zn4OXXrr2daS184C56VgLrp3NJ4QQB5CEz0NkN69Zmxc6f23lNewjL/FI3zNQ\nyOI6BSzbQyerLAe+ypmsxwecD+Asj5N9Dc4VCgy2xRgahlj3ErOVaY4eGWW1MMv8l0+xXkmQqx3F\nauYY85aZHoGZmMNqbRnsITAb0IxCsI6vfVCg6zGU2SQcMriv6xjvHHonbx94O+Nt48zkpsmU5q7s\nCDSQGOBY+zE+MvWfsCbLuOvH0OE6RJroRgw3246RuICVqjBXDL5lqIA5OkjP4+vYZ1/Fa7fp7UwR\nckr0NqZZHPKZSbmMto1eOX8oMUC2nmUqN0U6lGYwOXhbe7Jv3M60p5QhmF0gmxpjvhAjnYb0UAxS\nI5Rf/jwNF0aP/YMbdvP/t9+/9pcvofOA2jzWAlpfN8/mE0KIA0rC5yGyV9csX/tUnAouNcaONYEJ\ntK9QhgZg/mKNUq3O819xWZgcpjhtUCzkeXkqz5nJPF1DDm9/Rx+Vfjj9NZPSyhQ9a4q2ikORJDPx\nCLO5Oq/7M1iWhd22iFscwMuN46emIVRB12NQOIKKL9HRW6UjPILlaRqvfZNAc5YTjsuJUAh/cAxj\nuLX1kq99xsY1L11YR2eXWZsfQPkhtFFHxedIpdcZHYtQdapvGSpQGeoluF7ArS5yn/sGx3QdMx7C\nP9bDeRuWe6oMqhDRyVnCC6t0NOqk6w6m77IQnOEbhk3QDm57T/Yr+3X7Pitn66jZJsu0gmdvb+s/\n34zi12vouiJuRa75/ngwzt/9p2/jTLyPY+0apRT33w/f//13/jkQu2DzWIuNNs/mk0lIQogDSsLn\nIbGX1yzLsIjaUQJWgFw9R1uo7UrwzNayBKwAteV+LroWq8vw7Y8MUsJiZkUxn+khWfHoc8LMFef4\nbNMkEU0ymHiDVDDNcrnG66U0Z+0ywXyY1MAikaEaK7UFmoaJXziKv26DUceIL5Pqz/L0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AAgMQh5AIAACBxCLkAAABIHEIuAAAAEoeQCwANJp1O13oIAFB1hFwAaDCZTKbWQwCAqiPk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\n5VCL68o1bmF0hRarMjnFp3Tq5BT/RXUmp5hAPTdI+nKy+z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+ ""text/plain"": [ + """" + ] + }, + ""metadata"": { + ""image/png"": { + ""height"": 328, + ""width"": 348 + } + }, + ""output_type"": ""display_data"" + }, + { + ""name"": ""stdout"", + ""output_type"": ""stream"", + ""text"": [ + ""('top10', ['AD2D92', 'AD2D726', 'AD2D248', 'AD2D91', 'AD2D170', 'AD2D102', 'AD2D638', 'AD2D180', 'AD2D415', 'AD2D717'])\n"", + ""\n"", + ""('top20', ['AD2D92', 'AD2D726', 'AD2D248', 'AD2D91', 'AD2D170', 'AD2D102', 'AD2D638', 'AD2D180', 'AD2D415', 'AD2D717', 'AD2D482', 'AD2D492', 'AD2D414', 'AD2D326', 'AD2D258', 'AD2D648', 'AD2D703', 'AD2D93', 'AD2D336', 'AD2D247'])\n"", + ""\n"", + ""('top30', ['AD2D92', 'AD2D726', 'AD2D248', 'AD2D91', 'AD2D170', 'AD2D102', 'AD2D638', 'AD2D180', 'AD2D415', 'AD2D717', 'AD2D482', 'AD2D492', 'AD2D414', 'AD2D326', 'AD2D258', 'AD2D648', 'AD2D703', 'AD2D93', 'AD2D336', 'AD2D247', 'AD2D570', 'AD2D404', 'AD2D4', 'AD2D403', 'AD2D169', 'AD2D625', 'AD2D493', 'AD2D637', 'AD2D2', 'AD2D560'])\n"", + ""\n"", + ""('top40', ['AD2D92', 'AD2D726', 'AD2D248', 'AD2D91', 'AD2D170', 'AD2D102', 'AD2D638', 'AD2D180', 'AD2D415', 'AD2D717', 'AD2D482', 'AD2D492', 'AD2D414', 'AD2D326', 'AD2D258', 'AD2D648', 'AD2D703', 'AD2D93', 'AD2D336', 'AD2D247', 'AD2D570', 'AD2D404', 'AD2D4', 'AD2D403', 'AD2D169', 'AD2D625', 'AD2D493', 'AD2D637', 'AD2D2', 'AD2D560', 'AD2D325', 'AD2D181', 'AD2D716', 'AD2D171', 'AD2D627', 'AD2D561', 'AD2D249', 'AD2D13', 'AD2D481', 'AD2D549'])\n"", + ""\n"", + ""('top50', ['AD2D92', 'AD2D726', 'AD2D248', 'AD2D91', 'AD2D170', 'AD2D102', 'AD2D638', 'AD2D180', 'AD2D415', 'AD2D717', 'AD2D482', 'AD2D492', 'AD2D414', 'AD2D326', 'AD2D258', 'AD2D648', 'AD2D703', 'AD2D93', 'AD2D336', 'AD2D247', 'AD2D570', 'AD2D404', 'AD2D4', 'AD2D403', 'AD2D169', 'AD2D625', 'AD2D493', 'AD2D637', 'AD2D2', 'AD2D560', 'AD2D325', 'AD2D181', 'AD2D716', 'AD2D171', 'AD2D627', 'AD2D561', 'AD2D249', 'AD2D13', 'AD2D481', 'AD2D549', 'AD2D12', 'AD2D559', 'AD2D14', 'AD2D6', 'AD2D327', 'AD2D261', 'AD2D649', 'AD2D15', 'AD2D714', 'AD2D337'])\n"" + ] + }, + { + ""data"": { + ""image/png"": 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D3ScBZwFXAin4m+WLMWl9vMLnnE3OZ3JPkXcB/olsP89CRNTaHfa/fPuya/ezxO/uvO26u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+ ""text/plain"": [ + """" + ] + }, + ""metadata"": { + ""image/png"": { + ""height"": 690, + ""width"": 332 + } + }, + ""output_type"": ""display_data"" + }, + { + ""name"": ""stdout"", + ""output_type"": ""stream"", + ""text"": [ + ""\n"", + ""\n"", + ""************************************************************************************\n"", + ""\n"", + ""QSAR/ER_alpha_MACCSFingerprinter.csv\n"", + ""\n"", + ""from Remove useless descriptor\n"", + ""The initial set of 166 descriptors has been reduced to 141 descriptors.\n"", + ""from Remove correlation\n"", + ""The initial set of 141 descriptors has been reduced to 92 descriptors.\n"", + ""\n"", + ""Training set\n"", + ""------------\n"", + ""N: 861\n"", + ""R2: 0.9407\n"", + ""std_R2: 0.0027\n"", + ""RMSE: 0.4824\n"", + ""std_RMSE: 0.0052\n"", + ""\n"", + ""Cross-validation set\n"", + ""------------\n"", + ""N: 861\n"", + ""Q2: 0.7244\n"", + ""std_Q2: 0.0090\n"", + ""RMSE: 0.7343\n"", + ""std_RMSE: 0.0051\n"", + ""\n"", + ""External set\n"", + ""------------\n"", + ""N: 370\n"", + ""Q2_EXt: 0.7385\n"", + ""std_Q2_EXt: 0.0106\n"", + ""RMSE: 0.5316\n"", + ""std_RMSE: 0.0141\n"" + ] + }, + { + ""data"": { + ""image/png"": 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bRQiFK0Q5K6Qa65iVczKGmi7iKKjWlBbfLRm2yiDS5yJQNApZN2G/S0rVI1F8k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Yag846Vi+DZYmEk1kGlsJ3agZhV0CRRZoHArWWi0oeSYty6ZKIoi5x4b9Y58U3/5g+ZfRKnnE3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\nBtO2RTyhzyBaQCroD04QnhdOnL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sF9s0pGtE0otjiTuQMY1GTVx96sJqdDa9jtRr2mfHx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RvZi3FlhEzsx2SuvOuWY6ozeGHsj9L+Uz29bokQi4AAEAN1HO7giQ9mX3tkLcU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\n6qvynqYLQwj/Y6r1NJs02i+EcKf8Dv2vSPq5PFSPyudU3SDpHklrQw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+ ""text/plain"": [ + """" + ] + }, + ""metadata"": { + ""image/png"": { + ""height"": 328, + ""width"": 348 + } + }, + ""output_type"": ""display_data"" + }, + { + ""name"": ""stdout"", + ""output_type"": ""stream"", + ""text"": [ + ""('top10', ['MACCSFP86', 'MACCSFP139', 'MACCSFP92', 'MACCSFP97', 'MACCSFP36', 'MACCSFP77', 'MACCSFP154', 'MACCSFP118', 'MACCSFP131', 'MACCSFP113'])\n"", + ""\n"", + ""('top20', ['MACCSFP86', 'MACCSFP139', 'MACCSFP92', 'MACCSFP97', 'MACCSFP36', 'MACCSFP77', 'MACCSFP154', 'MACCSFP118', 'MACCSFP131', 'MACCSFP113', 'MACCSFP75', 'MACCSFP109', 'MACCSFP80', 'MACCSFP133', 'MACCSFP136', 'MACCSFP95', 'MACCSFP127', 'MACCSFP98', 'MACCSFP72', 'MACCSFP62'])\n"", + ""\n"", + ""('top30', ['MACCSFP86', 'MACCSFP139', 'MACCSFP92', 'MACCSFP97', 'MACCSFP36', 'MACCSFP77', 'MACCSFP154', 'MACCSFP118', 'MACCSFP131', 'MACCSFP113', 'MACCSFP75', 'MACCSFP109', 'MACCSFP80', 'MACCSFP133', 'MACCSFP136', 'MACCSFP95', 'MACCSFP127', 'MACCSFP98', 'MACCSFP72', 'MACCSFP62', 'MACCSFP142', 'MACCSFP57', 'MACCSFP155', 'MACCSFP140', 'MACCSFP144', 'MACCSFP151', 'MACCSFP93', 'MACCSFP65', 'MACCSFP50', 'MACCSFP132'])\n"", + ""\n"", + ""('top40', ['MACCSFP86', 'MACCSFP139', 'MACCSFP92', 'MACCSFP97', 'MACCSFP36', 'MACCSFP77', 'MACCSFP154', 'MACCSFP118', 'MACCSFP131', 'MACCSFP113', 'MACCSFP75', 'MACCSFP109', 'MACCSFP80', 'MACCSFP133', 'MACCSFP136', 'MACCSFP95', 'MACCSFP127', 'MACCSFP98', 'MACCSFP72', 'MACCSFP62', 'MACCSFP142', 'MACCSFP57', 'MACCSFP155', 'MACCSFP140', 'MACCSFP144', 'MACCSFP151', 'MACCSFP93', 'MACCSFP65', 'MACCSFP50', 'MACCSFP132', 'MACCSFP120', 'MACCSFP89', 'MACCSFP87', 'MACCSFP59', 'MACCSFP83', 'MACCSFP160', 'MACCSFP47', 'MACCSFP123', 'MACCSFP149', 'MACCSFP150'])\n"", + ""\n"", + ""('top50', ['MACCSFP86', 'MACCSFP139', 'MACCSFP92', 'MACCSFP97', 'MACCSFP36', 'MACCSFP77', 'MACCSFP154', 'MACCSFP118', 'MACCSFP131', 'MACCSFP113', 'MACCSFP75', 'MACCSFP109', 'MACCSFP80', 'MACCSFP133', 'MACCSFP136', 'MACCSFP95', 'MACCSFP127', 'MACCSFP98', 'MACCSFP72', 'MACCSFP62', 'MACCSFP142', 'MACCSFP57', 'MACCSFP155', 'MACCSFP140', 'MACCSFP144', 'MACCSFP151', 'MACCSFP93', 'MACCSFP65', 'MACCSFP50', 'MACCSFP132', 'MACCSFP120', 'MACCSFP89', 'MACCSFP87', 'MACCSFP59', 'MACCSFP83', 'MACCSFP160', 'MACCSFP47', 'MACCSFP123', 'MACCSFP149', 'MACCSFP150', 'MACCSFP91', 'MACCSFP145', 'MACCSFP115', 'MACCSFP42', 'MACCSFP146', 'MACCSFP90', 'MACCSFP108', 'MACCSFP106', 'MACCSFP114', 'MACCSFP38'])\n"" + ] + }, + { + ""data"": { + ""image/png"": 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wP20cPnL+pqjZW1X3zaOtnQmbn0u546ND56f8Y7PAfiKr6PvBWYF/afw4kSZJ2sJj7aH6I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ZzAAAg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\na9udwP/tjv9UVXeN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+ ""text/plain"": [ + """" + ] + }, + ""metadata"": { + ""image/png"": { + ""height"": 690, + ""width"": 333 + } + }, + ""output_type"": ""display_data"" + }, + { + ""name"": ""stdout"", + ""output_type"": ""stream"", + ""text"": [ + ""\n"", + ""\n"", + ""************************************************************************************\n"", + ""\n"", + ""QSAR/ER_alpha_SubstructureFingerprintCount.csv\n"", + ""\n"", + ""from Remove useless descriptor\n"", + ""The initial set of 307 descriptors has been reduced to 82 descriptors.\n"", + ""from Remove correlation\n"", + ""The initial set of 82 descriptors has been reduced to 64 descriptors.\n"", + ""\n"", + ""Training set\n"", + ""------------\n"", + ""N: 861\n"", + ""R2: 0.9479\n"", + ""std_R2: 0.0019\n"", + ""RMSE: 0.4734\n"", + ""std_RMSE: 0.0044\n"", + ""\n"", + ""Cross-validation set\n"", + ""------------\n"", + ""N: 861\n"", + ""Q2: 0.7191\n"", + ""std_Q2: 0.0101\n"", + ""RMSE: 0.7338\n"", + ""std_RMSE: 0.0068\n"", + ""\n"", + ""External set\n"", + ""------------\n"", + ""N: 370\n"", + ""Q2_EXt: 0.7340\n"", + ""std_Q2_EXt: 0.0166\n"", + ""RMSE: 0.5255\n"", + ""std_RMSE: 0.0159\n"" + ] + }, + { + ""data"": { + ""image/png"": 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f3ULWrBPufwFOepHx/iHn9GVLFeorxHPVj8xSsOuati+j34Px50HUV/DWU4iGi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xjyhGVOfxA7rGUz+eeL0oi5AIAANRBI5crSNJjuecueUlBMYMFjgEAAEBCNXrI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\nuy/+6X5mxsPtP/yDB81vftN/nm9vj1t6rV3rgf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+ ""text/plain"": [ + """" + ] + }, + ""metadata"": { + ""image/png"": { + ""height"": 328, + ""width"": 348 + } + }, + ""output_type"": ""display_data"" + }, + { + ""name"": ""stdout"", + ""output_type"": ""stream"", + ""text"": [ + ""('top10', ['SubFPC169', 'SubFPC23', 'SubFPC274', 'SubFPC302', 'SubFPC88', 'SubFPC2', 'SubFPC181', 'SubFPC18', 'SubFPC287', 'SubFPC137'])\n"", + ""\n"", + ""('top20', ['SubFPC169', 'SubFPC23', 'SubFPC274', 'SubFPC302', 'SubFPC88', 'SubFPC2', 'SubFPC181', 'SubFPC18', 'SubFPC287', 'SubFPC137', 'SubFPC1', 'SubFPC20', 'SubFPC5', 'SubFPC135', 'SubFPC100', 'SubFPC182', 'SubFPC172', 'SubFPC3', 'SubFPC183', 'SubFPC49'])\n"", + ""\n"", + ""('top30', ['SubFPC169', 'SubFPC23', 'SubFPC274', 'SubFPC302', 'SubFPC88', 'SubFPC2', 'SubFPC181', 'SubFPC18', 'SubFPC287', 'SubFPC137', 'SubFPC1', 'SubFPC20', 'SubFPC5', 'SubFPC135', 'SubFPC100', 'SubFPC182', 'SubFPC172', 'SubFPC3', 'SubFPC183', 'SubFPC49', 'SubFPC33', 'SubFPC4', 'SubFPC180', 'SubFPC303', 'SubFPC171', 'SubFPC85', 'SubFPC101', 'SubFPC12', 'SubFPC179', 'SubFPC143'])\n"", + ""\n"", + ""('top40', ['SubFPC169', 'SubFPC23', 'SubFPC274', 'SubFPC302', 'SubFPC88', 'SubFPC2', 'SubFPC181', 'SubFPC18', 'SubFPC287', 'SubFPC137', 'SubFPC1', 'SubFPC20', 'SubFPC5', 'SubFPC135', 'SubFPC100', 'SubFPC182', 'SubFPC172', 'SubFPC3', 'SubFPC183', 'SubFPC49', 'SubFPC33', 'SubFPC4', 'SubFPC180', 'SubFPC303', 'SubFPC171', 'SubFPC85', 'SubFPC101', 'SubFPC12', 'SubFPC179', 'SubFPC143', 'SubFPC133', 'SubFPC214', 'SubFPC9', 'SubFPC84', 'SubFPC173', 'SubFPC136', 'SubFPC170', 'SubFPC86', 'SubFPC16', 'SubFPC6'])\n"", + ""\n"", + ""('top50', ['SubFPC169', 'SubFPC23', 'SubFPC274', 'SubFPC302', 'SubFPC88', 'SubFPC2', 'SubFPC181', 'SubFPC18', 'SubFPC287', 'SubFPC137', 'SubFPC1', 'SubFPC20', 'SubFPC5', 'SubFPC135', 'SubFPC100', 'SubFPC182', 'SubFPC172', 'SubFPC3', 'SubFPC183', 'SubFPC49', 'SubFPC33', 'SubFPC4', 'SubFPC180', 'SubFPC303', 'SubFPC171', 'SubFPC85', 'SubFPC101', 'SubFPC12', 'SubFPC179', 'SubFPC143', 'SubFPC133', 'SubFPC214', 'SubFPC9', 'SubFPC84', 'SubFPC173', 'SubFPC136', 'SubFPC170', 'SubFPC86', 'SubFPC16', 'SubFPC6', 'SubFPC78', 'SubFPC70', 'SubFPC13', 'SubFPC15', 'SubFPC54', 'SubFPC188', 'SubFPC139', 'SubFPC103', 'SubFPC52', 'SubFPC28'])\n"" + ] + }, + { + ""data"": { + ""image/png"": 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n68TPgUtq/x7/p+vNe4PVJXlpVt4//uSRJkiZjEqvOj6OFzG8BB1bVT4c/TPI0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ktQLg6YkSZJ6YdCUJElSLzZb6g5oedpxmx1ZN7VuqbshSZI2Yo5oSpIkqRcGTUmSJPXCoClJkqRe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\nszksyYkD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+ ""text/plain"": [ + """" + ] + }, + ""metadata"": { + ""image/png"": { + ""height"": 690, + ""width"": 333 + } + }, + ""output_type"": ""display_data"" + }, + { + ""name"": ""stdout"", + ""output_type"": ""stream"", + ""text"": [ + ""\n"", + ""\n"", + ""************************************************************************************\n"", + ""\n"", + ""QSAR/ER_alpha_KlekotaRothFingerprinter.csv\n"", + ""\n"", + ""from Remove useless descriptor\n"", + ""The initial set of 4860 descriptors has been reduced to 844 descriptors.\n"", + ""from Remove correlation\n"", + ""The initial set of 844 descriptors has been reduced to 452 descriptors.\n"", + ""\n"", + ""Training set\n"", + ""------------\n"", + ""N: 861\n"", + ""R2: 0.9518\n"", + ""std_R2: 0.0027\n"", + ""RMSE: 0.4623\n"", + ""std_RMSE: 0.0041\n"", + ""\n"", + ""Cross-validation set\n"", + ""------------\n"", + ""N: 861\n"", + ""Q2: 0.7119\n"", + ""std_Q2: 0.0074\n"", + ""RMSE: 0.7459\n"", + ""std_RMSE: 0.0060\n"", + ""\n"", + ""External set\n"", + ""------------\n"", + ""N: 370\n"", + ""Q2_EXt: 0.7289\n"", + ""std_Q2_EXt: 0.0137\n"", + ""RMSE: 0.5423\n"", + ""std_RMSE: 0.0164\n"" + ] + }, + { + ""data"": { + ""image/png"": 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LUmsgHXfg743Rfm+TF3ueIFzy8v2jTqFkoLoWecAevfPoCb77mwqU530sgAM8+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jEb+iKn7lx9DvDT9EofjQyJyBFmUUSTls9FHtmsTlsPkIjleLr3M+7X32exskgwliSkxPDwM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CyqfWQe0/u+eUQwuxig0IIF8xsTN5De89i45ZjZm+V9D5JWUn/utTzoE61tkoP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\nF0K4c5Jj/Z2k9ZIuDSGcaOR5A0C9EXIBoCCigHunpN+W9LikD4UQdo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+ ""text/plain"": [ + """" + ] + }, + ""metadata"": { + ""image/png"": { + ""height"": 328, + ""width"": 348 + } + }, + ""output_type"": ""display_data"" + }, + { + ""name"": ""stdout"", + ""output_type"": ""stream"", + ""text"": [ + ""('top10', ['KRFP1148', 'KRFP1642', 'KRFP3560', 'KRFP1193', 'KRFP433', 'KRFP4736', 'KRFP341', 'KRFP1645', 'KRFP677', 'KRFP2981'])\n"", + ""\n"", + ""('top20', ['KRFP1148', 'KRFP1642', 'KRFP3560', 'KRFP1193', 'KRFP433', 'KRFP4736', 'KRFP341', 'KRFP1645', 'KRFP677', 'KRFP2981', 'KRFP2242', 'KRFP4820', 'KRFP45', 'KRFP2856', 'KRFP2', 'KRFP2979', 'KRFP3681', 'KRFP344', 'KRFP2855', 'KRFP3788'])\n"", + ""\n"", + ""('top30', ['KRFP1148', 'KRFP1642', 'KRFP3560', 'KRFP1193', 'KRFP433', 'KRFP4736', 'KRFP341', 'KRFP1645', 'KRFP677', 'KRFP2981', 'KRFP2242', 'KRFP4820', 'KRFP45', 'KRFP2856', 'KRFP2', 'KRFP2979', 'KRFP3681', 'KRFP344', 'KRFP2855', 'KRFP3788', 'KRFP1452', 'KRFP2667', 'KRFP3682', 'KRFP2547', 'KRFP2712', 'KRFP2265', 'KRFP2548', 'KRFP3602', 'KRFP2986', 'KRFP4287'])\n"", + ""\n"", + ""('top40', ['KRFP1148', 'KRFP1642', 'KRFP3560', 'KRFP1193', 'KRFP433', 'KRFP4736', 'KRFP341', 'KRFP1645', 'KRFP677', 'KRFP2981', 'KRFP2242', 'KRFP4820', 'KRFP45', 'KRFP2856', 'KRFP2', 'KRFP2979', 'KRFP3681', 'KRFP344', 'KRFP2855', 'KRFP3788', 'KRFP1452', 'KRFP2667', 'KRFP3682', 'KRFP2547', 'KRFP2712', 'KRFP2265', 'KRFP2548', 'KRFP3602', 'KRFP2986', 'KRFP4287', 'KRFP2428', 'KRFP669', 'KRFP1637', 'KRFP2673', 'KRFP14', 'KRFP3550', 'KRFP3892', 'KRFP1564', 'KRFP4254', 'KRFP639'])\n"", + ""\n"", + ""('top50', ['KRFP1148', 'KRFP1642', 'KRFP3560', 'KRFP1193', 'KRFP433', 'KRFP4736', 'KRFP341', 'KRFP1645', 'KRFP677', 'KRFP2981', 'KRFP2242', 'KRFP4820', 'KRFP45', 'KRFP2856', 'KRFP2', 'KRFP2979', 'KRFP3681', 'KRFP344', 'KRFP2855', 'KRFP3788', 'KRFP1452', 'KRFP2667', 'KRFP3682', 'KRFP2547', 'KRFP2712', 'KRFP2265', 'KRFP2548', 'KRFP3602', 'KRFP2986', 'KRFP4287', 'KRFP2428', 'KRFP669', 'KRFP1637', 'KRFP2673', 'KRFP14', 'KRFP3550', 'KRFP3892', 'KRFP1564', 'KRFP4254', 'KRFP639', 'KRFP3640', 'KRFP4260', 'KRFP2414', 'KRFP3591', 'KRFP2260', 'KRFP346', 'KRFP665', 'KRFP582', 'KRFP3789', 'KRFP3402'])\n"" + ] + }, + { + ""data"": { + ""image/png"": 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AG4NXAuUn2rKprJmx/bf9moOPpNlx/fcv+tVRVVyS5AnhikicNbOo+5xn999z81FuBj85z\nuZV0wfw8uvB6t0fpSe5PF95fSjca+tqqumvzf4UkSdLSaL4Ze1Xd2b/ecT3wRrqgucfgpusLOAk4\nAjg4yR9V1aWt+9jQycCfAO9JcsDcyGL/ysoj+jqfAuhHO+/x2si+/ixdyPxYVf350LkH0G3W/mK6\nkPoGA6YkSVruluSNP/08wUOTrAcOZ+OI5oKBsapu6bdDOgF4F3DgUvRxlCSnDhzObevzniQ/7f/9\n51U1uLXSScAqumkCFyU5C/hXwG8BjwD+ZL63Fi3CKXQB83rg+8A7u5cF3c2aqlqzmfeRJElqZsne\nXQ5QVUckuYVuRfW5SV5YVd+eoOkpdFsAvSrJcVX1zaXs54DXjCjbf+DfaxjYv7OfHrAf3Sr5V9Pt\nv3kH8A3gg1X1yQZ92qH/3oZu+6P5rGlwL0mSpCY2O2QutJ1QVR0FHDVUtv0CbTYA2y72XkN1Z2Fx\nSzsXuTXSXJvbgD/uP5tkXF+ravdNva4kSdK0NH93uSRJkrSkj8sl6b5kWvtiLhczM6NexibpvsqR\nTEmSJDVnyJQkSVJzhkxJkiQ1Z8iUJElSc4ZMSZIkNWfIlCRJUnOGTEmSJDVnyJQkSVJzhkxJkiQ1\nZ8iUJElSc4ZMSZIkNWfIlCRJUnOGTEmSJDW3Ytod0PKw8jErWTuzdtrdkCRJ9xKOZEqSJKk5Q6Yk\nSZKaM2RKkiSpOUOmJEmSmjNkSpIkqTlDpiRJkpozZEqSJKk5Q6YkSZKaM2RKkiSpOUOmJEmSmjNk\nSpIkqTnfXS4ALrz2QrI60+6GpEWqmZp2FyRpJEcyJUmS1JwhU5IkSc0ZMiVJktScIVOSJEnNGTIl\nSZLUnCFTkiRJzRkyJUmS1JwhU5IkSc0ZMiVJktScIVOSJEnNGTIlSZLUnCFTkiRJzRkyJUmS1Jwh\nU5IkSc2tmHYHJOmX1SyzY4/vzVavXn2345mZmSn1RNJy5UimJEmSmjNkSpIkqTlDpiRJkprb7JCZ\npJLUPOd2THJ5X+fYgfJ1c+36z11JbkpyfpLDk9xv3L3GfA4eqDs74vz6JJck+WCS7Yau/YIkpyX5\nVpIbkmxIcmWSv0my5zz9OXWB/jxtzN9tzyR/leSHSW5N8oMkX0jy4nnqPy/J55P8uP8d3+z/VlvN\ndw9JkqRpWbKFP0l2Bj4PbAO8uapOHlHtROBGYCvg8cD+wAnAnsB+Yy6/ep7yi0aUnQOs6f+9DfAi\n4E3AK5M8t6ou78/t0X8uAM4Gft736SXAfkneXVXvmOe+c79j2PWjKif5I+CtwDXA3/T1fg3YGdid\n7u82WP+lwF8CG4BPAz+m+/ucADwf+O15+iVJkjQVSxIyk+wFfAa4P3BAVZ0xT9X3V9W6gXZH0wXF\nfZPsVlXnjGpUVbOL6M6awfr9KOmZdEH27cBr+1PHj7pukm2BC4G3JfnTqrp2od8xTpLX0wXMjwFv\nqKrbhs7fb+j4ocBHgDuB3avqa335O+jC8CuSHFBVn5rk/pIkSVtC8zmZSQ4EPgfcBawaEzDvoaou\noxt5BHhO677197gd+HB/uMtA+YZ56n8f+Ard3+qJm3PvJA8AjgGuYkTAHOjfoFfQjXJ+ai5gDvT3\n7f3hf9qcfkmSJLXWdCQzyWF0j3CvA/apqlGPryc1HLZaSv89ci7p3SomjwR+HbgV+N481fbpRxzv\nBC4Dzq6qm0fU24suML4fuCvJbwLPoHsM/k9V9dURbfbov/9+xLlzgVuA5yV5QFXdutDvkbR0prFP\n5vB+lZK0XDQLmUmOB44ELgX2rqorN+EaT6Wbkwhw3ph6syOK11XVqRPcYwXwhv7wghHnnw3sS/e3\n2Y5u7uPD6OaVjpxjCfzp0PFPk/z3qvrgUPnc6OwG4Ot0AXPw3ucCr6iqfxkofmr/fcnwTavqjiRX\nAk+nG2W9eJ7+zV1/7Tyn5l2gJEmStClajmQeSTf6uGoRAfPwJIMLf14OPBh4b1XNF4gARr1a4hzg\n1BHluw+E0q2BvYEn0y22OWZE/WcPXf+nwGur6rQRdc+lW6RzPvAj4LHAy/r2Jye5vao+PFD/kf33\nW4HvAL9BNwd1B+C9dIuSzmBj0IYu4ALcNOL+g+UPn+e8JEnSFtcyZH6BLsB9Ismqqhq12nrYYSPK\nZqtq7POfqsq480N26z8AtwFXA6cAx1bV1SOufQpwSpIH0oW/Q4GPJ3l+VR06VPd/DDW/Anhfku8B\nfwsck+SjVXVnf35uDuwdwEsGFgv9vyQvo3scv1uSfz/Po/PNUlU7jyrvRzhXtr6fJEm672oZMl8K\nnE635c/ZSfaqqhsWaLNDVa3rA90z6cLfTJIr5hk53BSrF7kaHfjFwpqLgcP6BTtvTPKlqvqLCdp+\nLsn3gW2BfwP8v/7UXPD++vBq9Kq6JckXgNfRLUiaC5lzI5UPY7S58klCvaQlNI05mTWz4NTyJeFc\nUEkLaba6vF908nK6oPksYE2SR03YdkNVnQ/sQ/d4+kNJHtuqbw2c2X/vvog2c/Mqf3WgbG7h0HyB\n8Cf994NGtHnKcOV+fukOdCOjVyyib5IkSUuq6RZGVXUHcBDwcbpFLecOv1lngfbXAsfSBbPl9H+T\nt+2/75ikcpKH0S2mKWBwfupZfdm/STLqbz+3EGiwzdn996oR9V9AN4f1K64slyRJy0nzfTL7+YcH\nA39GN/p2bpLtF3GJk+i2QDo4yZNb928+SXaZp/xJwNv6w78bKH/0qACd5CF0C5AeCHypqq6bO1dV\n/0w3V/PxDM1HTfIiujmtN3L37Yr+gm6R0gH9yve5+g8E3t0ffmiiHylJkrSFLMkbf6qqgEOTrAcO\npwuae1bVpRO0vaXfDukE4F3AgUvRxxH+IcmP6LYWuprub/MkuhHEFcBJVfXFgfpPA76U5Kt02wv9\niG7Ecy/g0XSPrw8ZcZ/fp5tO8Cf9Pplfp3vk/Vt0+2weUlW/WEleVTf3bwn6C7opCJ+ie63kS+i2\nN/oLuldNSpIkLRvNRzIHVdURdI+/H0cXNJ8+YdNTgB8Ar0ryb5eqf0PeSRcWn0v3bvP/BPw74LN0\n2zL9wVD9y4GP0j3afwnwFrrFT1fTvYnnmVV11fBNquoauneUn0y3ldJhdHM9/xZ4flX95Yg2n6Vb\nIX8u3bzXN9NtF/WHdK/tnM7Mf0mSpHls9kjmQtsJVdVRwFFDZdsv0GYDG+dBTnyvobqzMPlSz6r6\nAPCBRdS/GnjjpPWH2v4LXVB88yLa/B/gxZtyP0mSpC1tSUcyJUmSdN+0JHMyJem+YBr7Yi4XMzOj\nXrwmSRs5kilJkqTmDJmSJElqzpApSZKk5gyZkiRJas6QKUmSpOYMmZIkSWrOkClJkqTmDJmSJElq\nzpApSZKk5gyZkiRJas6QKUmSpOYMmZIkSWrOkClJkqTmVky7A1oeVj5mJWtn1k67G5Ik6V7CkUxJ\nkiQ1Z8iUJElSc4ZMSZIkNWfIlCRJUnOGTEmSJDVnyJQkSVJzhkxJkiQ1Z8iUJElSc4ZMSZIkNWfI\nlCRJUnOGTEmSJDVnyJQkSVJzK6bdAS0PF157IVmdaXdD0oCaqWl3QZI2mSOZkiRJas6QKUmSpOYM\nmZIkSWrOkClJkqTmDJmSJElqzpApSZKk5gyZkiRJas6QKUmSpOYMmZIkSWrOkClJkqTmDJmSJElq\nzpApSZKk5gyZkiRJam7FtDsgScvVLLNjj+9tVq9efbfjmZmZKfVE0r2BI5mSJElqzpApSZKk5gyZ\nkiRJam6zQ2aSSlLznNsxyeV9nWMHytfNtes/dyW5Kcn5SQ5Pcr9x9xrzOXig7uyI8+uTXJLkg0m2\nG7r2dkmOSnJGksv6PlWSHcf89l2SHJfkzCQ/7Otfs8DfK0len+SCJD9L8vMkX0tyaJJ7/PdI8rgk\nf9rX/2GSW5P8IMmXk7x2vr+VJEnSNC3Zwp8kOwOfB7YB3lxVJ4+odiJwI7AV8Hhgf+AEYE9gvzGX\nXz1P+UUjys4B1vT/3gZ4EfAm4JVJnltVl/fnng28GyjgSuAm4OFj+gBwEHAYcDvwHeBRC9QH+F99\nux8BnwRuAfYCPgQ8D3j1UP0nAb8DXAB8FvgxsDWwD/A/gN9L8qKqumOCe0uSJG0RSxIyk+wFfAa4\nP3BAVZ0xT9X3V9W6gXZH0wXFfZPsVlXnjGpUVbOL6M6awfr9yN+ZdEH27cBr+1NfA14AfKOqbk6y\nBthtgWufCnwM+HZV3TbfiO7AvV9GFzCvBHapquv78vsDf0kXGD9bVZ8ZaPYV4BFVddfQte4H/APw\nH+jC+ekL9FWSJGmLaT4nM8mBwOeAu4BVYwLmPVTVZXQjjwDPad23/h63Ax/uD3cZKL+mqr5cVTcv\n4loXVdXXq+q2CZu8rP9+31zA7K9zG/CO/vA/D93jtuGAOfA7PtsfPnnSPkuSJG0JTUcykxxG97j7\nOmCfqhr1+HpSt7fp1Ujpv8eOPC6BR/ffV4w4N1f2G0nuv1BwTbIV8OL+8JuN+idpjC29T+bwvpWS\n9MukWchMcjxwJHApsHdVXbkJ13gqsHt/eN6YerMjitdV1akT3GMF8Ib+8ILF9XCzzY1e7jDi3BP7\n7xX9v787eDLJNnSjnAF+jW4e547AJ6rqbye5eZK185x62iTtJUmSJtVyJPNIutHHVYsImIcnGVz4\n83LgwcB7q2q+QAQw6jUU59DNkRy2+0Ao3RrYm+7x8vXAMRP2s5W/Aw4E/jDJp6rqx/CL+ZWDQxaP\nGNF2G+7+uwt4L/C2JeqrJEnSJmsZMr9AF+A+kWRVVd04QZvDRpTNVtXYZ0RVlXHnh+zGxgU8twFX\nA6cAx1bV1Yu4TgufAn6P7u/0nSR/DWwAXgg8BriKLmyPmoP5XbodkLYCtqWb3/kuYNckvzkXWMep\nqp1HlfcjnCs36RdJkiSN0DJkvpRuhfNLgLOT7FVVNyzQZoeqWpfkgcAz6cLfTJIrquq0Rv1avcjV\n6Eumqu5Msh/wh8DvAq+hC5lr6EZx/6Kv+qNx16ALoycmuY5uG6R3MbRgSFJ7W3pOZs1s2WnjzgGV\n1FKz1eVVdStdUDodeBawJskk+0ZSVRuq6ny6vR9/CnwoyWNb9W05qarbq+o9VfX/VdUDq+rhVfVb\nwDr6x/iLmG5wZv+9+xJ0VZIkaZM13cKo3xD8IODjwDOAc4ffrLNA+2uBY4FfZf4N1++tDqDbV/ST\ni2izbf/tRuySJGlZab5PZv8492Dgz4Cn0AXN7RdxiZPotkA6OMm9bv/HJA8dUfZM4I+BnwDHD51b\n2c/DHG7zELo3JkG3oEiSJGnZWJI3/lRVAYcmWQ8cThc096yqSydoe0u/HdIJdHMND1yKPo6S5NSB\nw7ltfd6T5Kf9v/+8qs4bqP804L8NXeYRQ9d5y+DG68AX+7/Lt+imBuwE/CawHtivqn4wdL13As9P\n8hW6uZi3AI+jm1rwcLo3Ah23mN8pSZK01Jbs3eUAVXVEklvottk5N8kLq+rbEzQ9BXgr8Kokx1XV\nltps/DUjyvYf+Pca7r5/56NHtHnwUNksG/fHhG5xzwF0C38eBHyf7g1Ex1XVNSPu/xHgZ3RvJ9q9\nv/5PgLV081//h+8tlyRJy81mh8yFthOqqqOAo4bKtl+gzQY2zjec+F5DdWdhcUtBF7k1ElW1ho1v\nD5q0zR/TPRqftP7f4eNwSZL0S6b5nExJkiRpSR+XS9Ivsy29L+a0zcyMepmaJG0aRzIlSZLUnCFT\nkiRJzRkyJUmS1JwhU5IkSc0ZMiVJktScIVOSJEnNGTIlSZLUnCFTkiRJzRkyJUmS1JwhU5IkSc0Z\nMiVJktScIVOSJEnNGTIlSZLU3Ippd0DLw8rHrGTtzNppd0OSJN1LOJIpSZKk5gyZkiRJas6QKUmS\npOYMmZIkSWrOkClJkqTmDJmSJElqzpApSZKk5gyZkiRJas6QKUmSpOYMmZIkSWrOkClJkqTmDJmS\nJElqbsW0O6Dl4cJrLySrM+1uSPcKNVPT7oIkTZ0jmZIkSWrOkClJkqTmDJmSJElqzpApSZKk5gyZ\nkiRJas6QKUmSpOYMmZIkSWrOkClJkqTmDJmSJElqzpApSZKk5gyZkiRJas6QKUmSpOYMmZIkSWpu\nxbQ7IEnfE7IoAAAgAElEQVStzTI79vjeZvXq1Xc7npmZmVJPJGkjRzIlSZLUnCFTkiRJzRkyJUmS\n1NxUQmaSSlLznNsxyeV9nWMHytfNtes/dyW5Kcn5SQ5Pcr9x9xrzOXig7uyI8+uTXJLkg0m2W+B3\nPSDJt/p218xT5z8m+WySy5LcnOTnSS5O8pEkT52nzXuSnJXk6r4/P07y9SQzSbYe1ydJkqRpWFYL\nf5LsDHwe2AZ4c1WdPKLaicCNwFbA44H9gROAPYH9xlx+9TzlF40oOwdY0/97G+BFwJuAVyZ5blVd\nPs+1jgWeMKYPAL8LPAa4APghcBfwdOC1wKuT/FZVnTnU5gjgQuCLwI+AXwWeC8wCb+j7dPUC95Uk\nSdpilk3ITLIX8Bng/sABVXXGPFXfX1XrBtodTRcU902yW1WdM6pRVc0uojtrBuv3o6Rn0gXZt9MF\nwuH+704XBt8EfGjMtV9cVRtGtN8L+Afgff29Bj10njbHAG8D/nt/X0mSpGVhWczJTHIg8Dm6Ub1V\nYwLmPVTVZXQjjwDPWYLuUVW3Ax/uD3cZPp/kocCpwFlVdcoC17pHWOzLv0g3QrvjpG2A0/vvJ4+7\npyRJ0pY29ZHMJIfRPe6+DtinqkY9vp7U7W16NVL671FzST8APAJ43SZfPNkVeDjdY/FJzU0P+Oam\n3le6L9jS+2QO71spSfdFUw2ZSY4HjgQuBfauqis34RpPBXbvD88bU292RPG6qjp1gnusAN7QH14w\ndO5lwGuAQ6rqqoV7/It2rwCeATwIeArwYuDHwH8e0+YtwEOAhwHPBnalC5jHT3jPtfOcetqk/ZYk\nSZrEtEcyj6QbfVy1iIB5eJLBhT8vBx4MvLeq5gtRAKNegXEO3WPuYbsPhNKtgb3pHklfDxwzVynJ\no+geo59ZVR+dsP9zXgG8auD4UuCgqvramDZvAR41cPz3wMFV9S+LvLckSdKSmnbI/AJdgPtEklVV\ndeMEbQ4bUTZbVWOfT1VVxp0fslv/AbgNuBo4BTh2aBX3R+j+hocs4tpz/TkAOKCfz/kMuhD8f5K8\ncb7R1ap6NPwi3D6PbgTz60n2raoFH7NX1c6jyvsRzpWL/Q2SJEnzmXbIfCnd4pWXAGcn2auqblig\nzQ5VtS7JA4Fn0oW/mSRXVNVpjfq1eqHV6EleTTcn8jVV9YNNvVFV3Qx8Jcl+wNeADyX5UlWN3Gez\nb3Md8FdJLgQuAT5OF1QljbCl52TWzMhtgJeMc0AlLUdTXV1eVbfSPe4+HXgWsKYfpZuk7YaqOh/Y\nB/gpXTh77JJ19p7mRv4+NryBe1++7UDZwxe6WFXdBpwFPJBuD8wFVdU/A98Bnp5km034DZIkSUti\n2iOZVNUdSQ4CNgCvBs5Nsue4kbyh9tf2bwY6nm7D9dcvXW/v5qt0i3BGeR1wC/DJ/vjWCa+5bf99\nxyL6MRes71xEG0mSpCU19ZAJUFV39q93XA+8kS5o7jG46foCTqLbCP3gJH9UVZcuTU83qqpPA58e\ndS7J64CfVNUhQ+VbAw+rqitGtNkXeBnwMzbu+0mSpwDXVdVNQ/V/BTgaeCTwlar6yeb9IkmSpHaW\nRcgEqKoCDk2yHjicjSOaCwbGqrql3w7pBOBdwIFL29tN9jhgbZKvAd8Dvk+3N+Yz6R6R3063FdJg\nYHwxcFyS84ArgRvoVpjvBjyR7tWUW2r0VpIkaSLLJmTOqaojktxC97rEc5O8sKq+PUHTU4C3Aq9K\nclxVLccNyv8ZOI4uIO5Ftz3S7cBVwJ8BJ1bVxUNtvkT3FqBd6eatPhz4Od2Cn9OAD1TVj7dI7yVJ\nkiY0lZC50HZCVXUUcNRQ2fYLtNnAxjmNE99rqO4sbP4y1Pnu2Y9Qvn2R1/oWYzZolyRJWo6WxbvL\nJUmSdO+y7B6XS9Lm2tL7Yk7bzMyoF5pJ0nQ5kilJkqTmDJmSJElqzpApSZKk5gyZkiRJas6QKUmS\npOYMmZIkSWrOkClJkqTmDJmSJElqzpApSZKk5gyZkiRJas6QKUmSpOYMmZIkSWrOkClJkqTmDJmS\nJElqbsW0O6DlYeVjVrJ2Zu20uyFJku4lHMmUJElSc4ZMSZIkNWfIlCRJUnOGTEmSJDVnyJQkSVJz\nhkxJkiQ1Z8iUJElSc4ZMSZIkNWfIlCRJUnOGTEmSJDVnyJQkSVJzvrtcAFx47YVkdabdDemXQs3U\ntLsgScueI5mSJElqzpApSZKk5gyZkiRJas6QKUmSpOYMmZIkSWrOkClJkqTmDJmSJElqzpApSZKk\n5gyZkiRJas6QKUmSpOYMmZIkSWrOkClJkqTmDJmSJElqbsW0OyBJm2qW2bHH9warV6++2/HMzMyU\neiJJi+NIpiRJkpozZEqSJKk5Q6YkSZKa2+yQmaSS1DzndkxyeV/n2IHydXPt+s9dSW5Kcn6Sw5Pc\nb9y9xnwOHqg7O+L8+iSXJPlgku0W+F0PSPKtvt01I85vP0F/KslvDLV7T5Kzklzd9+fHSb6eZCbJ\n1mP6s1WSQ5Kcm+Qnfdsrknw6yVPG/RZJkqQtbckW/iTZGfg8sA3w5qo6eUS1E4Ebga2AxwP7AycA\newL7jbn86nnKLxpRdg6wpv/3NsCLgDcBr0zy3Kq6fJ5rHQs8YUwfbhzTj8cB/xG4AfinoXNHABcC\nXwR+BPwq8FxgFnhD36erBxskeQjw18AedL/xY8AGYFvgN4CnAJeM6askSdIWtSQhM8lewGeA+wMH\nVNUZ81R9f1WtG2h3NF2I2jfJblV1zqhGVTW7iO6sGazfj5KeSRdk3w68dkT/d6cLg28CPjRPH26E\n0UtZkxzX//PjVXXr0OmHVtWGEW2OAd4G/Pf+voP+jC5gHlpVfzai7ciRX0mSpGlpPiczyYHA54C7\ngFVjAuY9VNVldCOPAM9p3bf+HrcDH+4Pdxk+n+ShwKnAWVV1ymKv3we+g/vDDw+fHxUwe6f3308e\nut5K4CDg06MCZn/N2xfbT0mSpKXUdCQzyWF0j7uvA/apqlGPrye1lMEp/feouaQfAB4BvG4Tr/0S\n4NHAuVX13UW0m5se8M2h8oP6708meVhf73F0j+LP7oO5JLbcPpnDe1dKku6pWchMcjxwJHApsHdV\nXbkJ13gqsHt/eN6YerMjitdV1akT3GMF8Ib+8IKhcy8DXgMcUlVXLdzjkeauPXLUceBebwEeAjwM\neDawK13APH6o6tyI7hOAy4HBxUGV5EPAH1TVnQt1LMnaeU49baG2kiRJi9FyJPNIutHHVYsImIcn\nGVz483LgwcB7q2q+QAQw6pUX59A95h62+0Ao3RrYm+6R9PXAMXOVkjyK7vH2mVX10Qn7fzdJtgf2\nohtl/MsFqr8FeNTA8d8DB1fVvwzVe2T//SfAZ+nmkV4D/DpwCt38zX9hnvmhkiRJ09AyZH6BLsB9\nIsmqfmHMQg4bUTZbVWOfRVVVxp0fslv/AbgNuJounB07tIr7I3R/j0MWce1hr6d7FP+xEQt+7qaq\nHg2/CLfPoxvB/HqSfavqwoGqc/Nmvwu8amDE8qwkr6Bbqf6HSY6tqtsWuOfOo8r7Ec6V43+aJEnS\n5FqGzJfSLV55CXB2kr2q6oYF2uxQVeuSPBB4Jl34m0lyRVWd1qhfqxdajZ7k1XRzHV9TVT/YlJv0\nj+HnVqrfY8HPfKrqOuCvklxItw3Rx4FnDFSZC+t/O/xIvKq+keRK4EnATsA3NqXv0r3FlpqTWTMj\ntwZeEs7/lPTLqtnq8n7k7uV0QfNZwJp+lG6Sthuq6nxgH+CnwIeSPLZV3yYwN4r3seHN1PvybQfK\nHj7PNfYDHgOcU1XfW2wHquqfge8AT0+yzcCpuWvNNzL8k/77QYu9pyRJ0lJpurq8qu5IchDdRuGv\nBs5NsmdV3eONOfO0v7Z/M9DxdBudv75l/8b4Kt0inFFeB9wCfLI/nu8x+NyCn4lHMUeYC9aDI5Zf\nAn6Pu49uAt1bidi45dG6zbivJElSU803Y6+qO/vXO64H3kgXNPcY3HR9ASfRbYR+cJI/qqpLW/dx\nWFV9Gvj0qHNJXgf8pKrmnauZ5Al0bxIau+Cnf/3jdVV101D5rwBH0y3y+UpV/WTg9F8CxwGvSnJS\nVQ2+QegddKvT/7GqfjjmJ0qSJG1RS/LGn6oq4NAk64HD2TiiuWBgrKpb+u2QTgDeBRy4FH1s7BC6\nqQcLLfh5MXBckvOAK+lC6aPoFiY9EfghQ6O3VfXzPrR/Dvhyks8A36dbXb4r3asp39j010iSJG2m\n5m/8GVRVR9C9A/xxdEHz6RM2PQX4Ad3o3b9dqv61kGQruveUw8KPyr8EfBT4Nbr3tL+Vbh7rj+mm\nBzy9qr4z3Kiqvkj3dqK/BV4I/AHdvpmnAM/aEqO9kiRJi7HZI5kLbSdUVUcBRw2Vbb9Amw3Atou9\n11DdWRrsHTnB77uTEX2dp+63gP+8if34BvCKTWkrSZK0pS3pSKYkSZLum5ZkTqYkbQlbal/MaZqZ\nGfWCM0la/hzJlCRJUnOGTEmSJDVnyJQkSVJzhkxJkiQ1Z8iUJElSc4ZMSZIkNWfIlCRJUnOGTEmS\nJDVnyJQkSVJzhkxJkiQ1Z8iUJElSc4ZMSZIkNWfIlCRJUnOGTEmSJDW3Ytod0PKw8jErWTuzdtrd\nkCRJ9xKOZEqSJKk5Q6YkSZKaM2RKkiSpOUOmJEmSmjNkSpIkqTlDpiRJkpozZEqSJKk5Q6YkSZKa\nM2RKkiSpOUOmJEmSmjNkSpIkqTnfXS4ALrz2QrI60+6GtKRqpqbdBUm6z3AkU5IkSc0ZMiVJktSc\nIVOSJEnNGTIlSZLUnCFTkiRJzRkyJUmS1JwhU5IkSc0ZMiVJktScIVOSJEnNGTIlSZLUnCFTkiRJ\nzRkyJUmS1JwhU5IkSc2tmHYHJN13zTI79viXzerVq+92PDMzM6WeSNL0OZIpSZKk5gyZkiRJas6Q\nKUmSpOamEjKTVJKa59yOSS7v6xw7UL5url3/uSvJTUnOT3J4kvuNu9eYz8EDdWdHnF+f5JIkH0yy\n3dC1X5DktCTfSnJDkg1JrkzyN0n2nPBv8ZQkP+/v9b/mqfOKJCcl+XKSm8fVlSRJWg6W1cKfJDsD\nnwe2Ad5cVSePqHYicCOwFfB4YH/gBGBPYL8xl189T/lFI8rOAdb0/94GeBHwJuCVSZ5bVZf35/bo\nPxcAZwM/7/v0EmC/JO+uqnfM16EkK4DTgLvG9Bvg7cC/A34GXAM8bYH6kiRJU7VsQmaSvYDPAPcH\nDqiqM+ap+v6qWjfQ7mi6oLhvkt2q6pxRjapqdhHdWTNYvx8lPZMuyL4deG1/6vhR102yLXAh8LYk\nf1pV185zn7cBzwTeShee53MEXbi8DNgN+MdF/BZJkqQtblnMyUxyIPA5uhG9VWMC5j1U1WV0I48A\nz1mC7lFVtwMf7g93GSjfME/97wNfofv7PnFUnSTPBt4BHA18c4H7/2NVXVpVI6cYSJIkLTdTH8lM\nchjd4+7rgH2qatTj60nd3qZXI6X/XjDoJXkk8OvArcD3Rpx/EN1j8ouA44Fd23VT+uW11PtkDu9j\nKUlaOlMNmUmOB44ELgX2rqorN+EaTwV27w/PG1NvdkTxuqo6dYJ7rADe0B9eMOL8s4F96f6e29HN\nDX0Y3bzS60dc8nhgB2BlVd2RZESV9pKsneeUczwlSVJT0x7JPJJu9HHVIgLm4UkGF/68HHgw8N6q\nmi9EAYx69cY5wKkjyncfCKVbA3sDTwauB44ZUf/ZQ9f/KfDaqjptuGK/6vzNwH+rqu+M6a8kSdIv\nrWmHzC/QBbhPJFlVVTdO0OawEWWzVTX2OVhVLWa4cLf+A3AbcDVwCnBsVV094tqnAKckeSDdCOWh\nwMeTPL+qDp2rl+ThdKH2AuB9i+hPE1W186jyfoRz5RbujiRJuhebdsh8KXA63ZY/ZyfZq6puWKDN\nDlW1rg90z6QLfzNJrhg1criJVi9yNTrwi4VAFwOHJXkA8MYkX6qqv+ir/AndyOgLq+rORn2V7jWW\nek5mzSzt2jnnfErSRlNdXV5Vt9I97j4deBawJsmjJmy7oarOB/ahezz9oSSPXbLOLt6Z/ffuA2Ur\ngQcB3x3c8J2NWxL9Tl+2OYufJEmSpm7aI5n0C18OAjYArwbOTbJnVV0zYftr+zcDHU+34frrl663\ni7Jt/33HQNlngK+NqPsY4MXA5XSbwF+1pD2TJElaYlMPmQBVdWf/esf1wBvpguYeg5uuL+Akug3L\nD07yR1V16dL09O6S7FJV/zSi/El0G60D/N1ceVW9a57r7E4XMs+vqkOWoKuSJElb1LIImQD9RuOH\nJlkPHM7GEc0FA2NV3dJvh3QC8C7gwKXt7S/8Q5IfAV+nWxy0AngSsKr/90lV9cXNvUmS3wJ+qz98\ndP/975Oc2v/7+qp6y+beR5IkqZVlEzLnVNURSW6hGwk8N8kLq+rbEzQ9he71jK9KclxVjX2LTiPv\npHuv+XPp9sbcim5T+c8Cf15VX2h0n2cCrxkqeyIb3yb0z4AhU5IkLRtTCZkLbSdUVUcBRw2Vbb9A\nmw1snAc58b2G6s7C5Mtbq+oDwAcmrT/mOmvY+Eahze6XJEnStC2Ld5dLkiTp3mXZPS6XdN+x1Pti\nbmkzM6NeLCZJ902OZEqSJKk5Q6YkSZKaM2RKkiSpOUOmJEmSmjNkSpIkqTlDpiRJkpozZEqSJKk5\nQ6YkSZKaM2RKkiSpOUOmJEmSmjNkSpIkqTlDpiRJkpozZEqSJKk5Q6YkSZKaWzHtDmh5WPmYlayd\nWTvtbkiSpHsJRzIlSZLUnCFTkiRJzRkyJUmS1JwhU5IkSc0ZMiVJktScIVOSJEnNGTIlSZLUnCFT\nkiRJzRkyJUmS1JwhU5IkSc0ZMiVJktSc7y4XABdeeyFZnWl3Q2quZmraXZCk+yRHMiVJktScIVOS\nJEnNGTIlSZLUnCFTkiRJzRkyJUmS1JwhU5IkSc0ZMiVJktScIVOSJEnNGTIlSZLUnCFTkiRJzRky\nJUmS1JwhU5IkSc0ZMiVJktTciml3QNJ9zyyzY49/WaxevfpuxzMzM1PqiSQtP45kSpIkqTlDpiRJ\nkpozZEqSJKm5zQ6ZSSpJzXNuxySX93WOHShfN9eu/9yV5KYk5yc5PMn9xt1rzOfggbqzI86vT3JJ\nkg8m2W7o2tslOSrJGUku6/tUSXac4G+wY5KPJLkyyYYk1/e/5b9M0PZ3B/p3yIjz2y/wmz+10D0k\nSZK2tCVb+JNkZ+DzwDbAm6vq5BHVTgRuBLYCHg/sD5wA7AnsN+byq+cpv2hE2TnAmv7f2wAvAt4E\nvDLJc6vq8v7cs4F3AwVcCdwEPHxMHwBIsj/wCeB24HN924cBT+1/z/vGtH0ccDLwM+AhC9zqG8Bn\nR5R/a6E+SpIkbWlLEjKT7AV8Brg/cEBVnTFP1fdX1bqBdkfTBcV9k+xWVeeMalRVs4vozprB+v0o\n6Zl0QfbtwGv7U18DXgB8o6puTrIG2G3chZM8gy5gfgd4cVX9cOj8yBHZ/lyA/wncQPe3essCv+Oi\nRf5uSZKkqWk+JzPJgXQjencBq8YEzHuoqsvoRh4BntO6b/09bgc+3B/uMlB+TVV9uapuXsTljqUL\n0r8zHDAH7jWfPwD2oAu5P1/EPSVJkpa9piOZSQ6je9x9HbBPVY16fD2pcQFtc6X/HjmXdKILJA8F\nfpNu5PPiJLsAu9I9+r8Y+Iequm2etjsBxwMnVtW5SfaY4JaPTfJGYGu60c+vVtU3N7X/0nKylPtk\nDu9lKUnaMpqFzCTHA0cClwJ7V9WVm3CNpwK794fnjak3O6J4XVWdOsE9VgBv6A8vWFwP72ZnupHg\ndUlOB3576PxVSV5RVf93xP1PA64C3raI++3VfwavtQZ4TVVdNckFkqyd59TTFtEPSZKkBbUcyTyS\nbvRx1SIC5uFJBhf+vBx4MPDeqpovEAGMeq3GOcCpI8p3HwilWwN7A08GrgeOmbCfozyy/96PbpHQ\nQcDfAw8Ffh94K/D5JDtV1fUD7d4JPAvYtarWT3CfW4Cj6Rb9XNGX/VtgFvgPwFlJnllVPnKXJEnL\nRsuQ+QW6APeJJKuq6sYJ2hw2omy2qsY+36qqjDs/ZDc2LuC5Dbga/n/27jXcrqq++/73X4IgSq0F\nVEQlqSDYYh9NKqJoiWIgUQGByumxGASVW6sEbUsL2r033ITDbQsKCCLUKPWEBQ9FKK1CErklqOHB\nY5VDSAVJqaAgNQkE+D8vxtyyWFlr7bWTsbJ3ku/nutY1s8YcY86xNm9+jDnGmFwEzM/Mu8ZxnXaj\n81m3AN6TmaNbCf0K+OuIeCFldfk7gDMAIuIVlNHLv8/MG/u5SWb+NyWYtlocEftRRntfARxHWak/\n1rVmdCpvRjin99MfSZKkftQMmQcBlwMHAtdFxKzMvH+MNtMyc3lEbA28lBL+hiJiWWZeVqlfIwNa\nlT0aohP4SofzX6KEzD3ht4/JPw3cCnxofW+emY9GxCWUkPmn9BEypclqkHMyc2idp16PyfmektRd\ntdXlmfkw5XH35ZTHwQsj4tl9tl2dmUuAOcBDwIUR8dxafRuQnzbH1V0ee/+qOT61OT4deBHwYmB1\n64bqPPH4/xNN2bl99uEXzfFp4+y7JEnSQFVdXd6Mrh0FrAaOpjzW3Tcz7+6z/YrmzUBnUjZcf0fN\n/tWUmcsiYhnwBxHxwpZN3Uft0RxH56c+DFza5XLTKcH8Bkp47etROrBXc1zWs5YkSdIGVn2fzMx8\nDJgLfJwycrc4IqaO4xLnUbZAmhsRu9buX2WjbzE6q3kcDpRXVAInNl8/D5CZqzLzuE4f4KtN3U81\nZV9oudb0iFjrv1NE7Ntyj3+q/LskSZLWy0De+JOZCRwfEauAeTwxonlbH21XNtshnQOcChw5iD52\nEhELWr6ObutzVkQ81Pz7ksxs3VrpPGA2ZZrALRHxDWBb4M3AM4F/6PbWonH4B2DXiPgWMDoi/MeU\njdwBPpSZ31rPe0iSJFU1sHeXA2TmiRGxkrKienFEvD4zf9RH04soWwAdHhFnbMBNx9/WoeyQln8v\npGX/zmZ6wAGUVfJHU/bffJTynvELMvNzFfp0GXAw5Q1Ic4AtKSO9lwPnZ+Y3K9xDkiSpqvUOmWNt\nJ5SZpwCntJVNHaPNamCn8d6rre4wjG/J6ji3Rhpt8wjwf5rPOunV18y8lO5zOSVJkial6nMyJUmS\npIE+LpekTga5L+aGNDTU6eVjkiRwJFOSJEkDYMiUJElSdYZMSZIkVWfIlCRJUnWGTEmSJFVnyJQk\nSVJ1hkxJkiRVZ8iUJElSdYZMSZIkVWfIlCRJUnWGTEmSJFVnyJQkSVJ1hkxJkiRVZ8iUJElSdVMm\nugOaHKbvOJ2lQ0snuhuSJGkT4UimJEmSqjNkSpIkqTpDpiRJkqozZEqSJKk6Q6YkSZKqM2RKkiSp\nOkOmJEmSqjNkSpIkqTpDpiRJkqozZEqSJKk6Q6YkSZKq893lAuDmFTcTIzHR3ZB6yqGc6C5Ikvrk\nSKYkSZKqM2RKkiSpOkOmJEmSqjNkSpIkqTpDpiRJkqozZEqSJKk6Q6YkSZKqM2RKkiSpOkOmJEmS\nqjNkSpIkqTpDpiRJkqozZEqSJKk6Q6YkSZKqmzLRHZC0aRhmuOf3yWpkZORJ34eGhiaoJ5K0aXEk\nU5IkSdUZMiVJklSdIVOSJEnVTUjIjIiMiOxybpeIuKOpM7+lfPlou+bzeEQ8GBFLImJeRGzZ6149\nPnNb6g53OL8qIm6NiAsi4nld7vGMiDg1Ir4fEf8TEb+OiB9GxMdb+xURU/voT0bEa1razO2j/mPr\n8J9BkiRpYCbVwp+ImAFcDWwPvDczz+9Q7SPAA8AWwAuAQ4BzgH2BA3pcfqRL+S0dyhYBC5t/bw/s\nB7wbOCwi9srMO1r6vDvwb8BOwNeBa4AtganAYcAHgDVN9Qd69OP5wNuB+4Fvt/WvW5vXAK9r7ilJ\nkjRpTJqQGRGzgCuBpwBHZOYXu1Q9NzOXt7Q7jRLE3hQR+2Tmok6NMnN4HN1Z2Fq/GY28hhJkPwgc\n05RvA3wV2BbYOzOXtP2mKcBvRxkz8wHovOQ2Is5o/vnpzHy4pc0tdA7CRMSNzT8v7v+nSZIkDd6k\nmJMZEUcCVwGPA7N7BMy1ZObtlJFHgJcPoHtk5hqeCHJ7tpw6HtgV+Nv2gNm0ezQzO04LaNWE2LnN\n174CY0S8BNgL+DnwtX7aSJIkbSgTPpIZESdQHnffC8xpRu7W1Zqxq6yzaI6tofGo5vvnI2IqMAf4\nPeBnwL9m5v19XvtA4DnA4sz8SZ9t3tkcL81M52Rq0hnEPpnte1pKkiavCQ2ZEXEmcBJwG7B/Zt65\nDtfYDZjZfL2hR73hDsXLM3NBH/eYwhOh7qambEvg/wF+AbwDmM+T/56/iYj3ZeY/jnX9lmt/vI+6\nRMRTgbdSHsVf0k+bpt3SLqd27/cakiRJ/ZjokcyTKKOPs8cRMOdFROvCn0OBbYAPZ2a3EAXQ6TUe\ni4AFHcpntoTS7YD9KY/F7wNOb8p/n/L32w44AzgV+EdgFfBm4FzgkohYnpnXdetUMwI6i7Lg54oe\n/W91GGXE9GuZeVefbSRJkjaYiQ6Z11IC3GcjYnazMGYsJ3QoG87Mns/RMjN6nW+zT/MBeAS4C7gI\nmN8S6kbns24BfDwzT21pf2mzKOijlCDdNWRSRkED+FTrgp8xjGvkc1RmzuhU3oxwTh/PtSRJknqZ\n6JB5EHA5ZU7idRExq495jNMyc3lEbA28lBL+hiJiWWZeVqlfI32sRn+w5d9f6nD+S5SQuWeHc8Bv\nH8Mf03ztd8HPHwGvAu6mbPckTUqDmJOZQ2Ouoxs353lK0mBM6OryZuTuUErQfBmwMCKe3Wfb1c2K\n7jnAQ8CFEfHcgXV27fuvpIxwQtn/st2vmuNTe1zmAGBHYFFm/rTPW7vgR5IkTXoTvoVRZj5KWaX9\naZ7cOxYAACAASURBVGAPYHG3N+t0ab+CsujmaXTftHxQvt4c9+hwbrSs11zT0cDY7yjm1sCfUxb8\nXNpPG0mSpIkw4SEToBmRm0uZY/giStCcOo5LnEfZAmluROxau389XEDZ2/NvImKH0cImDI4uEPpc\np4YRsTPlTULjWfDzFuCZwDUu+JEkSZPZpAiZAFkcT1mVPY0SNPsKjM2j6zMpc0xPHaN6Nc1q9hFg\nF+CHEfGJiDgP+D7l7UDfAs7u0vw4yt9/XRb8+IYfSZI0qU2akDkqM0+kPP5+PiVo/lGfTS8C7gEO\nj4g/HlT/2jWryg8FfgocTlktvoby+snXZebq9jYRsQXlPeXQ/6PyFwOvxgU/kiRpIzAhq8vH2k4o\nM08BTmkrmzpGm9XATuO9V1vdYbq8W3yMdldS3rveb/3H6NDXMdr8B0+8dUiSJGlSm3QjmZIkSdr4\nTfQ+mZI2EYPYF3NDGBrq9DIwSdL6ciRTkiRJ1RkyJUmSVJ0hU5IkSdUZMiVJklSdIVOSJEnVGTIl\nSZJUnSFTkiRJ1RkyJUmSVJ0hU5IkSdUZMiVJklSdIVOSJEnVGTIlSZJUnSFTkiRJ1RkyJUmSVN2U\nie6AJofpO05n6dDSie6GJEnaRDiSKUmSpOoMmZIkSarOkClJkqTqDJmSJEmqzpApSZKk6gyZkiRJ\nqs6QKUmSpOoMmZIkSarOkClJkqTqDJmSJEmqzpApSZKk6nx3uQC4ecXNxEhMdDe0mcqhnOguSJIq\ncyRTkiRJ1RkyJUmSVJ0hU5IkSdUZMiVJklSdIVOSJEnVGTIlSZJUnSFTkiRJ1RkyJUmSVJ0hU5Ik\nSdUZMiVJklSdIVOSJEnVGTIlSZJUnSFTkiRJ1RkyJUmSVN2Uie6ApMlvmOGe3yejkZGRJ30fGhqa\noJ5I0ubJkUxJkiRVZ8iUJElSdesdMiMiIyK7nNslIu5o6sxvKV8+2q75PB4RD0bEkoiYFxFb9rpX\nj8/clrrDHc6viohbI+KCiHhe27X3joizI+I7EfGLiHg4Iu6MiEsiYpcev/+1EXF1RNzftLk9Is6M\niG17tHljRPxbRNzd9GlZRHwxIl7Zoe6WEXFCRHwyIm6JiEea33Jct+tLkiRNtIHNyYyIGcDVwPbA\nezPz/A7VPgI8AGwBvAA4BDgH2Bc4oMflR7qU39KhbBGwsPn39sB+wLuBwyJir8y8ozl3BbAD8C3g\nM8CjwCuBY4EjImJWZt7Y9huPBz7W1L0SuBuYAZwEvCEiXpOZD7a1OQv4a+B+4MvAfcAuwEHAoRFx\ndGb+U0uTpwHnNv++F/gv4Pldfr8kSdKkMJCQGRGzKKHrKcARmfnFLlXPzczlLe1OowTFN0XEPpm5\nqFOjzBweR3cWttZvRkmvoQTZDwLHNKfOAS7LzHvafsvJwOnAxcBLWsp3bNo8Brw6M7/dcu5vgfnA\nacD7WsqfA/wlJSz+cWb+d8u51wLXAacCrSFzJfAG4JbMXBERw4ArGCRJ0qRWfU5mRBwJXAU8Dszu\nETDXkpm3U0YeAV5eu2/NPdZQAiPAni3lZ7UHzMZZwCpgj4jYrqV8DrA18OXWgNk4G/gl8PaI2Kal\nfGfK3/ym1oDZ3P964CHKaGpr+SOZeU1mruj3N0qSJE20qiEzIk6gPGr+JbBPE5zW1Zo6veoommPH\nuaRtkvI4HMqo5ajnNMdlazXIfAz4T8qj7le0nLoNeATYMyK2f1KHIv4U2Bb4eh99kiRJmtSqPS6P\niDMpcxFvA/bPzDvX4Rq7ATObrzf0qDfcoXh5Zi7o4x5TgHc2X2/qo1tvoYS/JZn5QEv5fc1xWod7\n/A5l1BJgN+B6gMz8ZUScBPwD8OOI+DJlbuYLgQOBfwfe1UefpAlVe5/M9j0tJUkbv5pzMk+ijD7O\nHkfAnBcRrQt/DgW2AT6cmUt7tOs0J3ERsKBD+cyWULodsD+wKyUknt6rcxExDTiPMpL5/rbT1zbl\nb46IP8nM77ac+0vg95t/P7O1UWaeGxHLgX8E3tFy6nZgQftj9JoiotvfdPdB3VOSJG2eaobMaykB\n7rMRMbtt1K+bEzqUDWdmz2GNzIxe59vs03ygPKq+C7gImJ+Zd3VrFBHPoiwQ2gF4T/vK8sz8z4gY\noSzu+b8RcQXwc2A68Frg+8AfU+amtl73rymLgj4KnE9ZLb47cAbwmYh4aWb+9Th+nyRJ0qRTc07m\nQcBXKXMQr2tbJNPNtCYwPpWyXdD3gKGI+POK/RrJzGg+W2XmLpn5v/oImNdRHnWfkJkf61QvM/83\n8GeUx+4HAO+hjMS+CfhmU611BflMykKir2bm+zNzWWauzMybgYMpIfUDEfEH6/mbO8rMGZ0+wE8G\ncT9JkrT5qjaSmZkPR8ShlIU/hwELI+L1mXlvH21XA0siYg4l8FwYEd/ostp7oJqtib5BGV18T7eA\nOSozr6Dssdl+nb9p/vmdluI3Nce1FkRl5sqI+DYlbL6MDguKpMmi9pzMHOpnDd74OM9TkiZW1dXl\nmfkocBTwaWAPYHH7m3XGaL+C8ij5aXTfcH1gmr4uogTM48cKmD2u80Jgb+AHmfnDllNbNccd1m71\npPJH1uW+kiRJk0X1fTKb7XvmAh8HXkQJmlPHcYnzKJuVz42IXWv3r5uI2BlYTFnp/fbMvHiMJkTE\n73Yo244ymvs7lMVQrUYfob8zInZqazeHEkxXU946JEmStNEayBt/MjOB4yNiFTCPEjT3zczb+mi7\nstkO6RzK22+OHEQfO1gITAWWAlO7bJO0oPUNRcDfRcRs4EbK3MudKFsR/R7wgcy8pq39P1P2wXw9\n8B8R8SXKwp8XUx6lB/A3mXl/a6Pm0fvoCvCXNsdjIuLVzb9vyMxLxvNjJUmSBmlg7y4HyMwTI2Il\ncDIlaL4+M3/UR9OLgL8CDo+IMzLz+4PsZ2Nqc5zRfDpZCCxv+X49ZTX5QZRg+UvKfM6/z8wl7Y0z\n8/GIeANlgdARlPmX2zTtrgY+mpn/1uG+s3lihfyoVzWfUYZMSZI0aax3yBxrO6HMPAU4pa1s6hht\nVlNGBcd1r7a6w9D/6oRxbos02uZrwNfG2WYNcG7z6bfNzPH1TJIkaWJVn5MpSZIkGTIlSZJU3UDn\nZEraNNTeF3NDGBrq9PZZSdKG4kimJEmSqjNkSpIkqTpDpiRJkqozZEqSJKk6Q6YkSZKqM2RKkiSp\nOkOmJEmSqjNkSpIkqTpDpiRJkqozZEqSJKk6Q6YkSZKqM2RKkiSpOkOmJEmSqpsy0R3Q5DB9x+ks\nHVo60d2QJEmbCEcyJUmSVJ0hU5IkSdUZMiVJklSdIVOSJEnVGTIlSZJUnSFTkiRJ1RkyJUmSVJ0h\nU5IkSdUZMiVJklSdIVOSJEnVGTIlSZJUne8uFwA3r7iZGImJ7oYmuRzKie6CJGkj4UimJEmSqjNk\nSpIkqTpDpiRJkqozZEqSJKk6Q6YkSZKqM2RKkiSpOkOmJEmSqjNkSpIkqTpDpiRJkqozZEqSJKk6\nQ6YkSZKqM2RKkiSpOkOmJEmSqjNkSpIkqbopE90BSfUMM9zz+2QzMjLypO9DQ0MT1BNJUm2OZEqS\nJKk6Q6YkSZKqW++QGREZEdnl3C4RcUdTZ35L+fLRds3n8Yh4MCKWRMS8iNiy1716fOa21B3ucH5V\nRNwaERdExPParr13RJwdEd+JiF9ExMMRcWdEXBIRu3Toy5YRcXBEXBoRP4yIX0fEyoj4QUScGhHb\n9vn3e2tL/47rcH7XiDgpIq6LiLsi4pGIuDcivhIRr+3nHpIkSRvawOZkRsQM4Gpge+C9mXl+h2of\nAR4AtgBeABwCnAPsCxzQ4/IjXcpv6VC2CFjY/Ht7YD/g3cBhEbFXZt7RnLsC2AH4FvAZ4FHglcCx\nwBERMSszb2y57guBK4HfANcDXwOeDuwPfAg4PCL2zsz7uv2IiHg+cD7wP03bTk4DDgd+TPl7/hLY\nDTgQODAiTsjMj3a7hyRJ0kQYSMiMiFmUAPYU4IjM/GKXqudm5vKWdqdRguKbImKfzFzUqVFmDo+j\nOwtb6zejpNdQguwHgWOaU+cAl2XmPW2/5WTgdOBi4CUtpx4C3gN8KjN/01L/KZTf/kZgCHhvp05F\nRACfBO5v6v9ll/7/K3BWZv5/be33Af4d+D8R8cXMXNGlvSRJ0gZXfU5mRBwJXAU8DszuETDXkpm3\nU0YeAV5eu2/NPdZQAiPAni3lZ7UHzMZZwCpgj4jYrqX+zzPzY60Bsyl/BBidGjCzR1feB7yOEnJ/\n061SZi5oD5hN+egI7VOAV/W4jyRJ0gZXNWRGxAmUR82/BPbJzOvX43Jr6vSqo2iOHeeStknKo3OA\nx/q8/mjfH+10MiJeDJwJfCQzF/d5zXHfR5IkaaJUe1weEWcCJwG3Aftn5p3rcI3deGL074Ye9YY7\nFC/PzAV93GMK8M7m6019dOstwLbAksx8oI/6AG9vjv/a5f6XAT8DTu7zemuJiJ0pj/xXAusTVLUJ\nq71PZvu+lpIkdVNzTuZJlJG12eMImPMionXhz6HANsCHM3Npj3addmxeBCzoUD6zJZRuR1mYsytw\nH2WuZVcRMQ04jzJS+P5edVvaHAi8C7gbOLtDlb8DXga8OjNX9XPNDvfYijJivBXw15n5qz7bdfub\n7r4u/ZAkSeqmZsi8lhLgPhsRs/sc9TuhQ9lwZvYcLsnM6HW+zT7NB+AR4C7gImB+Zt7VrVFEPIuy\nQGgH4D1tK8u7tXkV8FnKHMtD28NfRLyCMnr59/1cr8s9tqCMhO4NfAH48LpcR5IkaZBqhsyDgMsp\nW+tc12z5c/8YbaZl5vKI2Bp4KSX8DUXEssy8rFK/Rsa5Gn00YF5H2SrohMz8WB9tXkkJpY8DczLz\n223npwCfBm6lbHE0bk3A/CfKI/zLgbdmZj/zSgHIzBldrrsUmL4ufZIkSeqkWsjMzIcj4lDKY9zD\ngIUR8frMvLePtquBJRExB/gJcGFEfKPLau+BiogdgW9QHiG/p8+A+RrKPpmPU+ajLulQ7enAi5p/\nry47GK3lExHxCcqCoHlt99iS8rd9C2W09OjM7HchkjZTtedk5lDf/0/TF+d4StKmq+o+mZn5aEQc\nBawGjgYWR8S+mXl3n+1XNG8GOpOy4fo7avZvLM1bgK4DdgGOz8yLx2hCRLwO+BfgYUrA/E6Xqg8D\nl3Y5N50yT/MG4KfAkx6lN3tvXk4ZLf40cExmPj7mD5IkSZog1Tdjz8zHmtc7rqIsgFkcEa9r3XR9\nDOcBJwJzI+LszLytdh87aVZrXw/sDLy9z5Xq+wFfpqzwntVpP8tRzSKftV4b2VxnmBIyP5WZl7Sd\n24qyWfsbKCH1nQZMSZI02Q3kjT/NPMHjI2IVMI8nRjTHDIyZubLZDukc4FTgyEH0sYOFwFRgKTC1\nyzZJC0bDcrPd0leArSmvezwoIg5qbzDe+aAdXEQJmPcBPwf+rsOj9oWZuXA97yNJklTNwN5dDpCZ\nJ0bESsqK6sXNHM0f9dH0IuCvKO//PiMzvz/IfjamNscZzaeThcDy5t87UgImlK2XDu3SZng9+zWt\nOW5P2f6om4XreR9JkqRq1jtkjrWdUGaeApzSVjZ1jDargZ3Ge6+2usOMI+CNc1skmpHDcbXpca1h\nuvQ1M2fWuIckSdKGVP3d5ZIkSZIhU5IkSdUNdE6mpA2r9r6YgzY01OkNsZKkTYEjmZIkSarOkClJ\nkqTqDJmSJEmqzpApSZKk6gyZkiRJqs6QKUmSpOoMmZIkSarOkClJkqTqDJmSJEmqzpApSZKk6gyZ\nkiRJqs6QKUmSpOoMmZIkSapuykR3QJPD9B2ns3Ro6UR3Q5IkbSIcyZQkSVJ1hkxJkiRVZ8iUJElS\ndYZMSZIkVWfIlCRJUnWGTEmSJFVnyJQkSVJ1hkxJkiRVZ8iUJElSdYZMSZIkVWfIlCRJUnW+u1wA\n3LziZmIkJrobmqRyKCe6C5KkjYwjmZIkSarOkClJkqTqDJmSJEmqzpApSZKk6gyZkiRJqs6QKUmS\npOoMmZIkSarOkClJkqTqDJmSJEmqzpApSZKk6gyZkiRJqs6QKUmSpOoMmZIkSarOkClJkqTqpkx0\nByStv2GGe36fTEZGRp70fWhoaIJ6IkkaJEcyJUmSVJ0hU5IkSdUZMiVJklTdeofMiMiIyC7ndomI\nO5o681vKl4+2az6PR8SDEbEkIuZFxJa97tXjM7el7nCH86si4taIuCAinjfG79oqIn7YtLu7R72X\nRMRnIuL25vo/j4jrI+LwiPidtrrbRcRxEfGllvoPRsQNEXFse/22tq+KiKsj4pdNu+83f6stev0O\nSZKkiTCwhT8RMQO4GtgeeG9mnt+h2keAB4AtgBcAhwDnAPsCB/S4/EiX8ls6lC0CFjb/3h7YD3g3\ncFhE7JWZd3S51nxg5x59ICIOAK4EHge+Cvxzc4+Dgc8Drwfe0dLkLcCFwArgeuBnwLMpv/sSYE5E\nvCUznxTaI+Ig4ApgNfAF4JeUv885wN7NdSVJkiaNgYTMiJhFCV9PAY7IzC92qXpuZi5vaXcaJSi+\nKSL2ycxFnRpl5vA4urOwtX4zSnoNJch+EDimQ/9nAidSwuiFPa59JuVvOLO1rxHxQeB7wHERcVpm\n/qw5dStwIPC1zHy8pf7JwLeBQymB84qWc78LfAJ4rLnPd5vyDwHXAX8WEUdk5uf7+WNIkiRtCNXn\nZEbEkcBVlNG92T0C5loy83bKyCPAy2v3rbnHGuDi5uue7eebULcA+EZmXjTG5f4A+HV7GM7M/wJu\nar7u0FJ+XWb+S2vAbKk/eq+Zbff4s+Yanx8NmE2b1ZSQDPC/xuinJEnSBlV1JDMiTqA8wr0XmJOZ\nnR5f92tNnV51FM2x01zSjwLPBI7t4zo/AmZExKsz84bfXjziWZQAuwL4cZ99Gv29j7aVv645/muH\nNouBlcCrImKrzHy4z3tpE1d7n8z2vS0lSRpLtZAZEWcCJwG3Aftn5p3rcI3deGIk74Ye9YY7FC/P\nzAV93GMK8M7m601t5w4G3gYc1/KIu5cTKaO2X4+IrwDLKHMy30yZa3pUZq7qs09HN1/bw+RuzfHW\n9naZ+WhE3An8EWVU9T/GuM/SLqd2H6uPkiRJ41FzJPMkymjc7HEEzHkR0brw51BgG+DDmdktEAF0\nekXIIspj7nYzW0LpdsD+wK7AfcDpo5Ui4tmUx+jXZOal/XQ+M78ZEa8ELgcOazn1EPBJ4Af9XIcy\nt3MP4OrMvLbt3DOa44Nd2o6W/16f95IkSRq4miHzWkqA+2xEzM7MB/poc0KHsuHM7PlsLjOj1/k2\n+zQfgEeAuyjzH+dn5l0t9T5B+Xsc1++FmwVOnwe+SxmJ/AnwHOAvKAH2jc0CpvZH4K3XeB/wgabt\nn/d773WRmTO69GEpMH2Q95YkSZuXmiHzIMqI3oHAdRExKzPvH6PNtMxcHhFbAy+lhL+hiFiWmZdV\n6tfIWKvRI+JoypZAb8vMe/q5aET8PmU7oZXAwZm5sjm1DHh/REyjPDZ/K51HWImIv6Bs4/RjYN/M\n/GWHaqMjlc/ocK61vJ9Qr81E7TmZOdRxK9x14vxOSdo8VFtd3iw6OZQSNF8GLGweQffTdnVmLgHm\nUB41XxgRz63Vtz6MjuJ9qn0D96Z8p5ay0cfSr6IsELqpJWC2ur45dhs9nAecB/wQeG2zwryTnzbH\nF3W4xhRgGmWx0LIev0+SJGmDqrq6vFmIchRl0/CjgcURsW9mdn1jTlv7Fc2bgc6kbLj+jjGa1HIj\n8PQu546ljFZ+rvk+uoJ7q+a4w1otnlz+SPuJiDiJ8htvAWZl5n09+nYd8P8Cs1v6MOpPKXNYF7uy\nXJIkTSbV98nMzMeAucDHKaNviyNi6jgucR5lC6S5EbFr7f51kplfyMzjOn2aKr9qKRtdLX4jZQRx\n74jYr/V6EfF84F3N12+0nfsQJWAupTwi7xUwobxF6D7giIj4k5brbA387+Zrrw3jJUmSNriBvPGn\neS3i8RGxCpjHEyOat/XRdmWzHdI5wKnAkYPo4/rKzHuaNxSNANdExFU8sfDnEMrI6Jcy8+rRNhHx\nNspvegz4JvC+iLXWMD1pK6bM/HVEvIMSNhdGxOcpr5U8kLK90T9T5oZKkiRNGgN7dzlAZp4YESuB\nkylB8/WZ+aM+ml4E/BVweESckZnfH2Q/11VmnhoR3wOOp8zRfCPl0foPgMt44s1Co6Y1xy0o4buT\ntbZiyswvR8Q+wCmUea9bA7cD7wc+2v6uc0mSpIm23iFzrO2EMvMUSjhqLZs6RpvVwE7jvVdb3WFY\n/yW2ffy+rwBfGXSfMvP/Am9Yl7aSJEkbWvU5mZIkSdJAH5dL2jBq74s5SENDnV7YJUna1DiSKUmS\npOoMmZIkSarOkClJkqTqDJmSJEmqzpApSZKk6gyZkiRJqs6QKUmSpOoMmZIkSarOkClJkqTqDJmS\nJEmqzpApSZKk6gyZkiRJqs6QKUmSpOqmTHQHNDlM33E6S4eWTnQ3JEnSJsKRTEmSJFVnyJQkSVJ1\nhkxJkiRVZ8iUJElSdYZMSZIkVWfIlCRJUnWGTEmSJFVnyJQkSVJ1hkxJkiRVZ8iUJElSdYZMSZIk\nVee7ywXAzStuJkZioruhSSiHcqK7IEnaCDmSKUmSpOoMmZIkSarOkClJkqTqDJmSJEmqzpApSZKk\n6gyZkiRJqs6QKUmSpOoMmZIkSarOkClJkqTqDJmSJEmqzpApSZKk6gyZkiRJqs6QKUmSpOoMmZIk\nSapuykR3QFIdwwz3/D7RRkZGnvR9aGhognoiSdoQHMmUJElSdYZMSZIkVWfIlCRJUnUbTciMiIyI\n7HJul4i4o6kzv6V8+Wi75vN4RDwYEUsiYl5EbNnrXj0+c1vqDnc4vyoibo2ICyLieWP8rq0i4odN\nu7u71FkwRn927+uPKEmStIFs9At/ImIGcDWwPfDezDy/Q7WPAA8AWwAvAA4BzgH2BQ7ocfmRLuW3\ndChbBCxs/r09sB/wbuCwiNgrM+/ocq35wM49+tBq9He0u6/P9pIkSRvERh0yI2IWcCXwFOCIzPxi\nl6rnZubylnanUYLimyJin8xc1KlRZg6PozsLW+s3o6TXUILsB4FjOvR/JnAiJYxe2Mc9nvQ7JEmS\nJquN5nF5u4g4ErgKeByY3SNgriUzb6eMPAK8fADdIzPXABc3X/dsPx8RvwssAL6RmRcNog+SJEkT\nZaMcyYyIEyiPu+8F5mRmp8fX/VpTp1cdRXPsNJf0o8AzgWPHcb05TTh9DLgduC4zf71+XdSmqtY+\nme37W0qS1I+NLmRGxJnAScBtwP6Zeec6XGM3YGbz9YYe9YY7FC/PzAV93GMK8M7m601t5w4G3gYc\nl5k/G7vHv/Wxtu8PRcTfZuYF/TSOiKVdTrlwSJIkVbXRhUxKwFxDeUTeb8CcFxGtC38OBbYBPpyZ\n3YIXQKdXkiyiPOZuN7MllG4H7A/sSlmUc/popYh4NuUx+jWZeWmf/V9MWdy0BPhv4LnAwU3/zo+I\nNZl5cY/2kiRJG9TGGDKvpQS4z0bE7MzstNq63QkdyoYzs+dzwMyMXufb7NN8AB4B7gIuAuZn5l0t\n9T5B+bsf1++FM/Mf24qWAX8fET8F/gU4PSIuzczHxrjOjE7lzQjn9H77I0mSNJaNMWQeBFwOHAhc\nFxGzMvP+MdpMy8zlEbE18FJK+BuKiGWZeVmlfo2MtRo9Io6mbJn0tsy8Z31vmJlXRcTPgZ2APwR+\nsL7X1Kaj1pzMHOq4Pe24ObdTkjYvG93q8sx8mPK4+3LgZcDC5hF0P21XZ+YSYA7wEHBhRDx3YJ1d\n2+ho4afaN1RvyndqKfu9Pq/5i+b4tLpdlSRJWncb40gmmfloRBwFrAaOBhZHxL6Z2fGNOR3ar2je\nDHQmZcP1dwyut09yI/D0LueOBVYCn2u+PzzWxSLiGZRFOwmMewGUJEnSoGyUIRMgMx9rXu+4CngX\nJWi+bhyblZ9H2Qh9bkScnZm3DaanT8jMLwBf6HQuIo4FfpWZx7WVPweY0h6gI+LplAVIWwP/npn3\nDqTTkiRJ62Cje1zeKovjgXOBaZSguWufbVdSRjKnAKcOrpfrbXdgeUR8MyIujYgzIuLTlC2c3kxZ\nBNT3IiJJkqQNYaMOmaMy80TKO8CfTwmaf9Rn04uAe4DDI+KPB9W/9XQHcCllzuWBwF9SFj/dRXld\n5UvHudemJEnSwG00j8vH2k4oM08BTmkrmzpGm9WUldnjuldb3WFY/2W83e7ZbH/0rvW9viRJ0oa0\nSYxkSpIkaXLZaEYyJfVWa1/MQRka6vQCLUnSpsqRTEmSJFVnyJQkSVJ1hkxJkiRVZ8iUJElSdYZM\nSZIkVWfIlCRJUnWGTEmSJFVnyJQkSVJ1hkxJkiRVZ8iUJElSdYZMSZIkVWfIlCRJUnWGTEmSJFU3\nZaI7oMlh+o7TWTq0dKK7IUmSNhGOZEqSJKk6Q6YkSZKqM2RKkiSpOkOmJEmSqjNkSpIkqTpDpiRJ\nkqozZEqSJKk6Q6YkSZKqM2RKkiSpOkOmJEmSqjNkSpIkqTrfXS4Abl5xMzESE90NTYAcyonugiRp\nE+RIpiRJkqozZEqSJKk6Q6YkSZKqM2RKkiSpOkOmJEmSqjNkSpIkqTpDpiRJkqozZEqSJKk6Q6Yk\nSZKqM2RKkiSpOkOmJEmSqjNkSpIkqTpDpiRJkqozZEqSJKm6KRPdAUnjM8xwz+8TYWRk5Enfh4aG\nJqgnkqTJwpFMSZIkVWfIlCRJUnWGTEmSJFW33iEzIjIissu5XSLijqbO/Jby5aPtms/jEfFgRCyJ\niHkRsWWve/X4zG2pO9zh/KqIuDUiLoiI57Vde++IODsivhMRv4iIhyPizoi4JCJ26fH7XxIRn4mI\n25vr/zwiro+IwyNirb9vh9/e+vmvHvfZIiKOi4jFEfGr5l7LIuILEfGibu0kSZImwsAW/kTEw37/\nJgAAIABJREFUDOBqYHvgvZl5fodqHwEeALYAXgAcApwD7Asc0OPyI13Kb+lQtghY2Px7e2A/4N3A\nYRGxV2be0Zy7AtgB+BbwGeBR4JXAscARETErM29s+40HAFcCjwNfBf65ucfBwOeB1wPv6NCnB4Fz\nO5T/T6cfFRFPB74CvK75jZ8CVgM7Aa8BXgTc2qmtJEnSRBhIyIyIWZTw9RTgiMz8Ypeq52bm8pZ2\np1FC1JsiYp/MXNSpUWYOj6M7C1vrN6Ok11CC7AeBY5pT5wCXZeY9bb/lZOB04GLgJW3XPpPyN5zZ\n2teI+CDwPeC4iDgtM3/W1u6Bcf6Gj1MC5vGZ+fH2k91GfiVJkiZK9TmZEXEkcBVldG92j4C5lsy8\nnTLyCPDy2n1r7rGGEhgB9mwpP6s9YDbOAlYBe0TEdm3n/gD4dXsYzsz/Am5qvu6wPv2NiOnAUcAX\nOgXM5n5r1ucekiRJtVUdyYyIEygjgvcCczKz0+Prfg0yOEVz7DiXtE1SHp0DPNZ27kfAjIh4dWbe\n8NuLRzyLEmBXAD/ucM2tIuKtlCkCvwG+DyzOzPbrQwmYAJ+LiGdQphE8H7gfuK4J5tqMre8+me17\nXEqSVEO1kBkRZwInAbcB+2fmnetwjd2Amc3XG3rUG+5QvDwzF/RxjynAO5uvN/Wq23gLsC2wJDMf\naDt3ImXU9usR8RVgGWVO5pspc02PysxVHa75HOCytrI7I+KYDlMERkd0dwbuAFpHUzMiLgTe1yWg\nPklELO1yavex2kqSJI1HzZHMkyijj7PHETDnRUTrwp9DgW2AD2dmt0AE0Ol1IouABR3KZ7aE0u2A\n/YFdgfsocy27iohpwHmUkcz3t5/PzG9GxCuBy4HDWk49BHwS+EGHy34S+CZlFPQhyiP3v6AE32si\n4pWZ+b2W+s9qjv8AfJkyj/Ru4BXARZRFTL+ASfDaF0mSpEbNkHktJcB9NiJmdxj16+SEDmXDmdnz\n+V1mRq/zbfZpPgCPAHdRwtn8zLyrW6Pmkfc1lDmV72lfWd7UmUVZRf5d4GjgJ5RRyr+gBNg3NguY\nRh+30+G3/RA4PiL+B/gAJSwe3HJ+dN7sT4DDW0YsvxERfwbcDLw/IuZn5iO9/hCZOaPLb10KTO/V\nVpIkaTxqhsyDKCN6BwLXNVv+3D9Gm2mZuTwitgZeSgl/QxGxLDPbHyevq5FxruQeDZjXAbsBJ2Tm\nxzrU+X3gC8BK4ODMXNmcWkYJfdMoj83fSucR1nYXUULmn7aVj4b1f2l/JJ6Z34uIO4EXAi+mrGjX\nZmZ952TmUD9Tk3tzXqckqV211eWZ+TDlcfflwMuAhRHx7D7brs7MJcAcyiPkCyPiubX6Nh4RsSNl\nX80/pIxgfrRL1VcBzwRuagmYra5vjh1HDzv4RXN8Wlv5T5tjt5HhXzXHp/Z5H0mSpIGruoVR81j4\nKODTwB7A4vY364zRfgUwnxK0NvjQSNPXRZSFMMd3GsFssVVz7LZF0Wh5z0fYLfZqjsvayr/eHPdo\nbxARW1HmlwIs7/M+kiRJA1d9n8zmke5cygbiL6IEzanjuMR5lC2Q5kbErmNVriUidgYWUx49vz0z\nLx6jyY2UBUF7R8R+bdd6PvCu5us3WspfHBHtI5U0f5/RNyL9U9vpK4B7gMMjYs+2cx8CngFc3+zN\nKUmSNCkM5I0/mZmUxSyrgHmUoLlvZt7WR9uVzXZI5wCnAkcOoo8dLASmAkuBqV22SVow+oaizLyn\neUPRCGVV+FU8sfDnEODpwJcy8+qW9ocDH4iIxcB/UqYGvBB4I7A15TWcH269YWb+pnkn+1XANyPi\nSuDnlNXlrwb+mycCrSRJ0qQwsHeXA2TmiRGxEjiZEjRfn5k/6qPpRcBfUUbvzsjM7w+yn42pzXEG\n3edRLqTlsXRmnhoR3wOOp8zRfCNlIdAPKPtgto+GXk9ZTPQyYG/KtIAHKHuCXkZ5reVaqzAy89+b\nUcwPUd6H/gzgvyh/p9O6vKlIkiRpwqx3yBxrO6HMPAU4pa1s6hhtVgM7jfdebXWHGcfekePcFqm1\n3VeAr/RZdxFPvDZzvPf5HvBn69JWkiRpQ6s+J1OSJEka6ONySfWt776YgzA01OklXJKkzZkjmZIk\nSarOkClJkqTqDJmSJEmqzpApSZKk6gyZkiRJqs6QKUmSpOoMmZIkSarOkClJkqTqDJmSJEmqzpAp\nSZKk6gyZkiRJqs6QKUmSpOoMmZIkSapuykR3QJPD9B2ns3Ro6UR3Q5IkbSIcyZQkSVJ1hkxJkiRV\nZ8iUJElSdYZMSZIkVWfIlCRJUnWGTEmSJFVnyJQkSVJ1hkxJkiRVZ8iUJElSdYZMSZIkVWfIlCRJ\nUnWGTEmSJFU3ZaI7oMnh5hU3EyMx0d3QBpRDOdFdkCRtwhzJlCRJUnWGTEmSJFVnyJQkSVJ1hkxJ\nkiRVZ8iUJElSdYZMSZIkVWfIlCRJUnWGTEmSJFVnyJQkSVJ1hkxJkiRVZ8iUJElSdYZMSZIkVWfI\nlCRJUnVTJroDkvozzHDP7xvayMjIk74PDQ1NUE8kSZORI5mSJEmqzpApSZKk6gyZkiRJqm6TDpkR\nkRGRXc7tEhF3NHXmt5QvH23XfB6PiAcjYklEzIuILXvdq8dnbkvd4Q7nV0XErRFxQUQ8r+3ae0fE\n2RHxnYj4RUQ8HBF3RsQlEbFLpT+XJElSNZvlwp+ImAFcDWwPvDczz+9Q7SPAA8AWwAuAQ4BzgH2B\nA3pcfqRL+S0dyhYBC5t/bw/sB7wbOCwi9srMO5pzVwA7AN8CPgM8CrwSOBY4IiJmZeaNPfokSZK0\nQW12ITMiZgFXAk8BjsjML3apem5mLm9pdxolKL4pIvbJzEWdGmXm8Di6s7C1fjNKeg0lyH4QOKY5\ndQ5wWWbe0/ZbTgZOBy4GXjKO+0qSJA3UJv24vF1EHAlcBTwOzO4RMNeSmbdTRh4BXj6A7pGZayiB\nEWDPlvKz2gNm4yxgFbBHRGw3iD5JkiSti81mJDMiTqCMCN4LzMnMTo+v+7WmTq86iubYcS5pm6Q8\nOgd4bDDd0WS1vvtktu9zKUlSTZtFyIyIM4GTgNuA/TPzznW4xm7AzObrDT3qDXcoXp6ZC/q4xxTg\nnc3Xm/ro1luAbYElmflAH9df2uXU7n3cS5IkqW+bRcikBMw1lEfk/QbMeRHRuvDnUGAb4MOZ2S2s\nAXR67ckiYEGH8pktoXQ7YH9gV+A+ylzLriJiGnAeZSTz/b3qSpIkbWibS8i8lhLgPhsRs/sZ9QNO\n6FA2nJk9nzFmZvQ632af5gPwCHAXcBEwPzPv6tYoIp5FWSC0A/CefleWZ+aMLtdbCkwfR78lSZJ6\n2lxC5kHA5cCBwHXNlj/3j9FmWmYuj4itgZdSwt9QRCzLzMsq9WtknKvRRwPmdcBuwAmZ+bFKfdFG\nZn3nZOZQP9N+u3NOpySpl81idXlmPkx53H058DJgYUQ8u8+2qzNzCTAHeAi4MCKeO7DO9hARO1L2\n1fxDygjmRyeiH5IkSWPZLEImQGY+ChwFfBrYA1jc/madMdqvAOYDT6P7husD0/R1EWWRzvGOYEqS\npMlsswmZAJn5GDAX+DjwIkrQnDqOS5xH2QJpbkTsWrt/3UTEzsBi4IXA2zPz4jGaSJIkTajNZU7m\nb2VmAsdHxCpgHiVo7puZt/XRdmWzHdI5wKnAkYPt7W8tBKYCS4GpXbZJWtD6hiJJkqSJtNmFzFGZ\neWJErAROpgTN12fmj/poehHwV8DhEXFGZn5/oB0tpjbHGc2nk4XA8g3QF0mSpDFt0iFzrO2EMvMU\n4JS2sqljtFkN7DTee7XVHYb+lwaPc1skSZKkCbdZzcmUJEnShrFJj2RKm5L13ReztqGhTi+3kiSp\ncCRTkiRJ1RkyJUmSVJ0hU5IkSdUZMiVJklSdIVOSJEnVGTIlSZJUnSFTkiRJ1RkyJUmSVJ0hU5Ik\nSdUZMiVJklSdIVOSJEnVGTIlSZJUnSFTkiRJ1U2Z6A5ocpi+43SWDi2d6G5IkqRNhCOZkiRJqs6Q\nKUmSpOoMmZIkSarOkClJkqTqDJmSJEmqzpApSZKk6gyZkiRJqs6QKUmSpOoMmZIkSarOkClJkqTq\nDJmSJEmqzpApSZKk6qZMdAc0Ody84mZiJCa6GxqQHMqJ7oIkaTPjSKYkSZKqM2RKkiSpOkOmJEmS\nqjNkSpIkqTpDpiRJkqozZEqSJKk6Q6YkSZKqM2RKkiSpOkOmJEmSqjNkSpIkqTpDpiRJkqozZEqS\nJKk6Q6YkSZKqmzLRHZC0tmGGe37f0EZGRp70fWhoaIJ6IknaWDiSKUmSpOoMmZIkSarOkClJkqTq\n1jtkRkRGRHY5t0tE3NHUmd9Svny0XfN5PCIejIglETEvIrbsda8en7ktdYc7nF8VEbdGxAUR8by2\na+8dEWdHxHci4hcR8XBE3BkRl0TELj1+/2sj4uqIuL9pc3tEnBkR23aoO7eP3/BYh3bbRsTpEfGT\niFgdEb+KiGsjYt9u/ZIkSZpIA1v4ExEzgKuB7YH3Zub5Hap9BHgA2AJ4AXAIcA6wL3BAj8uPdCm/\npUPZImBh8+/tgf2AdwOHRcRemXlHc+4KYAfgW8BngEeBVwLHAkdExKzMvLHtNx4PfKypeyVwNzAD\nOAl4Q0S8JjMfbOtft76/BngdcE3bPZ4J3AD8IfAj4CLg6cBBwNcj4rjMvLTLNSVJkibEQEJmRMyi\nhK6nAEdk5he7VD03M5e3tDuNEsTeFBH7ZOaiTo0yc3gc3VnYWr8ZJb2GEmQ/CBzTnDoHuCwz72n7\nLScDpwMXAy9pKd+xafMY8OrM/HbLub8F5gOnAe9r6fctdA7CRMRogL247dQwJWBeCRyemY+29Ou7\nwHkRcW1m3j3G30GSJGmDqT4nMyKOBK4CHgdm9wiYa8nM2ykjjwAvr9235h5reCLI7dlSflZ7wGyc\nBawC9oiI7VrK5wBbA19uDZiNs4FfAm+PiG3G6lNEvATYC/g58LW20wc3x78bDZhNf/8b+AfgqcDb\nx7qHJEnShlR1JDMiTqCM7t0LzGlG7tbVmjq96iiaY8e5pG2S8jgcyqjlqOc0x2VrNch8LCL+E3gZ\n8Arg+jHu8c7meGlmts/J7HqflrJ9gVPHuIc2Yuu7T2b7PpeSJA1atZAZEWdS5iLeBuyfmXeuwzV2\nA2Y2X2/oUW+4Q/HyzFzQxz2m8ESou6mPbr0F2BZYkpkPtJTf1xyndbjH7wA7N193o0fIjIinAm+l\nBNhLOlS5D9ixuc+P2879Qcs9xhQRS7uc2r2f9pIkSf2qOZJ5EmX0cfY4Aua8iGhd+HMosA3w4czs\nFogAOr1uZBGwoEP5zJZQuh2wP7ArJbyd3qtzETENOI8ykvn+ttPXNuVvjog/yczvtpz7S+D3m38/\ns9c9gMOA3wO+lpl3dTj/NeA4YCQijhgd6YyIHYAT+7yHJEnSBlUzZF77/7d353FyVXXexz+/IQgo\nroCKwBgEVGZ4HAiKCApBFoOCqIiIowyMILgCouADSCc6Ijgim0vcGVDHAVT0URhRIEFEUMPiQpQl\nxAVZBERRkrD9nj/ObbkUVd3VndNdWT7v16te1XXvOfeeOoH0N+fecy4lwH0lImZ0jPr1ckiXbTMz\nc8Rre5kZI+3vsH3zArgP+B1lhvZxPUIdABHxVMoEoXWAt3fOLM/M30TELMrknh9GxNco91ROA3YA\nfgY8j3Jv6kiGR1U/3WP/sZR+fS1wdURcCDyOMrv8Zko4H+0cw23estv2ZoRzWj/HkCRJ6kfNkLkH\ncBbwSuCiZsmfO0eps2FmLoyI1YHNKeFvKCIWZOaZldo1a4yz0YcD5kWUy9CHZOYnu5XLzP+IiPmU\nsLw7ZUT2GmA34OWUkHn7COf5Z2AbytJH5/U4xy0R8QLg/c1x30YZhf0fyhJQ1490Dq0YlvaezBzq\n5/bj3rynU5I0VtVml2fmEsrl7rMoE17mRMTT+qy7ODMvp8zYvgf4VEQ8o1bbxqJZmmgOZdmgt2fm\nqSOVz8yvZeZ2mfn4zHxsZr4oM8+jBEyAn4xQfaQJP+1z3JaZ78jMqZn5mMx8Rma+kzKKOdo5JEmS\nJl3VJYyaJXbeAJwBbAZc0vlknVHq30JZX/Jx9F60fMI0bZ1LmQhzcK8RzD6OsxGwLfDzzPxFjzKr\nA2+iTPgZ72Lq+zbvXxlnfUmSpAlRfZ3MZkRuP8o9hs+mBM2pYzjEaZQlkPaLiE1qt6+XiHgmcAmw\nEfDvmdm5KHq3Ok/osm0tyhOD/oEyGaqXvSgTds4f5d7Qf4iINbtsfxMlZF4GnDtaWyVJkibThDzx\nJzMTODgiFgGHUoLmjpl5fR91722WQzqJsvbjPhPRxi7mAFOBecDUHssknd5+QhFwbETMAH5EuS9y\nPco9qU8CDs/M8x99iL8bvlQ+Wph9LHBbRHwPuJEyyWdbyiMv5wN7ZWZfE38kSZImy4Q9uxwgMw+L\niHuBoyhBc6fM/GUfVWcD7wX2jogPZ+bPJrKdjanN+5bNq5s5wMLW54sps7L3oATLu4ALgRObe0y7\niohNgRczwoSfliXAV5vyOzfbrgeOpjyW895R6kuSJE26pQ6Zoy0nlJlHUwJRe9vUUeospowKjulc\nHWVnQv9Tcse4LNJwne/w6MdA9lNvPg8/dWi0svcDbx7rOSRJkgap+j2ZkiRJ0oReLpc0Pku7LmZt\nQ0PdHrIlSVJvjmRKkiSpOkOmJEmSqjNkSpIkqTpDpiRJkqozZEqSJKk6Q6YkSZKqM2RKkiSpOkOm\nJEmSqjNkSpIkqTpDpiRJkqozZEqSJKk6Q6YkSZKqM2RKkiSpOkOmJEmSqpsy6AZo2TBt3WnMG5o3\n6GZIkqQVhCOZkiRJqs6QKUmSpOoMmZIkSarOkClJkqTqDJmSJEmqzpApSZKk6gyZkiRJqs6QKUmS\npOoMmZIkSarOkClJkqTqDJmSJEmqzmeXC4Arb7mSmBWDbobGIIdy0E2QJKknRzIlSZJUnSFTkiRJ\n1RkyJUmSVJ0hU5IkSdUZMiVJklSdIVOSJEnVGTIlSZJUnSFTkiRJ1RkyJUmSVJ0hU5IkSdUZMiVJ\nklSdIVOSJEnVGTIlSZJU3ZRBN0Ba2cxk5oifJ8OsWbMe8XloaGjS2yBJWrE5kilJkqTqDJmSJEmq\nzpApSZKk6pY6ZEZERkT22LdxRNzYlDmutX3hcL3m9VBE/DkiLo+IQyNi1ZHONcJrv1bZmV32L4qI\n6yLiExGxfsext4uIMyPiFxFxZ0QsjoibIuJbEbHjKH2wcUR8tim/OCLuaL7L4T3KvyIiLoiI3zdt\nWhARZ0fEi7qUXTUiDomIL0bE1RFxX/NdDhipTZIkSYM0YRN/ImJL4DxgbeCdmfnxLsVOAe4GVgH+\nEXgNcBKwI7D7CIef1WP71V22zQXmND+vDewCvA14XURsnZk3Nvte2ryuAC4C/ta06ZXA7hHxH5n5\n/i7f8zXAV4D7gW8DNwFPBJ7TfJ8TO8qfABwB3AmcC9wBbAzsAewZEftm5pdaVR4HnNz8fBtwK7BB\nj+8vSZK0TJiQkBkROwNfBx4DvD4zz+5R9OTMXNiq90FKUNwtIrbPzLndKmXmzDE0Z067fDNKej4l\nyB4D7N/sOr7bcSNiPeBK4KiI+GRm3tLatxklYF4LvDwzb+2ou2rH56cD76GExedl5u2tfTtQwu0H\ngHbIvBd4OXB1Zt4SETMBpwJLkqRlWvV7MiNiH8qI3kPAjBEC5qNk5g2UkUeAF9RuW3OO+4HPNB+3\nam1f3KP8zcBllL56Vsfu4yhB+l87A2brXG3PbI5zRTtgNmUvBu4B1unYfl9mnt8Ot5IkScu6qiOZ\nEXEI5XL3bcCumdnt8nW/OgNaTdG8d72X9BEFI54KvBBYAvy6tf0JwCuAazJzfkRsBbyYcul/PnBB\nZt7XcbjrgfuArSJi7cy8o3W87YDHUy6hayUy3nUyO9e6lCRpWVItZEbE8cCRlCD1ssy8aRzHeA4w\nvfl46QjlZnbZvDAzT+/jHFOAtzQfr+iy//nAbpS+WZ9yb+gTKfeV3tEquiVlVHJhRJwF7NVxqN9G\nxGsz8yfDGzLzrog4EvgYcG1EnEu5N3Mjyr2f3wMOGu07jFdEzOux67kTdU5JkrRyqjmSeSRl9HHG\nGALmoRHRnvizJ/BY4KOZ2SsQQfd7EucCp3fZPr0VStcCXgZsQplw86Eu5Z/fcfx7gP0z88yOck9t\n3ncH/gy8Afhf4AnA24H3AudFxKbtcJqZJ0fEQuALwIGt490AnN55GV2SJGl5VDNkfpcS4L4SETMy\n8+4+6hzSZdvMzBzxOmBmxkj7O2zfvKBcqv4dMBs4LjN/1+XYs4HZEbE6sCFwMHBGRGybmQe3ig7f\nz7oK8PbM/Grz+U/AERGxEWV2+YHAh4crRcQRlHs5TwU+Tpkt/tymzJcjYvPMPGIM369vmbllt+3N\nCOe0iTinJElaOdUMmXsAZ1Eu+14UETtn5p2j1NkwMxc2gW5zSvgbiogFXUYOx2vWGGejA3+fCDQf\nOCQiVgMOiojvZ+Y5TZHhEJ3AN7sc4huUkPn3yUURMR04AfhGZr67VfbKiHg1cB1weETMzswFY22z\nlk/jvSczh0a9pbgn7+eUJE20arPLM3MJ5XL3WcAWwJyIeFqfdRdn5uXArpTL05+KiGfUalsF5zfv\n01vbhicBLc7MRV3q/Kl5X6O1bbfm/eLOwpl5L/Bjyp/JFuNuqSRJ0jKg6hJGmfkA5d7EM4DNgEs6\nn6wzSv1bKJeSH0fvBdcHYb3m/YHhDc1I4wJgjebSeKfNmvf2/amrNe/r0N3w9s5Z6ZIkScuV6utk\nZuaDwH7Ap4FnU4Lm1DEc4jTKEkj7RcQmtdvXS7MEUbftGwFHNR+/07F7+ClGJzSz1ofrrA8c1nz8\naqv8D5r3tzSLvLfPsyuwLbCYsi6nJEnScmtCnviTmQkcHBGLgEMpQXPHzLy+j7r3NsshnUR5+s0+\nE9HGLi6IiNuBqyiTg6ZQlhaa0fx8WmZ+r6POac3+PYGrI+JCylqXrwKeDHys46lF5wDfB3YC5kfE\nNygTfzalXEoP4H2d97JGxPt4eJmhzZv3/SPixc3Pl2bm55bmy0uSJNU0Yc8uB8jMwyLiXspI4CUR\nsVNm/rKPqrMpSwDtHREfzsyfTWQ7G8dSnmu+NWVZolUoI6rnAp/LzO92VsjMByJid8os+X0p628+\nAFwDfCIz/7uj/EMR8XLKEkevB15NWbLpLspz3k/NzAu6tG0GD8+QH7ZN8xpmyJQkScuMpQ6Zoy0n\nlJlHA0d3bJs6Sp3FPHwfZN/n6ig7E/qftpuZp1KWFRqT5qk+/9m8+il/P3By8+r3HNPH2i5JkqRB\nqn5PpiRJkjShl8slPdp418WsaWio20OzJEmqx5FMSZIkVWfIlCRJUnWGTEmSJFVnyJQkSVJ1hkxJ\nkiRVZ8iUJElSdYZMSZIkVWfIlCRJUnWGTEmSJFVnyJQkSVJ1hkxJkiRVZ8iUJElSdYZMSZIkVWfI\nlCRJUnVTBt0ALRumrTuNeUPzBt0MSZK0gnAkU5IkSdUZMiVJklSdIVOSJEnVGTIlSZJUnSFTkiRJ\n1RkyJUmSVJ0hU5IkSdUZMiVJklSdIVOSJEnVGTIlSZJUnSFTkiRJ1fnscgFw5S1XErNi0M1Qn3Io\nB90ESZJG5EimJEmSqjNkSpIkqTpDpiRJkqozZEqSJKk6Q6YkSZKqM2RKkiSpOkOmJEmSqjNkSpIk\nqTpDpiRJkqozZEqSJKk6Q6YkSZKqM2RKkiSpOkOmJEmSqpsy6AZIK4uZzBzx82SYNWvWIz4PDQ1N\nehskSSsHRzIlSZJUnSFTkiRJ1RkyJUmSVN1AQmZEZERkj30bR8SNTZnjWtsXDtdrXg9FxJ8j4vKI\nODQiVh3pXCO89muVndll/6KIuC4iPhER63cce7uIODMifhERd0bE4oi4KSK+FRE79tkXz46IvzXn\n+lKfdd7Yat8B/dSRJEmaTMvUxJ+I2BI4D1gbeGdmfrxLsVOAu4FVgH8EXgOcBOwI7D7C4Wf12H51\nl21zgTnNz2sDuwBvA14XEVtn5o3Nvpc2ryuAi4C/NW16JbB7RPxHZr6/V4MiYgpwJvDQCO3urLMB\n8HHgr8Ca/daTJEmaTMtMyIyInYGvA48BXp+ZZ/coenJmLmzV+yAlKO4WEdtn5txulTJz5hiaM6dd\nvhklPZ8SZI8B9m92Hd/tuBGxHnAlcFREfDIzb+lxnqOAzYH3UsLziCIigC8Cd1L66j19fh9JkqRJ\ntUzckxkR+wDfpozozRghYD5KZt5AGXkEeMEENI/MvB/4TPNxq9b2xT3K3wxcRunfZ3UrExHPB94P\nfBD4WZ9NeRdl5HR/yqipJEnSMmngI5kRcQjlcvdtwK6Z2e3ydb/ur9OqrqJ573ov6SMKRjwVeCGw\nBPh1l/1rUC6TXw0cD7y4j2Nu2pQ9JTMviYiX9t90LYuWZp3MzvUuJUla1gw0ZEbE8cCRwPXAyzLz\npnEc4znA9ObjpSOUm9ll88LMPL2Pc0wB3tJ8vKLL/ucDu1H6c33KvaFPpNxXekeXQx4PbAhMy8wH\nylXwUc9/JvBbyiX2cYmIeT12PXe8x5QkSepm0COZR1JGH2eMIWAeGhHtiT97Ao8FPpqZvUIUQLdH\nm8wFTu+yfXorlK4FvAzYBLgD+FCX8s/vOP49wP6ZeWZnwWbW+TuB92XmtSO0t+1YYAvgxZm5qM86\nkiRJAzPokPldSoD7SkTMyMy7+6hzSJdtMzNzxOuHmTnycOEjbd+8AO4DfgfMBo7LzN91OfZsYHZE\nrE4ZoTwYOCMits3Mg4fLRcSTKKH2CuDEfhoSES+kjF6emJk/GsN3eJTM3LLHOeYB05bm2JIkSW2D\nDpl7AGdRlvy5KCJ2zsw7R6mzYWYubALd5pTwNxQRC7qNHI7TrDHORgf+PhFoPnBIRKyTaq1RAAAg\nAElEQVQGHBQR38/Mc5oiH6OMjO6UmQ+OdrzmMvkZwHWUSUJagSzNPZk5NOqtwV15L6ckabIMdHZ5\nZi6hXO4+i3I5eE5EPK3Puosz83JgV8rl6U9FxDMmrLFjd37zPr21bRqwBvCr9oLvwMXN/n9ttg1P\nfloTeDawKbC4o87w5fnPNttOntBvI0mSNAaDHsmkmfjyBmAxsC9wSUTsmJm/77P+Lc2TgY6nLLh+\n4MS1dkzWa94faG37OvDTLmXXBV4O3EhZBP63zfYlwOd7HH8aJZhfSpnBvlSX0iVJkmoaeMgEyMwH\nm8c7LgIOogTNl7YXXR/FacBhwH4R8ZHMvH5iWvpIEbFVZv64y/aNeHgW+HeGt2fmB3ocZzolZF6e\nmQe0yi8Cuj42spmYtAXwX5n5uXF+BUmSpAmxTIRMgMxM4OCIWAQcysMjmqMGxsy8t1kO6STgA8A+\nE9vav7sgIm4HrqJMDpoCbATMaH4+LTO/N0ltkSRJWmYsE0/8acvMw4DjgA0oQfOf+6w6G/gDsHdE\nPG+i2tfhWMqknK0pzzZ/K/AvwLmUZZneNUntkCRJWqYMZCRztOWEMvNo4OiObVNHqbOYh++D7Ptc\nHWVnQv9TfjPzVODUfsuPcJw5PPxEoX7rzGQMbZUkSZpMy9xIpiRJkpZ/y8w9mdKKbmnWxaxlaKjb\ng68kSarPkUxJkiRVZ8iUJElSdYZMSZIkVWfIlCRJUnWGTEmSJFVnyJQkSVJ1hkxJkiRVZ8iUJElS\ndYZMSZIkVWfIlCRJUnWGTEmSJFVnyJQkSVJ1hkxJkiRVZ8iUJElSdVMG3QAtG6atO415Q/MG3QxJ\nkrSCcCRTkiRJ1RkyJUmSVJ0hU5IkSdUZMiVJklSdIVOSJEnVGTIlSZJUnSFTkiRJ1RkyJUmSVJ0h\nU5IkSdUZMiVJklSdIVOSJEnV+exyAXDlLVcSs2LQzVAfcigH3QRJkkblSKYkSZKqM2RKkiSpOkOm\nJEmSqjNkSpIkqTpDpiRJkqozZEqSJKk6Q6YkSZKqM2RKkiSpOkOmJEmSqjNkSpIkqTpDpiRJkqoz\nZEqSJKk6Q6YkSZKqmzLoBkgri5nMHPHzRJs1a9YjPg8NDU3q+SVJKxdHMiVJklSdIVOSJEnVGTIl\nSZJU3VKHzIjIiMge+zaOiBubMse1ti8crte8HoqIP0fE5RFxaESsOtK5Rnjt1yo7s8v+RRFxXUR8\nIiLW7zj2thHxkYj4SUT8MSKWRMRNEfG5iNh4hO//fyLiyxFxQ3P8myPi4ojYOyIe1b9dvnv7dWuX\n8lNH+c5f7dU2SZKkQZmwiT8RsSVwHrA28M7M/HiXYqcAdwOrAP8IvAY4CdgR2H2Ew8/qsf3qLtvm\nAnOan9cGdgHeBrwuIrbOzBubfV8D1gEuA74MPAC8CHgz8PqI2Dkzf9TxHXcHvg48BHwLOKc5x6uB\nrwI7AQd2adOfgZO7bP9rj+8FcA1wbpftvxihjiRJ0kBMSMiMiJ0p4esxwOsz8+weRU/OzIWteh+k\nBMXdImL7zJzbrVJmzhxDc+a0yzejpOdTguwxwP7NrpOAMzPzDx3f5SjgQ8BngP/TcezjKX04vd3W\niDiGEgoPiIgPZuZvO+rdPcbvAHD1OOpIkiQNRPV7MiNiH+DblNG9GSMEzEfJzBsoI48AL6jdtuYc\n91MCI8BWre0ndAbMxgnAImCziFirY9+zgL90huHMvBW4ovm4TpWGS5IkLUeqjmRGxCGUEcHbgF0z\ns9vl637dX6dVXUXz3vVe0g5JuXQO8GDHvl8CW0bEizPz0r8fPOKplAB7C3Btl2OuFhFvpNwi8Dfg\nZ8Almdl5/LZnRMRBwFrAncCPMvNnfbRfy6jxrpPZud6lJEnLomohMyKOB44Ergdelpk3jeMYzwGm\nNx8vHaHczC6bF2bm6X2cYwrwlubjFSOVbewFPB64PDPv7th3GGXU9vsR8U1gAeWezFdR7jV9Q2Yu\n6nLMpwNndmy7KSL273WLALBz82p/lznAv3W5HN9VRMzrseu5/dSXJEnqV82RzCMpo48zxhAwD42I\n9sSfPYHHAh/NzF6BCKDbo0rmAqd32T69FUrXAl4GbALcQbnXsqeI2BA4jTKS+e7O/Zn5g4h4EXAW\n8LrWrnuALwI/73LYLwI/oIyC3kO55P4OSvA9PyJelJnXtMrfC3yQMulnQbPtecBMYAfgwojYPDP/\nNtJ3kSRJmkw1Q+Z3KQHuKxExo8uoXzeHdNk2MzNHvB6YmTHS/g7bNy+A+4DfAbOB4zLzd70qNZe8\nz6fcU/n2zpnlTZmdKbPIfwrsC/yKMkr5DkqAfUUzgWn4cjtdvtsvgIMj4q/A4ZTw+OpW+duBYzvq\nXBIRu1BGe18IHECZqT+izNyyx3edB0wbrb4kSVK/aobMPSgjeq8ELmqW/LlzlDobZubCiFgd2JwS\n/oYiYkFmdl5OHq9ZY52V3QTMi4DnAIdk5ie7lHkK8D+UkcZXZ+a9za4FwLubUdBXAW+k+whrp9mU\nkLldP23MzAci4nOUkLkdfYRMLVvGe09mDvVzK/GjeS+nJGkyVZtdnplLKJe7zwK2AOZExNP6rLs4\nMy8HdqVcQv5URDyjVtvGIiLWpayr+U+UEcxTexTdBngycEUrYLZd3Lx3HT3s4o/N++P6LD/eOpIk\nSROu6hJGzWXhNwBnAJtRLuuuP3KtR9S/BTiOEpomfdilaetcykSYg7uNYLas1rz3WqJoePt9fZ5+\n6+Z9wYillr6OJEnShKu+TmazDM9+wKeBZ1OC5tQxHOI0yhJI+0XEJrXb10tEPBO4BNgI+PfM/Mwo\nVX5EmRC0bXN/ZPtYGwAHNR8vbG3fNCIeNerY9M/wE5G+1LFvWo/HU+5Imd3+qDqSJEmDNiFP/MnM\npExmWQQcSgmaO2bm9X3UvbdZDukk4APAPhPRxi7mAFOBecDUHssknT78hKLM/EPzhKJZlFnh3+bh\niT+vAdYEvpGZ57Xq7w0cHhGXAL+h3BqwEfAKYHXKYzg/2nHOjwGbRMRlwO+bbc8DXtr8/P7MvGx8\nX1mSJGliTNizywEy87CIuBc4ihI0d8rMX/ZRdTbwXmDviPjwJC06PrV535Le91HOARYOf8jMD0TE\nNcDBlHs0X0GZCPRzyjqYnaOhF1MmE20BbEu5LeBuyizxMymPteyc1XEmZbb5Cyj3rK5KGek9C/h4\nZv5gTN9SkiRpEix1yBxtOaHMPBo4umPb1FHqLAbWG+u5OsrOhP6n745xWaR2vW8C3+yz7Fwefmxm\nv8f/PPD5cTRNkiRpYKrfkylJkiRN6OVySQ8b77qYtQwNdXtQliRJE8ORTEmSJFVnyJQkSVJ1hkxJ\nkiRVZ8iUJElSdYZMSZIkVWfIlCRJUnWGTEmSJFVnyJQkSVJ1hkxJkiRVZ8iUJElSdYZMSZIkVWfI\nlCRJUnWGTEmSJFVnyJQkSVJ1UwbdAC0bpq07jXlD8wbdDEmStIJwJFOSJEnVGTIlSZJUnSFTkiRJ\n1RkyJUmSVJ0hU5IkSdUZMiVJklSdIVOSJEnVGTIlSZJUnSFTkiRJ1RkyJUmSVJ0hU5IkSdX57HIB\ncOUtVxKzYtDNUIccykE3QZKkcXEkU5IkSdUZMiVJklSdIVOSJEnVGTIlSZJUnSFTkiRJ1RkyJUmS\nVJ0hU5IkSdUZMiVJklSdIVOSJEnVGTIlSZJUnSFTkiRJ1RkyJUmSVJ0hU5IkSdUZMiVJklTdlEE3\nQFpRzWTmiJ8n0qxZsx7xeWhoaNLOLUkSOJIpSZKkCWDIlCRJUnVLHTIjIiMie+zbOCJubMoc19q+\ncLhe83ooIv4cEZdHxKERsepI5xrhtV+r7Mwu+xdFxHUR8YmIWL/j2NtFxJkR8YuIuDMiFkfETRHx\nrYjYsUtbpvbRnoyIl3Sp+4qIuCAift+0aUFEnB0RL+rxvVeLiLdHxI8j4o6I+GtEzI+IUyPimT3/\ncCRJkgZkwu7JjIgtgfOAtYF3ZubHuxQ7BbgbWAX4R+A1wEnAjsDuIxx+Vo/tV3fZNheY0/y8NrAL\n8DbgdRGxdWbe2Ox7afO6ArgI+FvTplcCu0fEf2Tm+1vHvXuEdmwA/DtwJ/Dj9o6IOAE4otl3LnAH\nsDGwB7BnROybmV9qlZ8CXAhsC/wK+G9gCfAC4J3AvhGxTWZe26MtkiRJk25CQmZE7Ax8HXgM8PrM\nPLtH0ZMzc2Gr3gcpQXG3iNg+M+d2q5SZM8fQnDnt8s0o6fmUIHsMsH+z6/hux42I9YArgaMi4pOZ\neUvThruh+0yOiPhw8+MZmbmktf3pwHuA24DnZebtrX07UMLtB4AvtQ73akrAvBDYJTMfatWZBRzb\nHPPfR+kHSZKkSVP9nsyI2Af4NvAQMGOEgPkomXkDZeQRykhddZl5P/CZ5uNWre2Le5S/GbiM0lfP\nGu34TYjdr/n4mY7dz2yOc0U7YDbnuRi4B1ino87wOb/TDpiNbzbvnXUkSZIGqmrIjIhDgC8DdwHb\nN8FpvO6v06quonnvei/pIwpGPBV4IeUS9a/7OPYrgacDl2Tmrzr2XQ/cB2wVEWt3nGc74PHA9zvq\n/LJ53zUiOv+8dmveO+tIkiQNVLXL5RFxPHAkJUi9LDNvGscxngNMbz5eOkK5mV02L8zM0/s4xxTg\nLc3HK7rsfz4lvE0B1qfcG/pEyn2ld4x2/NaxP925IzPviogjgY8B10bEuZR7MzeihNPvAQd1VPsO\n5daD1wA/j4jvU4LqlsCLgdOAT/TRLg3YeNbJ7FzvUpKk5UXNezKPpIw+zhhDwDw0ItoTf/YEHgt8\nNDPnjVCv28rSc4HTu2yf3gqlawEvAzahTLj5UJfyz+84/j3A/pl55gjtAcqMc2BnSnD8WrcymXly\n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+ ""text/plain"": [ + """" + ] + }, + ""metadata"": { + ""image/png"": { + ""height"": 690, + ""width"": 332 + } + }, + ""output_type"": ""display_data"" + }, + { + ""name"": ""stdout"", + ""output_type"": ""stream"", + ""text"": [ + ""\n"", + ""\n"", + ""************************************************************************************\n"", + ""\n"", + ""QSAR/ER_alpha_SubstructureFingerprinter.csv\n"", + ""\n"", + ""from Remove useless descriptor\n"", + ""The initial set of 307 descriptors has been reduced to 80 descriptors.\n"", + ""from Remove correlation\n"", + ""The initial set of 80 descriptors has been reduced to 66 descriptors.\n"", + ""\n"", + ""Training set\n"", + ""------------\n"", + ""N: 861\n"", + ""R2: 0.9042\n"", + ""std_R2: 0.0043\n"", + ""RMSE: 0.5480\n"", + ""std_RMSE: 0.0064\n"", + ""\n"", + ""Cross-validation set\n"", + ""------------\n"", + ""N: 861\n"", + ""Q2: 0.7009\n"", + ""std_Q2: 0.0110\n"", + ""RMSE: 0.7507\n"", + ""std_RMSE: 0.0083\n"", + ""\n"", + ""External set\n"", + ""------------\n"", + ""N: 370\n"", + ""Q2_EXt: 0.7115\n"", + ""std_Q2_EXt: 0.0194\n"", + ""RMSE: 0.5539\n"", + ""std_RMSE: 0.0214\n"" + ] + }, + { + ""data"": { + ""image/png"": 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LnOoqi9Ni2Hd/mvqeB/7h8dQH4xQMbmBBCnCSH9D/5Vgf0ZLIm79xMcSM5z3uF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+D7gH2H7af7KMY97bFiW1QP+5xs/I0Ycnhui4GbAopJL4ylu4VnJ4jk9Q2Leh+x2I5qGvf1N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BIHEIuAAAAEoeQCwAAgMQh5AIAACBxCLkAAABIHEIuAAAAEoeQCwAAgMQh5AJAg8lkMrUe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\nHi7OejA0FCE5fb6zs7iS2rJlEVr7+6MHuVCIY8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+ZdZjZr5nZ7Wb2iJntNbNhMztgZj8x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\nhFwAAAC0HEIuAAAAWg4hFwAAAC2HkAsAAICWQ8gFAABAy/l/uhMA4A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+ ""text/plain"": [ + """" + ] + }, + ""metadata"": { + ""image/png"": { + ""height"": 328, + ""width"": 348 + } + }, + ""output_type"": ""display_data"" + }, + { + ""name"": ""stdout"", + ""output_type"": ""stream"", + ""text"": [ + ""('top10', ['SubFP169', 'SubFP23', 'SubFP88', 'SubFP18', 'SubFP181', 'SubFP137', 'SubFP20', 'SubFP2', 'SubFP184', 'SubFP1'])\n"", + ""\n"", + ""('top20', ['SubFP169', 'SubFP23', 'SubFP88', 'SubFP18', 'SubFP181', 'SubFP137', 'SubFP20', 'SubFP2', 'SubFP184', 'SubFP1', 'SubFP183', 'SubFP5', 'SubFP135', 'SubFP182', 'SubFP49', 'SubFP172', 'SubFP275', 'SubFP100', 'SubFP33', 'SubFP3'])\n"", + ""\n"", + ""('top30', ['SubFP169', 'SubFP23', 'SubFP88', 'SubFP18', 'SubFP181', 'SubFP137', 'SubFP20', 'SubFP2', 'SubFP184', 'SubFP1', 'SubFP183', 'SubFP5', 'SubFP135', 'SubFP182', 'SubFP49', 'SubFP172', 'SubFP275', 'SubFP100', 'SubFP33', 'SubFP3', 'SubFP302', 'SubFP4', 'SubFP180', 'SubFP303', 'SubFP171', 'SubFP101', 'SubFP85', 'SubFP179', 'SubFP170', 'SubFP143'])\n"", + ""\n"", + ""('top40', ['SubFP169', 'SubFP23', 'SubFP88', 'SubFP18', 'SubFP181', 'SubFP137', 'SubFP20', 'SubFP2', 'SubFP184', 'SubFP1', 'SubFP183', 'SubFP5', 'SubFP135', 'SubFP182', 'SubFP49', 'SubFP172', 'SubFP275', 'SubFP100', 'SubFP33', 'SubFP3', 'SubFP302', 'SubFP4', 'SubFP180', 'SubFP303', 'SubFP171', 'SubFP101', 'SubFP85', 'SubFP179', 'SubFP170', 'SubFP143', 'SubFP12', 'SubFP136', 'SubFP214', 'SubFP133', 'SubFP9', 'SubFP86', 'SubFP173', 'SubFP84', 'SubFP16', 'SubFP274'])\n"", + ""\n"", + ""('top50', ['SubFP169', 'SubFP23', 'SubFP88', 'SubFP18', 'SubFP181', 'SubFP137', 'SubFP20', 'SubFP2', 'SubFP184', 'SubFP1', 'SubFP183', 'SubFP5', 'SubFP135', 'SubFP182', 'SubFP49', 'SubFP172', 'SubFP275', 'SubFP100', 'SubFP33', 'SubFP3', 'SubFP302', 'SubFP4', 'SubFP180', 'SubFP303', 'SubFP171', 'SubFP101', 'SubFP85', 'SubFP179', 'SubFP170', 'SubFP143', 'SubFP12', 'SubFP136', 'SubFP214', 'SubFP133', 'SubFP9', 'SubFP86', 'SubFP173', 'SubFP84', 'SubFP16', 'SubFP274', 'SubFP52', 'SubFP188', 'SubFP6', 'SubFP15', 'SubFP13', 'SubFP17', 'SubFP103', 'SubFP78', 'SubFP70', 'SubFP28'])\n"" + ] + }, + { + ""data"": { + ""image/png"": 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e3LnpkB/VHkiRp1sYSMpOcnuR1/VY8w+f2B1b1H6/vy5v68qwkywauPYBu0/Pp\nrE5y4MA9uwEX0H33z8zzKyy6qroP+B7d6O3HB8/1U+Uf6z9+Ywd3TZIkaUbjeibzCGA1cFeSbwGT\nC1cOotsTci+6PS2/AFBVNya5nm6/y5uSXEc3DX4i8DWm/3nGbwNbk3yebmr8eOBlwGa6LYJ2mCQf\nYfu2QS/vy7OSHN2//1ZVXTJwy+/Qbbr+sSSvA26g+9u8ge550r8HPrHoHZckSZqjcYXMdcAPgNcC\nL6ULfnvSbdK+EbgcuHxoZfnJdCOQJwPv7e//EN2iobdP09b76bb/WQUs79u4CDh3kTZin84JPH36\n/ii2/wISwJMhs6q+nuTX6fbzPBb498DjwK3AHwLnL+HpfkmS9Aw2rn0yb6f76cWLZ7p24J4H6ILi\nqhGnM3ygqlayfdX1uv41Uxsr5tCfjaPabVX/wD23AL891/skSZLGaaku/JEkSdJObKz7ZErSXMx3\nX8xWJiYmZr5IkgQ4kilJkqRFYMiUJElSc4ZMSZIkNWfIlCRJUnOGTEmSJDVnyJQkSVJzhkxJkiQ1\nZ8iUJElSc4ZMSZIkNWfIlCRJUnOGTEmSJDVnyJQkSVJzhkxJkiQ1t2zcHdB4HfrCQ9k8sXnc3ZAk\nSbsYRzIlSZLUnCFTkiRJzRkyJUmS1JwhU5IkSc0ZMiVJktScIVOSJEnNGTIlSZLUnCFTkiRJzRky\nJUmS1JwhU5IkSc0ZMiVJktScIVOSJEnNLRt3BzReW+7cQtZm3N3QM0hN1Li7IEnaARzJlCRJUnOG\nTEmSJDVnyJQkSVJzhkxJkiQ1Z8iUJElSc4ZMSZIkNWfIlCRJUnOGTEmSJDVnyJQkSVJzhkxJkiQ1\nZ8iUJElSc4ZMSZIkNWfIlCRJUnPLxt0BSTuXNayZ9nNra9eufcrniYmJRW1PktSGI5mSJElqzpAp\nSZKk5naKkJlkfZJKsnzcfZEkSdLMmoTMJLsnWZVkU5L7kzya5J4ktyS5JMlJLdqZY5829sF0qtf6\ngWtXjDj/SJI7klyZ5JVDdT8ryeokn0mytb+2krx7hj49P8n5Sb6b5MEk9yXZnOScJM8dcf1vJPnD\nJNckuatv445mfyRJkqRFsuCFP0l2B74KnAA8AFwN3AHsAbwEOAM4BLhqoW3N02eBbSOObx1x7DZg\nff9+b+BI4DTg1CSnVdWGgXN/3L+/G7gLOGC6TvSjsDcCzwc2AtcAewKvB84HzkxyZFX9bOC2M4DV\nwKPA3wHyr4zMAAAgAElEQVQvmK4NSZKkpaLF6vLT6QLmd4Bjq+qngyeTPAc4okE787W+qjbO8tpt\nVbVm8ECStcC5wDpgMmQ+BLwR2FpVdyZZA8y05PUcuoC5pqqeXC7bh/RrgdcAbwMuG+w7XUj+26p6\nJEnN8ntIkiSNVYvp8qP6cv1wwASoqoeq6puTn5Os6ad9Vwxfm2T58FT2cH+TfCDJ95M83E9nX5hk\nnwbfYyoX9+VBSZ4HUFWPVNU1VXXnHOo5uC+fMqJbVY/Tjf4CPG/o3NaqurmqHplHvyVJksamxUjm\nfX354gZ1zeRC4NXAFcCXgeOB9wHHJDm6qh5ehDYz8H4hI4l/Szfi+ybg5icrT3YD3gA8AVy3gPql\nsZjrPpnD+15KknZNLULmF4EPA2f3i1c2AJur6rYGdQ97FfDyybqTfBS4EjiVbjr6vBH3rBw1ajo8\nLT6N9/TlrVV171w7POB84M3AeUmOA7bQPbf6emB/4N1VdfM0989bks1TnDpkMdqTJElacMisqpuT\nnAlcBJzZv0hyP3A9cGlVfWWh7fQuGgyvVfVEknOAU4B3MTpkvnOKutaMOLa8f74SusU9RwDH0I0y\nfnCefZ7s6z1JjgQuBd5C9wwmdKOjfwJ8fSH1S5IkLSVNflayqq5IsgE4DjgaeEVfngKckuQyYGVV\nLXThyqYRbd+a5Ha6gLhfVT0wdMlxc1j4cyDbF/A8BvyYbqR2XVXdMM8+A0+uLr8K2Itu0dC3gecA\nJ9MtKjo5ySur6ocLaWeUqjpsij5tBg5t3Z4kSVKz3y6vqkfpVklfC0+umn4r3cjdO+im0b+0wGbu\nnuL4XXQBcV+6bZTma1NVrVjA/dNZD/wa8LKquqU/9g/Ap5PsSbcl0gSwcpHalxbFXJ/JrIm5/V/T\nZzglaee0aL/4U1WPV9UVdIt1YPv08BN9OSrg7jdDtVPtE7l/Xz5tdftS0D+reixw/0DAHDS5+n7k\niKMkSdLOZkf8rOSDfTm5SvsnfTlq8/LDZ6jr2OEDSQ7u69o2Yqp8qdijL/dJsseI85NbF7lVkSRJ\n2iUsOGQmOT3J6/qteIbP7Q+s6j9e35c39eVZSZYNXHsA3abn01md5MCBe3YDLqD7Hp+Z51dYdFV1\nH/A9utHbjw+e66fKP9Z//MYO7pokSdKiaPFM5hF0P314V5JvAZMLVw6i2xNyL7o9Lb8AUFU3Jrme\nbr/Lm5JcRzcNfiLwNab/ecZvA1uTfJ5uavx44GXAZrotgnaYJB9h+xZAL+/Ls5Ic3b//VlVdMnDL\n79Btuv6xJK8DbqD727yB7nnSvwc+MdTGIcBHhpr+haHN6j+4wK2VJEmSmmsRMtcBPwBeC7yULvjt\nSbdJ+0bgcuDyoZXlJ9ONQJ4MvLe//0N0i4bePk1b76fb/mcVsLxv4yLg3EXaiH06J/D06fuj2P4L\nSABPhsyq+nqSX6fbz/NY4N8DjwO3An8InD9iun9/nr4F03OGjq0BDJmSJGlJabFP5u10P7148UzX\nDtzzAF1QXDXidIYPVNVKtq+6Xte/ZmpjxRz6s3FUu63qH7jnFuC353D9RubYL0mSpKVgRyz8kSRJ\n0jNMs30yJT0zzHVfzIWamJiY+SJJ0pLjSKYkSZKaM2RKkiSpOUOmJEmSmjNkSpIkqTlDpiRJkpoz\nZEqSJKk5Q6YkSZKaM2RKkiSpOUOmJEmSmjNkSpIkqTlDpiRJkpozZEqSJKk5Q6YkSZKaM2RKkiSp\nuWXj7oDG69AXHsrmic3j7oYkSdrFOJIpSZKk5gyZkiRJas6QKUmSpOYMmZIkSWrOkClJkqTmDJmS\nJElqzpApSZKk5gyZkiRJas6QKUmSpOYMmZIkSWrOkClJkqTm/O3yZ7gtd24hazPubmiJqYkadxck\nSTs5RzIlSZLUnCFTkiRJzRkyJUmS1JwhU5IkSc0ZMiVJktScIVOSJEnNGTIlSZLUnCFTkiRJzRky\nJUmS1JwhU5IkSc0ZMiVJktScIVOSJEnNGTIlSZLU3LJxd0DS4ljDmmk/t7J27dqnfJ6YmFiUdiRJ\nOxdHMiVJktScIVOSJEnN7RQhM8n6JJVk+bj7IkmSpJk1CZlJdk+yKsmmJPcneTTJPUluSXJJkpNa\ntDPHPm3sg+lUr/UD164Ycf6RJHckuTLJK4fqflaS1Uk+k2Rrf20lefc0/Xl1ks8l+W6S+5I8nOSH\nSa5K8psjrl8+Q/8nX8c0/cNJkiQ1sOCFP0l2B74KnAA8AFwN3AHsAbwEOAM4BLhqoW3N02eBbSOO\nbx1x7DZgff9+b+BI4DTg1CSnVdWGgXN/3L+/G7gLOGCGfrymf90IXAf8E/AvgZOAE5P8QVV9fOD6\nB4C1T6ulcwDwLuA+4KYZ2pUkSdrhWqwuP50uYH4HOLaqfjp4MslzgCMatDNf66tq4yyv3VZVawYP\nJFkLnAusAyZD5kPAG4GtVXVnkjXATEtq/+Nw3X39LwK2AL+X5D9X1Z0AVfUAjF4OnOQP+7eXVdXP\nZ/5akiRJO1aL6fKj+nL9cMAEqKqHquqbk5+TrOmneVcMXzswRbx+qv4m+UCS7/fTzXckuTDJPg2+\nx1Qu7suDkjwPoKoeqaprJgPhbFTVw1Mc/xFwA92/xcEz1ZPkWcDK/uP/Mdv2JUmSdqQWI5n39eWL\nG9Q1kwuBVwNXAF8GjgfeBxyT5OipgtwCZeB9Na88eT7dSO/Pgf82i1tOAvYHrq+q77fuj3Zdc9kn\nc3jvS0mS5qpFyPwi8GHg7CTPpZtS3lxVtzWoe9irgJdP1p3ko8CVwKnAOcB5I+5ZOWrUdNTU9RTe\n05e3VtW9c+3wsCSHA2+m+9v/C+BEYF/gvbOs/9/05afn0ObmKU4dMts6JEmS5mLBIbOqbk5yJnAR\ncGb/Isn9wPXApVX1lYW207toMLxW1RNJzgFOoVsIMypkvnOKutaMOLa8f74SusU9RwDHAE8AH5xn\nn4cdzlOf33wQOKuqPjfTjf0WTq+jGz3+y0b9kSRJaq7Jz0pW1RVJNgDHAUcDr+jLU4BTklwGrKyq\nhU43bxrR9q1JbqcLiPv1C2YGHTeHhT8Hsj0APgb8mG6kdl1V3TDPPg/391PAp5LsCRwEnA1cluRV\nVXX2DLevopu+/+xcFvxU1WGjjvcjnIfOth5JkqTZavbb5VX1KHBt/5rc2uitwKXAO+im0b+0wGbu\nnuL4XXQBcV+6rX/ma1NVrVjA/bPWPz/6PWB1kmcD/zbJ16vqC6OuT7IMOKv/6IIfzdlcnsmsidn/\nf9DnNyVJoyzaL/5U1eNVdQXdYh3o9oiEbuoZRgfc/Wao9gVTHN+/L5+2un0ncU1frpjmmhOBF9IF\n4dksEJIkSRqbHfGzkg/25eQq7Z/05ajNyw+foa5jhw8kObiva9uIqfKdxYv68rFprplc8OMopiRJ\nWvIWHDKTnJ7kdUmeVleS/emeI4RuERBs/4Was/op4MlrD6Db9Hw6q5McOHDPbsAFdN/jM/P8CjtE\nkt+Y4vgvAb/Xf7x6imsOBF6PC34kSdJOosUzmUcAq4G7knwL+GF//CDgTcBedHtafgGgqm5Mcj3d\nfpc3JbmObhr8ROBrTP/zjN8Gtib5PN3U+PHAy4DNwPkNvsusJfkI27cAenlfnpXk6P79t6rqkoFb\nrk1yD3AzcDvd3/6X6H4taRnwn6rqb6Zo7t10QXpOC34kSZLGpUXIXAf8AHgt8FK64Lcn3ajbRuBy\n4PKhleUn041Angy8t7//Q3SLht4+TVvvB95CNzq6vG/jIuDcRdqIfTon8PTp+6PY/gtIAIMh81y6\n0cgj6QL17nQLmb4EXFJVXxvVSL+A6l39R6fKJUnSTqHFPpm30/304sUzXTtwzwN0QXHViNMZPlBV\nK9n+U4rr+tdMbayYQ382jmq3Vf399Z8EPjmXe/r7Hmf7M5uSJEk7hR2x8EeSJEnPMM32yZS0tMxl\nX8yFmJiYmPkiSdIzjiOZkiRJas6QKUmSpOYMmZIkSWrOkClJkqTmDJmSJElqzpApSZKk5gyZkiRJ\nas6QKUmSpOYMmZIkSWrOkClJkqTmDJmSJElqzpApSZKk5gyZkiRJas6QKUmSpOaWjbsDGq9DX3go\nmyc2j7sbkiRpF+NIpiRJkpozZEqSJKk5Q6YkSZKaM2RKkiSpOUOmJEmSmjNkSpIkqTlDpiRJkpoz\nZEqSJKk5Q6YkSZKaM2RKkiSpOUOmJEmSmvO3y5/htty5hazNuLuhMamJGncXJEm7KEcyJUmS1Jwh\nU5IkSc0ZMiVJktScIVOSJEnNGTIlSZLUnCFTkiRJzRkyJUmS1JwhU5IkSc0ZMiVJktScIVOSJEnN\nGTIlSZLUnCFTkiRJzRkyJUmS1NyycXdA0sKtYc20nxdi7dq1T/k8MTHRrG5J0q7LkUxJkiQ1Z8iU\nJElSc7tsyEyyPkklWT7uvkiSJD3TjC1kJtk9yaokm5Lcn+TRJPckuSXJJUlOGkOfNvbBdKrX+oFr\nV4w4/0iSO5JcmeSVQ3X/cpIPJ7kuye39tXcn+XKS42bo1zuT3JTkH5P8tO/nmxfpzyBJkrRgY1n4\nk2R34KvACcADwNXAHcAewEuAM4BDgKvG0T/gs8C2Ece3jjh2G7C+f783cCRwGnBqktOqakN/7jzg\nt4C/A/4KuB/4FeAk4KQkq6vqk8OVJ/kj4Hfp/j5/Qvc3+lfAV5K8t6r+9/l8QUmSpMU0rtXlp9MF\nzO8Ax1bVTwdPJnkOcMQ4OtZbX1UbZ3nttqpaM3ggyVrgXGAdMBky/xr4RFXdPHTtscDfABckubKq\n7hw4dxRdwPzvwK9X1U/64xcAm4E/SvLVqto2t68nSZK0uMY1XX5UX64fDpgAVfVQVX1z8nOSNf10\n9Irha5MsH57KHrJbkg8k+X6Sh/vp7AuT7NPii0zh4r48KMnzAKpq/XDA7I9vAjbSjVAeNXT67L78\nD5MBs79nW9/Gs4GzmvZckiSpgXGNZN7Xly/eAW1dCLwauAL4MnA88D7gmCRHV9XDi9BmBt7XLK5/\ntC8fGzr+mr786xH3XAN8vL/GjQv1FLPdJ3N4D0xJkloZV8j8IvBh4Owkz6WbUt5cVbctQluvAl4+\nWXeSjwJXAqcC59A9Kzls5ahR0+Fp8Wm8py9vrap7p7swyYHAbwIPAdcPHN8beBHwj4NT6AN+0Jcz\nBvUkm6c4dchM90qSJM3HWEJmVd2c5EzgIuDM/kWS++mC1qVV9ZVGzV00GF6r6okk5wCnAO9idMh8\n5xR1rRlxbHmSyeN70z1LegzwBPDB6TqW5NnAn9NNe39ocEoc2Lcvn/Y4wdDx/aZrQ5IkaRzG9rOS\nVXVFkg3AccDRwCv68hTglCSXASurajbTzdPZNKLtW5PcThcQ96uqB4YuOW4OC38OZPt09WPAj+lG\natdV1Q1T3dSvsP8c3Ujr54E/mmV7c1ZVh03Rh83AoYvVriRJeuYa62+XV9WjwLX9azJ4vRW4FHgH\n3TT6lxbYzN1THL+LLiDuS7eN0nxtqqoVc7mh/55/BryN7lnRM0eE6cmRyn0ZbfL4QvquXdRsn8ms\niZn/D+dzm5Kk+VhSv/hTVY9X1RV0i3Vg+8KXJ/pyVCieabr4BVMc378vp5qOXhRJngX8Bd1el5cD\nZ1TV8IIfquqfgB8B/0OSF46o6pf78v9brL5KkiTN15IKmQMe7MvJVdqTzyoeMOLaw2eo69jhA0kO\n7uvaNmKqfNEk2YNu0dHbgMuA366qx6e55bq+PGHEuTcMXSNJkrRkjCVkJjk9yeuSPK39JPsDq/qP\nk6utb+rLs5IsG7j2ALpNz6ezul/BPXnPbsAFdN/9M/P8CnPWL/LZAJwM/ClwVlU9Mf1dfKov/5ck\nvzBQ13K6Few/Zwd+B0mSpNka1zOZRwCrgbuSfAv4YX/8IOBNwF50e1p+AaCqbkxyPd1+lzcluY5u\nGvxE4GuMHuGc9G1ga5LP002NHw+8jO4Xc85v/L2m8yngjcC9dNPg5yYZvmbj4IKjqrohyf8GfAC4\nJckX6DZt/y3gF4H3+ms/kiRpKRpXyFxHt8/ja4GX0gW/Pek2ad9I96zi5UOLYU6mG4E8GXhvf/+H\n6BYNvX2att4PvIVudHR538ZFwLmLtBH7VA7qy3/O9KOvGwc/VNXvJvmvdCOX/4bu+dQtwAVV9dVF\n6KckSdKCjWufzNvpfhbx4pmuHbjnAbqguGrE6acNCVbVSmBl/3Fd/5qpjRVz6M/GUe22qHvEveuB\n9fO9X5IkaUdbqgt/JEmStBMb6z6ZktqY7b6Y8zExMTHzRZIkDXEkU5IkSc0ZMiVJktScIVOSJEnN\nGTIlSZLUnCFTkiRJzRkyJUmS1JwhU5IkSc0ZMiVJktScIVOSJEnNGTIlSZLUnCFTkiRJzRkyJUmS\n1JwhU5IkSc0ZMiVJktTcsnF3QON16AsPZfPE5nF3Q5Ik7WIcyZQkSVJzhkxJkiQ1Z8iUJElSc4ZM\nSZIkNWfIlCRJUnOGTEmSJDVnyJQkSVJzhkxJkiQ1Z8iUJElSc4ZMSZIkNWfIlCRJUnP+dvkz3JY7\nt5C1GXc3NKAmatxdkCRpwRzJlCRJUnOGTEmSJDVnyJQkSVJzhkxJkiQ1Z8iUJElSc4ZMSZIkNWfI\nlCRJUnOGTEmSJDVnyJQkSVJzhkxJkiQ1Z8iUJElSc4ZMSZIkNWfIlCRJUnPLxt0BaVe2hjXTfp6v\ntWvXPuXzxMREk3olSWrFkUxJkiQ1Z8iUJElSc4bMXpL1SSrJ8nH3RZIkaWe304TMJLsnWZVkU5L7\nkzya5J4ktyS5JMlJY+jTxj6YTvVaP3DtihHnH0lyR5Irk7xyqO5fTvLhJNclub2/9u4kX05y3I7+\nrpIkSXOxUyz8SbI78FXgBOAB4GrgDmAP4CXAGcAhwFVj6uJngW0jjm8dcew2YH3/fm/gSOA04NQk\np1XVhv7cecBvAX8H/BVwP/ArwEnASUlWV9UnW30BSZKklnaKkAmcThcwvwMcW1U/HTyZ5DnAEePo\nWG99VW2c5bXbqmrN4IEka4FzgXXAZMj8a+ATVXXz0LXHAn8DXJDkyqq6cyEdlyRJWgw7y3T5UX25\nfjhgAlTVQ1X1zcnPSdb009Erhq9Nsnx4KnvIbkk+kOT7SR7up7MvTLJPiy8yhYv78qAkzwOoqvXD\nAbM/vgnYSDeKe9TweUmSpKVgZxnJvK8vX7wD2roQeDVwBfBl4HjgfcAxSY6uqocXoc0MvK9ZXP9o\nXz62CH3RIprNPpnDe2BKkrQz2llC5heBDwNnJ3ku3ZTy5qq6bRHaehXw8sm6k3wUuBI4FTiH7lnJ\nYStHjZoOT4tP4z19eWtV3TvdhUkOBH4TeAi4fjaVJ9k8xalDZtk/SZKkOdkpQmZV3ZzkTOAi4Mz+\nRZL76YLWpVX1lUbNXTQYXqvqiSTnAKcA72J0yHznFHWtGXFseZLJ43vTPUt6DPAE8MHpOpbk2cCf\nA88GPlRVP5nuekmSpHHZKUImQFVdkWQDcBxwNPCKvjwFOCXJZcDKqprNdPN0No1o+9Ykt9MFxP2q\n6oGhS46bw8KfA4HJ3wB8DPgx3Ujtuqq6Yaqb+hX2n6Mbaf088EezbI+qOmyKOjcDh862HkmSpNna\naUImQFU9ClzbvyaD11uBS4F30E2jf2mBzdw9xfG76ALivnTbKM3XpqpaMZcb+u/5Z8Db6J4VPbNB\nmNYYzOaZzJqY+Z/W5zYlSUvdzrK6fKSqeryqrqBbrAPwmr58oi9Hhej9Zqj2BVMc378vn7a6fTEl\neRbwF8C/Ai4HzqgqF/xIkqQlbacOmQMe7MvJVdqTzyoeMOLaw2eo69jhA0kO7uvaNmKqfNEk2YNu\n0dHbgMuA366qx3dU+5IkSfO1U4TMJKcneV2Sp/U3yf7Aqv7j5Grrm/ryrCTLBq49gG7T8+ms7ldw\nT96zG3AB3d/qM/P8CnPWL/LZAJwM/ClwVlU9Mf1dkiRJS8PO8kzmEcBq4K4k3wJ+2B8/CHgTsBfd\nnpZfAKiqG5NcT7ff5U1JrqObBj8R+BqjRzgnfRvYmuTzdFPjxwMvAzYD5zf+XtP5FPBG4F7gR8C5\nSYav2TiHBUeSJEk7zM4SMtf9/+zdfbTlVX3n+fcHSholKrMyKnSapiBpw7QdxdIMiGAVRgQfsEqg\nTWPTWjjWxFnGoC7xIUu5t9ruToKrGisTMp0OQom96BFc4mNobcWqGqADSUFJkokJaSwasnhQECUS\nhqfv/PH7Xepw6tznfevULd6vtc76nd/T3vvc+udTe//2/gG3Aa8DXkoX/A6mW6R9K92zilcMTYZZ\nS9cDuRZ4X3//h+kmDb1thro+ALyVrnd0ZV/HZuCCJVqIfTpH9dv/mZl7X7cufVMkSZLmZ1mEzKq6\nk+7VixfPdu3APQ/SBcUNI07v0SVYVeuB9f3upv4zWx1r5tGeraPqbVG2JEnSvmZZPJMpSZKk5WVZ\n9GRKy9Vc1sVciImJidkvkiRpjOzJlCRJUnOGTEmSJDVnyJQkSVJzhkxJkiQ1Z8iUJElSc4ZMSZIk\nNWfIlCRJUnOGTEmSJDVnyJQkSVJzhkxJkiQ1Z8iUJElSc4ZMSZIkNWfIlCRJUnOGTEmSJDW3YtwN\n0HitOnwVOyZ2jLsZkiRpP2NPpiRJkpozZEqSJKk5Q6YkSZKaM2RKkiSpOUOmJEmSmjNkSpIkqTlD\npiRJkpozZEqSJKk5Q6YkSZKaM2RKkiSpOUOmJEmSmvPd5c9wN999M9mYcTfjGa0matxNkCSpOXsy\nJUmS1JwhU5IkSc0ZMiVJktScIVOSJEnNGTIlSZLUnCFTkiRJzRkyJUmS1JwhU5IkSc0ZMiVJktSc\nIVOSJEnNGTIlSZLUnCFTkiRJzRkyJUmS1NyKcTdA2t9MMjnj/kJs3LjxafsTExOLLlOSpKVkT6Yk\nSZKaM2RKkiSpuWURMpNsSVJJVo67LZIkSZpdk5CZ5MAkG5JsS/JAkseS3Jfk1iSXJHlLi3rm2aat\nfTCd7rNl4No1I84/muSuJFcledVQ2c9Kcl6Sy5Ls7K+tJO+epU0vTHJhkj9P8lCS+5PsSHJ+kufO\n4Te9OMlP+7r+04L/OJIkSUts0RN/khwIfA04DXgQ+DpwF3AQ8BLg7cAxwFcWW9cCfRbYNeL4zhHH\n7gC29N8PAY4HzgLOSHJWVV09cO7T/fd7gXuAI2ZqRN8LeyPwQmArcA1wMPB64ELgnCTHV9XfT3P/\nCuBzwJMz1SNJkrQvaDG7/Gy6gPldYHVV/XjwZJLnAMc1qGehtlTV1jleu6uqJgcPJNkIXABsAqZC\n5sPAG4GdVXV3kklgtum+59MFzMmqemqqcB/Svwm8FvjnwOXT3P+bwLF9OZvn+HskSZLGosVw+Qn9\ndstwwASoqoer6jtT+0km++HeNcPXJlk5PJQ93N4kH0zyvSSP9MPZFyV5XoPfMZ2L++1RSV4AUFWP\nVtU1VXX3PMo5ut8+rUe3qp6g6/0FeMGoG5O8EvgE8Eng1nnUKUmSNBYtejLv77cvblDWbC4CXgNc\nCXwZOBV4P3BSkhOr6pElqDMD32sR5fwFXY/vm4Bbnio8OQB4A90w+LV7VJ48m26YfCfw28CJi2iD\nxmC2dTKH18CUJGl/0CJkfhH4CPCefvLK1cCOqrqjQdnDXg0cO1V2ko8BVwFn0A0jf3LEPetH9ZoO\nD4vP4L399vaq+uF8GzzgQuDNwCeTnAzcTPfc6uuBw4B3V9UtI+77beAoYFVVPZ5kxCUzS7JjmlPH\nzLswSZKkOVh0yKyqW5KcQ/ec4Dn9hyQPANuBS6vqq4utp7d5MLxW1ZNJzgfWAe9idMh85zRlTY44\ntrJ/vhK6yT3HASfR9TJ+aIFtnmrrfUmOBy4F3kr3DCZ0vaN/CHxr+J4kvwK8D/hoVf2/i6lfkiRp\nb2ryWsmqujLJ1cDJdMO5L++364B1SS4H1lfVYoabAbaNqPv2JHfSBcRDq+rBoUtOnsfEnyPZPYHn\nceAHdD21m6rqhgW2GXhqdvlXgGfTTRq6HngOsJZuUtHaJK+qqu/31x9KN9P9xv78glXVK6Zp0w5g\n1WLKliRJGqXZu8ur6jG6WdLfhKdmTZ9J13P3Drph9C8tspp7pzl+D11AfD7dMkoLta2q1izi/pls\nAX4JeFlVTU3e+QnwB0kOplsSaQJY35/798DPAq/rJwdpmZrtmcyamP3/Xj63KUlabpbsjT9V9URV\nXUk3WQd2Dw9PrfM4KuAeOkuxL5rm+GH9do/Z7fuC/lnV1cADAwFz0NTs+8Eex1V0vZ7fG1wkfuDa\nf9kfG7XepyRJ0lg168mcwUP9dmrGyo/67ajFy185S1mr6Z7zfEqSo/uydo0YKt9XHNRvn5fkoKp6\ndOj81NJFg8e/CPzpiLIOpxtu/+90i7r/j4btlCRJaqLFG3/OBn4IfLuqnhw6dxiwod+dCoc39dtz\nk3yuqh7vrz2CbtHzmZyX5PKB2eUHAJ+i65G9bLG/ZalU1f1J/hL4X+jWu/zE1Ll+qPzj/e63B+75\n16PK6mfKvxH446qa8TWWkiRJ49KiJ/M44DzgniTXAd/vjx9Ftybks+nWtPwCQFXdmGQ73XqXNyW5\nlm4Y/HTgG8z8esbrgZ1JPk83NH4q8DJgB90SQXtNko+yewmgY/vtuUmm1rG8rqouGbjlN+gWXf94\nklOAG+j+Nm+ge570b4DfWfKGS5Ik7QUtQuYm4DbgdcBL6YLfwXSLtG8FrgCuGJpZvpauB3It3RI9\ntwEfpps09LYZ6voA3fI/G4CVfR2bgQuWaCH2mZxGN3w/6AR2vwEJ4KmQWVXfSvLLdOt5rgZ+HXgC\nuB34LeDCfXi4X5IkaV5arJN5J92rFy+e7dqBex6kC4obRpzeY7XxqlrP7lnXm5jDkj7zmSXeL3E0\nr1XOFzILvZ/086/me99QGVuZZ1slSZL2tiWbXS5JkqRnrr0xu1x6RpltXcyFmJiYmP0iSZL2IfZk\nSpcstaMAACAASURBVJIkqTlDpiRJkpozZEqSJKk5Q6YkSZKaM2RKkiSpOUOmJEmSmjNkSpIkqTlD\npiRJkpozZEqSJKk5Q6YkSZKaM2RKkiSpOUOmJEmSmjNkSpIkqTlDpiRJkppbMe4GaLxWHb6KHRM7\nxt0MSZK0n7EnU5IkSc0ZMiVJktScIVOSJEnNGTIlSZLUnCFTkiRJzRkyJUmS1JwhU5IkSc0ZMiVJ\nktScIVOSJEnNGTIlSZLUnCFTkiRJzfnu8me4m+++mWzMuJvxjFQTNe4mSJK0ZOzJlCRJUnOGTEmS\nJDVnyJQkSVJzhkxJkiQ1Z8iUJElSc4ZMSZIkNWfIlCRJUnOGTEmSJDVnyJQkSVJzhkxJkiQ1Z8iU\nJElSc4ZMSZIkNWfIlCRJUnOGTEmSJDW3YtwNkPYnk0zOuD9XGzdufNr+xMTEAlskSdJ42JMpSZKk\n5gyZvSRbklSSleNuiyRJ0nK3bEJmkgOTbEiyLckDSR5Lcl+SW5NckuQtY2jT1j6YTvfZMnDtmhHn\nH01yV5KrkrxqqOxnJTkvyWVJdvbXVpJ37+3fKUmSNF/L4pnMJAcCXwNOAx4Evg7cBRwEvAR4O3AM\n8JUxNfGzwK4Rx3eOOHYHsKX/fghwPHAWcEaSs6rq6oFzn+6/3wvcAxzRqL2SJElLalmETOBsuoD5\nXWB1Vf148GSS5wDHjaNhvS1VtXWO1+6qqsnBA0k2AhcAm4CpkPkw8EZgZ1XdnWQScPaHJElaFpbL\ncPkJ/XbLcMAEqKqHq+o7U/tJJvuh5TXD1yZZOTyUPeSAJB9M8r0kj/TD2RcleV6LHzKNi/vtUUle\nAFBVj1bVNVV19xLWK0mStCSWS8i8v9++eC/UdRHwCWAbsBn4IfB+4NokBy9RnRn4XktUhyRJ0l6z\nXIbLvwh8BHhPkufSDSnvqKo7lqCuVwPHTpWd5GPAVcAZwPnAJ0fcs35Ur+nwsPgM3ttvb6+qH863\nwdp3zbRO5vBamJIk7U+WRcisqluSnEPXs3hO/yHJA8B24NKq+mqj6jYPhteqejLJ+cA64F2MDpnv\nnKasyRHHVvbPV0I3uec44CTgSeBDC2zzjJLsmObUMUtRnyRJ0rIImQBVdWWSq4GTgROBl/fbdcC6\nJJcD66tqscPN20bUfXuSO+kC4qFV9eDQJSfPY+LPkeyewPM48AO6ntpNVXXDAtssSZK0T1k2IROg\nqh4Dvtl/ppY2OhO4FHgH3TD6lxZZzb3THL+HLiA+n24ZpYXaVlVrFnH/vFXVK0Yd73s4V+3NtkiS\npGeGZRUyh1XVE8CVSX4J+DjwWrqQ+WR/yajfd+gsxb4I+KsRxw/rt3vMbpemM9MzmTUxfae7z2tK\nkpa75TK7fDYP9dupWdo/6rejFi9/5SxlrR4+kOTovqxdI4bKJUmSNGRZhMwkZyc5Jcke7U1yGLCh\n393eb2/qt+cmWTFw7RF0i57P5LwkRw7ccwDwKbq/1WUL/AmSJEnPKMtluPw44DzgniTXAd/vjx8F\nvAl4NvBl4AsAVXVjku3Aa4CbklxLNwx+OvANZn494/XAziSfpxsaPxV4GbADuLDx75pRko+yewb4\nsf323CQn9t+vq6pL9mabJEmS5mK5hMxNwG3A64CX0gW/g+kWad8KXAFcMTSzfC1dD+Ra4H39/R+m\nmzT0thnq+gDwVrre0ZV9HZuBC6rqkVY/aI5OY8/h+xPY/QYkAEOmJEna5yyLkFlVd9K9evHi2a4d\nuOdBuqC4YcTpDB+oqvXA+n53U/+ZrY4182jP1lH1tipfkiRpX7IsnsmUJEnS8mLIlCRJUnPLYrhc\nWi5mWhdzPiYmJma/SJKkfZg9mZIkSWrOkClJkqTmDJmSJElqzpApSZKk5gyZkiRJas6QKUmSpOYM\nmZIkSWrOkClJkqTmDJmSJElqzpApSZKk5gyZkiRJas6QKUmSpOYMmZIkSWpuxbgboPFadfgqdkzs\nGHczJEnSfsaeTEmSJDVnyJQkSVJzhkxJkiQ1Z8iUJElSc4ZMSZIkNWfIlCRJUnOGTEmSJDVnyJQk\nSVJzhkxJkiQ1Z8iUJElSc4ZMSZIkNee7y5/hbr77ZrIx427Gfq8matxNkCRpr7InU5IkSc0ZMiVJ\nktScIVOSJEnNGTIlSZLUnCFTkiRJzRkyJUmS1JwhU5IkSc0ZMiVJktScIVOSJEnNGTIlSZLUnCFT\nkiRJzRkyJUmS1JwhU5IkSc0ZMiVJktTcinE3QFquJpmccX8uNm7c+LT9iYmJRbRIkqR9hz2ZkiRJ\nam5ZhMwkW5JUkpXjboskSZJm1yRkJjkwyYYk25I8kOSxJPcluTXJJUne0qKeebZpax9Mp/tsGbh2\nzYjzjya5K8lVSV41VPazkpyX5LIkO/trK8m7Z2nTC5NcmOTPkzyU5P4kO5Kcn+S5I65/V5IvJfmb\nJD9J8tMkf5nkD5P8YrM/liRJUmOLfiYzyYHA14DTgAeBrwN3AQcBLwHeDhwDfGWxdS3QZ4FdI47v\nHHHsDmBL//0Q4HjgLOCMJGdV1dUD5z7df78XuAc4YqZG9L2wNwIvBLYC1wAHA68HLgTOSXJ8Vf39\nwG3nAIf3990DPEn3Nz0XeEeSdVV1zUz1SpIkjUOLiT9n0wXM7wKrq+rHgyeTPAc4rkE9C7WlqrbO\n8dpdVTU5eCDJRuACYBMwFTIfBt4I7Kyqu5NMArPN2DifLmBOVtVTsz36kP5N4LXAPwcuH7jnjVX1\nyHBBSU7p79lEF1YlSZL2KS2Gy0/ot1uGAyZAVT1cVd+Z2k8y2Q8trxm+NsnK4aHs4fYm+WCS7yV5\npB/OvijJ8xr8julc3G+PSvICgKp6tKquqaq751HO0f32aT26VfUEXe8vwAuGzu0RMPvj/5Wu1/gX\n5lG/JEnSXtMiZN7fb1/coKzZXAR8AtgGbAZ+CLwfuDbJwUtUZwa+1yLK+Yt++6anFZ4cALyBbij8\n2jk1KDkROBT4s0W0R5Ikacm0GC7/IvAR4D395JWrgR1VdUeDsoe9Gjh2quwkHwOuAs6gG47+5Ih7\n1o/qNR0eFp/Be/vt7VX1w/k2eMCFwJuBTyY5GbiZ7rnV1wOHAe+uqltG3ZjkLOCfAc+mC/NvBB4A\nfn0R7VFjM62TObwepiRJ+7tFh8yquiXJOXQ9i+f0H5I8AGwHLq2qry62nt7mwfBaVU8mOR9YB7yL\n0SHzndOUNTni2Mr++UroJvccB5xE18v4oQW2eaqt9yU5HrgUeCvdM5jQ9Y7+IfCtGW4/C/jVgf3b\ngLdX1Z/Ope4kO6Y5dcxc7pckSZqvJksYVdWVwD8GTqULel/ry14HfCXJZ5NkhiLmatuIum8H7qQL\niIeOuOfkqsrwZ5ryj6SbwDNBNwz/C3Q9tScNzCxfkH52+Xbgl+h6Ip9PN3P8/wD+JfAnSY4adW9V\n/Yu+zc+n6839PnB9kvWLaZMkSdJSafZayap6jG7G8zfhqVnTZ9L13L2Dbhj9S4us5t5pjt9DFxCf\nTzchZqG2VdWaRdw/ky10AfNlVXVrf+wnwB/0z5N+mi7crp+ugKr6CXBDktOBPwX+ryTfqqq7Zqq4\nql4x6njfw7lqnr9DkiRpVkv27vJ+1vSVSX4J+Djd8PCX6Iaep6t7VE/koBcBfzXi+GH9do/Z7fuC\n/lnV1cADAwFz0NTs+5FhcFhVPZrk23Sh9XjgC00aqkWZ6ZnMmhg9Z8xnNSVJ+6u98VrJh/rt1BD1\nj/rtqMXLXzlLWauHDyQ5ui9rV1UtphdzKR3Ub5+X5KAR56eWLnp0HmX+XL99fMGtkiRJWiKLDplJ\nzk5ySr8Uz/C5w4AN/e72fntTvz03yYqBa4+gW/R8JuclOXLgngOAT9H9jssW+BOWXFXdD/wlXe/t\nJwbP9UPlH+93vz1w/Gf7AL2HJG+mmzz0d4x4TlWSJGncWgyXHwecB9yT5Dq6SSkAR9GtCfls4Mv0\nQ7pVdWOS7cBrgJuSXEs3DH468A1mfj3j9cDOJJ+nGxo/FXgZsINuiaC9JslH2T07+9h+e26/hiXA\ndVV1ycAtv0G36PrH+zf23ED3t3kD3fOkfwP8zsD1RwA7kvwp3SMCf0v3OMGxdEPkj9Ete/QjJEmS\n9jEtQuYmuiV1Xge8lC74HUy3SPtW4ArgiqoafChtLV0P5Frgff39H6abNPS2Ger6AF0P3gZgZV/H\nZuCC6d6Os4ROY8/h+xPY/QYkgKdCZlV9K8kv063nuZpujcsngNuB3wIuHBruv6M/vho4BfhZumD5\nP4A/oFvO6S9b/iBJkqRWWqyTeSfdqxcvnu3agXsepAuKG0ac3mN5oapaz+5Z15v6z2x1rJlHe7aO\nqrdV+QP33Ar8qzle+yN2D6NLkiQtK3tj4o8kSZKeYQyZkiRJam7J1smU9nczrYs5VxMTE4tviCRJ\n+yB7MiVJktScIVOSJEnNGTIlSZLUnCFTkiRJzRkyJUmS1JwhU5IkSc0ZMiVJktScIVOSJEnNGTIl\nSZLUnCFTkiRJzRkyJUmS1JwhU5IkSc0ZMiVJktTcinE3QOO16vBV7JjYMe5mSJKk/Yw9mZIkSWrO\nkClJkqTmDJmSJElqzpApSZKk5gyZkiRJas6QKUmSpOYMmZIkSWrOkClJkqTmDJmSJElqzpApSZKk\n5gyZkiRJas53lz/D3Xz3zWRjxt2M/VZN1LibIEnSWNiTKUmSpOYMmZIkSWrOkClJkqTmDJmSJElq\nzpApSZKk5gyZkiRJas6QKUmSpOYMmZIkSWrOkClJkqTmDJmSJElqzpApSZKk5gyZkiRJas6QKUmS\npOYMmZIkSWpuxbgbIC1Hk0zOuD+bjRs3Pm1/YmJikS2SJGnfYk+mJEmSmjNkSpIkqTlDZi/JliSV\nZOW42yJJkrTcLZuQmeTAJBuSbEvyQJLHktyX5NYklyR5yxjatLUPptN9tgxcu2bE+UeT3JXkqiSv\nGip75Sxl/997+/dKkiTN1bKY+JPkQOBrwGnAg8DXgbuAg4CXAG8HjgG+MqYmfhbYNeL4zhHH7gC2\n9N8PAY4HzgLOSHJWVV09dP13gS+NKOfPF9RSSZKkvWBZhEzgbLqA+V1gdVX9ePBkkucAx42jYb0t\nVbV1jtfuqqrJwQNJNgIXAJuA4ZC5c/h6SZKkfd1yGS4/od9uGQ6YAFX1cFV9Z2o/yWQ/pLxm+NqB\nYegt09R1QJIPJvlekkf64eyLkjyvxQ+ZxsX99qgkL1jCeiRJkvaK5dKTeX+/ffFeqOsi4DXAlcCX\ngVOB9wMnJTmxqh5Zgjoz8L2Gzv3DJL8G/Czd3+G/VdWtS9AGLcJ062QOr4cpSdIzxXIJmV8EPgK8\nJ8lz6YaUd1TVHUtQ16uBY6fKTvIx4CrgDOB84JMj7lk/qtd0HsPc7+23t1fVD4fOndJ/npJkK/DO\nqvofcyk8yY5pTh0zx/ZJkiTNy7IImVV1S5JzgM3AOf2HJA8A24FLq+qrjarbPBheq+rJJOcD64B3\nMTpkvnOasiZHHFuZZOr4IXTPkp4EPAl8aOC6h/u6vgTc3h97aV/mycC3kxxbVT+d/SdJkiTtXcsi\nZAJU1ZVJrqYLWCcCL++364B1SS4H1lfV8HDzfG0bUfftSe6kC4iHVtWDQ5ecPI+JP0cCU+8QfBz4\nAV1P7aaqumGgzvvoJgMN2p7k9cB1dOH03XTBe0ZV9YpRx/sezlVzbLckSdKcLZuQCVBVjwHf7D9T\nSxudCVwKvINuGH3Ucj/zce80x++hC4jPp1tGaaG2VdWahd5cVY8nuYQuZL6GOYRMLb3pnsmsidH/\n5/FZTUnS/m65zC4fqaqeqKor6SbrALy23z7Zb0eF6ENnKfZF0xw/rN/uMbt9DH7Qbw8ZayskSZKm\nsaxD5oCH+u3ULO0f9dsjRlz7ylnKWj18IMnRfVm7RgyVj8Px/fb2Ga+SJEkak2URMpOcneSUJHu0\nN8lhwIZ+d3u/vanfnptkxcC1R7Dnc47Dzkty5MA9BwCfovtbXbbAnzBvSVZN83t/BfhAv/uf9lZ7\nJEmS5mO5PJN5HHAecE+S64Dv98ePAt4EPJtuTcsvAFTVjUm20z2zeFOSa+mGwU8HvsHoHs4p1wM7\nk3yebmj8VOBlwA7gwsa/ayb/HvgnSW6ge4UmdLPLpx4J+MTgRCFJkqR9yXIJmZuA24DX0QWtU4GD\n6RYn3wpcAVwxNLN8LV0P5Frgff39H6abNPS2Ger6APBWut7RlX0dm4ELlmgh9ul8rm/HLwNvAJ5F\nNynpSuD3qur/2YttkSRJmpdlETKr6k66Vy9ePNu1A/c8SBcUN4w4neEDVbUeWN/vbuo/s9WxZh7t\n2Tqq3hmu/wzwmbleL0mStC9ZFs9kSpIkaXlZFj2Z0r5munUx52piYmL2iyRJWsbsyZQkSVJzhkxJ\nkiQ1Z8iUJElSc4ZMSZIkNWfIlCRJUnOGTEmSJDVnyJQkSVJzhkxJkiQ1Z8iUJElSc4ZMSZIkNWfI\nlCRJUnOGTEmSJDVnyJQkSVJzK8bdAI3XqsNXsWNix7ibIUmS9jP2ZEqSJKk5Q6YkSZKaM2RKkiSp\nOUOmJEmSmjNkSpIkqTlDpiRJkpozZEqSJKk5Q6YkSZKaM2RKkiSpOUOmJEmSmjNkSpIkqTnfXf4M\nd/PdN5ONGXcz9is1UeNugiRJY2dPpiRJkpozZEqSJKk5Q6YkSZKaM2RKkiSpOUOmJEmSmjNkSpIk\nqTlDpiRJkpozZEqSJKk5Q6YkSZKaM2RKkiSpOUOmJEmSmjNkSpIkqTlDpiRJkpozZEqSJKm5FeNu\ngLRcTDI54/5sNm7c+LT9iYmJRbZIkqR9lz2ZkiRJas6QKUmSpOaWRchMsiVJJVk57rZIkiRpdk1C\nZpIDk2xIsi3JA0keS3JfkluTXJLkLS3qmWebtvbBdLrPloFr14w4/2iSu5JcleRVQ2U/K8l5SS5L\nsrO/tpK8e4b2vCbJ55L8eZL7kzyS5PtJvpLkV6a5Z8sM7a8kxzT7g0mSJDW06Ik/SQ4EvgacBjwI\nfB24CzgIeAnwduAY4CuLrWuBPgvsGnF854hjdwBb+u+HAMcDZwFnJDmrqq4eOPfp/vu9wD3AEbO0\n47X950bgWuCnwD8G3gKcnuTfVNUnprl3M93fdtgPZ6lTkiRpLFrMLj+bLmB+F1hdVT8ePJnkOcBx\nDepZqC1VtXWO1+6qqsnBA0k2AhcAm4CpkPkw8EZgZ1XdnWQSmG2q8G8Pl92X/3PAzcBvJvn9qrp7\nxL2frqpdc/wNkiRJY9diuPyEfrtlOGACVNXDVfWdqf0kk/1Q75rha5OsHB7KHm5vkg8m+V4/3HxX\nkouSPK/B75jOxf32qCQvAKiqR6vqmmkC4UhV9cg0x/8WuIHu3+LoxTZWkiRpX9CiJ/P+fvviBmXN\n5iLgNcCVwJeBU4H3AyclOXG6ILdIGfhezQtPXkjX0/v/AX81zWVv6IP0E8DfANdW1U9at0XzM906\nmcPrYUqS9EzUImR+EfgI8J4kz6UbUt5RVXc0KHvYq4Fjp8pO8jHgKuAM4HzgkyPuWT+q13TU0PU0\n3ttvb6+qRT8DmeSVwJvp/vb/CDgdeD7wvhnK//2h/YeSfKyqLh559Z517pjmlBOHJEnSklh0yKyq\nW5KcQzc55Zz+Q5IHgO3ApVX11cXW09s8GF6r6skk5wPrgHcxOmS+c5qyJkccW9k/Xwnd5J7jgJOA\nJ4EPLbDNw17J05/ffAg4t6o+N+La7cAfAX8M3Af8Q+Ct/f2/l+SxqvqPjdolSZLUTJPXSlbVlUmu\nBk4GTgRe3m/XAeuSXA6sr6rFDjdvG1H37UnupAuIh1bV8Czsk+cx8edIdgfAx4Ef0PXUbqqqGxbY\n5uH2/gfgPyQ5GDgKeA9weZJXV9V7hq69dOj224FNSf4K+Crwb5N8pqqemKXOV4w63vdwrlrgT5Ek\nSZpWs3eXV9VjwDf7z9TSRmcClwLvoBtG/9Iiq7l3muP30AXE5zN6qZ+52lZVaxZx/5z1z4/+JXBe\nkn8A/FqSb1XVF+Zw79eS/C3wc8A/Bf5saVurUaZ7JrMmRv9fymc1JUnPJEv2xp+qeqKqrqSbrAPd\nGpHQDT3D6IB76CzFvmia44f12z1mty8T1/TbNfO45wf99pC2TZEkSVq8vfFayYf67dQs7R/121GL\nl79ylrJWDx9IcnRf1q4RQ+XLxc/128fncnGS59NN2ing+0vVKEmSpIVadMhMcnaSU5LsUVaSw4AN\n/e72fntTvz03yYqBa4+gW/R8JuclOXLgngOAT9H9jssW+BP2iiT/6zTHfx74zX736wPHD0vyj0Zc\n/zN0byU6GPhWVU33CIEkSdLYtHgm8zjgPOCeJNexu2ftKOBNwLPp1rT8AkBV3ZhkO916lzcluZZu\nGPx04BvM/HrG64GdST5PNzR+KvAyYAdwYYPfMmdJPsruJYCO7bfnJjmx/35dVV0ycMs3k9wH3ALc\nSfe3/3m6tyWtAP7PqvqvA9cfA3wryX8D/ppudvnPAafQPR5wOzDtu9IlSZLGqUXI3ATcBrwOeCld\n8DuYbpH2rcAVwBVDM8vX0vVArgXe19//YbpJQ2+boa4P0C3hswFY2dexGbhgiRZin8lp7Dl8fwK7\n34AEMBgyLwBeT/c+9NOBA+kmMn0JuKSqvjFU1n8HPgP8Mt37zQ+le53lXwG/B/xuVT2EJEnSPqjF\nOpl30r16cU4Lg/f3PEgXFDeMOJ3hA1W1Hljf727qP7PVsWYe7dk6qt5W5ffX/y7wu/O4/k7g1+ZT\nhyRJ0r5ib0z8kSRJ0jNMs3Uypf3ddOtiztXExMTsF0mStJ+wJ1OSJEnNGTIlSZLUnCFTkiRJzRky\nJUmS1JwhU5IkSc0ZMiVJktScIVOSJEnNGTIlSZLUnCFTkiRJzRkyJUmS1JwhU5IkSc0ZMiVJktSc\nIVOSJEnNrRh3AzReqw5fxY6JHeNuhiRJ2s/YkylJkqTmDJmSJElqzpApSZKk5gyZkiRJas6QKUmS\npOYMmZIkSWrOkClJkqTmDJmSJElqzpApSZKk5gyZkiRJas6QKUmSpOZ8d/kz3M1330w2ZtzNWJZq\nosbdBEmS9ln2ZEqSJKk5Q6YkSZKaM2RKkiSpOUOmJEmSmjNkSpIkqTlDpiRJkpozZEqSJKk5Q6Yk\nSZKaM2RKkiSpOUOmJEmSmjNkSpIkqTlDpiRJkpozZEqSJKk5Q6YkSZKaWzHuBkj7okkmZ9yfycaN\nG5+2PzEx0aBFkiQtL/ZkSpIkqTlDpiRJkppbFiEzyZYklWTluNsiSZKk2TUJmUkOTLIhybYkDyR5\nLMl9SW5NckmSt7SoZ55t2toH0+k+WwauXTPi/KNJ7kpyVZJXDZX9rCTnJbksyc7+2kry7lna9MIk\nFyb58yQPJbk/yY4k5yd57og63prkM/31P0nycJI/S/Kvh6+XJEnalyx64k+SA4GvAacBDwJfB+4C\nDgJeArwdOAb4ymLrWqDPArtGHN854tgdwJb++yHA8cBZwBlJzqqqqwfOfbr/fi9wD3DETI3oe2Fv\nBF4IbAWuAQ4GXg9cCJyT5Piq+vv+lp8Hvgj8FPgO3d/1Z4BTgU8Av5rk1VX1w5nqlSRJGocWs8vP\npguY3wVWV9WPB08meQ5wXIN6FmpLVW2d47W7qmpy8ECSjcAFwCZgKmQ+DLwR2FlVdyeZBGabQnw+\nXcCcrKqnph/3If2bwGuBfw5c3p96CHgv8Nmq+unA9QfRhc839XW+b46/TZIkaa9pMVx+Qr/dMhww\nAarq4ar6ztR+ksl+aHnN8LVJVg4PZQ+3N8kHk3wvySP9cPZFSZ7X4HdM5+J+e1SSFwBU1aNVdU1V\n3T2Pco7ut0/r0a2qJ+h6KQFeMHD8b6vq9wcD5lTdwL/rd9fMo35JkqS9pkVP5v399sUNyprNRcBr\ngCuBL9MNHb8fOCnJiVX1yBLUmYHvtYhy/oKux/dNwC1PFZ4cALwBeBK4do5lPdZvH19EezQPo9bJ\nHF4PU5Ik7dYiZH4R+Ajwnn4yytXAjqq6o0HZw14NHDtVdpKPAVcBZ9ANR39yxD3rR/WaDg+Lz+C9\n/fb2RT7/eCHwZuCTSU4GbqZ7bvX1wGHAu6vqlhnuH/Sufvtf5nJxkh3TnDpmjvVJkiTNy6JDZlXd\nkuQcYDNwTv8hyQPAduDSqvrqYuvpbR4Mr1X1ZJLzgXV0wWtUyHznNGVNjji2sn++ErrJPccBJ9H1\nMn5ogW2eaut9SY4HLgXeSvcMJnS9o38IfGsu5fQz9X+NbnLVhYtpkyRJ0lJp8lrJqroyydXAycCJ\nwMv77TpgXZLLgfVVtZjhZoBtI+q+PcmddAHx0Kp6cOiSk+cx8edIdk/geRz4AV1P7aaqumGBbQae\nml3+FeDZdJOGrgeeA6ylm1S0Nsmrqur7M5RxAnAF3YzzM6vqR3Opu6peMU15O4BVc/8VkiRJc9Ps\n3eVV9RjdLOlvwlOzps+k67l7B90w+pcWWc290xy/hy4gPp9uGaWF2lZVaxZx/0y2AL8EvKyqbu2P\n/QT4gyQH0y2JNAGsH3Vzv1bnNXS9qm+oqpuWqJ0aYdQzmTUx+v9MPqspSdISvvGnqp6oqivpJuvA\n7uHhJ/vtqIB76CzFvmia44f12z1mt+8L+mdVVwMPDATMQVOz76frcTwJ+Abd0Prrq+r6JWmoJElS\nI3vjtZIP9dupWdpTQ7yjFi9/5SxlrR4+kOTovqxdI4bK9xUH9dvn9etcDptauujR4RNJXks3wedx\n4JSq+uOlaaIkSVI7iw6ZSc5Ockq/FM/wucOADf3u9n47Ncx7bpIVA9ceQbfo+UzOS3LkwD0HAJ+i\n+x2XLfAnLLmquh/4S7re208MnuuHyj/e73576Nzr6d6m9PfAr1TVnyx9ayVJkhavxTOZxwHnAfck\nuQ6YmrhyFN2akM+mW9PyCwBVdWOS7XTrXd6U5Fq6YfDT6YaEZ3o94/XAziSfpxsaPxV4GbCDDfMk\n3QAAIABJREFUvTzTOslH2b0E0LH99twkJ/bfr6uqSwZu+Q26Rdc/nuQU4Aa6v80b6J4n/RvgdwbK\n/0W6v9vBwB/RTQxaO9yOeSzFJEmStNe0CJmbgNuA1wEvpQt+B9Mt0r6Vbjb0FUMzy9fS9UCupXst\n4m3Ah+kmDb1thro+QLf8zwZgZV/HZuCCJVqIfSansefw/QnsfgMSwFMhs6q+leSX6dbzXA38OvAE\ncDvwW8CFQ8P9h9P9HaGbQHXmNO2YXGD7JUmSlkyLdTLvpHv14sWzXTtwz4N0QXHDiNMZPlBV69k9\n63pT/5mtjjXzaM/WUfW2Kn/gnluBf7VUbZIkSdpX7I2JP5IkSXqGabZOprQ/GbUu5lxNTEzMfpEk\nSfs5ezIlSZLUnCFTkiRJzRkyJUmS1JwhU5IkSc0ZMiVJktScIVOSJEnNGTIlSZLUnCFTkiRJzRky\nJUmS1JwhU5IkSc0ZMiVJktScIVOSJEnNGTIlSZLU3IpxN0DjterwVeyY2DHuZkiSpP2MPZmSJElq\nzpApSZKk5gyZkiRJas6QKUmSpOYMmZIkSWrOkClJkqTmDJmSJElqzpApSZKk5gyZkiRJas6QKUmS\npOYMmZIkSWrOkClJkqTmVoy7ARqvm+++mWzMuJux7NREjbsJkiTt0+zJlCRJUnOGTEmSJDVnyJQk\nSVJzhkxJkiQ1Z8iUJElSc4ZMSZIkNWfIlCRJUnOGTEmSJDVnyJQkSVJzhkxJkiQ1Z8iUJElSc4ZM\nSZIkNWfIlCRJUnMrxt0AaV8zyeSM+zPZuHHj0/YnJiYatEiSpOXHnkxJkiQ1Z8iUJElSc/ttyEyy\nJUklWTnutkiSJD3TjC1kJjkwyYYk25I8kOSxJPcluTXJJUneMoY2be2D6XSfLQPXrhlx/tEkdyW5\nKsmr5lDfJQP3/sI01/xCksv6ch9NcneSzyX5+YY/XZIkqamxTPxJciDwNeA04EHg68BdwEHAS4C3\nA8cAXxlH+4DPArtGHN854tgdwJb++yHA8cBZwBlJzqqqq0dVkOR04H8D/g74mWmueSVwLfBc4NvA\nfwaOBP4F8JYka6rqlrn9JEmSpL1nXLPLz6YLmN8FVlfVjwdPJnkOcNw4GtbbUlVb53jtrqqaHDyQ\nZCNwAbAJ2CNkJnkB8IfA54HDgNXTlP0ZuoD5waq6aOD+E4GtwGVJXl5VNce2SpIk7RXjGi4/od9u\nGQ6YAFX1cFV9Z2o/yWQ/pLxm+NokK4eHsocckOSDSb6X5JF+2PmiJM9r8UOmcXG/PaoPlMP+Y799\n73QFJDkaeClwH7B58FxVXUfXE/wy4KRFt1aSJKmxcfVk3t9vX7wX6roIeA1wJfBl4FTg/cBJSU6s\nqkeWoM4MfH9aL2OS9cA6YF1V3Z8MXvo0h/XbXVX15Ijzt/fbXwG2L7ypms2odTKH18OUJElPN66Q\n+UXgI8B7kjyXbkh5R1XdsQR1vRo4dqrsJB8DrgLOAM4HPjninvWjek2Hh8VnMNVDeXtV/XDqYJIj\n6Xol/1NVfXmWMqbuOzJJRgyJH91vf3G2xiTZMc2pY2a7V5IkaSHGEjKr6pYk59AFrnP6D0keoOuV\nu7Sqvtqous2D4bWqnkxyPl1v4rsYHTLfOU1ZkyOOrUwydfwQumdJTwKeBD40dVGSA+gmFP0d8Buz\nNbqq/jrJbcA/6a9/asg8yQnAm/vd/2m2siRJkva2sb1WsqquTHI1cDJwIvDyfrsOWJfkcmB9g0kt\n20bUfXuSO+kC4qFV9eDQJSfPY+LPkcDUuwMfB35A11O7qapuGLjuA3QTfN5UVT+aY9nvAa4BPp3k\nzXSz24+g64X9M+BYujA7o6p6xajjfQ/nqjm2RZIkac7G+u7yqnoM+Gb/mVra6EzgUuAddMPoX1pk\nNfdOc/weuoD4fLpllBZqW1WtmemCJC8G/i1wWVX90VwLrqprkxwPfJzuudLVdM9ifgT4W7rZ6fct\nsN2ao1HPZNbE6P/7+KymJEmdfeqNP1X1RFVdSTdZB+C1/Xaqt25UKD50lmJfNM3xqYk1e8xuXwL/\nFPgHwLnDC7ize/mi2/pj6wZvrKpbqurMqnpBVR1UVcf0yxn9s/6SP9kL7ZckSZqXsfZkzuChfjs1\n9XpqePmIEde+cpayVjM0+7pfHugIupnbi+nFnKtddGtejvImusB7FfATRi8C/zRJnkW31uhjwBea\ntFCSJKmhcb3x52y62dPfHl6eJ8lhwIZ+dyoc3tRvz03yuap6vL/2CLpFz2dyXpLLB2aXHwB8iq4X\n97JF/5g5qKqdwLtHnUuylS5k/mZV/c3QuUOAR6rqiYFjK4DfBX4B+J2qumep2i1JkrRQ4+rJPA44\nD7gnyXXA9/vjR9H17D2bbk3LLwBU1Y1JttM9l3hTkmvphsFPB77B6B7OKdcDO5N8nm5o/FS6Rcx3\nABc2/l2tnQxckuRbdK/d/Bm6NyX9PN3f5hNjbJskSdK0xhUyNwG3Aa+je6vNqcDBdIu0bwWuAK4Y\nmlm+lq4Hci3wvv7+D9NNGnrbDHV9AHgrXe/oyr6OzcAFS7QQe0t/TReSVwMvBB6mm2E+wZ5/H0mS\npH3GuNbJvJPu1YsXz3btwD0P0gXFDSNO7/HanKpaD6zvdzf1n9nqWDOP9mwdVe98zVRnVf013Wx7\nSZKkZWWfml0uSZKk/cO+OrtcGptR62LO1cTExOwXSZL0DGBPpiRJkpozZEqSJKk5Q6YkSZKaM2RK\nkiSpOUOmJEmSmjNkSpIkqTlDpiRJkpozZEqSJKk5Q6YkSZKaM2RKkiSpOUOmJEmSmjNkSpIkqTlD\npiRJkppbMe4GaLxWHb6KHRM7xt0MSZK0n7EnU5IkSc0ZMiVJktScIVOSJEnNGTIlSZLUnCFTkiRJ\nzRkyJUmS1JwhU5IkSc0ZMiVJktScIVOSJEnNGTIlSZLUnCFTkiRJzRkyJUmS1NyKcTdA43Xz3TeT\njRl3M5aFmqhxN0GSpGXDnkxJkiQ1Z8iUJElSc4ZMSZIkNWfIlCRJUnOGTEmSJDVnyJQkSVJzhkxJ\nkiQ1Z8iUJElSc4ZMSZIkNWfIlCRJUnOGTEmSJDVnyJQkSVJzhkxJkiQ1t2LcDZD2BZNMzrg/ysaN\nG5+2PzEx0bBFkiQtb/ZkSpIkqTlDpiRJkppbFiEzyZYklWTluNsiSZKk2TUJmUkOTLIhybYkDyR5\nLMl9SW5NckmSt7SoZ55t2toH0+k+WwauXTPi/KNJ7kpyVZJXDZX9rCTnJbksyc7+2kry7hnas2uW\n9lSSTwzV8dYkn0ny50l+kuThJH+W5F8nee6S/OEkSZIaWPTEnyQHAl8DTgMeBL4O3AUcBLwEeDtw\nDPCVxda1QJ8Fdo04vnPEsTuALf33Q4DjgbOAM5KcVVVXD5z7dP/9XuAe4IhZ2vFp4NARxwP8Jt2/\nxTUDx38e+CLwU+A7dH/XnwFOBT4B/GqSV1fVD2epV5Ikaa9rMbv8bLqA+V1gdVX9ePBkkucAxzWo\nZ6G2VNXWOV67q6omBw8k2QhcAGwCpkLmw8AbgZ1VdXeSSWDGqcVV9elRx5OcSvfvcEtV/enAqYeA\n9wKfraqfDlx/EF34fFNf5/vm+NskSZL2mhbD5Sf02y3DAROgqh6uqu9M7SeZ7IeG1wxfm2Tl8FD2\ncHuTfDDJ95I80g9nX5TkeQ1+x3Qu7rdHJXkBQFU9WlXXVNXdDcr/3/vtHwwerKq/rarfHwyYU3UD\n/67fXdOgfkmSpOZa9GTe329f3KCs2VwEvAa4Evgy3dDx+4GTkpxYVY8sQZ0Z+F5NC05eBJwO/B1w\nxTxufazfPt6yPdpt1DqZw+tiSpKk6bUImV8EPgK8p5+McjWwo6ruaFD2sFcDx06VneRjwFXAGcD5\nwCdH3LN+VK/p8LD4DN7bb29fgucf3wU8i64X+KF53gfwX+ZycZId05w6Zh51SpIkzdmiQ2ZV3ZLk\nHGAzcE7/IckDwHbg0qr66mLr6W0eDK9V9WSS84F1dMFrVMh85zRlTY44trJ/vhK6yT3HAScBTwIf\nWmCbR0oSYGo2+n+cx31vAX6NbnLVhS3bJEmS1EqT10pW1ZVJrgZOBk4EXt5v1wHrklwOrK+qxQ43\nbxtR9+1J7qQLiIdW1YNDl5w8j4k/R7J7As/jwA/oemo3VdUNC2zzdF4HHA3cPDThZ1pJTqAbVv8p\ncGZV/Wgu91XVK6Ypbwewam7NlSRJmrtm7y6vqseAb/afqaWNzgQuBd5BN4z+pUVWc+80x++hC4jP\np1tGaaG2VdWaRdw/H1MTfubUi9mv1XkNXa/qG6rqpqVqmEY/k1kTT/8/ks9oSpI0vSV7409VPVFV\nV9JN1gF4bb99st+OCrij1pEc9KJpjh/Wb/eY3b4vSvJCYC1znPCT5CTgG3QTj15fVdcvbQslSZIW\nZ2+8VnJqQsvULO2pId5Ri5e/cpayVg8fSHJ0X9auEUPl+6pz6Sb8/OfZJvwkeS3dBJ/HgVOq6o/3\nQvskSZIWZdEhM8nZSU5JskdZSQ4DNvS72/vt1DDvuUlWDFx7BN2i5zM5L8mRA/ccAHyK7ndctsCf\nsFcNTfj5g1mufT3d25T+HviVqvqTJW6eJElSEy2eyTwOOA+4J8l1wPf740fRvZXm2XRrWn4BoKpu\nTLKdbr3Lm5JcSzcMfjrdkPBMr2e8HtiZ5PN0Q+OnAi8DdrCXZ1on+Si7lwA6tt+em+TE/vt1VXXJ\niFtfC/wC3YSf6ZYWIskv0v3dDgb+CFibZO3wdfNYikmSJGmvaREyNwG30c2Wfild8DuYbpH2rXTP\nHF4xNLN8LV0P5Fq61yLeBnyYbtLQ22ao6wPAW+l6R1f2dWwGLliihdhnchp7Dt+fwO43IAGMCplz\nnfBzON3fEboJVGdOc93kLOVIkiTtdS3WybyT7tWLF8927cA9D9IFxQ0jTmf4QFWtB9b3u5v6z2x1\nrJlHe7aOqrdV+UP3/Srwq0vRJkmSpH3F3pj4I0mSpGeYZutkSsvZqHUxZzMxMTH7RZIkPUPZkylJ\nkqTmDJmSJElqzpApSZKk5gyZkiRJas6QKUmSpOYMmZIkSWrOkClJkqTmDJmSJElqzpApSZKk5gyZ\nkiRJas6QKUmSpOYMmZIkSWrOkClJkqTmDJmSJElqbsW4G6DxWnX4KnZM7Bh3MyRJ0n7GnkxJkiQ1\nZ8iUJElSc4ZMSZIkNWfIlCRJUnOGTEmSJDVnyJQkSVJzhkxJkiQ1Z8iUJElSc4ZMSZIkNWfIlCRJ\nUnOGTEmSJDXnu8uf4W6++2ayMeNuxj6vJmrcTZAkaVmxJ1OSJEnNGTIlSZLUnCFTkiRJzRkyJUnS\n/9/enYdbUtVn3//e0ggICg4oPobQOAWijwMOKIg0xgmVQcQBgwhG1LzG4Gz0VU73kzdXErFFTEhM\nNNgO0QhEcI5osEElgdiAmldRE2wEIyIgCmGG3/NH1Qmbzd5n6F6nd5/u7+e69lVdVauq1l4H+ty9\nqtYqqTlDpiRJkpozZEqSJKk5Q6YkSZKaM2RKkiSpOUOmJEmSmjNkSpIkqTlDpiRJkpozZEqSJKk5\nQ6YkSZKaWzLpCkgbg+Usn3F92IoVK+60PjU11bhGkiQtbvZkSpIkqTlDpiRJkppbFCEzyaoklWTp\npOsiSZKk2TUJmUm2SHJ0krOSXJ3kliRXJPlOkg8lObDFdeZZp9V9MB33WTVQdtmI/TcnuSzJKUme\nPHTuhyV5W5Izk1zal/15ks8k2W9MfdbOUp9K8q6hY1bNUn63BWk8SZKk9bTeA3+SbAF8Hng2cA3w\nBeAy4O7AI4CXArsBn13fa62jjwBrR2y/cMS2S4BV/Z+3BZ4EHAockuTQqjqt3/fHwIuB7wFfBK4G\nfgs4EDgwyTFV9f6hc78P2GHENQO8g+5n8aUx3+EEurYdduWY8pIkSRPVYnT5YXQB89vAvlX1q8Gd\nSe4B7NngOutqVVWtnmPZtVW1fHBDkhXAscBKYDpk/hPw51V1wVDZfYGvAMclOaWqfja9r6reN+qC\nSZ5F93O4oKq+NaZe76uqtXP8DpIkSRPX4nb5Xv1y1XDABKiq66vqa9PrSZb3t3qXDZdNsnT4VvZw\nfZO8MclFSW7sb2cfn+ReDb7HOCf2y12T7AhQVauGA2a//SxgNV0v7l7D+8d4Vb/8m/WspyRJ0kaj\nRU/mVf3y4Q3ONZvjgacCJwOfAZ4FvB7YJ8lTqurGBbhmBv5ccyh/S7+8ddYTJw8ADgCuAz4xQ9H9\n+yB9G/AfwJlV9es51EXraHiezOF5MSVJ0sxahMxPA28DXpPknnS3lNdU1SUNzj1sb+Ax0+dO8nbg\nFOAQ4C10z0oOO3JUr+nwbfEZvLZfXlxVMz4DmWQX4HeA64Gz53DuVwBb0vUCXztDub8aWr82ydur\n6sSRpe9arzVjdjlwSJIkLYj1DplVdUGSw+kGpxzef0hyNV3QOqmqPre+1+mdMBheq+r2JG8BDqYL\nbKNC5svHnGv5iG1Lk0xv35buWdJ9gNuBN89UsSRbAX8PbAW8tap+OUv5AK/sV/92TLGz6QYW/Stw\nBfC/gOcDU8BfJrmlqsYdK0mSNDFNXitZVScnOQ3YD3gK8Nh+eTBwcJKPAkdW1VxuN8/krBHXvjjJ\npXQBcYeqGh6Fvd88Bv7sQhfgoLvd/Qu6ntqVVXXOuIP6EfYfo+tp/RTwnjlc6+nAg4Hzxw34qaqT\nhjZdDKxM8gPgc8CfJPm7qrptpgtV1ePG1HsNsMcc6ipJkjQvzd5dXlW3AGf0n+ng9QLgJOAIutvo\np6/nZX4+ZvvldAFxe0ZP9TNXZ1XVsvkc0H/PjwMvpHtW9PA5hunpAT/z7omsqs8n+SnwIOC3ge/O\n9xya2fAzmTV15x+pz2hKkjSzBXvjT1XdVlUn0w3WAXhav7y9X44KuKPmkRz0gDHbd+qXdxndvpCS\nbAl8EngJ3cCdl1bVXAb83B84iNkH/MzkF/1y23U8XpIkacFsiNdKTg9omR6lPf2s4s4jyj5+lnPt\nO7whyYP7c60dcat8wSS5O92goxcCHwVeNttt6wFH0Q34+eQsA37GXXt7ukE7Bfx4vsdLkiQttPUO\nmUkOS/KMJHc5V5KdgKP71enR1uf1y6OSLBkouzPdpOczOaYfwT19zN2A4+i+x4fX8SvMWz/I5zS6\n3si/A46qqttnPup/jh0c8DN2bswkOyX5jRHbt6N7K9HWwFeratwjBJIkSRPT4pnMPYFjgMuTfIM7\netZ2BZ4LbEM3p+WpAFV1bpKz6ea7PC/JmXS3wQ8AvszoHs5p3wQuTPIpulvjzwIeDawB3t3gu8zV\nB4Dn0L3W8afAsV12vJPVYwYcPQ14KN2An3FTC0HXU/nVJP8C/JBudPmDgGfQPR5wMXeEVUmSpI1K\ni5C5EvgR3WjpR9EFv63pJmlfTffM4SeGBsMcRNcDeRDwuv74t9INGnrRDNd6A90UPkcDS/trnAAc\nu0ATsY+za7+8HzP3vq4esW2uA37+k66X9Al070TfgW7+zR8Afwm8f11utUuSJG0ILebJvJTu1Ytz\nmhi8P+YauqB49Ijdd+kSrKojgSP71ZX9Z7ZrLJtHfVaPum6Lc4849sXAi+dQ7lLg1et6HUmSpEna\nEAN/JEmStJlpNk+mtJgNz4s5m6mpqdkLSZK0GbMnU5IkSc0ZMiVJktScIVOSJEnNGTIlSZLUnCFT\nkiRJzRkyJUmS1JwhU5IkSc0ZMiVJktScIVOSJEnNGTIlSZLUnCFTkiRJzRkyJUmS1JwhU5IkSc0Z\nMiVJktTckklXQJO1xwP3YM3UmklXQ5IkbWLsyZQkSVJzhkxJkiQ1Z8iUJElSc4ZMSZIkNWfIlCRJ\nUnOGTEmSJDVnyJQkSVJzhkxJkiQ1Z8iUJElSc4ZMSZIkNWfIlCRJUnO+u3wzd/7PzicrMulqbLRq\nqiZdBUmSFiV7MiVJktScIVOSJEnNGTIlSZLUnCFTkiRJzRkyJUmS1JwhU5IkSc0ZMiVJktScIVOS\nJEnNGTIlSZLUnCFTkiRJzRkyJUmS1JwhU5IkSc0ZMiVJktTckklXQJqk5SyfcX2UFStW3Gl9amqq\nYY0kSdo02JMpSZKk5gyZkiRJam5RhMwkq5JUkqWTroskSZJm1yRkJtkiydFJzkpydZJbklyR5DtJ\nPpTkwBbXmWedVvfBdNxn1UDZZSP235zksiSnJHny0Lm3THJMkg8nubAvW0leOYd6vTzJeUmuS/Kr\nvp7Pm6H8NklWJPlBkhv7dj05ye7r1UCSJEkLaL0H/iTZAvg88GzgGuALwGXA3YFHAC8FdgM+u77X\nWkcfAdaO2H7hiG2XAKv6P28LPAk4FDgkyaFVddrAvvf1f/45cDmw82wVSfIe4E107fNBujZ6CfC5\nJK+rqr8cKr8V8BVgb+BbwAn9dV4IPDfJ06rq3NmuK0mStKG1GF1+GF3A/Dawb1X9anBnknsAeza4\nzrpaVVWr51h2bVUtH9yQZAVwLLASmA6Z1wPPAS6sqp8lWQ7MOMQ4yV50AfM/gSdU1S/77ccBa4D3\nJPl8Va0dOOyNdAHzVODFVXV7f8yngNOBk5L87+ntkiRJG4sWt8v36perhgMmQFVdX1Vfm15Psry/\ntbxsuGySpcO3sofrm+SNSS7qbx1fluT4JPdq8D3GObFf7ppkR4CqurmqvlRVP5vHeV7TL/9kOmD2\n51rbX2Mr4Kjp7UkycMxbB4NkVX0G+Drw28C+8/s6kiRJC69FT+ZV/fLhDc41m+OBpwInA58BngW8\nHtgnyVOq6sYFuGYG/lzrcZ6n9ct/GrHvS8C7+jLTPaIPAX4T+GFV/XjMMfv0x3xtxH6tg+F5Mofn\nxJQkSXPTImR+Gngb8Jok96S7pbymqi5pcO5hewOPmT53krcDpwCHAG8B/njEMUeO6jUdvi0+g9f2\ny4ur6sr5VhggybbAg4DrxvR+/qhfDgb13+qXPxxz2lHHjLv+mjG7dpvtWEmSpHWx3iGzqi5Icjjd\noJTD+w9JrgbOBk6qqs+t73V6JwyG16q6PclbgIOBVzA6ZL58zLmWj9i2tH++ErrBPXvS9RbeDrx5\nHesMsH2/vMvjBEPbd1jPYyRJkjYKTV4rWVUnJzkN2A94CvDYfnkwcHCSjwJHVtX63G4GOGvEtS9O\ncildQNyhqq4ZKrLfPAb+7MIdt6tvBX5B11O7sqrOWcc6T1xVPW7U9r6Hc48NXB1JkrQZaPbu8qq6\nBTij/0xPbfQC4CTgCLrb6Kev52V+Pmb75XQBcXu6aZTW1VlVtWw9jh9nutdx+zH7p7cP1n1djtF6\nGn4ms6bu+u8in9OUJGl2C/bGn6q6rapOphusA3cMfJkeJT0q4M526/cBY7bv1C/H3VqeqKr6b+Cn\nwHZJHjiiyMP65eDzlz/ol+OeuRx1jCRJ0kZhQ7xW8tp+OT1Ke3r6nlGTlz9+lnPdZbqeJA/uz7V2\nxK3yjcmZ/fLZI/btP1QGuvk0fwI8PMmuczxGkiRpo7DeITPJYUmekeQu50qyE3B0v3p2vzyvXx6V\nZMlA2Z3pJj2fyTFJdhk45m7AcXTf48Pr+BU2lA/0y/83yb2nN/bvY38tcBMD36F/fnX6mHcPtm+S\ng+gGJH2PEc+pSpIkTVqLZzL3BI4BLk/yDWB6TsddgecC29DNaXkqQFWdm+Rsuvkuz0tyJt1t8AOA\nLzPz6xm/CVzYv/HmV3TzZD6a7o05727wXeYsyR9xxxRAj+mXRyV5Sv/nb1TVh6bLV9U5Sd5L9xaf\n7yQ5le61ki8G7gO8buhtPwDvBZ5H92rLc5P8M93cmS+ke+vQK3zbjyRJ2hi1CJkr6eZsfDrwKLrg\ntzXdJO2rgU8AnxgaWX4QXQ/kQcDr+uPfSjdo6EUzXOsNwPPpekeX9tc4ATh2gSZin8mzuevt+724\n4w1IAB8a3FlVb0ryXbqey1fRPZ96PnBcVX1++AJVdVOSZwB/RPf6zjcAv6YbQDVVVd9r9F0kSZKa\najFP5qV0r0U8cbayA8dcQxcUjx6xO8MbqupI4Mh+dWX/me0ay+ZRn9Wjrtvq/EPHrQJWzaP89XSP\nEcz2KIEkSdJGY0MM/JEkSdJmptk8mdJiNDwv5lxMTU3NXkiSpM2cPZmSJElqzpApSZKk5gyZkiRJ\nas6QKUmSpOYMmZIkSWrOkClJkqTmDJmSJElqzpApSZKk5gyZkiRJas6QKUmSpOYMmZIkSWrOkClJ\nkqTmDJmSJElqzpApSZKk5pZMugKarD0euAdrptZMuhqSJGkTY0+mJEmSmjNkSpIkqTlDpiRJkpoz\nZEqSJKk5Q6YkSZKaM2RKkiSpOUOmJEmSmjNkSpIkqTlDpiRJkpozZEqSJKk5Q6YkSZKa893lm7nz\nf3Y+WZFJV2OjVFM16SpIkrRo2ZMpSZKk5gyZkiRJas6QKUmSpOYMmZIkSWrOkClJkqTmDJmSJElq\nzpApSZKk5gyZkiRJas6QKUmSpOYMmZIkSWrOkClJkqTmDJmSJElqzpApSZKk5pZMugLSpCxn+Yzr\nw1asWHGn9ampqcY1kiRp02FPpiRJkpozZEqSJKm5TTZkJlmVpJIsnXRdJEmSNjcTC5lJtkhydJKz\nklyd5JYkVyT5TpIPJTlwAnVa3QfTcZ9VA2WXjdh/c5LLkpyS5MlD5945yV8lOTfJ5UluSvJfSb6e\n5KgkW46oz1OTfCzJvye5KsmNSX6c5LNJfmcDNIkkSdI6mcjAnyRbAJ8Hng1cA3wBuAy4O/AI4KXA\nbsBnJ1E/4CPA2hHbLxyx7RJgVf/nbYEnAYcChyQ5tKpO6/c9BPhd4FzgdOBq4L7A/sBJwMuSPLOq\nbh0499P6z7nAmcB/A78JHAgckOT/q6p3reN3lCRJWjCTGl1+GF3A/Dawb1X9anBnknsAe06iYr1V\nVbV6jmXXVtXywQ1JVgDHAiuB6ZB5DnDvqrp9qOyWwBnAfsAhwMkDu/9s+Nz9MQ8Czgcl8l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+ ""text/plain"": [ + """" + ] + }, + ""metadata"": { + ""image/png"": { + ""height"": 690, + ""width"": 332 + } + }, + ""output_type"": ""display_data"" + } + ], + ""source"": [ + ""import glob # to read multiple files \n"", + ""from rdkit import Chem, DataStructs\n"", + ""from rdkit.Chem import AllChem\n"", + ""import os\n"", + ""\n"", + ""%config InlineBackend.figure_format = 'retina'\n"", + ""\n"", + ""! rm Result/CHEMBL206_IC50_Revision.csv\n"", + ""\n"", + ""outfile = open('Result/CHEMBL206_IC50_Revision.csv', 'a')\n"", + ""\n"", + ""print >> outfile, 'Filename,N_train,Descriptors,Remove STDEV,Remove correlation,'+\\\n"", + "" 'R2_train,R2_train_std,' + \\\n"", + "" 'MAE_train,MAE_train_std,N_CV,Q2_CV,Q2_CV_std,MAE_CV,' + \\\n"", + "" 'MAE_CV_std,N_External,Q2_External,Q2_External_std,MAE_External,MAE_External_std'\n"", + ""\n"", + ""path = r'QSAR/'\n"", + ""\n"", + ""for f in glob.glob(path + '*.csv'):\n"", + "" df = pd.read_csv(f)\n"", + "" df = df.apply(lambda x: pd.to_numeric(x,errors='ignore'))\n"", + "" df = df.fillna(method='ffill')\n"", + "" Y = df[\""pIC50\""].as_matrix().astype(np.float)\n"", + "" data = df.ix[:,2:]\n"", + "" \n"", + "" print '\\n\\n************************************************************************************'\n"", + "" print ''\n"", + "" print (f)\n"", + "" print ''\n"", + "" \n"", + "" data, des1, des2 = Remove_useless_descriptor(data, 0.05) # Remove correlation cut off 95%\n"", + "" data, des3, des4 = correlation(data, 0.7) # Remove correlation cut off 0.7\n"", + ""\n"", + "" h = data.columns.tolist()\n"", + "" hx = np.array(h)\n"", + ""\n"", + "" data = data.as_matrix().astype(np.float)\n"", + "" X = np.array(data)\n"", + "" \n"", + "" # Prepare empty lists to plot QSAR model\n"", + "" R2_train = []\n"", + "" RMSE_train = []\n"", + "" Q2_CV = []\n"", + "" RMSE_CV = []\n"", + "" Q2_External = []\n"", + "" RMSE_External = []\n"", + "" importances_dict = defaultdict(list)\n"", + "" \n"", + "" # Prepare empty lists to plot the performance of accuracy.\n"", + "" acclist_realRF = []\n"", + "" acclist_realRF_predictTrain = []\n"", + "" acclist_predictionOnTest_scrambledtrain = []\n"", + "" acclist_predictionOnTrain_scrambledtrain = []\n"", + "" \n"", + "" for i in range(10):\n"", + "" R2_train, RMSE_train, Q2_CV, RMSE_CV, Q2_External, RMSE_External, Feature, \\\n"", + "" X_internal, X_external, Y_internal, Y_external, rf, prediction, importances_dict = build_model(X, Y, i, hx, f)\n"", + "" \n"", + "" acclist_predictionOnTest_scrambledtrain, acclist_predictionOnTrain_scrambledtrain = Y_scrambling( \\\n"", + "" X_internal, X_external, Y_internal, Y_external)\n"", + "" R2_train_mean, RMSE_train_mean, Q2_CV_mean, RMSE_CV_mean, Q2_External_mean, \\\n"", + "" RMSE_External_mean, importances_mean = mean(R2_train, RMSE_train, Q2_CV, RMSE_CV, \\\n"", + "" Q2_External, RMSE_External, importances_dict)\n"", + "" R2_train_std, RMSE_train_std, Q2_CV_std, RMSE_CV_std, Q2_External_std,\\\n"", + "" RMSE_External_std, importances_std = std(R2_train, RMSE_train, Q2_CV, RMSE_CV, \\\n"", + "" Q2_External, RMSE_External, importances_dict)\n"", + "" print_output(R2_train_mean, RMSE_train_mean, Q2_CV_mean, RMSE_CV_mean, Q2_External_mean, \n"", + "" RMSE_External_mean, R2_train_std, RMSE_train_std, Q2_CV_std, RMSE_CV_std, Q2_External_std, \n"", + "" RMSE_External_std, X_internal, X_external, X, f, des1, des2)\n"", + "" plot_model(f, X_internal, X_external, Y_internal, Y_external,\n"", + "" R2_train_mean, Q2_External_mean,\n"", + "" importances_mean, importances_std, Feature, prediction,\n"", + "" acclist_predictionOnTest_scrambledtrain, acclist_predictionOnTrain_scrambledtrain)\n"", + "" \n"", + ""outfile.close()\n"", + "" #END"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": null, + ""metadata"": { + ""collapsed"": true + }, + ""outputs"": [], + ""source"": [] + }, + { + ""cell_type"": ""code"", + ""execution_count"": null, + ""metadata"": { + ""collapsed"": true + }, + ""outputs"": [], + ""source"": [] + }, + { + ""cell_type"": ""code"", + ""execution_count"": null, + ""metadata"": { + ""collapsed"": true + }, + ""outputs"": [], + ""source"": [] + } + ], + ""metadata"": { + ""kernelspec"": { + ""display_name"": ""Python 2"", + ""language"": ""python"", + ""name"": ""python2"" + }, + ""language_info"": { + ""codemirror_mode"": { + ""name"": ""ipython"", + ""version"": 2 + }, + ""file_extension"": "".py"", + ""mimetype"": ""text/x-python"", + ""name"": ""python"", + ""nbconvert_exporter"": ""python"", + ""pygments_lexer"": ""ipython2"", + ""version"": ""2.7.13"" + } + }, + ""nbformat"": 4, + ""nbformat_minor"": 2 +} +","Unknown" +"Inhibition","chaninlab/estrogen-receptor-alpha-qsar","03_Fingerprint_gen.ipynb",".ipynb","1197350","13075","{ + ""cells"": [ + { + ""cell_type"": ""code"", + ""execution_count"": 1, + ""metadata"": {}, + ""outputs"": [ + { + ""data"": { + ""text/plain"": [ + ""u'/Users/zeromtmu/Desktop/Thesis/ER_alpha_Regression'"" + ] + }, + ""execution_count"": 1, + ""metadata"": {}, + ""output_type"": ""execute_result"" + } + ], + ""source"": [ + ""pwd"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 2, + ""metadata"": { + ""collapsed"": true + }, + ""outputs"": [], + ""source"": [ + ""list_Fp = ['Fingerprinter.xml', 'ExtendedFingerprinter.xml', 'EStateFingerprinter.xml',\n"", + "" 'GraphOnlyFingerprinter.xml', 'MACCSFingerprinter.xml', 'PubchemFingerprinter.xml',\n"", + "" 'SubstructureFingerprinter.xml', 'KlekotaRothFingerprinter.xml', 'AtomPairs2DFingerprinter.xml']"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 7, + ""metadata"": { + ""collapsed"": true + }, + ""outputs"": [], + ""source"": [ + ""def Fingerprint(name , Fp):\n"", + "" \n"", + "" group = name\n"", + "" group = [name.replace(\"".smi\"", \"".csv\"") for name in group]\n"", + "" result = ', '.join(group)\n"", + "" \n"", + "" Fp_name = Fp\n"", + "" Fp_name = Fp.replace(\"".xml\"", \""_\"")\n"", + "" \n"", + "" print name\n"", + "" print result\n"", + "" print Fp\n"", + "" print Fp_name\n"", + ""\n"", + "" ! java -jar -Xms64m -Xmx200m /Users/zeromtmu/Desktop/Thesis/PaDEL-Descriptor/PaDEL-Descriptor.jar \\\n"", + "" -removesalt -standardizenitro \\\n"", + "" -fingerprints -descriptortypes /Users/zeromtmu/Desktop/Thesis/PaDEL-Descriptor/$Fp \\\n"", + "" -dir /Users/zeromtmu/Desktop/Thesis/ER_alpha_Regression/smiles/$name \\\n"", + "" -file /Users/zeromtmu/Desktop/Thesis/ER_alpha_Regression/Fingerprint/$Fp_name$result"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 8, + ""metadata"": { + ""scrolled"": true + }, + ""outputs"": [ + { + ""name"": ""stdout"", + ""output_type"": ""stream"", + ""text"": [ + ""Test_les_ER_alpha.smi\n"", + ""Test_les_ER_alpha.csv\n"", + ""Fingerprinter.xml\n"", + ""Fingerprinter_\n"", + ""Processing CHEMBL1532515 in Test_les_ER_alpha.smi (1/19). \n"", + ""Processing CHEMBL1566820 in Test_les_ER_alpha.smi (2/19). \n"", + ""Processing CHEMBL501952 in Test_les_ER_alpha.smi (3/19). \n"", + ""Processing CHEMBL482644 in Test_les_ER_alpha.smi (4/19). \n"", + ""Processing CHEMBL33899 in Test_les_ER_alpha.smi (5/19). \n"", + ""Processing CHEMBL2041093 in Test_les_ER_alpha.smi (8/19). Average speed: 0.25 s/mol.\n"", + ""Processing CHEMBL2041096 in Test_les_ER_alpha.smi (7/19). Average speed: 0.36 s/mol.\n"", + ""Processing CHEMBL2441807 in Test_les_ER_alpha.smi (9/19). Average speed: 0.22 s/mol.\n"", + ""Processing CHEMBL482642 in Test_les_ER_alpha.smi (6/19). Average speed: 0.72 s/mol.\n"", + ""Processing CHEMBL2441815 in Test_les_ER_alpha.smi (10/19). Average speed: 0.18 s/mol.\n"", + ""Processing CHEMBL2441809 in Test_les_ER_alpha.smi (11/19). Average speed: 0.16 s/mol.\n"", + ""Processing CHEMBL2441813 in Test_les_ER_alpha.smi (12/19). Average speed: 0.14 s/mol.\n"", + ""Processing CHEMBL2441814 in Test_les_ER_alpha.smi (14/19). Average speed: 0.12 s/mol.\n"", + ""Processing CHEMBL2441810 in Test_les_ER_alpha.smi (13/19). Average speed: 0.13 s/mol.\n"", + ""Processing CHEMBL2441811 in Test_les_ER_alpha.smi (15/19). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL2441816 in Test_les_ER_alpha.smi (16/19). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL2441812 in Test_les_ER_alpha.smi (17/19). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL2441808 in Test_les_ER_alpha.smi (18/19). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL444663 in Test_les_ER_alpha.smi (19/19). Average speed: 0.09 s/mol.\n"", + ""Descriptor calculation completed in 1.308 secs . Average speed: 0.07 s/mol.\n"", + ""Test_les_ER_alpha.smi\n"", + ""Test_les_ER_alpha.csv\n"", + ""ExtendedFingerprinter.xml\n"", + ""ExtendedFingerprinter_\n"", + ""Processing CHEMBL1566820 in Test_les_ER_alpha.smi (2/19). \n"", + ""Processing CHEMBL1532515 in Test_les_ER_alpha.smi (1/19). \n"", + ""Processing CHEMBL501952 in Test_les_ER_alpha.smi (3/19). \n"", + ""Processing CHEMBL482644 in Test_les_ER_alpha.smi (4/19). \n"", + ""Processing CHEMBL33899 in Test_les_ER_alpha.smi (5/19). \n"", + ""Processing CHEMBL2041096 in Test_les_ER_alpha.smi (7/19). Average speed: 0.45 s/mol.\n"", + ""Processing CHEMBL482642 in Test_les_ER_alpha.smi (6/19). Average speed: 0.88 s/mol.\n"", + ""Processing CHEMBL2041093 in Test_les_ER_alpha.smi (8/19). Average speed: 0.31 s/mol.\n"", + ""Processing CHEMBL2441807 in Test_les_ER_alpha.smi (9/19). Average speed: 0.24 s/mol.\n"", + ""Processing CHEMBL2441815 in Test_les_ER_alpha.smi (10/19). Average speed: 0.20 s/mol.\n"", + ""Processing CHEMBL2441809 in Test_les_ER_alpha.smi (11/19). Average speed: 0.17 s/mol.\n"", + ""Processing CHEMBL2441813 in Test_les_ER_alpha.smi (12/19). Average speed: 0.15 s/mol.\n"", + ""Processing CHEMBL2441810 in Test_les_ER_alpha.smi (13/19). Average speed: 0.14 s/mol.\n"", + ""Processing CHEMBL2441814 in Test_les_ER_alpha.smi (14/19). Average speed: 0.12 s/mol.\n"", + ""Processing CHEMBL2441811 in Test_les_ER_alpha.smi (15/19). Average speed: 0.12 s/mol.\n"", + ""Processing CHEMBL2441816 in Test_les_ER_alpha.smi (16/19). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL2441812 in Test_les_ER_alpha.smi (17/19). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL2441808 in Test_les_ER_alpha.smi (18/19). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL444663 in Test_les_ER_alpha.smi (19/19). Average speed: 0.09 s/mol.\n"", + ""Descriptor calculation completed in 1.416 secs . Average speed: 0.07 s/mol.\n"", + ""Test_les_ER_alpha.smi\n"", + ""Test_les_ER_alpha.csv\n"", + ""EStateFingerprinter.xml\n"", + ""EStateFingerprinter_\n"", + ""Processing CHEMBL1532515 in Test_les_ER_alpha.smi (1/19). \n"", + ""Processing CHEMBL1566820 in Test_les_ER_alpha.smi (2/19). \n"", + ""Processing CHEMBL501952 in Test_les_ER_alpha.smi (3/19). \n"", + ""Processing CHEMBL482644 in Test_les_ER_alpha.smi (4/19). \n"", + ""Processing CHEMBL33899 in Test_les_ER_alpha.smi (5/19). \n"", + ""Processing CHEMBL2041096 in Test_les_ER_alpha.smi (7/19). Average speed: 0.40 s/mol.\n"", + ""Processing CHEMBL2041093 in Test_les_ER_alpha.smi (8/19). Average speed: 0.27 s/mol.\n"", + ""Processing CHEMBL482642 in Test_les_ER_alpha.smi (6/19). Average speed: 0.79 s/mol.\n"", + ""Processing CHEMBL2441807 in Test_les_ER_alpha.smi (9/19). Average speed: 0.21 s/mol.\n"", + ""Processing CHEMBL2441815 in Test_les_ER_alpha.smi (10/19). Average speed: 0.18 s/mol.\n"", + ""Processing CHEMBL2441809 in Test_les_ER_alpha.smi (11/19). Average speed: 0.15 s/mol.\n"", + ""Processing CHEMBL2441813 in Test_les_ER_alpha.smi (12/19). Average speed: 0.13 s/mol.\n"", + ""Processing CHEMBL2441810 in Test_les_ER_alpha.smi (13/19). Average speed: 0.12 s/mol.\n"", + ""Processing CHEMBL2441814 in Test_les_ER_alpha.smi (14/19). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL2441811 in Test_les_ER_alpha.smi (15/19). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL2441816 in Test_les_ER_alpha.smi (16/19). Average speed: 0.09 s/mol.\n"", + ""Processing CHEMBL2441812 in Test_les_ER_alpha.smi (17/19). Average speed: 0.08 s/mol.\n"", + ""Processing CHEMBL2441808 in Test_les_ER_alpha.smi (18/19). Average speed: 0.07 s/mol.\n"", + ""Processing CHEMBL444663 in Test_les_ER_alpha.smi (19/19). Average speed: 0.07 s/mol.\n"", + ""Descriptor calculation completed in 1.147 secs . Average speed: 0.06 s/mol.\n"", + ""Test_les_ER_alpha.smi\n"", + ""Test_les_ER_alpha.csv\n"", + ""GraphOnlyFingerprinter.xml\n"", + ""GraphOnlyFingerprinter_\n"", + ""Processing CHEMBL1532515 in Test_les_ER_alpha.smi (1/19). \n"", + ""Processing CHEMBL1566820 in Test_les_ER_alpha.smi (2/19). \n"", + ""Processing CHEMBL501952 in Test_les_ER_alpha.smi (3/19). \n"", + ""Processing CHEMBL482644 in Test_les_ER_alpha.smi (4/19). \n"", + ""Processing CHEMBL33899 in Test_les_ER_alpha.smi (5/19). \n"", + ""Processing CHEMBL2041096 in Test_les_ER_alpha.smi (7/19). Average speed: 0.41 s/mol.\n"", + ""Processing CHEMBL482642 in Test_les_ER_alpha.smi (6/19). Average speed: 0.78 s/mol.\n"", + ""Processing CHEMBL2041093 in Test_les_ER_alpha.smi (8/19). Average speed: 0.28 s/mol.\n"", + ""Processing CHEMBL2441807 in Test_les_ER_alpha.smi (9/19). Average speed: 0.21 s/mol.\n"", + ""Processing CHEMBL2441815 in Test_les_ER_alpha.smi (10/19). Average speed: 0.18 s/mol.\n"", + ""Processing CHEMBL2441809 in Test_les_ER_alpha.smi (11/19). Average speed: 0.15 s/mol.\n"", + ""Processing CHEMBL2441810 in Test_les_ER_alpha.smi (13/19). Average speed: 0.12 s/mol.\n"", + ""Processing CHEMBL2441813 in Test_les_ER_alpha.smi (12/19). Average speed: 0.14 s/mol.\n"", + ""Processing CHEMBL2441814 in Test_les_ER_alpha.smi (14/19). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL2441811 in Test_les_ER_alpha.smi (15/19). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL2441816 in Test_les_ER_alpha.smi (16/19). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL2441808 in Test_les_ER_alpha.smi (18/19). Average speed: 0.09 s/mol.\n"", + ""Processing CHEMBL2441812 in Test_les_ER_alpha.smi (17/19). Average speed: 0.09 s/mol.\n"", + ""Processing CHEMBL444663 in Test_les_ER_alpha.smi (19/19). Average speed: 0.09 s/mol.\n"", + ""Descriptor calculation completed in 1.344 secs . Average speed: 0.07 s/mol.\n"", + ""Test_les_ER_alpha.smi\n"", + ""Test_les_ER_alpha.csv\n"", + ""MACCSFingerprinter.xml\n"", + ""MACCSFingerprinter_\n"", + ""Processing CHEMBL1532515 in Test_les_ER_alpha.smi (1/19). \n"", + ""Processing CHEMBL1566820 in Test_les_ER_alpha.smi (2/19). \n"", + ""Processing CHEMBL501952 in Test_les_ER_alpha.smi (3/19). \n"", + ""Processing CHEMBL482644 in Test_les_ER_alpha.smi (4/19). \n"", + ""Processing CHEMBL482642 in Test_les_ER_alpha.smi (6/19). Average speed: 0.45 s/mol.\n"", + ""Processing CHEMBL33899 in Test_les_ER_alpha.smi (5/19). Average speed: 0.45 s/mol.\n"", + ""Processing CHEMBL2041096 in Test_les_ER_alpha.smi (7/19). Average speed: 0.36 s/mol.\n"", + ""Processing CHEMBL2041093 in Test_les_ER_alpha.smi (8/19). Average speed: 0.27 s/mol.\n"", + ""Processing CHEMBL2441807 in Test_les_ER_alpha.smi (9/19). Average speed: 0.23 s/mol.\n"", + ""Processing CHEMBL2441815 in Test_les_ER_alpha.smi (10/19). Average speed: 0.20 s/mol.\n"", + ""Processing CHEMBL2441809 in Test_les_ER_alpha.smi (11/19). Average speed: 0.18 s/mol.\n"", + ""Processing CHEMBL2441813 in Test_les_ER_alpha.smi (12/19). Average speed: 0.17 s/mol.\n"", + ""Processing CHEMBL2441810 in Test_les_ER_alpha.smi (13/19). Average speed: 0.15 s/mol.\n"", + ""Processing CHEMBL2441814 in Test_les_ER_alpha.smi (14/19). Average speed: 0.14 s/mol.\n"", + ""Processing CHEMBL2441811 in Test_les_ER_alpha.smi (15/19). Average speed: 0.14 s/mol.\n"", + ""Processing CHEMBL2441816 in Test_les_ER_alpha.smi (16/19). Average speed: 0.13 s/mol.\n"", + ""Processing CHEMBL2441808 in Test_les_ER_alpha.smi (18/19). Average speed: 0.12 s/mol.\n"", + ""Processing CHEMBL2441812 in Test_les_ER_alpha.smi (17/19). Average speed: 0.13 s/mol.\n"", + ""Processing CHEMBL444663 in Test_les_ER_alpha.smi (19/19). Average speed: 0.12 s/mol.\n"" + ] + }, + { + ""name"": ""stdout"", + ""output_type"": ""stream"", + ""text"": [ + ""Descriptor calculation completed in 1.862 secs . Average speed: 0.10 s/mol.\n"", + ""Test_les_ER_alpha.smi\n"", + ""Test_les_ER_alpha.csv\n"", + ""PubchemFingerprinter.xml\n"", + ""PubchemFingerprinter_\n"", + ""Processing CHEMBL1532515 in Test_les_ER_alpha.smi (1/19). \n"", + ""Processing CHEMBL1566820 in Test_les_ER_alpha.smi (2/19). \n"", + ""Processing CHEMBL501952 in Test_les_ER_alpha.smi (3/19). \n"", + ""Processing CHEMBL482644 in Test_les_ER_alpha.smi (4/19). \n"", + ""Processing CHEMBL33899 in Test_les_ER_alpha.smi (5/19). Average speed: 1.20 s/mol.\n"", + ""Processing CHEMBL482642 in Test_les_ER_alpha.smi (6/19). Average speed: 1.24 s/mol.\n"", + ""Processing CHEMBL2041096 in Test_les_ER_alpha.smi (7/19). Average speed: 0.70 s/mol.\n"", + ""Processing CHEMBL2041093 in Test_les_ER_alpha.smi (8/19). Average speed: 0.47 s/mol.\n"", + ""Processing CHEMBL2441807 in Test_les_ER_alpha.smi (9/19). Average speed: 0.40 s/mol.\n"", + ""Processing CHEMBL2441815 in Test_les_ER_alpha.smi (10/19). Average speed: 0.31 s/mol.\n"", + ""Processing CHEMBL2441809 in Test_les_ER_alpha.smi (11/19). Average speed: 0.34 s/mol.\n"", + ""Processing CHEMBL2441813 in Test_les_ER_alpha.smi (12/19). Average speed: 0.37 s/mol.\n"", + ""Processing CHEMBL2441810 in Test_les_ER_alpha.smi (13/19). Average speed: 0.33 s/mol.\n"", + ""Processing CHEMBL2441814 in Test_les_ER_alpha.smi (14/19). Average speed: 0.30 s/mol.\n"", + ""Processing CHEMBL2441811 in Test_les_ER_alpha.smi (15/19). Average speed: 0.29 s/mol.\n"", + ""Processing CHEMBL2441816 in Test_les_ER_alpha.smi (16/19). Average speed: 0.27 s/mol.\n"", + ""Processing CHEMBL2441812 in Test_les_ER_alpha.smi (17/19). Average speed: 0.27 s/mol.\n"", + ""Processing CHEMBL2441808 in Test_les_ER_alpha.smi (18/19). Average speed: 0.26 s/mol.\n"", + ""Processing CHEMBL444663 in Test_les_ER_alpha.smi (19/19). Average speed: 0.25 s/mol.\n"", + ""Descriptor calculation completed in 4.53 secs . Average speed: 0.21 s/mol.\n"", + ""Test_les_ER_alpha.smi\n"", + ""Test_les_ER_alpha.csv\n"", + ""SubstructureFingerprinter.xml\n"", + ""SubstructureFingerprinter_\n"", + ""Processing CHEMBL1532515 in Test_les_ER_alpha.smi (1/19). \n"", + ""Processing CHEMBL1566820 in Test_les_ER_alpha.smi (2/19). \n"", + ""Processing CHEMBL501952 in Test_les_ER_alpha.smi (3/19). \n"", + ""Processing CHEMBL482644 in Test_les_ER_alpha.smi (4/19). \n"", + ""Processing CHEMBL33899 in Test_les_ER_alpha.smi (5/19). \n"", + ""Processing CHEMBL2041093 in Test_les_ER_alpha.smi (8/19). Average speed: 0.53 s/mol.\n"", + ""Processing CHEMBL2041096 in Test_les_ER_alpha.smi (7/19). Average speed: 0.53 s/mol.\n"", + ""Processing CHEMBL482642 in Test_les_ER_alpha.smi (6/19). Average speed: 0.50 s/mol.\n"", + ""Processing CHEMBL2441807 in Test_les_ER_alpha.smi (9/19). Average speed: 0.25 s/mol.\n"", + ""Processing CHEMBL2441815 in Test_les_ER_alpha.smi (10/19). Average speed: 0.21 s/mol.\n"", + ""Processing CHEMBL2441809 in Test_les_ER_alpha.smi (11/19). Average speed: 0.24 s/mol.\n"", + ""Processing CHEMBL2441810 in Test_les_ER_alpha.smi (13/19). Average speed: 0.20 s/mol.\n"", + ""Processing CHEMBL2441813 in Test_les_ER_alpha.smi (12/19). Average speed: 0.22 s/mol.\n"", + ""Processing CHEMBL2441814 in Test_les_ER_alpha.smi (14/19). Average speed: 0.19 s/mol.\n"", + ""Processing CHEMBL2441811 in Test_les_ER_alpha.smi (15/19). Average speed: 0.17 s/mol.\n"", + ""Processing CHEMBL2441816 in Test_les_ER_alpha.smi (16/19). Average speed: 0.16 s/mol.\n"", + ""Processing CHEMBL2441812 in Test_les_ER_alpha.smi (17/19). Average speed: 0.16 s/mol.\n"", + ""Processing CHEMBL2441808 in Test_les_ER_alpha.smi (18/19). Average speed: 0.15 s/mol.\n"", + ""Processing CHEMBL444663 in Test_les_ER_alpha.smi (19/19). Average speed: 0.14 s/mol.\n"", + ""Descriptor calculation completed in 2.88 secs . Average speed: 0.11 s/mol.\n"", + ""Test_les_ER_alpha.smi\n"", + ""Test_les_ER_alpha.csv\n"", + ""KlekotaRothFingerprinter.xml\n"", + ""KlekotaRothFingerprinter_\n"", + ""Processing CHEMBL1532515 in Test_les_ER_alpha.smi (1/19). \n"", + ""Processing CHEMBL1566820 in Test_les_ER_alpha.smi (2/19). \n"", + ""Processing CHEMBL501952 in Test_les_ER_alpha.smi (3/19). \n"", + ""Processing CHEMBL482644 in Test_les_ER_alpha.smi (4/19). \n"", + ""Processing CHEMBL33899 in Test_les_ER_alpha.smi (5/19). Average speed: 2.79 s/mol.\n"", + ""Processing CHEMBL482642 in Test_les_ER_alpha.smi (6/19). Average speed: 2.87 s/mol.\n"", + ""Processing CHEMBL2041096 in Test_les_ER_alpha.smi (7/19). Average speed: 1.51 s/mol.\n"", + ""Processing CHEMBL2041093 in Test_les_ER_alpha.smi (8/19). Average speed: 1.01 s/mol.\n"", + ""Processing CHEMBL2441807 in Test_les_ER_alpha.smi (9/19). Average speed: 1.08 s/mol.\n"", + ""Processing CHEMBL2441815 in Test_les_ER_alpha.smi (10/19). Average speed: 0.86 s/mol.\n"", + ""Processing CHEMBL2441809 in Test_les_ER_alpha.smi (11/19). Average speed: 0.90 s/mol.\n"", + ""Processing CHEMBL2441813 in Test_les_ER_alpha.smi (12/19). Average speed: 0.77 s/mol.\n"", + ""Processing CHEMBL2441810 in Test_les_ER_alpha.smi (13/19). Average speed: 0.81 s/mol.\n"", + ""Processing CHEMBL2441814 in Test_les_ER_alpha.smi (14/19). Average speed: 0.72 s/mol.\n"", + ""Processing CHEMBL2441811 in Test_les_ER_alpha.smi (15/19). Average speed: 0.66 s/mol.\n"", + ""Processing CHEMBL2441816 in Test_les_ER_alpha.smi (16/19). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL2441812 in Test_les_ER_alpha.smi (17/19). Average speed: 0.62 s/mol.\n"", + ""Processing CHEMBL2441808 in Test_les_ER_alpha.smi (18/19). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL444663 in Test_les_ER_alpha.smi (19/19). Average speed: 0.66 s/mol.\n"", + ""Descriptor calculation completed in 10.25 secs . Average speed: 0.53 s/mol.\n"", + ""Test_les_ER_alpha.smi\n"", + ""Test_les_ER_alpha.csv\n"", + ""AtomPairs2DFingerprinter.xml\n"", + ""AtomPairs2DFingerprinter_\n"", + ""Processing CHEMBL1532515 in Test_les_ER_alpha.smi (1/19). \n"", + ""Processing CHEMBL1566820 in Test_les_ER_alpha.smi (2/19). \n"", + ""Processing CHEMBL501952 in Test_les_ER_alpha.smi (3/19). \n"", + ""Processing CHEMBL482644 in Test_les_ER_alpha.smi (4/19). \n"", + ""Processing CHEMBL482642 in Test_les_ER_alpha.smi (6/19). Average speed: 0.66 s/mol.\n"", + ""Processing CHEMBL2041096 in Test_les_ER_alpha.smi (7/19). Average speed: 0.33 s/mol.\n"", + ""Descriptor calculation completed in 0.912 secs . Average speed: 0.05 s/mol.\n"", + ""Test_gra_ER_alpha.smi\n"", + ""Test_gra_ER_alpha.csv\n"", + ""Fingerprinter.xml\n"", + ""Fingerprinter_\n"", + ""Processing CHEMBL192325 in Test_gra_ER_alpha.smi (1/283). \n"", + ""Processing CHEMBL1331671 in Test_gra_ER_alpha.smi (2/283). \n"", + ""Processing CHEMBL1393 in Test_gra_ER_alpha.smi (3/283). \n"", + ""Processing CHEMBL1349227 in Test_gra_ER_alpha.smi (4/283). \n"", + ""Processing CHEMBL1383468 in Test_gra_ER_alpha.smi (5/283). \n"", + ""Processing CHEMBL1481853 in Test_gra_ER_alpha.smi (6/283). Average speed: 0.49 s/mol.\n"", + ""Processing CHEMBL1528804 in Test_gra_ER_alpha.smi (7/283). Average speed: 0.49 s/mol.\n"", + ""Processing CHEMBL1491627 in Test_gra_ER_alpha.smi (8/283). Average speed: 0.40 s/mol.\n"", + ""Processing CHEMBL1565048 in Test_gra_ER_alpha.smi (9/283). Average speed: 0.30 s/mol.\n"", + ""Processing CHEMBL1468795 in Test_gra_ER_alpha.smi (10/283). Average speed: 0.27 s/mol.\n"", + ""Processing CHEMBL1408468 in Test_gra_ER_alpha.smi (11/283). Average speed: 0.24 s/mol.\n"", + ""Processing CHEMBL1610665 in Test_gra_ER_alpha.smi (12/283). Average speed: 0.21 s/mol.\n"", + ""Processing CHEMBL1441655 in Test_gra_ER_alpha.smi (13/283). Average speed: 0.19 s/mol.\n"", + ""Processing CHEMBL1500491 in Test_gra_ER_alpha.smi (16/283). Average speed: 0.14 s/mol.\n"", + ""Processing CHEMBL1353351 in Test_gra_ER_alpha.smi (14/283). Average speed: 0.15 s/mol.\n"", + ""Processing CHEMBL1557815 in Test_gra_ER_alpha.smi (17/283). Average speed: 0.13 s/mol.\n"", + ""Processing CHEMBL1379474 in Test_gra_ER_alpha.smi (15/283). Average speed: 0.15 s/mol.\n"", + ""Processing CHEMBL1405411 in Test_gra_ER_alpha.smi (18/283). Average speed: 0.13 s/mol.\n"", + ""Processing CHEMBL1341487 in Test_gra_ER_alpha.smi (19/283). Average speed: 0.12 s/mol.\n"", + ""Processing CHEMBL1599210 in Test_gra_ER_alpha.smi (22/283). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL1492422 in Test_gra_ER_alpha.smi (21/283). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL1543038 in Test_gra_ER_alpha.smi (20/283). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL1510990 in Test_gra_ER_alpha.smi (25/283). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL1412224 in Test_gra_ER_alpha.smi (24/283). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL1579100 in Test_gra_ER_alpha.smi (23/283). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL1304094 in Test_gra_ER_alpha.smi (26/283). Average speed: 0.09 s/mol.\n"", + ""Processing CHEMBL516241 in Test_gra_ER_alpha.smi (27/283). Average speed: 0.09 s/mol.\n"", + ""Descriptor calculation completed in 8.932 secs . Average speed: 0.03 s/mol.\n"", + ""Test_gra_ER_alpha.smi\n"", + ""Test_gra_ER_alpha.csv\n"", + ""ExtendedFingerprinter.xml\n"", + ""ExtendedFingerprinter_\n"", + ""Processing CHEMBL192325 in Test_gra_ER_alpha.smi (1/283). \n"", + ""Processing CHEMBL1481853 in Test_gra_ER_alpha.smi (6/283). \n"", + ""Processing CHEMBL1491627 in Test_gra_ER_alpha.smi (8/283). \n"", + ""Processing CHEMBL1610665 in Test_gra_ER_alpha.smi (12/283). \n"", + ""Processing CHEMBL1331671 in Test_gra_ER_alpha.smi (2/283). \n"", + ""Processing CHEMBL1528804 in Test_gra_ER_alpha.smi (7/283). \n"", + ""Processing CHEMBL1468795 in Test_gra_ER_alpha.smi (10/283). \n"", + ""Processing CHEMBL1393 in Test_gra_ER_alpha.smi (3/283). \n"", + ""Processing CHEMBL1565048 in Test_gra_ER_alpha.smi (9/283). \n"", + ""Processing CHEMBL1441655 in Test_gra_ER_alpha.smi (13/283). \n"", + ""Processing CHEMBL1349227 in Test_gra_ER_alpha.smi (4/283). \n"", + ""Processing CHEMBL1383468 in Test_gra_ER_alpha.smi (5/283). \n"", + ""Processing CHEMBL1408468 in Test_gra_ER_alpha.smi (11/283). \n"" + ] + }, + { + ""name"": ""stdout"", + ""output_type"": ""stream"", + ""text"": [ + ""Processing CHEMBL1500491 in Test_gra_ER_alpha.smi (16/283). Average speed: 1.33 s/mol.\r\n"", + ""Processing CHEMBL1405411 in Test_gra_ER_alpha.smi (18/283). Average speed: 0.28 s/mol.\r\n"", + ""Processing CHEMBL1492422 in Test_gra_ER_alpha.smi (21/283). Average speed: 0.18 s/mol.\r\n"", + ""Processing CHEMBL1579100 in Test_gra_ER_alpha.smi (23/283). Average speed: 0.15 s/mol.\r\n"", + ""Processing CHEMBL475503 in Test_gra_ER_alpha.smi (28/283). Average speed: 0.10 s/mol.\r\n"", + ""Processing CHEMBL496881 in Test_gra_ER_alpha.smi (32/283). Average speed: 0.08 s/mol.\r\n"", + ""Processing CHEMBL196509 in Test_gra_ER_alpha.smi (36/283). Average speed: 0.07 s/mol.\r\n"", + ""Processing CHEMBL205989 in Test_gra_ER_alpha.smi (40/283). Average speed: 0.07 s/mol.\r\n"", + ""Processing CHEMBL400480 in Test_gra_ER_alpha.smi (44/283). Average speed: 0.06 s/mol.\r\n"", + ""Processing CHEMBL43064 in Test_gra_ER_alpha.smi (50/283). Average speed: 0.06 s/mol.\r\n"", + ""Processing CHEMBL251515 in Test_gra_ER_alpha.smi (56/283). Average speed: 0.06 s/mol.\r\n"", + ""Processing CHEMBL65695 in Test_gra_ER_alpha.smi (60/283). Average speed: 0.05 s/mol.\r\n"", + ""Processing CHEMBL1333664 in Test_gra_ER_alpha.smi (64/283). Average speed: 0.05 s/mol.\r\n"", + ""Processing CHEMBL1407108 in Test_gra_ER_alpha.smi (67/283). Average speed: 0.05 s/mol.\r\n"", + ""Processing CHEMBL1494151 in Test_gra_ER_alpha.smi (74/283). Average speed: 0.05 s/mol.\r\n"", + ""Processing CHEMBL1377527 in Test_gra_ER_alpha.smi (78/283). Average speed: 0.05 s/mol.\r\n"", + ""Processing CHEMBL1330656 in Test_gra_ER_alpha.smi (80/283). Average speed: 0.05 s/mol.\r\n"", + ""Processing CHEMBL203519 in Test_gra_ER_alpha.smi (84/283). Average speed: 0.05 s/mol.\r\n"", + ""Processing CHEMBL1307842 in Test_gra_ER_alpha.smi (88/283). Average speed: 0.05 s/mol.\r\n"", + ""Processing CHEMBL513735 in Test_gra_ER_alpha.smi (90/283). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL1438401 in Test_gra_ER_alpha.smi (97/283). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL1485581 in Test_gra_ER_alpha.smi (99/283). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL386630 in Test_gra_ER_alpha.smi (105/283). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL212276 in Test_gra_ER_alpha.smi (108/283). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL377560 in Test_gra_ER_alpha.smi (112/283). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL206278 in Test_gra_ER_alpha.smi (117/283). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL364333 in Test_gra_ER_alpha.smi (120/283). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL196577 in Test_gra_ER_alpha.smi (124/283). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL1539136 in Test_gra_ER_alpha.smi (127/283). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL1585598 in Test_gra_ER_alpha.smi (132/283). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL1586766 in Test_gra_ER_alpha.smi (136/283). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL1391679 in Test_gra_ER_alpha.smi (140/283). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL1321083 in Test_gra_ER_alpha.smi (145/283). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL189541 in Test_gra_ER_alpha.smi (148/283). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL1350947 in Test_gra_ER_alpha.smi (149/283). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL382102 in Test_gra_ER_alpha.smi (153/283). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL1387920 in Test_gra_ER_alpha.smi (160/283). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL1342736 in Test_gra_ER_alpha.smi (165/283). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL457241 in Test_gra_ER_alpha.smi (172/283). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL204341 in Test_gra_ER_alpha.smi (174/283). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL232468 in Test_gra_ER_alpha.smi (177/283). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL1433155 in Test_gra_ER_alpha.smi (180/283). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL1455614 in Test_gra_ER_alpha.smi (183/283). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL1504200 in Test_gra_ER_alpha.smi (185/283). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL1379474 in Test_gra_ER_alpha.smi (15/283). Average speed: 1.32 s/mol.\r\n"", + ""Processing CHEMBL1543038 in Test_gra_ER_alpha.smi (20/283). Average speed: 0.18 s/mol.\r\n"", + ""Processing CHEMBL1510990 in Test_gra_ER_alpha.smi (25/283). Average speed: 0.11 s/mol.\r\n"", + ""Processing CHEMBL470783 in Test_gra_ER_alpha.smi (29/283). Average speed: 0.09 s/mol.\r\n"", + ""Processing CHEMBL378473 in Test_gra_ER_alpha.smi (34/283). Average speed: 0.07 s/mol.\r\n"", + ""Processing CHEMBL438977 in Test_gra_ER_alpha.smi (39/283). Average speed: 0.07 s/mol.\r\n"", + ""Processing CHEMBL205988 in Test_gra_ER_alpha.smi (42/283). Average speed: 0.07 s/mol.\r\n"", + ""Processing CHEMBL201364 in Test_gra_ER_alpha.smi (47/283). Average speed: 0.06 s/mol.\r\n"", + ""Processing CHEMBL65354 in Test_gra_ER_alpha.smi (51/283). Average speed: 0.06 s/mol.\r\n"", + ""Processing CHEMBL385358 in Test_gra_ER_alpha.smi (54/283). Average speed: 0.06 s/mol.\r\n"", + ""Processing CHEMBL457242 in Test_gra_ER_alpha.smi (58/283). Average speed: 0.05 s/mol.\r\n"", + ""Processing CHEMBL1340716 in Test_gra_ER_alpha.smi (63/283). Average speed: 0.05 s/mol.\r\n"", + ""Processing CHEMBL1337538 in Test_gra_ER_alpha.smi (65/283). Average speed: 0.05 s/mol.\r\n"", + ""Processing CHEMBL1393680 in Test_gra_ER_alpha.smi (69/283). Average speed: 0.05 s/mol.\r\n"", + ""Processing CHEMBL1488579 in Test_gra_ER_alpha.smi (71/283). Average speed: 0.05 s/mol.\r\n"", + ""Processing CHEMBL1461750 in Test_gra_ER_alpha.smi (76/283). Average speed: 0.05 s/mol.\r\n"", + ""Processing CHEMBL1485768 in Test_gra_ER_alpha.smi (83/283). Average speed: 0.05 s/mol.\r\n"", + ""Processing CHEMBL1496108 in Test_gra_ER_alpha.smi (85/283). Average speed: 0.05 s/mol.\r\n"", + ""Processing CHEMBL457036 in Test_gra_ER_alpha.smi (86/283). Average speed: 0.05 s/mol.\r\n"", + ""Processing CHEMBL1334452 in Test_gra_ER_alpha.smi (93/283). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL1565196 in Test_gra_ER_alpha.smi (98/283). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL194012 in Test_gra_ER_alpha.smi (100/283). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL201809 in Test_gra_ER_alpha.smi (107/283). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL1459339 in Test_gra_ER_alpha.smi (111/283). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL264349 in Test_gra_ER_alpha.smi (115/283). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL209650 in Test_gra_ER_alpha.smi (119/283). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL1334570 in Test_gra_ER_alpha.smi (125/283). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL1318598 in Test_gra_ER_alpha.smi (126/283). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL1333501 in Test_gra_ER_alpha.smi (134/283). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL1545617 in Test_gra_ER_alpha.smi (139/283). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL1573688 in Test_gra_ER_alpha.smi (143/283). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL1306474 in Test_gra_ER_alpha.smi (146/283). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL65221 in Test_gra_ER_alpha.smi (150/283). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL1343134 in Test_gra_ER_alpha.smi (158/283). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL1344624 in Test_gra_ER_alpha.smi (161/283). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL434183 in Test_gra_ER_alpha.smi (163/283). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL65657 in Test_gra_ER_alpha.smi (170/283). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL205009 in Test_gra_ER_alpha.smi (173/283). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL1070 in Test_gra_ER_alpha.smi (176/283). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL1082723 in Test_gra_ER_alpha.smi (178/283). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL1365404 in Test_gra_ER_alpha.smi (181/283). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL1582133 in Test_gra_ER_alpha.smi (184/283). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL1557815 in Test_gra_ER_alpha.smi (17/283). Average speed: 0.46 s/mol.\r\n"", + ""Processing CHEMBL1341487 in Test_gra_ER_alpha.smi (19/283). Average speed: 0.21 s/mol.\r\n"", + ""Processing CHEMBL1412224 in Test_gra_ER_alpha.smi (24/283). Average speed: 0.14 s/mol.\r\n"", + ""Processing CHEMBL516241 in Test_gra_ER_alpha.smi (27/283). Average speed: 0.10 s/mol.\r\n"", + ""Processing CHEMBL398840 in Test_gra_ER_alpha.smi (31/283). Average speed: 0.08 s/mol.\r\n"", + ""Processing CHEMBL293388 in Test_gra_ER_alpha.smi (37/283). Average speed: 0.07 s/mol.\r\n"", + ""Processing CHEMBL204959 in Test_gra_ER_alpha.smi (38/283). Average speed: 0.07 s/mol.\r\n"", + ""Processing CHEMBL206218 in Test_gra_ER_alpha.smi (43/283). Average speed: 0.06 s/mol.\r\n"", + ""Processing CHEMBL311662 in Test_gra_ER_alpha.smi (46/283). Average speed: 0.06 s/mol.\r\n"", + ""Processing CHEMBL212923 in Test_gra_ER_alpha.smi (53/283). Average speed: 0.06 s/mol.\r\n"", + ""Processing CHEMBL65021 in Test_gra_ER_alpha.smi (57/283). Average speed: 0.05 s/mol.\r\n"", + ""Processing CHEMBL313062 in Test_gra_ER_alpha.smi (61/283). Average speed: 0.05 s/mol.\r\n"", + ""Processing CHEMBL1528600 in Test_gra_ER_alpha.smi (66/283). Average speed: 0.05 s/mol.\r\n"", + ""Processing CHEMBL1484399 in Test_gra_ER_alpha.smi (70/283). Average speed: 0.05 s/mol.\r\n"", + ""Processing CHEMBL1299324 in Test_gra_ER_alpha.smi (73/283). Average speed: 0.05 s/mol.\r\n"", + ""Processing CHEMBL1305162 in Test_gra_ER_alpha.smi (75/283). Average speed: 0.05 s/mol.\r\n"", + ""Processing CHEMBL1707275 in Test_gra_ER_alpha.smi (79/283). Average speed: 0.05 s/mol.\r\n"", + ""Processing CHEMBL1500073 in Test_gra_ER_alpha.smi (82/283). Average speed: 0.05 s/mol.\r\n"", + ""Processing CHEMBL1342429 in Test_gra_ER_alpha.smi (89/283). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL1402732 in Test_gra_ER_alpha.smi (92/283). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL1373202 in Test_gra_ER_alpha.smi (94/283). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL1299346 in Test_gra_ER_alpha.smi (96/283). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL1483760 in Test_gra_ER_alpha.smi (102/283). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL1606939 in Test_gra_ER_alpha.smi (103/283). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL81591 in Test_gra_ER_alpha.smi (104/283). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL1353787 in Test_gra_ER_alpha.smi (110/283). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL377166 in Test_gra_ER_alpha.smi (114/283). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL379827 in Test_gra_ER_alpha.smi (118/283). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL125596 in Test_gra_ER_alpha.smi (121/283). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL345000 in Test_gra_ER_alpha.smi (122/283). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL1449807 in Test_gra_ER_alpha.smi (129/283). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL1505040 in Test_gra_ER_alpha.smi (131/283). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL1301796 in Test_gra_ER_alpha.smi (133/283). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL1505994 in Test_gra_ER_alpha.smi (138/283). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL1331281 in Test_gra_ER_alpha.smi (142/283). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL65868 in Test_gra_ER_alpha.smi (147/283). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL1595235 in Test_gra_ER_alpha.smi (152/283). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL195058 in Test_gra_ER_alpha.smi (155/283). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL1322881 in Test_gra_ER_alpha.smi (156/283). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL294209 in Test_gra_ER_alpha.smi (164/283). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL1594663 in Test_gra_ER_alpha.smi (166/283). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL392000 in Test_gra_ER_alpha.smi (169/283). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL381622 in Test_gra_ER_alpha.smi (175/283). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL1359279 in Test_gra_ER_alpha.smi (179/283). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL1438484 in Test_gra_ER_alpha.smi (182/283). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL1583952 in Test_gra_ER_alpha.smi (186/283). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL1353351 in Test_gra_ER_alpha.smi (14/283). Average speed: 1.32 s/mol.\r\n"", + ""Processing CHEMBL1599210 in Test_gra_ER_alpha.smi (22/283). Average speed: 0.15 s/mol.\r\n"", + ""Processing CHEMBL1304094 in Test_gra_ER_alpha.smi (26/283). Average speed: 0.11 s/mol.\r\n"", + ""Processing CHEMBL397343 in Test_gra_ER_alpha.smi (30/283). Average speed: 0.09 s/mol.\r\n"", + ""Processing CHEMBL396946 in Test_gra_ER_alpha.smi (33/283). Average speed: 0.08 s/mol.\r\n"", + ""Processing CHEMBL371311 in Test_gra_ER_alpha.smi (35/283). Average speed: 0.07 s/mol.\r\n"", + ""Processing CHEMBL388474 in Test_gra_ER_alpha.smi (41/283). Average speed: 0.06 s/mol.\r\n"", + ""Processing CHEMBL66598 in Test_gra_ER_alpha.smi (45/283). Average speed: 0.06 s/mol.\r\n"", + ""Processing CHEMBL57978 in Test_gra_ER_alpha.smi (48/283). Average speed: 0.06 s/mol.\r\n"", + ""Processing CHEMBL200903 in Test_gra_ER_alpha.smi (49/283). Average speed: 0.06 s/mol.\r\n"", + ""Processing CHEMBL379384 in Test_gra_ER_alpha.smi (52/283). Average speed: 0.06 s/mol.\r\n"", + ""Processing CHEMBL397344 in Test_gra_ER_alpha.smi (55/283). Average speed: 0.06 s/mol.\r\n"", + ""Processing CHEMBL361266 in Test_gra_ER_alpha.smi (59/283). Average speed: 0.05 s/mol.\r\n"", + ""Processing CHEMBL438863 in Test_gra_ER_alpha.smi (62/283). Average speed: 0.05 s/mol.\r\n"", + ""Processing CHEMBL1495161 in Test_gra_ER_alpha.smi (68/283). Average speed: 0.05 s/mol.\r\n"", + ""Processing CHEMBL1509842 in Test_gra_ER_alpha.smi (72/283). Average speed: 0.05 s/mol.\r\n"", + ""Processing CHEMBL380106 in Test_gra_ER_alpha.smi (77/283). Average speed: 0.05 s/mol.\r\n"", + ""Processing CHEMBL1559976 in Test_gra_ER_alpha.smi (81/283). Average speed: 0.05 s/mol.\r\n"", + ""Processing CHEMBL1575022 in Test_gra_ER_alpha.smi (87/283). Average speed: 0.05 s/mol.\r\n"", + ""Processing CHEMBL1465900 in Test_gra_ER_alpha.smi (91/283). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL1502446 in Test_gra_ER_alpha.smi (95/283). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL1606174 in Test_gra_ER_alpha.smi (101/283). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL379441 in Test_gra_ER_alpha.smi (106/283). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL377622 in Test_gra_ER_alpha.smi (109/283). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL438616 in Test_gra_ER_alpha.smi (113/283). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL195723 in Test_gra_ER_alpha.smi (116/283). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL537887 in Test_gra_ER_alpha.smi (123/283). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL1556511 in Test_gra_ER_alpha.smi (128/283). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL1486906 in Test_gra_ER_alpha.smi (130/283). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL1365444 in Test_gra_ER_alpha.smi (135/283). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL1581440 in Test_gra_ER_alpha.smi (137/283). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL1442547 in Test_gra_ER_alpha.smi (141/283). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL1393628 in Test_gra_ER_alpha.smi (144/283). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL1405097 in Test_gra_ER_alpha.smi (151/283). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL192326 in Test_gra_ER_alpha.smi (154/283). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL1367559 in Test_gra_ER_alpha.smi (157/283). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL1582458 in Test_gra_ER_alpha.smi (159/283). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL1329411 in Test_gra_ER_alpha.smi (162/283). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL1422039 in Test_gra_ER_alpha.smi (167/283). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL188951 in Test_gra_ER_alpha.smi (168/283). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL381489 in Test_gra_ER_alpha.smi (171/283). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL1333846 in Test_gra_ER_alpha.smi (187/283). Average speed: 0.04 s/mol.\r\n"", + ""Descriptor calculation completed in 9.537 secs . Average speed: 0.03 s/mol.\r\n"" + ] + }, + { + ""name"": ""stdout"", + ""output_type"": ""stream"", + ""text"": [ + ""Test_gra_ER_alpha.smi\n"", + ""Test_gra_ER_alpha.csv\n"", + ""EStateFingerprinter.xml\n"", + ""EStateFingerprinter_\n"", + ""Processing CHEMBL192325 in Test_gra_ER_alpha.smi (1/283). \n"", + ""Processing CHEMBL1383468 in Test_gra_ER_alpha.smi (5/283). \n"", + ""Processing CHEMBL1331671 in Test_gra_ER_alpha.smi (2/283). \n"", + ""Processing CHEMBL1481853 in Test_gra_ER_alpha.smi (6/283). \n"", + ""Processing CHEMBL1565048 in Test_gra_ER_alpha.smi (9/283). \n"", + ""Processing CHEMBL1393 in Test_gra_ER_alpha.smi (3/283). \n"", + ""Processing CHEMBL1528804 in Test_gra_ER_alpha.smi (7/283). \n"", + ""Processing CHEMBL1349227 in Test_gra_ER_alpha.smi (4/283). \n"", + ""Processing CHEMBL1491627 in Test_gra_ER_alpha.smi (8/283). \n"", + ""Processing CHEMBL1408468 in Test_gra_ER_alpha.smi (11/283). Average speed: 0.31 s/mol.\n"", + ""Processing CHEMBL1468795 in Test_gra_ER_alpha.smi (10/283). Average speed: 0.36 s/mol.\n"", + ""Processing CHEMBL1610665 in Test_gra_ER_alpha.smi (12/283). Average speed: 0.27 s/mol.\n"", + ""Processing CHEMBL1441655 in Test_gra_ER_alpha.smi (13/283). Average speed: 0.24 s/mol.\n"", + ""Processing CHEMBL1353351 in Test_gra_ER_alpha.smi (14/283). Average speed: 0.20 s/mol.\n"", + ""Processing CHEMBL1379474 in Test_gra_ER_alpha.smi (15/283). Average speed: 0.20 s/mol.\n"", + ""Processing CHEMBL1500491 in Test_gra_ER_alpha.smi (16/283). Average speed: 0.19 s/mol.\n"", + ""Processing CHEMBL1557815 in Test_gra_ER_alpha.smi (17/283). Average speed: 0.18 s/mol.\n"", + ""Processing CHEMBL1405411 in Test_gra_ER_alpha.smi (18/283). Average speed: 0.17 s/mol.\n"", + ""Processing CHEMBL1341487 in Test_gra_ER_alpha.smi (19/283). Average speed: 0.15 s/mol.\n"", + ""Processing CHEMBL1543038 in Test_gra_ER_alpha.smi (20/283). Average speed: 0.15 s/mol.\n"", + ""Processing CHEMBL1492422 in Test_gra_ER_alpha.smi (21/283). Average speed: 0.14 s/mol.\n"", + ""Processing CHEMBL1599210 in Test_gra_ER_alpha.smi (22/283). Average speed: 0.12 s/mol.\n"", + ""Processing CHEMBL1579100 in Test_gra_ER_alpha.smi (23/283). Average speed: 0.13 s/mol.\n"", + ""Processing CHEMBL1412224 in Test_gra_ER_alpha.smi (24/283). Average speed: 0.12 s/mol.\n"", + ""Processing CHEMBL1510990 in Test_gra_ER_alpha.smi (25/283). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL1304094 in Test_gra_ER_alpha.smi (26/283). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL516241 in Test_gra_ER_alpha.smi (27/283). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL475503 in Test_gra_ER_alpha.smi (28/283). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL470783 in Test_gra_ER_alpha.smi (29/283). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL397343 in Test_gra_ER_alpha.smi (30/283). Average speed: 0.09 s/mol.\n"", + ""Processing CHEMBL398840 in Test_gra_ER_alpha.smi (31/283). Average speed: 0.09 s/mol.\n"", + ""Processing CHEMBL378473 in Test_gra_ER_alpha.smi (34/283). Average speed: 0.09 s/mol.\n"", + ""Processing CHEMBL496881 in Test_gra_ER_alpha.smi (32/283). Average speed: 0.09 s/mol.\n"", + ""Processing CHEMBL396946 in Test_gra_ER_alpha.smi (33/283). Average speed: 0.09 s/mol.\n"", + ""Processing CHEMBL371311 in Test_gra_ER_alpha.smi (35/283). Average speed: 0.08 s/mol.\n"", + ""Processing CHEMBL196509 in Test_gra_ER_alpha.smi (36/283). Average speed: 0.08 s/mol.\n"", + ""Processing CHEMBL293388 in Test_gra_ER_alpha.smi (37/283). Average speed: 0.08 s/mol.\n"", + ""Processing CHEMBL204959 in Test_gra_ER_alpha.smi (38/283). Average speed: 0.08 s/mol.\n"", + ""Processing CHEMBL438977 in Test_gra_ER_alpha.smi (39/283). Average speed: 0.08 s/mol.\n"", + ""Processing CHEMBL205989 in Test_gra_ER_alpha.smi (40/283). Average speed: 0.08 s/mol.\n"", + ""Processing CHEMBL388474 in Test_gra_ER_alpha.smi (41/283). Average speed: 0.08 s/mol.\n"", + ""Processing CHEMBL205988 in Test_gra_ER_alpha.smi (42/283). Average speed: 0.07 s/mol.\n"", + ""Processing CHEMBL400480 in Test_gra_ER_alpha.smi (44/283). Average speed: 0.07 s/mol.\n"", + ""Processing CHEMBL206218 in Test_gra_ER_alpha.smi (43/283). Average speed: 0.07 s/mol.\n"", + ""Processing CHEMBL66598 in Test_gra_ER_alpha.smi (45/283). Average speed: 0.07 s/mol.\n"", + ""Processing CHEMBL311662 in Test_gra_ER_alpha.smi (46/283). Average speed: 0.07 s/mol.\n"", + ""Processing CHEMBL201364 in Test_gra_ER_alpha.smi (47/283). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL57978 in Test_gra_ER_alpha.smi (48/283). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL200903 in Test_gra_ER_alpha.smi (49/283). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL65354 in Test_gra_ER_alpha.smi (51/283). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL43064 in Test_gra_ER_alpha.smi (50/283). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL379384 in Test_gra_ER_alpha.smi (52/283). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL212923 in Test_gra_ER_alpha.smi (53/283). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL385358 in Test_gra_ER_alpha.smi (54/283). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL397344 in Test_gra_ER_alpha.smi (55/283). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL65021 in Test_gra_ER_alpha.smi (57/283). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL251515 in Test_gra_ER_alpha.smi (56/283). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL361266 in Test_gra_ER_alpha.smi (59/283). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL457242 in Test_gra_ER_alpha.smi (58/283). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL65695 in Test_gra_ER_alpha.smi (60/283). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL313062 in Test_gra_ER_alpha.smi (61/283). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL438863 in Test_gra_ER_alpha.smi (62/283). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL1340716 in Test_gra_ER_alpha.smi (63/283). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL1333664 in Test_gra_ER_alpha.smi (64/283). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL1337538 in Test_gra_ER_alpha.smi (65/283). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL1528600 in Test_gra_ER_alpha.smi (66/283). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL1407108 in Test_gra_ER_alpha.smi (67/283). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL1495161 in Test_gra_ER_alpha.smi (68/283). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL1393680 in Test_gra_ER_alpha.smi (69/283). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL1484399 in Test_gra_ER_alpha.smi (70/283). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL1488579 in Test_gra_ER_alpha.smi (71/283). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL1509842 in Test_gra_ER_alpha.smi (72/283). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL1299324 in Test_gra_ER_alpha.smi (73/283). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL1494151 in Test_gra_ER_alpha.smi (74/283). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL1461750 in Test_gra_ER_alpha.smi (76/283). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL1305162 in Test_gra_ER_alpha.smi (75/283). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL380106 in Test_gra_ER_alpha.smi (77/283). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL1377527 in Test_gra_ER_alpha.smi (78/283). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL1707275 in Test_gra_ER_alpha.smi (79/283). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL1330656 in Test_gra_ER_alpha.smi (80/283). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL1559976 in Test_gra_ER_alpha.smi (81/283). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL1500073 in Test_gra_ER_alpha.smi (82/283). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1485768 in Test_gra_ER_alpha.smi (83/283). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL203519 in Test_gra_ER_alpha.smi (84/283). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1496108 in Test_gra_ER_alpha.smi (85/283). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL457036 in Test_gra_ER_alpha.smi (86/283). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1575022 in Test_gra_ER_alpha.smi (87/283). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1307842 in Test_gra_ER_alpha.smi (88/283). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1342429 in Test_gra_ER_alpha.smi (89/283). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL513735 in Test_gra_ER_alpha.smi (90/283). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1465900 in Test_gra_ER_alpha.smi (91/283). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1334452 in Test_gra_ER_alpha.smi (93/283). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1402732 in Test_gra_ER_alpha.smi (92/283). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1373202 in Test_gra_ER_alpha.smi (94/283). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1502446 in Test_gra_ER_alpha.smi (95/283). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1299346 in Test_gra_ER_alpha.smi (96/283). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1438401 in Test_gra_ER_alpha.smi (97/283). Average speed: 0.04 s/mol.\n"" + ] + }, + { + ""name"": ""stdout"", + ""output_type"": ""stream"", + ""text"": [ + ""Processing CHEMBL1565196 in Test_gra_ER_alpha.smi (98/283). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1485581 in Test_gra_ER_alpha.smi (99/283). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL194012 in Test_gra_ER_alpha.smi (100/283). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1606174 in Test_gra_ER_alpha.smi (101/283). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1483760 in Test_gra_ER_alpha.smi (102/283). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1606939 in Test_gra_ER_alpha.smi (103/283). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL81591 in Test_gra_ER_alpha.smi (104/283). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL386630 in Test_gra_ER_alpha.smi (105/283). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL379441 in Test_gra_ER_alpha.smi (106/283). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL201809 in Test_gra_ER_alpha.smi (107/283). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL212276 in Test_gra_ER_alpha.smi (108/283). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL377622 in Test_gra_ER_alpha.smi (109/283). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1353787 in Test_gra_ER_alpha.smi (110/283). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1459339 in Test_gra_ER_alpha.smi (111/283). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL377560 in Test_gra_ER_alpha.smi (112/283). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL377166 in Test_gra_ER_alpha.smi (114/283). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL438616 in Test_gra_ER_alpha.smi (113/283). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL264349 in Test_gra_ER_alpha.smi (115/283). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL195723 in Test_gra_ER_alpha.smi (116/283). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL206278 in Test_gra_ER_alpha.smi (117/283). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL379827 in Test_gra_ER_alpha.smi (118/283). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL209650 in Test_gra_ER_alpha.smi (119/283). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL364333 in Test_gra_ER_alpha.smi (120/283). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL125596 in Test_gra_ER_alpha.smi (121/283). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL345000 in Test_gra_ER_alpha.smi (122/283). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL537887 in Test_gra_ER_alpha.smi (123/283). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1334570 in Test_gra_ER_alpha.smi (125/283). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL196577 in Test_gra_ER_alpha.smi (124/283). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1318598 in Test_gra_ER_alpha.smi (126/283). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL1539136 in Test_gra_ER_alpha.smi (127/283). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL1556511 in Test_gra_ER_alpha.smi (128/283). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL1449807 in Test_gra_ER_alpha.smi (129/283). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL1486906 in Test_gra_ER_alpha.smi (130/283). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL1505040 in Test_gra_ER_alpha.smi (131/283). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL1585598 in Test_gra_ER_alpha.smi (132/283). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL1301796 in Test_gra_ER_alpha.smi (133/283). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL1365444 in Test_gra_ER_alpha.smi (135/283). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL1333501 in Test_gra_ER_alpha.smi (134/283). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL1586766 in Test_gra_ER_alpha.smi (136/283). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL1581440 in Test_gra_ER_alpha.smi (137/283). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL1505994 in Test_gra_ER_alpha.smi (138/283). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL1545617 in Test_gra_ER_alpha.smi (139/283). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL1391679 in Test_gra_ER_alpha.smi (140/283). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL1442547 in Test_gra_ER_alpha.smi (141/283). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL1331281 in Test_gra_ER_alpha.smi (142/283). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL1573688 in Test_gra_ER_alpha.smi (143/283). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL1393628 in Test_gra_ER_alpha.smi (144/283). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL1321083 in Test_gra_ER_alpha.smi (145/283). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL1306474 in Test_gra_ER_alpha.smi (146/283). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL65868 in Test_gra_ER_alpha.smi (147/283). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL189541 in Test_gra_ER_alpha.smi (148/283). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL1350947 in Test_gra_ER_alpha.smi (149/283). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL65221 in Test_gra_ER_alpha.smi (150/283). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL1405097 in Test_gra_ER_alpha.smi (151/283). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL1595235 in Test_gra_ER_alpha.smi (152/283). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL382102 in Test_gra_ER_alpha.smi (153/283). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL195058 in Test_gra_ER_alpha.smi (155/283). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL192326 in Test_gra_ER_alpha.smi (154/283). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL1322881 in Test_gra_ER_alpha.smi (156/283). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL1367559 in Test_gra_ER_alpha.smi (157/283). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL1343134 in Test_gra_ER_alpha.smi (158/283). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL1582458 in Test_gra_ER_alpha.smi (159/283). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL1387920 in Test_gra_ER_alpha.smi (160/283). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL1344624 in Test_gra_ER_alpha.smi (161/283). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL1329411 in Test_gra_ER_alpha.smi (162/283). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL434183 in Test_gra_ER_alpha.smi (163/283). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL294209 in Test_gra_ER_alpha.smi (164/283). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL1342736 in Test_gra_ER_alpha.smi (165/283). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL1594663 in Test_gra_ER_alpha.smi (166/283). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL1422039 in Test_gra_ER_alpha.smi (167/283). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL188951 in Test_gra_ER_alpha.smi (168/283). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL392000 in Test_gra_ER_alpha.smi (169/283). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL65657 in Test_gra_ER_alpha.smi (170/283). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL381489 in Test_gra_ER_alpha.smi (171/283). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL457241 in Test_gra_ER_alpha.smi (172/283). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL205009 in Test_gra_ER_alpha.smi (173/283). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL381622 in Test_gra_ER_alpha.smi (175/283). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL1070 in Test_gra_ER_alpha.smi (176/283). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL204341 in Test_gra_ER_alpha.smi (174/283). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL232468 in Test_gra_ER_alpha.smi (177/283). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL1082723 in Test_gra_ER_alpha.smi (178/283). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL1359279 in Test_gra_ER_alpha.smi (179/283). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL1433155 in Test_gra_ER_alpha.smi (180/283). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL1365404 in Test_gra_ER_alpha.smi (181/283). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL1455614 in Test_gra_ER_alpha.smi (183/283). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL1438484 in Test_gra_ER_alpha.smi (182/283). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL1582133 in Test_gra_ER_alpha.smi (184/283). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL1504200 in Test_gra_ER_alpha.smi (185/283). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL1583952 in Test_gra_ER_alpha.smi (186/283). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL1333846 in Test_gra_ER_alpha.smi (187/283). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL1352844 in Test_gra_ER_alpha.smi (188/283). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL1500059 in Test_gra_ER_alpha.smi (189/283). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL1300705 in Test_gra_ER_alpha.smi (190/283). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL1450059 in Test_gra_ER_alpha.smi (191/283). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL1432915 in Test_gra_ER_alpha.smi (192/283). Average speed: 0.03 s/mol.\n"" + ] + }, + { + ""name"": ""stdout"", + ""output_type"": ""stream"", + ""text"": [ + ""Processing CHEMBL1391501 in Test_gra_ER_alpha.smi (194/283). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL1539668 in Test_gra_ER_alpha.smi (193/283). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL1331278 in Test_gra_ER_alpha.smi (195/283). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL1565816 in Test_gra_ER_alpha.smi (197/283). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL101 in Test_gra_ER_alpha.smi (196/283). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL1564026 in Test_gra_ER_alpha.smi (198/283). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL1334162 in Test_gra_ER_alpha.smi (199/283). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL1572214 in Test_gra_ER_alpha.smi (200/283). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL1586116 in Test_gra_ER_alpha.smi (201/283). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL1478743 in Test_gra_ER_alpha.smi (202/283). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL1299808 in Test_gra_ER_alpha.smi (205/283). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL1589718 in Test_gra_ER_alpha.smi (204/283). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL1583911 in Test_gra_ER_alpha.smi (203/283). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL1579818 in Test_gra_ER_alpha.smi (206/283). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL377616 in Test_gra_ER_alpha.smi (207/283). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL1095097 in Test_gra_ER_alpha.smi (208/283). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL213457 in Test_gra_ER_alpha.smi (209/283). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL214291 in Test_gra_ER_alpha.smi (210/283). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL104367 in Test_gra_ER_alpha.smi (211/283). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL362507 in Test_gra_ER_alpha.smi (212/283). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL190238 in Test_gra_ER_alpha.smi (213/283). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL79335 in Test_gra_ER_alpha.smi (215/283). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL195515 in Test_gra_ER_alpha.smi (214/283). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL380978 in Test_gra_ER_alpha.smi (217/283). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL103 in Test_gra_ER_alpha.smi (216/283). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL311034 in Test_gra_ER_alpha.smi (218/283). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL379024 in Test_gra_ER_alpha.smi (219/283). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL244971 in Test_gra_ER_alpha.smi (220/283). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL245132 in Test_gra_ER_alpha.smi (221/283). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL1450825 in Test_gra_ER_alpha.smi (222/283). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL1390728 in Test_gra_ER_alpha.smi (223/283). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL204389 in Test_gra_ER_alpha.smi (224/283). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL377565 in Test_gra_ER_alpha.smi (225/283). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL245576 in Test_gra_ER_alpha.smi (226/283). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL245771 in Test_gra_ER_alpha.smi (227/283). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL1402012 in Test_gra_ER_alpha.smi (228/283). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL1349449 in Test_gra_ER_alpha.smi (230/283). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL1492286 in Test_gra_ER_alpha.smi (229/283). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL1441028 in Test_gra_ER_alpha.smi (231/283). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL1366918 in Test_gra_ER_alpha.smi (233/283). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL1340515 in Test_gra_ER_alpha.smi (232/283). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL1351935 in Test_gra_ER_alpha.smi (234/283). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL1519846 in Test_gra_ER_alpha.smi (235/283). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL1464700 in Test_gra_ER_alpha.smi (236/283). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL1463596 in Test_gra_ER_alpha.smi (237/283). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL1539548 in Test_gra_ER_alpha.smi (238/283). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL1603680 in Test_gra_ER_alpha.smi (239/283). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL246140 in Test_gra_ER_alpha.smi (240/283). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL1392987 in Test_gra_ER_alpha.smi (241/283). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL1547093 in Test_gra_ER_alpha.smi (242/283). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL1390198 in Test_gra_ER_alpha.smi (243/283). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL515671 in Test_gra_ER_alpha.smi (244/283). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL1985765 in Test_gra_ER_alpha.smi (245/283). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL1971815 in Test_gra_ER_alpha.smi (246/283). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL1965461 in Test_gra_ER_alpha.smi (247/283). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL2000120 in Test_gra_ER_alpha.smi (248/283). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL3261407 in Test_gra_ER_alpha.smi (249/283). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL3191473 in Test_gra_ER_alpha.smi (250/283). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL3211537 in Test_gra_ER_alpha.smi (251/283). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL3234863 in Test_gra_ER_alpha.smi (253/283). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL3234858 in Test_gra_ER_alpha.smi (252/283). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL3207950 in Test_gra_ER_alpha.smi (254/283). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL3213194 in Test_gra_ER_alpha.smi (255/283). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL3196436 in Test_gra_ER_alpha.smi (256/283). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL3263752 in Test_gra_ER_alpha.smi (257/283). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL3208853 in Test_gra_ER_alpha.smi (258/283). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL3208616 in Test_gra_ER_alpha.smi (259/283). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL3326454 in Test_gra_ER_alpha.smi (260/283). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL3261425 in Test_gra_ER_alpha.smi (261/283). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL3427415 in Test_gra_ER_alpha.smi (262/283). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL3578295 in Test_gra_ER_alpha.smi (264/283). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL3427385 in Test_gra_ER_alpha.smi (263/283). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL3597500 in Test_gra_ER_alpha.smi (265/283). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL3597503 in Test_gra_ER_alpha.smi (266/283). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL3597506 in Test_gra_ER_alpha.smi (267/283). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL3597507 in Test_gra_ER_alpha.smi (268/283). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL3597510 in Test_gra_ER_alpha.smi (269/283). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL3597513 in Test_gra_ER_alpha.smi (270/283). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL3597514 in Test_gra_ER_alpha.smi (271/283). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL3421892 in Test_gra_ER_alpha.smi (272/283). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL3421871 in Test_gra_ER_alpha.smi (273/283). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL3427414 in Test_gra_ER_alpha.smi (274/283). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL3427411 in Test_gra_ER_alpha.smi (275/283). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL3427406 in Test_gra_ER_alpha.smi (276/283). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL3775464 in Test_gra_ER_alpha.smi (278/283). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL3774852 in Test_gra_ER_alpha.smi (277/283). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL3775389 in Test_gra_ER_alpha.smi (279/283). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL3775798 in Test_gra_ER_alpha.smi (280/283). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL3827018 in Test_gra_ER_alpha.smi (282/283). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL3775473 in Test_gra_ER_alpha.smi (281/283). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL3827876 in Test_gra_ER_alpha.smi (283/283). Average speed: 0.03 s/mol.\n"", + ""Descriptor calculation completed in 10.357 secs . Average speed: 0.04 s/mol.\n"", + ""Test_gra_ER_alpha.smi\n"", + ""Test_gra_ER_alpha.csv\n"", + ""GraphOnlyFingerprinter.xml\n"", + ""GraphOnlyFingerprinter_\n"", + ""Processing CHEMBL192325 in Test_gra_ER_alpha.smi (1/283). \n"", + ""Processing CHEMBL1383468 in Test_gra_ER_alpha.smi (5/283). \n"", + ""Processing CHEMBL1331671 in Test_gra_ER_alpha.smi (2/283). \n"", + ""Processing CHEMBL1481853 in Test_gra_ER_alpha.smi (6/283). \n"", + ""Processing CHEMBL1528804 in Test_gra_ER_alpha.smi (7/283). \n"", + ""Processing CHEMBL1393 in Test_gra_ER_alpha.smi (3/283). \n"", + ""Processing CHEMBL1349227 in Test_gra_ER_alpha.smi (4/283). \n"", + ""Processing CHEMBL1491627 in Test_gra_ER_alpha.smi (8/283). \n"" + ] + }, + { + ""name"": ""stdout"", + ""output_type"": ""stream"", + ""text"": [ + ""Processing CHEMBL1408468 in Test_gra_ER_alpha.smi (11/283). Average speed: 0.24 s/mol.\n"", + ""Processing CHEMBL1441655 in Test_gra_ER_alpha.smi (13/283). Average speed: 0.20 s/mol.\n"", + ""Processing CHEMBL1468795 in Test_gra_ER_alpha.smi (10/283). Average speed: 0.35 s/mol.\n"", + ""Processing CHEMBL1565048 in Test_gra_ER_alpha.smi (9/283). Average speed: 0.68 s/mol.\n"", + ""Processing CHEMBL1610665 in Test_gra_ER_alpha.smi (12/283). Average speed: 0.24 s/mol.\n"", + ""Processing CHEMBL1353351 in Test_gra_ER_alpha.smi (14/283). Average speed: 0.18 s/mol.\n"", + ""Processing CHEMBL1379474 in Test_gra_ER_alpha.smi (15/283). Average speed: 0.17 s/mol.\n"", + ""Processing CHEMBL1500491 in Test_gra_ER_alpha.smi (16/283). Average speed: 0.16 s/mol.\n"", + ""Processing CHEMBL1557815 in Test_gra_ER_alpha.smi (17/283). Average speed: 0.15 s/mol.\n"", + ""Processing CHEMBL1405411 in Test_gra_ER_alpha.smi (18/283). Average speed: 0.14 s/mol.\n"", + ""Processing CHEMBL1341487 in Test_gra_ER_alpha.smi (19/283). Average speed: 0.14 s/mol.\n"", + ""Processing CHEMBL1543038 in Test_gra_ER_alpha.smi (20/283). Average speed: 0.13 s/mol.\n"", + ""Processing CHEMBL1492422 in Test_gra_ER_alpha.smi (21/283). Average speed: 0.12 s/mol.\n"", + ""Processing CHEMBL1599210 in Test_gra_ER_alpha.smi (22/283). Average speed: 0.12 s/mol.\n"", + ""Processing CHEMBL1579100 in Test_gra_ER_alpha.smi (23/283). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL1412224 in Test_gra_ER_alpha.smi (24/283). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL1510990 in Test_gra_ER_alpha.smi (25/283). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL1304094 in Test_gra_ER_alpha.smi (26/283). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL475503 in Test_gra_ER_alpha.smi (28/283). Average speed: 0.09 s/mol.\n"", + ""Processing CHEMBL516241 in Test_gra_ER_alpha.smi (27/283). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL397343 in Test_gra_ER_alpha.smi (30/283). Average speed: 0.09 s/mol.\n"", + ""Processing CHEMBL470783 in Test_gra_ER_alpha.smi (29/283). Average speed: 0.09 s/mol.\n"", + ""Processing CHEMBL398840 in Test_gra_ER_alpha.smi (31/283). Average speed: 0.09 s/mol.\n"", + ""Processing CHEMBL496881 in Test_gra_ER_alpha.smi (32/283). Average speed: 0.08 s/mol.\n"", + ""Processing CHEMBL396946 in Test_gra_ER_alpha.smi (33/283). Average speed: 0.08 s/mol.\n"", + ""Processing CHEMBL378473 in Test_gra_ER_alpha.smi (34/283). Average speed: 0.08 s/mol.\n"", + ""Processing CHEMBL371311 in Test_gra_ER_alpha.smi (35/283). Average speed: 0.08 s/mol.\n"", + ""Processing CHEMBL293388 in Test_gra_ER_alpha.smi (37/283). Average speed: 0.07 s/mol.\n"", + ""Processing CHEMBL196509 in Test_gra_ER_alpha.smi (36/283). Average speed: 0.07 s/mol.\n"", + ""Processing CHEMBL438977 in Test_gra_ER_alpha.smi (39/283). Average speed: 0.07 s/mol.\n"", + ""Processing CHEMBL204959 in Test_gra_ER_alpha.smi (38/283). Average speed: 0.07 s/mol.\n"", + ""Processing CHEMBL205988 in Test_gra_ER_alpha.smi (42/283). Average speed: 0.07 s/mol.\n"", + ""Processing CHEMBL311662 in Test_gra_ER_alpha.smi (46/283). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL379384 in Test_gra_ER_alpha.smi (52/283). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL385358 in Test_gra_ER_alpha.smi (54/283). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL457242 in Test_gra_ER_alpha.smi (58/283). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL1340716 in Test_gra_ER_alpha.smi (63/283). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL1528600 in Test_gra_ER_alpha.smi (66/283). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL1299324 in Test_gra_ER_alpha.smi (73/283). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL1305162 in Test_gra_ER_alpha.smi (75/283). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL1461750 in Test_gra_ER_alpha.smi (76/283). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL1500073 in Test_gra_ER_alpha.smi (82/283). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL1307842 in Test_gra_ER_alpha.smi (88/283). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1465900 in Test_gra_ER_alpha.smi (91/283). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1373202 in Test_gra_ER_alpha.smi (94/283). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1438401 in Test_gra_ER_alpha.smi (97/283). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL194012 in Test_gra_ER_alpha.smi (100/283). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL81591 in Test_gra_ER_alpha.smi (104/283). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL201809 in Test_gra_ER_alpha.smi (107/283). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL377560 in Test_gra_ER_alpha.smi (112/283). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL379827 in Test_gra_ER_alpha.smi (118/283). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL364333 in Test_gra_ER_alpha.smi (120/283). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL537887 in Test_gra_ER_alpha.smi (123/283). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1539136 in Test_gra_ER_alpha.smi (127/283). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1585598 in Test_gra_ER_alpha.smi (132/283). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1505994 in Test_gra_ER_alpha.smi (138/283). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1331281 in Test_gra_ER_alpha.smi (142/283). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL65868 in Test_gra_ER_alpha.smi (147/283). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL382102 in Test_gra_ER_alpha.smi (153/283). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1387920 in Test_gra_ER_alpha.smi (160/283). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1422039 in Test_gra_ER_alpha.smi (167/283). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL188951 in Test_gra_ER_alpha.smi (168/283). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL392000 in Test_gra_ER_alpha.smi (169/283). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL232468 in Test_gra_ER_alpha.smi (177/283). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1359279 in Test_gra_ER_alpha.smi (179/283). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1333846 in Test_gra_ER_alpha.smi (187/283). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1300705 in Test_gra_ER_alpha.smi (190/283). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1450059 in Test_gra_ER_alpha.smi (191/283). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL101 in Test_gra_ER_alpha.smi (196/283). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1572214 in Test_gra_ER_alpha.smi (200/283). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL377616 in Test_gra_ER_alpha.smi (207/283). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL362507 in Test_gra_ER_alpha.smi (212/283). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL195515 in Test_gra_ER_alpha.smi (214/283). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL311034 in Test_gra_ER_alpha.smi (218/283). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1390728 in Test_gra_ER_alpha.smi (223/283). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL205989 in Test_gra_ER_alpha.smi (40/283). Average speed: 0.07 s/mol.\n"", + ""Processing CHEMBL400480 in Test_gra_ER_alpha.smi (44/283). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL43064 in Test_gra_ER_alpha.smi (50/283). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL251515 in Test_gra_ER_alpha.smi (56/283). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL361266 in Test_gra_ER_alpha.smi (59/283). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL438863 in Test_gra_ER_alpha.smi (62/283). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL1337538 in Test_gra_ER_alpha.smi (65/283). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL1393680 in Test_gra_ER_alpha.smi (69/283). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL1494151 in Test_gra_ER_alpha.smi (74/283). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL1377527 in Test_gra_ER_alpha.smi (78/283). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL1330656 in Test_gra_ER_alpha.smi (80/283). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL203519 in Test_gra_ER_alpha.smi (84/283). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL1496108 in Test_gra_ER_alpha.smi (85/283). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1342429 in Test_gra_ER_alpha.smi (89/283). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1402732 in Test_gra_ER_alpha.smi (92/283). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1502446 in Test_gra_ER_alpha.smi (95/283). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1485581 in Test_gra_ER_alpha.smi (99/283). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1606174 in Test_gra_ER_alpha.smi (101/283). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1606939 in Test_gra_ER_alpha.smi (103/283). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL379441 in Test_gra_ER_alpha.smi (106/283). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1353787 in Test_gra_ER_alpha.smi (110/283). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL377166 in Test_gra_ER_alpha.smi (114/283). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL206278 in Test_gra_ER_alpha.smi (117/283). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL125596 in Test_gra_ER_alpha.smi (121/283). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL345000 in Test_gra_ER_alpha.smi (122/283). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1556511 in Test_gra_ER_alpha.smi (128/283). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1505040 in Test_gra_ER_alpha.smi (131/283). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1333501 in Test_gra_ER_alpha.smi (134/283). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1581440 in Test_gra_ER_alpha.smi (137/283). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1442547 in Test_gra_ER_alpha.smi (141/283). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1393628 in Test_gra_ER_alpha.smi (144/283). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1405097 in Test_gra_ER_alpha.smi (151/283). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL192326 in Test_gra_ER_alpha.smi (154/283). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL195058 in Test_gra_ER_alpha.smi (155/283). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1582458 in Test_gra_ER_alpha.smi (159/283). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1344624 in Test_gra_ER_alpha.smi (161/283). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL434183 in Test_gra_ER_alpha.smi (163/283). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL381489 in Test_gra_ER_alpha.smi (171/283). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1504200 in Test_gra_ER_alpha.smi (185/283). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1352844 in Test_gra_ER_alpha.smi (188/283). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1432915 in Test_gra_ER_alpha.smi (192/283). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1565816 in Test_gra_ER_alpha.smi (197/283). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1334162 in Test_gra_ER_alpha.smi (199/283). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1478743 in Test_gra_ER_alpha.smi (202/283). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL214291 in Test_gra_ER_alpha.smi (210/283). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL190238 in Test_gra_ER_alpha.smi (213/283). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL79335 in Test_gra_ER_alpha.smi (215/283). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL379024 in Test_gra_ER_alpha.smi (219/283). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL377565 in Test_gra_ER_alpha.smi (225/283). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL206218 in Test_gra_ER_alpha.smi (43/283). Average speed: 0.07 s/mol.\n"", + ""Processing CHEMBL201364 in Test_gra_ER_alpha.smi (47/283). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL212923 in Test_gra_ER_alpha.smi (53/283). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL397344 in Test_gra_ER_alpha.smi (55/283). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL313062 in Test_gra_ER_alpha.smi (61/283). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL1495161 in Test_gra_ER_alpha.smi (68/283). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL1484399 in Test_gra_ER_alpha.smi (70/283). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL1509842 in Test_gra_ER_alpha.smi (72/283). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL380106 in Test_gra_ER_alpha.smi (77/283). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL1559976 in Test_gra_ER_alpha.smi (81/283). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL1575022 in Test_gra_ER_alpha.smi (87/283). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL513735 in Test_gra_ER_alpha.smi (90/283). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1299346 in Test_gra_ER_alpha.smi (96/283). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL377622 in Test_gra_ER_alpha.smi (109/283). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1459339 in Test_gra_ER_alpha.smi (111/283). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL264349 in Test_gra_ER_alpha.smi (115/283). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL209650 in Test_gra_ER_alpha.smi (119/283). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1334570 in Test_gra_ER_alpha.smi (125/283). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1449807 in Test_gra_ER_alpha.smi (129/283). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1486906 in Test_gra_ER_alpha.smi (130/283). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1365444 in Test_gra_ER_alpha.smi (135/283). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1586766 in Test_gra_ER_alpha.smi (136/283). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1391679 in Test_gra_ER_alpha.smi (140/283). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1321083 in Test_gra_ER_alpha.smi (145/283). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL189541 in Test_gra_ER_alpha.smi (148/283). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL65221 in Test_gra_ER_alpha.smi (150/283). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1367559 in Test_gra_ER_alpha.smi (157/283). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1343134 in Test_gra_ER_alpha.smi (158/283). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL294209 in Test_gra_ER_alpha.smi (164/283). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1594663 in Test_gra_ER_alpha.smi (166/283). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL65657 in Test_gra_ER_alpha.smi (170/283). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL205009 in Test_gra_ER_alpha.smi (173/283). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1070 in Test_gra_ER_alpha.smi (176/283). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1082723 in Test_gra_ER_alpha.smi (178/283). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1433155 in Test_gra_ER_alpha.smi (180/283). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1455614 in Test_gra_ER_alpha.smi (183/283). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1582133 in Test_gra_ER_alpha.smi (184/283). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1539668 in Test_gra_ER_alpha.smi (193/283). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1391501 in Test_gra_ER_alpha.smi (194/283). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1564026 in Test_gra_ER_alpha.smi (198/283). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1589718 in Test_gra_ER_alpha.smi (204/283). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1299808 in Test_gra_ER_alpha.smi (205/283). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1579818 in Test_gra_ER_alpha.smi (206/283). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL213457 in Test_gra_ER_alpha.smi (209/283). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL104367 in Test_gra_ER_alpha.smi (211/283). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL380978 in Test_gra_ER_alpha.smi (217/283). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL245132 in Test_gra_ER_alpha.smi (221/283). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL204389 in Test_gra_ER_alpha.smi (224/283). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL388474 in Test_gra_ER_alpha.smi (41/283). Average speed: 0.07 s/mol.\n"", + ""Processing CHEMBL66598 in Test_gra_ER_alpha.smi (45/283). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL57978 in Test_gra_ER_alpha.smi (48/283). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL200903 in Test_gra_ER_alpha.smi (49/283). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL65354 in Test_gra_ER_alpha.smi (51/283). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL65021 in Test_gra_ER_alpha.smi (57/283). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL65695 in Test_gra_ER_alpha.smi (60/283). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL1333664 in Test_gra_ER_alpha.smi (64/283). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL1407108 in Test_gra_ER_alpha.smi (67/283). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL1488579 in Test_gra_ER_alpha.smi (71/283). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL1707275 in Test_gra_ER_alpha.smi (79/283). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL1485768 in Test_gra_ER_alpha.smi (83/283). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL457036 in Test_gra_ER_alpha.smi (86/283). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1334452 in Test_gra_ER_alpha.smi (93/283). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1565196 in Test_gra_ER_alpha.smi (98/283). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1483760 in Test_gra_ER_alpha.smi (102/283). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL386630 in Test_gra_ER_alpha.smi (105/283). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL212276 in Test_gra_ER_alpha.smi (108/283). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL438616 in Test_gra_ER_alpha.smi (113/283). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL195723 in Test_gra_ER_alpha.smi (116/283). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL196577 in Test_gra_ER_alpha.smi (124/283). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1318598 in Test_gra_ER_alpha.smi (126/283). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1301796 in Test_gra_ER_alpha.smi (133/283). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1545617 in Test_gra_ER_alpha.smi (139/283). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1573688 in Test_gra_ER_alpha.smi (143/283). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1306474 in Test_gra_ER_alpha.smi (146/283). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1350947 in Test_gra_ER_alpha.smi (149/283). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1595235 in Test_gra_ER_alpha.smi (152/283). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1322881 in Test_gra_ER_alpha.smi (156/283). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1329411 in Test_gra_ER_alpha.smi (162/283). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1342736 in Test_gra_ER_alpha.smi (165/283). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL457241 in Test_gra_ER_alpha.smi (172/283). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL204341 in Test_gra_ER_alpha.smi (174/283). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL381622 in Test_gra_ER_alpha.smi (175/283). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1365404 in Test_gra_ER_alpha.smi (181/283). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1438484 in Test_gra_ER_alpha.smi (182/283). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1583952 in Test_gra_ER_alpha.smi (186/283). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1500059 in Test_gra_ER_alpha.smi (189/283). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1331278 in Test_gra_ER_alpha.smi (195/283). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1586116 in Test_gra_ER_alpha.smi (201/283). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1583911 in Test_gra_ER_alpha.smi (203/283). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1095097 in Test_gra_ER_alpha.smi (208/283). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL103 in Test_gra_ER_alpha.smi (216/283). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL244971 in Test_gra_ER_alpha.smi (220/283). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL1450825 in Test_gra_ER_alpha.smi (222/283). Average speed: 0.03 s/mol.\n"", + ""Descriptor calculation completed in 9.502 secs . Average speed: 0.03 s/mol.\n"" + ] + }, + { + ""name"": ""stdout"", + ""output_type"": ""stream"", + ""text"": [ + ""Test_gra_ER_alpha.smi\n"", + ""Test_gra_ER_alpha.csv\n"", + ""MACCSFingerprinter.xml\n"", + ""MACCSFingerprinter_\n"", + ""Processing CHEMBL192325 in Test_gra_ER_alpha.smi (1/283). \n"", + ""Processing CHEMBL1383468 in Test_gra_ER_alpha.smi (5/283). \n"", + ""Processing CHEMBL1331671 in Test_gra_ER_alpha.smi (2/283). \n"", + ""Processing CHEMBL1393 in Test_gra_ER_alpha.smi (3/283). \n"", + ""Processing CHEMBL1349227 in Test_gra_ER_alpha.smi (4/283). \n"", + ""Processing CHEMBL1481853 in Test_gra_ER_alpha.smi (6/283). Average speed: 1.40 s/mol.\n"", + ""Processing CHEMBL1528804 in Test_gra_ER_alpha.smi (7/283). Average speed: 1.42 s/mol.\n"", + ""Processing CHEMBL1491627 in Test_gra_ER_alpha.smi (8/283). Average speed: 0.75 s/mol.\n"", + ""Processing CHEMBL1565048 in Test_gra_ER_alpha.smi (9/283). Average speed: 0.64 s/mol.\n"", + ""Processing CHEMBL1468795 in Test_gra_ER_alpha.smi (10/283). Average speed: 0.58 s/mol.\n"", + ""Processing CHEMBL1408468 in Test_gra_ER_alpha.smi (11/283). Average speed: 0.51 s/mol.\n"", + ""Processing CHEMBL1610665 in Test_gra_ER_alpha.smi (12/283). Average speed: 0.46 s/mol.\n"", + ""Processing CHEMBL1353351 in Test_gra_ER_alpha.smi (14/283). Average speed: 0.45 s/mol.\n"", + ""Processing CHEMBL1441655 in Test_gra_ER_alpha.smi (13/283). Average speed: 0.47 s/mol.\n"", + ""Processing CHEMBL1379474 in Test_gra_ER_alpha.smi (15/283). Average speed: 0.42 s/mol.\n"", + ""Processing CHEMBL1500491 in Test_gra_ER_alpha.smi (16/283). Average speed: 0.39 s/mol.\n"", + ""Processing CHEMBL1405411 in Test_gra_ER_alpha.smi (18/283). Average speed: 0.37 s/mol.\n"", + ""Processing CHEMBL1557815 in Test_gra_ER_alpha.smi (17/283). Average speed: 0.38 s/mol.\n"", + ""Processing CHEMBL1341487 in Test_gra_ER_alpha.smi (19/283). Average speed: 0.37 s/mol.\n"", + ""Processing CHEMBL1543038 in Test_gra_ER_alpha.smi (20/283). Average speed: 0.35 s/mol.\n"", + ""Processing CHEMBL1599210 in Test_gra_ER_alpha.smi (22/283). Average speed: 0.32 s/mol.\n"", + ""Processing CHEMBL1492422 in Test_gra_ER_alpha.smi (21/283). Average speed: 0.33 s/mol.\n"", + ""Processing CHEMBL1579100 in Test_gra_ER_alpha.smi (23/283). Average speed: 0.32 s/mol.\n"", + ""Processing CHEMBL1412224 in Test_gra_ER_alpha.smi (24/283). Average speed: 0.31 s/mol.\n"", + ""Processing CHEMBL1510990 in Test_gra_ER_alpha.smi (25/283). Average speed: 0.30 s/mol.\n"", + ""Processing CHEMBL1304094 in Test_gra_ER_alpha.smi (26/283). Average speed: 0.29 s/mol.\n"", + ""Processing CHEMBL516241 in Test_gra_ER_alpha.smi (27/283). Average speed: 0.29 s/mol.\n"", + ""Processing CHEMBL475503 in Test_gra_ER_alpha.smi (28/283). Average speed: 0.28 s/mol.\n"", + ""Processing CHEMBL470783 in Test_gra_ER_alpha.smi (29/283). Average speed: 0.28 s/mol.\n"", + ""Processing CHEMBL397343 in Test_gra_ER_alpha.smi (30/283). Average speed: 0.28 s/mol.\n"", + ""Processing CHEMBL398840 in Test_gra_ER_alpha.smi (31/283). Average speed: 0.27 s/mol.\n"", + ""Processing CHEMBL496881 in Test_gra_ER_alpha.smi (32/283). Average speed: 0.26 s/mol.\n"", + ""Processing CHEMBL396946 in Test_gra_ER_alpha.smi (33/283). Average speed: 0.26 s/mol.\n"", + ""Processing CHEMBL378473 in Test_gra_ER_alpha.smi (34/283). Average speed: 0.26 s/mol.\n"", + ""Processing CHEMBL371311 in Test_gra_ER_alpha.smi (35/283). Average speed: 0.25 s/mol.\n"", + ""Processing CHEMBL196509 in Test_gra_ER_alpha.smi (36/283). Average speed: 0.25 s/mol.\n"", + ""Processing CHEMBL204959 in Test_gra_ER_alpha.smi (38/283). Average speed: 0.24 s/mol.\n"", + ""Processing CHEMBL293388 in Test_gra_ER_alpha.smi (37/283). Average speed: 0.24 s/mol.\n"", + ""Processing CHEMBL438977 in Test_gra_ER_alpha.smi (39/283). Average speed: 0.24 s/mol.\n"", + ""Processing CHEMBL205989 in Test_gra_ER_alpha.smi (40/283). Average speed: 0.24 s/mol.\n"", + ""Processing CHEMBL388474 in Test_gra_ER_alpha.smi (41/283). Average speed: 0.23 s/mol.\n"", + ""Processing CHEMBL205988 in Test_gra_ER_alpha.smi (42/283). Average speed: 0.22 s/mol.\n"", + ""Processing CHEMBL400480 in Test_gra_ER_alpha.smi (44/283). Average speed: 0.21 s/mol.\n"", + ""Processing CHEMBL206218 in Test_gra_ER_alpha.smi (43/283). Average speed: 0.22 s/mol.\n"", + ""Processing CHEMBL66598 in Test_gra_ER_alpha.smi (45/283). Average speed: 0.21 s/mol.\n"", + ""Processing CHEMBL311662 in Test_gra_ER_alpha.smi (46/283). Average speed: 0.20 s/mol.\n"", + ""Processing CHEMBL201364 in Test_gra_ER_alpha.smi (47/283). Average speed: 0.20 s/mol.\n"", + ""Processing CHEMBL57978 in Test_gra_ER_alpha.smi (48/283). Average speed: 0.20 s/mol.\n"", + ""Processing CHEMBL200903 in Test_gra_ER_alpha.smi (49/283). Average speed: 0.20 s/mol.\n"", + ""Processing CHEMBL43064 in Test_gra_ER_alpha.smi (50/283). Average speed: 0.20 s/mol.\n"", + ""Processing CHEMBL65354 in Test_gra_ER_alpha.smi (51/283). Average speed: 0.19 s/mol.\n"", + ""Processing CHEMBL379384 in Test_gra_ER_alpha.smi (52/283). Average speed: 0.19 s/mol.\n"", + ""Processing CHEMBL212923 in Test_gra_ER_alpha.smi (53/283). Average speed: 0.19 s/mol.\n"", + ""Processing CHEMBL385358 in Test_gra_ER_alpha.smi (54/283). Average speed: 0.19 s/mol.\n"", + ""Processing CHEMBL397344 in Test_gra_ER_alpha.smi (55/283). Average speed: 0.19 s/mol.\n"", + ""Processing CHEMBL65021 in Test_gra_ER_alpha.smi (57/283). Average speed: 0.19 s/mol.\n"", + ""Processing CHEMBL251515 in Test_gra_ER_alpha.smi (56/283). Average speed: 0.19 s/mol.\n"", + ""Processing CHEMBL457242 in Test_gra_ER_alpha.smi (58/283). Average speed: 0.19 s/mol.\n"", + ""Processing CHEMBL313062 in Test_gra_ER_alpha.smi (61/283). Average speed: 0.18 s/mol.\n"", + ""Processing CHEMBL361266 in Test_gra_ER_alpha.smi (59/283). Average speed: 0.18 s/mol.\n"", + ""Processing CHEMBL65695 in Test_gra_ER_alpha.smi (60/283). Average speed: 0.18 s/mol.\n"", + ""Processing CHEMBL438863 in Test_gra_ER_alpha.smi (62/283). Average speed: 0.18 s/mol.\n"", + ""Processing CHEMBL1340716 in Test_gra_ER_alpha.smi (63/283). Average speed: 0.18 s/mol.\n"", + ""Processing CHEMBL1333664 in Test_gra_ER_alpha.smi (64/283). Average speed: 0.17 s/mol.\n"", + ""Processing CHEMBL1337538 in Test_gra_ER_alpha.smi (65/283). Average speed: 0.18 s/mol.\n"", + ""Processing CHEMBL1528600 in Test_gra_ER_alpha.smi (66/283). Average speed: 0.17 s/mol.\n"", + ""Processing CHEMBL1407108 in Test_gra_ER_alpha.smi (67/283). Average speed: 0.17 s/mol.\n"", + ""Processing CHEMBL1393680 in Test_gra_ER_alpha.smi (69/283). Average speed: 0.17 s/mol.\n"", + ""Processing CHEMBL1495161 in Test_gra_ER_alpha.smi (68/283). Average speed: 0.17 s/mol.\n"", + ""Processing CHEMBL1484399 in Test_gra_ER_alpha.smi (70/283). Average speed: 0.17 s/mol.\n"", + ""Processing CHEMBL1509842 in Test_gra_ER_alpha.smi (72/283). Average speed: 0.17 s/mol.\n"", + ""Processing CHEMBL1488579 in Test_gra_ER_alpha.smi (71/283). Average speed: 0.17 s/mol.\n"", + ""Processing CHEMBL1494151 in Test_gra_ER_alpha.smi (74/283). Average speed: 0.17 s/mol.\n"", + ""Processing CHEMBL1299324 in Test_gra_ER_alpha.smi (73/283). Average speed: 0.17 s/mol.\n"", + ""Processing CHEMBL1305162 in Test_gra_ER_alpha.smi (75/283). Average speed: 0.17 s/mol.\n"", + ""Processing CHEMBL1461750 in Test_gra_ER_alpha.smi (76/283). Average speed: 0.16 s/mol.\n"", + ""Processing CHEMBL380106 in Test_gra_ER_alpha.smi (77/283). Average speed: 0.16 s/mol.\n"", + ""Processing CHEMBL1377527 in Test_gra_ER_alpha.smi (78/283). Average speed: 0.16 s/mol.\n"", + ""Processing CHEMBL1707275 in Test_gra_ER_alpha.smi (79/283). Average speed: 0.16 s/mol.\n"", + ""Processing CHEMBL1559976 in Test_gra_ER_alpha.smi (81/283). Average speed: 0.16 s/mol.\n"", + ""Processing CHEMBL1330656 in Test_gra_ER_alpha.smi (80/283). Average speed: 0.16 s/mol.\n"", + ""Processing CHEMBL1500073 in Test_gra_ER_alpha.smi (82/283). Average speed: 0.16 s/mol.\n"", + ""Processing CHEMBL1485768 in Test_gra_ER_alpha.smi (83/283). Average speed: 0.16 s/mol.\n"", + ""Processing CHEMBL203519 in Test_gra_ER_alpha.smi (84/283). Average speed: 0.16 s/mol.\n"", + ""Processing CHEMBL1496108 in Test_gra_ER_alpha.smi (85/283). Average speed: 0.16 s/mol.\n"", + ""Processing CHEMBL457036 in Test_gra_ER_alpha.smi (86/283). Average speed: 0.15 s/mol.\n"", + ""Processing CHEMBL1575022 in Test_gra_ER_alpha.smi (87/283). Average speed: 0.15 s/mol.\n"", + ""Processing CHEMBL1307842 in Test_gra_ER_alpha.smi (88/283). Average speed: 0.15 s/mol.\n"", + ""Processing CHEMBL1342429 in Test_gra_ER_alpha.smi (89/283). Average speed: 0.15 s/mol.\n"", + ""Processing CHEMBL513735 in Test_gra_ER_alpha.smi (90/283). Average speed: 0.15 s/mol.\n"", + ""Processing CHEMBL1465900 in Test_gra_ER_alpha.smi (91/283). Average speed: 0.15 s/mol.\n"", + ""Processing CHEMBL1402732 in Test_gra_ER_alpha.smi (92/283). Average speed: 0.15 s/mol.\n"", + ""Processing CHEMBL1334452 in Test_gra_ER_alpha.smi (93/283). Average speed: 0.14 s/mol.\n"", + ""Processing CHEMBL1373202 in Test_gra_ER_alpha.smi (94/283). Average speed: 0.14 s/mol.\n"", + ""Processing CHEMBL1502446 in Test_gra_ER_alpha.smi (95/283). Average speed: 0.14 s/mol.\n"", + ""Processing CHEMBL1299346 in Test_gra_ER_alpha.smi (96/283). Average speed: 0.14 s/mol.\n"" + ] + }, + { + ""name"": ""stdout"", + ""output_type"": ""stream"", + ""text"": [ + ""Processing CHEMBL1438401 in Test_gra_ER_alpha.smi (97/283). Average speed: 0.14 s/mol.\n"", + ""Processing CHEMBL1485581 in Test_gra_ER_alpha.smi (99/283). Average speed: 0.14 s/mol.\n"", + ""Processing CHEMBL1565196 in Test_gra_ER_alpha.smi (98/283). Average speed: 0.14 s/mol.\n"", + ""Processing CHEMBL194012 in Test_gra_ER_alpha.smi (100/283). Average speed: 0.14 s/mol.\n"", + ""Processing CHEMBL1606174 in Test_gra_ER_alpha.smi (101/283). Average speed: 0.14 s/mol.\n"", + ""Processing CHEMBL1483760 in Test_gra_ER_alpha.smi (102/283). Average speed: 0.14 s/mol.\n"", + ""Processing CHEMBL1606939 in Test_gra_ER_alpha.smi (103/283). Average speed: 0.14 s/mol.\n"", + ""Processing CHEMBL81591 in Test_gra_ER_alpha.smi (104/283). Average speed: 0.13 s/mol.\n"", + ""Processing CHEMBL386630 in Test_gra_ER_alpha.smi (105/283). Average speed: 0.13 s/mol.\n"", + ""Processing CHEMBL379441 in Test_gra_ER_alpha.smi (106/283). Average speed: 0.13 s/mol.\n"", + ""Processing CHEMBL201809 in Test_gra_ER_alpha.smi (107/283). Average speed: 0.13 s/mol.\n"", + ""Processing CHEMBL212276 in Test_gra_ER_alpha.smi (108/283). Average speed: 0.13 s/mol.\n"", + ""Processing CHEMBL377622 in Test_gra_ER_alpha.smi (109/283). Average speed: 0.13 s/mol.\n"", + ""Processing CHEMBL1353787 in Test_gra_ER_alpha.smi (110/283). Average speed: 0.13 s/mol.\n"", + ""Processing CHEMBL1459339 in Test_gra_ER_alpha.smi (111/283). Average speed: 0.13 s/mol.\n"", + ""Processing CHEMBL377560 in Test_gra_ER_alpha.smi (112/283). Average speed: 0.13 s/mol.\n"", + ""Processing CHEMBL438616 in Test_gra_ER_alpha.smi (113/283). Average speed: 0.13 s/mol.\n"", + ""Processing CHEMBL377166 in Test_gra_ER_alpha.smi (114/283). Average speed: 0.13 s/mol.\n"", + ""Processing CHEMBL195723 in Test_gra_ER_alpha.smi (116/283). Average speed: 0.12 s/mol.\n"", + ""Processing CHEMBL264349 in Test_gra_ER_alpha.smi (115/283). Average speed: 0.12 s/mol.\n"", + ""Processing CHEMBL206278 in Test_gra_ER_alpha.smi (117/283). Average speed: 0.12 s/mol.\n"", + ""Processing CHEMBL209650 in Test_gra_ER_alpha.smi (119/283). Average speed: 0.12 s/mol.\n"", + ""Processing CHEMBL379827 in Test_gra_ER_alpha.smi (118/283). Average speed: 0.12 s/mol.\n"", + ""Processing CHEMBL364333 in Test_gra_ER_alpha.smi (120/283). Average speed: 0.12 s/mol.\n"", + ""Processing CHEMBL125596 in Test_gra_ER_alpha.smi (121/283). Average speed: 0.12 s/mol.\n"", + ""Processing CHEMBL345000 in Test_gra_ER_alpha.smi (122/283). Average speed: 0.12 s/mol.\n"", + ""Processing CHEMBL537887 in Test_gra_ER_alpha.smi (123/283). Average speed: 0.12 s/mol.\n"", + ""Processing CHEMBL196577 in Test_gra_ER_alpha.smi (124/283). Average speed: 0.12 s/mol.\n"", + ""Processing CHEMBL1334570 in Test_gra_ER_alpha.smi (125/283). Average speed: 0.12 s/mol.\n"", + ""Processing CHEMBL1318598 in Test_gra_ER_alpha.smi (126/283). Average speed: 0.12 s/mol.\n"", + ""Processing CHEMBL1539136 in Test_gra_ER_alpha.smi (127/283). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL1556511 in Test_gra_ER_alpha.smi (128/283). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL1449807 in Test_gra_ER_alpha.smi (129/283). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL1486906 in Test_gra_ER_alpha.smi (130/283). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL1505040 in Test_gra_ER_alpha.smi (131/283). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL1585598 in Test_gra_ER_alpha.smi (132/283). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL1301796 in Test_gra_ER_alpha.smi (133/283). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL1333501 in Test_gra_ER_alpha.smi (134/283). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL1365444 in Test_gra_ER_alpha.smi (135/283). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL1586766 in Test_gra_ER_alpha.smi (136/283). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL1581440 in Test_gra_ER_alpha.smi (137/283). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL1505994 in Test_gra_ER_alpha.smi (138/283). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL1545617 in Test_gra_ER_alpha.smi (139/283). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL1391679 in Test_gra_ER_alpha.smi (140/283). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL1442547 in Test_gra_ER_alpha.smi (141/283). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL1331281 in Test_gra_ER_alpha.smi (142/283). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL1393628 in Test_gra_ER_alpha.smi (144/283). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL1573688 in Test_gra_ER_alpha.smi (143/283). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL1321083 in Test_gra_ER_alpha.smi (145/283). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL1306474 in Test_gra_ER_alpha.smi (146/283). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL65868 in Test_gra_ER_alpha.smi (147/283). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL189541 in Test_gra_ER_alpha.smi (148/283). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL1350947 in Test_gra_ER_alpha.smi (149/283). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL65221 in Test_gra_ER_alpha.smi (150/283). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL1405097 in Test_gra_ER_alpha.smi (151/283). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL1595235 in Test_gra_ER_alpha.smi (152/283). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL382102 in Test_gra_ER_alpha.smi (153/283). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL192326 in Test_gra_ER_alpha.smi (154/283). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL195058 in Test_gra_ER_alpha.smi (155/283). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL1322881 in Test_gra_ER_alpha.smi (156/283). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL1343134 in Test_gra_ER_alpha.smi (158/283). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL1367559 in Test_gra_ER_alpha.smi (157/283). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL1582458 in Test_gra_ER_alpha.smi (159/283). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL1387920 in Test_gra_ER_alpha.smi (160/283). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL1344624 in Test_gra_ER_alpha.smi (161/283). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL1329411 in Test_gra_ER_alpha.smi (162/283). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL434183 in Test_gra_ER_alpha.smi (163/283). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL1342736 in Test_gra_ER_alpha.smi (165/283). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL294209 in Test_gra_ER_alpha.smi (164/283). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL1594663 in Test_gra_ER_alpha.smi (166/283). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL1422039 in Test_gra_ER_alpha.smi (167/283). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL188951 in Test_gra_ER_alpha.smi (168/283). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL392000 in Test_gra_ER_alpha.smi (169/283). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL65657 in Test_gra_ER_alpha.smi (170/283). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL381489 in Test_gra_ER_alpha.smi (171/283). Average speed: 0.09 s/mol.\n"", + ""Processing CHEMBL457241 in Test_gra_ER_alpha.smi (172/283). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL205009 in Test_gra_ER_alpha.smi (173/283). Average speed: 0.09 s/mol.\n"", + ""Processing CHEMBL204341 in Test_gra_ER_alpha.smi (174/283). Average speed: 0.09 s/mol.\n"", + ""Processing CHEMBL381622 in Test_gra_ER_alpha.smi (175/283). Average speed: 0.09 s/mol.\n"", + ""Processing CHEMBL1070 in Test_gra_ER_alpha.smi (176/283). Average speed: 0.09 s/mol.\n"", + ""Processing CHEMBL232468 in Test_gra_ER_alpha.smi (177/283). Average speed: 0.09 s/mol.\n"", + ""Processing CHEMBL1082723 in Test_gra_ER_alpha.smi (178/283). Average speed: 0.09 s/mol.\n"", + ""Processing CHEMBL1359279 in Test_gra_ER_alpha.smi (179/283). Average speed: 0.09 s/mol.\n"", + ""Processing CHEMBL1433155 in Test_gra_ER_alpha.smi (180/283). Average speed: 0.09 s/mol.\n"", + ""Processing CHEMBL1365404 in Test_gra_ER_alpha.smi (181/283). Average speed: 0.09 s/mol.\n"", + ""Processing CHEMBL1438484 in Test_gra_ER_alpha.smi (182/283). Average speed: 0.09 s/mol.\n"", + ""Processing CHEMBL1455614 in Test_gra_ER_alpha.smi (183/283). Average speed: 0.09 s/mol.\n"", + ""Processing CHEMBL1582133 in Test_gra_ER_alpha.smi (184/283). Average speed: 0.09 s/mol.\n"", + ""Processing CHEMBL1504200 in Test_gra_ER_alpha.smi (185/283). Average speed: 0.09 s/mol.\n"", + ""Processing CHEMBL1583952 in Test_gra_ER_alpha.smi (186/283). Average speed: 0.09 s/mol.\n"", + ""Processing CHEMBL1352844 in Test_gra_ER_alpha.smi (188/283). Average speed: 0.09 s/mol.\n"", + ""Processing CHEMBL1333846 in Test_gra_ER_alpha.smi (187/283). Average speed: 0.09 s/mol.\n"", + ""Processing CHEMBL1500059 in Test_gra_ER_alpha.smi (189/283). Average speed: 0.09 s/mol.\n"", + ""Processing CHEMBL1300705 in Test_gra_ER_alpha.smi (190/283). Average speed: 0.09 s/mol.\n"" + ] + }, + { + ""name"": ""stdout"", + ""output_type"": ""stream"", + ""text"": [ + ""Processing CHEMBL1450059 in Test_gra_ER_alpha.smi (191/283). Average speed: 0.09 s/mol.\n"", + ""Processing CHEMBL1432915 in Test_gra_ER_alpha.smi (192/283). Average speed: 0.09 s/mol.\n"", + ""Processing CHEMBL1539668 in Test_gra_ER_alpha.smi (193/283). Average speed: 0.09 s/mol.\n"", + ""Processing CHEMBL1391501 in Test_gra_ER_alpha.smi (194/283). Average speed: 0.09 s/mol.\n"", + ""Processing CHEMBL1331278 in Test_gra_ER_alpha.smi (195/283). Average speed: 0.09 s/mol.\n"", + ""Processing CHEMBL101 in Test_gra_ER_alpha.smi (196/283). Average speed: 0.09 s/mol.\n"", + ""Processing CHEMBL1564026 in Test_gra_ER_alpha.smi (198/283). Average speed: 0.09 s/mol.\n"", + ""Processing CHEMBL1565816 in Test_gra_ER_alpha.smi (197/283). Average speed: 0.09 s/mol.\n"", + ""Processing CHEMBL1334162 in Test_gra_ER_alpha.smi (199/283). Average speed: 0.09 s/mol.\n"", + ""Processing CHEMBL1572214 in Test_gra_ER_alpha.smi (200/283). Average speed: 0.09 s/mol.\n"", + ""Processing CHEMBL1586116 in Test_gra_ER_alpha.smi (201/283). Average speed: 0.09 s/mol.\n"", + ""Processing CHEMBL1583911 in Test_gra_ER_alpha.smi (203/283). Average speed: 0.09 s/mol.\n"", + ""Processing CHEMBL1478743 in Test_gra_ER_alpha.smi (202/283). Average speed: 0.09 s/mol.\n"", + ""Processing CHEMBL1589718 in Test_gra_ER_alpha.smi (204/283). Average speed: 0.09 s/mol.\n"", + ""Processing CHEMBL1299808 in Test_gra_ER_alpha.smi (205/283). Average speed: 0.09 s/mol.\n"", + ""Processing CHEMBL1579818 in Test_gra_ER_alpha.smi (206/283). Average speed: 0.09 s/mol.\n"", + ""Processing CHEMBL377616 in Test_gra_ER_alpha.smi (207/283). Average speed: 0.09 s/mol.\n"", + ""Processing CHEMBL1095097 in Test_gra_ER_alpha.smi (208/283). Average speed: 0.09 s/mol.\n"", + ""Processing CHEMBL213457 in Test_gra_ER_alpha.smi (209/283). Average speed: 0.09 s/mol.\n"", + ""Processing CHEMBL214291 in Test_gra_ER_alpha.smi (210/283). Average speed: 0.09 s/mol.\n"", + ""Processing CHEMBL104367 in Test_gra_ER_alpha.smi (211/283). Average speed: 0.09 s/mol.\n"", + ""Processing CHEMBL362507 in Test_gra_ER_alpha.smi (212/283). Average speed: 0.09 s/mol.\n"", + ""Processing CHEMBL190238 in Test_gra_ER_alpha.smi (213/283). Average speed: 0.09 s/mol.\n"", + ""Processing CHEMBL195515 in Test_gra_ER_alpha.smi (214/283). Average speed: 0.09 s/mol.\n"", + ""Processing CHEMBL79335 in Test_gra_ER_alpha.smi (215/283). Average speed: 0.09 s/mol.\n"", + ""Processing CHEMBL103 in Test_gra_ER_alpha.smi (216/283). Average speed: 0.09 s/mol.\n"", + ""Processing CHEMBL380978 in Test_gra_ER_alpha.smi (217/283). Average speed: 0.09 s/mol.\n"", + ""Processing CHEMBL311034 in Test_gra_ER_alpha.smi (218/283). Average speed: 0.09 s/mol.\n"", + ""Processing CHEMBL379024 in Test_gra_ER_alpha.smi (219/283). Average speed: 0.09 s/mol.\n"", + ""Processing CHEMBL244971 in Test_gra_ER_alpha.smi (220/283). Average speed: 0.09 s/mol.\n"", + ""Processing CHEMBL245132 in Test_gra_ER_alpha.smi (221/283). Average speed: 0.09 s/mol.\n"", + ""Processing CHEMBL1450825 in Test_gra_ER_alpha.smi (222/283). Average speed: 0.09 s/mol.\n"", + ""Processing CHEMBL1390728 in Test_gra_ER_alpha.smi (223/283). Average speed: 0.09 s/mol.\n"", + ""Processing CHEMBL204389 in Test_gra_ER_alpha.smi (224/283). Average speed: 0.09 s/mol.\n"", + ""Processing CHEMBL377565 in Test_gra_ER_alpha.smi (225/283). Average speed: 0.09 s/mol.\n"", + ""Processing CHEMBL245576 in Test_gra_ER_alpha.smi (226/283). Average speed: 0.09 s/mol.\n"", + ""Processing CHEMBL245771 in Test_gra_ER_alpha.smi (227/283). Average speed: 0.09 s/mol.\n"", + ""Processing CHEMBL1402012 in Test_gra_ER_alpha.smi (228/283). Average speed: 0.08 s/mol.\n"", + ""Processing CHEMBL1492286 in Test_gra_ER_alpha.smi (229/283). Average speed: 0.08 s/mol.\n"", + ""Processing CHEMBL1349449 in Test_gra_ER_alpha.smi (230/283). Average speed: 0.09 s/mol.\n"", + ""Processing CHEMBL1441028 in Test_gra_ER_alpha.smi (231/283). Average speed: 0.08 s/mol.\n"", + ""Processing CHEMBL1340515 in Test_gra_ER_alpha.smi (232/283). Average speed: 0.08 s/mol.\n"", + ""Processing CHEMBL1366918 in Test_gra_ER_alpha.smi (233/283). Average speed: 0.08 s/mol.\n"", + ""Processing CHEMBL1351935 in Test_gra_ER_alpha.smi (234/283). Average speed: 0.08 s/mol.\n"", + ""Processing CHEMBL1519846 in Test_gra_ER_alpha.smi (235/283). Average speed: 0.08 s/mol.\n"", + ""Processing CHEMBL1463596 in Test_gra_ER_alpha.smi (237/283). Average speed: 0.08 s/mol.\n"", + ""Processing CHEMBL1464700 in Test_gra_ER_alpha.smi (236/283). Average speed: 0.08 s/mol.\n"", + ""Processing CHEMBL1539548 in Test_gra_ER_alpha.smi (238/283). Average speed: 0.08 s/mol.\n"", + ""Processing CHEMBL246140 in Test_gra_ER_alpha.smi (240/283). Average speed: 0.08 s/mol.\n"", + ""Processing CHEMBL1603680 in Test_gra_ER_alpha.smi (239/283). Average speed: 0.08 s/mol.\n"", + ""Processing CHEMBL1392987 in Test_gra_ER_alpha.smi (241/283). Average speed: 0.08 s/mol.\n"", + ""Processing CHEMBL1547093 in Test_gra_ER_alpha.smi (242/283). Average speed: 0.08 s/mol.\n"", + ""Processing CHEMBL1390198 in Test_gra_ER_alpha.smi (243/283). Average speed: 0.08 s/mol.\n"", + ""Processing CHEMBL515671 in Test_gra_ER_alpha.smi (244/283). Average speed: 0.08 s/mol.\n"", + ""Processing CHEMBL1985765 in Test_gra_ER_alpha.smi (245/283). Average speed: 0.08 s/mol.\n"", + ""Processing CHEMBL1971815 in Test_gra_ER_alpha.smi (246/283). Average speed: 0.08 s/mol.\n"", + ""Processing CHEMBL1965461 in Test_gra_ER_alpha.smi (247/283). Average speed: 0.08 s/mol.\n"", + ""Processing CHEMBL2000120 in Test_gra_ER_alpha.smi (248/283). Average speed: 0.08 s/mol.\n"", + ""Processing CHEMBL3261407 in Test_gra_ER_alpha.smi (249/283). Average speed: 0.08 s/mol.\n"", + ""Processing CHEMBL3191473 in Test_gra_ER_alpha.smi (250/283). Average speed: 0.08 s/mol.\n"", + ""Processing CHEMBL3211537 in Test_gra_ER_alpha.smi (251/283). Average speed: 0.08 s/mol.\n"", + ""Processing CHEMBL3234858 in Test_gra_ER_alpha.smi (252/283). Average speed: 0.08 s/mol.\n"", + ""Processing CHEMBL3234863 in Test_gra_ER_alpha.smi (253/283). Average speed: 0.08 s/mol.\n"", + ""Processing CHEMBL3207950 in Test_gra_ER_alpha.smi (254/283). Average speed: 0.08 s/mol.\n"", + ""Processing CHEMBL3213194 in Test_gra_ER_alpha.smi (255/283). Average speed: 0.08 s/mol.\n"", + ""Processing CHEMBL3196436 in Test_gra_ER_alpha.smi (256/283). Average speed: 0.08 s/mol.\n"", + ""Processing CHEMBL3263752 in Test_gra_ER_alpha.smi (257/283). Average speed: 0.08 s/mol.\n"", + ""Processing CHEMBL3208853 in Test_gra_ER_alpha.smi (258/283). Average speed: 0.08 s/mol.\n"", + ""Processing CHEMBL3326454 in Test_gra_ER_alpha.smi (260/283). Average speed: 0.08 s/mol.\n"", + ""Processing CHEMBL3208616 in Test_gra_ER_alpha.smi (259/283). Average speed: 0.08 s/mol.\n"", + ""Processing CHEMBL3261425 in Test_gra_ER_alpha.smi (261/283). Average speed: 0.08 s/mol.\n"", + ""Processing CHEMBL3427415 in Test_gra_ER_alpha.smi (262/283). Average speed: 0.08 s/mol.\n"", + ""Processing CHEMBL3427385 in Test_gra_ER_alpha.smi (263/283). Average speed: 0.08 s/mol.\n"", + ""Processing CHEMBL3578295 in Test_gra_ER_alpha.smi (264/283). Average speed: 0.08 s/mol.\n"", + ""Processing CHEMBL3597500 in Test_gra_ER_alpha.smi (265/283). Average speed: 0.08 s/mol.\n"", + ""Processing CHEMBL3597503 in Test_gra_ER_alpha.smi (266/283). Average speed: 0.08 s/mol.\n"", + ""Processing CHEMBL3597506 in Test_gra_ER_alpha.smi (267/283). Average speed: 0.08 s/mol.\n"", + ""Processing CHEMBL3597510 in Test_gra_ER_alpha.smi (269/283). Average speed: 0.09 s/mol.\n"", + ""Processing CHEMBL3597507 in Test_gra_ER_alpha.smi (268/283). Average speed: 0.08 s/mol.\n"", + ""Processing CHEMBL3597513 in Test_gra_ER_alpha.smi (270/283). Average speed: 0.09 s/mol.\n"", + ""Processing CHEMBL3597514 in Test_gra_ER_alpha.smi (271/283). Average speed: 0.09 s/mol.\n"", + ""Processing CHEMBL3421892 in Test_gra_ER_alpha.smi (272/283). Average speed: 0.09 s/mol.\n"", + ""Processing CHEMBL3421871 in Test_gra_ER_alpha.smi (273/283). Average speed: 0.09 s/mol.\n"", + ""Processing CHEMBL3427414 in Test_gra_ER_alpha.smi (274/283). Average speed: 0.09 s/mol.\n"", + ""Processing CHEMBL3427406 in Test_gra_ER_alpha.smi (276/283). Average speed: 0.09 s/mol.\n"", + ""Processing CHEMBL3427411 in Test_gra_ER_alpha.smi (275/283). Average speed: 0.09 s/mol.\n"", + ""Processing CHEMBL3774852 in Test_gra_ER_alpha.smi (277/283). Average speed: 0.09 s/mol.\n"", + ""Processing CHEMBL3775464 in Test_gra_ER_alpha.smi (278/283). Average speed: 0.09 s/mol.\n"", + ""Processing CHEMBL3775389 in Test_gra_ER_alpha.smi (279/283). Average speed: 0.09 s/mol.\n"", + ""Processing CHEMBL3775798 in Test_gra_ER_alpha.smi (280/283). Average speed: 0.09 s/mol.\n"", + ""Processing CHEMBL3827018 in Test_gra_ER_alpha.smi (282/283). Average speed: 0.09 s/mol.\n"", + ""Processing CHEMBL3775473 in Test_gra_ER_alpha.smi (281/283). Average speed: 0.09 s/mol.\n"", + ""Processing CHEMBL3827876 in Test_gra_ER_alpha.smi (283/283). Average speed: 0.09 s/mol.\n"", + ""Descriptor calculation completed in 26.559 secs . Average speed: 0.09 s/mol.\n"" + ] + }, + { + ""name"": ""stdout"", + ""output_type"": ""stream"", + ""text"": [ + ""Test_gra_ER_alpha.smi\n"", + ""Test_gra_ER_alpha.csv\n"", + ""PubchemFingerprinter.xml\n"", + ""PubchemFingerprinter_\n"", + ""Processing CHEMBL192325 in Test_gra_ER_alpha.smi (1/283). \n"", + ""Processing CHEMBL1331671 in Test_gra_ER_alpha.smi (2/283). \n"", + ""Processing CHEMBL1393 in Test_gra_ER_alpha.smi (3/283). \n"", + ""Processing CHEMBL1349227 in Test_gra_ER_alpha.smi (4/283). \n"", + ""Processing CHEMBL1481853 in Test_gra_ER_alpha.smi (6/283). Average speed: 1.66 s/mol.\n"", + ""Processing CHEMBL1383468 in Test_gra_ER_alpha.smi (5/283). Average speed: 3.25 s/mol.\n"", + ""Processing CHEMBL1528804 in Test_gra_ER_alpha.smi (7/283). Average speed: 1.80 s/mol.\n"", + ""Processing CHEMBL1491627 in Test_gra_ER_alpha.smi (8/283). Average speed: 0.97 s/mol.\n"", + ""Processing CHEMBL1565048 in Test_gra_ER_alpha.smi (9/283). Average speed: 0.81 s/mol.\n"", + ""Processing CHEMBL1468795 in Test_gra_ER_alpha.smi (10/283). Average speed: 0.81 s/mol.\n"", + ""Processing CHEMBL1408468 in Test_gra_ER_alpha.smi (11/283). Average speed: 0.74 s/mol.\n"", + ""Processing CHEMBL1610665 in Test_gra_ER_alpha.smi (12/283). Average speed: 0.56 s/mol.\n"", + ""Processing CHEMBL1441655 in Test_gra_ER_alpha.smi (13/283). Average speed: 0.53 s/mol.\n"", + ""Processing CHEMBL1353351 in Test_gra_ER_alpha.smi (14/283). Average speed: 0.48 s/mol.\n"", + ""Processing CHEMBL1379474 in Test_gra_ER_alpha.smi (15/283). Average speed: 0.45 s/mol.\n"", + ""Processing CHEMBL1500491 in Test_gra_ER_alpha.smi (16/283). Average speed: 0.42 s/mol.\n"", + ""Processing CHEMBL1557815 in Test_gra_ER_alpha.smi (17/283). Average speed: 0.42 s/mol.\n"", + ""Processing CHEMBL1405411 in Test_gra_ER_alpha.smi (18/283). Average speed: 0.38 s/mol.\n"", + ""Processing CHEMBL1341487 in Test_gra_ER_alpha.smi (19/283). Average speed: 0.39 s/mol.\n"", + ""Processing CHEMBL1543038 in Test_gra_ER_alpha.smi (20/283). Average speed: 0.35 s/mol.\n"", + ""Processing CHEMBL1492422 in Test_gra_ER_alpha.smi (21/283). Average speed: 0.34 s/mol.\n"", + ""Processing CHEMBL1599210 in Test_gra_ER_alpha.smi (22/283). Average speed: 0.34 s/mol.\n"", + ""Processing CHEMBL1579100 in Test_gra_ER_alpha.smi (23/283). Average speed: 0.32 s/mol.\n"", + ""Processing CHEMBL1412224 in Test_gra_ER_alpha.smi (24/283). Average speed: 0.32 s/mol.\n"", + ""Processing CHEMBL1510990 in Test_gra_ER_alpha.smi (25/283). Average speed: 0.30 s/mol.\n"", + ""Processing CHEMBL1304094 in Test_gra_ER_alpha.smi (26/283). Average speed: 0.30 s/mol.\n"", + ""Processing CHEMBL516241 in Test_gra_ER_alpha.smi (27/283). Average speed: 0.29 s/mol.\n"", + ""Processing CHEMBL475503 in Test_gra_ER_alpha.smi (28/283). Average speed: 0.28 s/mol.\n"", + ""Processing CHEMBL470783 in Test_gra_ER_alpha.smi (29/283). Average speed: 0.28 s/mol.\n"", + ""Processing CHEMBL397343 in Test_gra_ER_alpha.smi (30/283). Average speed: 0.27 s/mol.\n"", + ""Processing CHEMBL398840 in Test_gra_ER_alpha.smi (31/283). Average speed: 0.26 s/mol.\n"", + ""Processing CHEMBL496881 in Test_gra_ER_alpha.smi (32/283). Average speed: 0.25 s/mol.\n"", + ""Processing CHEMBL396946 in Test_gra_ER_alpha.smi (33/283). Average speed: 0.26 s/mol.\n"", + ""Processing CHEMBL378473 in Test_gra_ER_alpha.smi (34/283). Average speed: 0.27 s/mol.\n"", + ""Processing CHEMBL371311 in Test_gra_ER_alpha.smi (35/283). Average speed: 0.25 s/mol.\n"", + ""Processing CHEMBL196509 in Test_gra_ER_alpha.smi (36/283). Average speed: 0.25 s/mol.\n"", + ""Processing CHEMBL293388 in Test_gra_ER_alpha.smi (37/283). Average speed: 0.25 s/mol.\n"", + ""Processing CHEMBL204959 in Test_gra_ER_alpha.smi (38/283). Average speed: 0.25 s/mol.\n"", + ""Processing CHEMBL438977 in Test_gra_ER_alpha.smi (39/283). Average speed: 0.24 s/mol.\n"", + ""Processing CHEMBL205989 in Test_gra_ER_alpha.smi (40/283). Average speed: 0.24 s/mol.\n"", + ""Processing CHEMBL388474 in Test_gra_ER_alpha.smi (41/283). Average speed: 0.24 s/mol.\n"", + ""Processing CHEMBL205988 in Test_gra_ER_alpha.smi (42/283). Average speed: 0.24 s/mol.\n"", + ""Processing CHEMBL206218 in Test_gra_ER_alpha.smi (43/283). Average speed: 0.23 s/mol.\n"", + ""Processing CHEMBL400480 in Test_gra_ER_alpha.smi (44/283). Average speed: 0.23 s/mol.\n"", + ""Processing CHEMBL66598 in Test_gra_ER_alpha.smi (45/283). Average speed: 0.23 s/mol.\n"", + ""Processing CHEMBL311662 in Test_gra_ER_alpha.smi (46/283). Average speed: 0.22 s/mol.\n"", + ""Processing CHEMBL201364 in Test_gra_ER_alpha.smi (47/283). Average speed: 0.22 s/mol.\n"", + ""Processing CHEMBL57978 in Test_gra_ER_alpha.smi (48/283). Average speed: 0.22 s/mol.\n"", + ""Processing CHEMBL200903 in Test_gra_ER_alpha.smi (49/283). Average speed: 0.22 s/mol.\n"", + ""Processing CHEMBL43064 in Test_gra_ER_alpha.smi (50/283). Average speed: 0.22 s/mol.\n"", + ""Processing CHEMBL65354 in Test_gra_ER_alpha.smi (51/283). Average speed: 0.22 s/mol.\n"", + ""Processing CHEMBL379384 in Test_gra_ER_alpha.smi (52/283). Average speed: 0.22 s/mol.\n"", + ""Processing CHEMBL212923 in Test_gra_ER_alpha.smi (53/283). Average speed: 0.21 s/mol.\n"", + ""Processing CHEMBL385358 in Test_gra_ER_alpha.smi (54/283). Average speed: 0.21 s/mol.\n"", + ""Processing CHEMBL397344 in Test_gra_ER_alpha.smi (55/283). Average speed: 0.22 s/mol.\n"", + ""Processing CHEMBL251515 in Test_gra_ER_alpha.smi (56/283). Average speed: 0.21 s/mol.\n"", + ""Processing CHEMBL65021 in Test_gra_ER_alpha.smi (57/283). Average speed: 0.21 s/mol.\n"", + ""Processing CHEMBL457242 in Test_gra_ER_alpha.smi (58/283). Average speed: 0.22 s/mol.\n"", + ""Processing CHEMBL361266 in Test_gra_ER_alpha.smi (59/283). Average speed: 0.21 s/mol.\n"", + ""Processing CHEMBL65695 in Test_gra_ER_alpha.smi (60/283). Average speed: 0.21 s/mol.\n"", + ""Processing CHEMBL313062 in Test_gra_ER_alpha.smi (61/283). Average speed: 0.21 s/mol.\n"", + ""Processing CHEMBL438863 in Test_gra_ER_alpha.smi (62/283). Average speed: 0.21 s/mol.\n"", + ""Processing CHEMBL1340716 in Test_gra_ER_alpha.smi (63/283). Average speed: 0.21 s/mol.\n"", + ""Processing CHEMBL1333664 in Test_gra_ER_alpha.smi (64/283). Average speed: 0.21 s/mol.\n"", + ""Processing CHEMBL1337538 in Test_gra_ER_alpha.smi (65/283). Average speed: 0.21 s/mol.\n"", + ""Processing CHEMBL1528600 in Test_gra_ER_alpha.smi (66/283). Average speed: 0.21 s/mol.\n"", + ""Processing CHEMBL1407108 in Test_gra_ER_alpha.smi (67/283). Average speed: 0.21 s/mol.\n"", + ""Processing CHEMBL1495161 in Test_gra_ER_alpha.smi (68/283). Average speed: 0.20 s/mol.\n"", + ""Processing CHEMBL1393680 in Test_gra_ER_alpha.smi (69/283). Average speed: 0.21 s/mol.\n"", + ""Processing CHEMBL1484399 in Test_gra_ER_alpha.smi (70/283). Average speed: 0.21 s/mol.\n"", + ""Processing CHEMBL1488579 in Test_gra_ER_alpha.smi (71/283). Average speed: 0.20 s/mol.\n"", + ""Processing CHEMBL1509842 in Test_gra_ER_alpha.smi (72/283). Average speed: 0.20 s/mol.\n"", + ""Processing CHEMBL1299324 in Test_gra_ER_alpha.smi (73/283). Average speed: 0.20 s/mol.\n"", + ""Processing CHEMBL1494151 in Test_gra_ER_alpha.smi (74/283). Average speed: 0.20 s/mol.\n"", + ""Processing CHEMBL1461750 in Test_gra_ER_alpha.smi (76/283). Average speed: 0.19 s/mol.\n"", + ""Processing CHEMBL1305162 in Test_gra_ER_alpha.smi (75/283). Average speed: 0.20 s/mol.\n"", + ""Processing CHEMBL380106 in Test_gra_ER_alpha.smi (77/283). Average speed: 0.19 s/mol.\n"", + ""Processing CHEMBL1377527 in Test_gra_ER_alpha.smi (78/283). Average speed: 0.19 s/mol.\n"", + ""Processing CHEMBL1707275 in Test_gra_ER_alpha.smi (79/283). Average speed: 0.19 s/mol.\n"", + ""Processing CHEMBL1330656 in Test_gra_ER_alpha.smi (80/283). Average speed: 0.19 s/mol.\n"", + ""Processing CHEMBL1559976 in Test_gra_ER_alpha.smi (81/283). Average speed: 0.19 s/mol.\n"", + ""Processing CHEMBL1500073 in Test_gra_ER_alpha.smi (82/283). Average speed: 0.19 s/mol.\n"", + ""Processing CHEMBL1485768 in Test_gra_ER_alpha.smi (83/283). Average speed: 0.18 s/mol.\n"", + ""Processing CHEMBL203519 in Test_gra_ER_alpha.smi (84/283). Average speed: 0.19 s/mol.\n"", + ""Processing CHEMBL1496108 in Test_gra_ER_alpha.smi (85/283). Average speed: 0.18 s/mol.\n"", + ""Processing CHEMBL457036 in Test_gra_ER_alpha.smi (86/283). Average speed: 0.18 s/mol.\n"", + ""Processing CHEMBL1575022 in Test_gra_ER_alpha.smi (87/283). Average speed: 0.18 s/mol.\n"", + ""Processing CHEMBL1307842 in Test_gra_ER_alpha.smi (88/283). Average speed: 0.18 s/mol.\n"", + ""Processing CHEMBL1342429 in Test_gra_ER_alpha.smi (89/283). Average speed: 0.18 s/mol.\n"", + ""Processing CHEMBL513735 in Test_gra_ER_alpha.smi (90/283). Average speed: 0.18 s/mol.\n"", + ""Processing CHEMBL1465900 in Test_gra_ER_alpha.smi (91/283). Average speed: 0.17 s/mol.\n"", + ""Processing CHEMBL1402732 in Test_gra_ER_alpha.smi (92/283). Average speed: 0.18 s/mol.\n"", + ""Processing CHEMBL1334452 in Test_gra_ER_alpha.smi (93/283). Average speed: 0.18 s/mol.\n"", + ""Processing CHEMBL1373202 in Test_gra_ER_alpha.smi (94/283). Average speed: 0.17 s/mol.\n"", + ""Processing CHEMBL1502446 in Test_gra_ER_alpha.smi (95/283). Average speed: 0.17 s/mol.\n"" + ] + }, + { + ""name"": ""stdout"", + ""output_type"": ""stream"", + ""text"": [ + ""Processing CHEMBL1299346 in Test_gra_ER_alpha.smi (96/283). Average speed: 0.17 s/mol.\n"", + ""Processing CHEMBL1438401 in Test_gra_ER_alpha.smi (97/283). Average speed: 0.17 s/mol.\n"", + ""Processing CHEMBL1565196 in Test_gra_ER_alpha.smi (98/283). Average speed: 0.17 s/mol.\n"", + ""Processing CHEMBL1485581 in Test_gra_ER_alpha.smi (99/283). Average speed: 0.17 s/mol.\n"", + ""Processing CHEMBL194012 in Test_gra_ER_alpha.smi (100/283). Average speed: 0.17 s/mol.\n"", + ""Processing CHEMBL1606174 in Test_gra_ER_alpha.smi (101/283). Average speed: 0.17 s/mol.\n"", + ""Processing CHEMBL1483760 in Test_gra_ER_alpha.smi (102/283). Average speed: 0.17 s/mol.\n"", + ""Processing CHEMBL1606939 in Test_gra_ER_alpha.smi (103/283). Average speed: 0.17 s/mol.\n"", + ""Processing CHEMBL81591 in Test_gra_ER_alpha.smi (104/283). Average speed: 0.17 s/mol.\n"", + ""Processing CHEMBL386630 in Test_gra_ER_alpha.smi (105/283). Average speed: 0.16 s/mol.\n"", + ""Processing CHEMBL379441 in Test_gra_ER_alpha.smi (106/283). Average speed: 0.16 s/mol.\n"", + ""Processing CHEMBL201809 in Test_gra_ER_alpha.smi (107/283). Average speed: 0.16 s/mol.\n"", + ""Processing CHEMBL212276 in Test_gra_ER_alpha.smi (108/283). Average speed: 0.16 s/mol.\n"", + ""Processing CHEMBL377622 in Test_gra_ER_alpha.smi (109/283). Average speed: 0.16 s/mol.\n"", + ""Processing CHEMBL1353787 in Test_gra_ER_alpha.smi (110/283). Average speed: 0.16 s/mol.\n"", + ""Processing CHEMBL1459339 in Test_gra_ER_alpha.smi (111/283). Average speed: 0.16 s/mol.\n"", + ""Processing CHEMBL377560 in Test_gra_ER_alpha.smi (112/283). Average speed: 0.16 s/mol.\n"", + ""Processing CHEMBL438616 in Test_gra_ER_alpha.smi (113/283). Average speed: 0.16 s/mol.\n"", + ""Processing CHEMBL377166 in Test_gra_ER_alpha.smi (114/283). Average speed: 0.16 s/mol.\n"", + ""Processing CHEMBL264349 in Test_gra_ER_alpha.smi (115/283). Average speed: 0.16 s/mol.\n"", + ""Processing CHEMBL195723 in Test_gra_ER_alpha.smi (116/283). Average speed: 0.16 s/mol.\n"", + ""Processing CHEMBL206278 in Test_gra_ER_alpha.smi (117/283). Average speed: 0.16 s/mol.\n"", + ""Processing CHEMBL379827 in Test_gra_ER_alpha.smi (118/283). Average speed: 0.16 s/mol.\n"", + ""Processing CHEMBL209650 in Test_gra_ER_alpha.smi (119/283). Average speed: 0.16 s/mol.\n"", + ""Processing CHEMBL364333 in Test_gra_ER_alpha.smi (120/283). Average speed: 0.16 s/mol.\n"", + ""Processing CHEMBL125596 in Test_gra_ER_alpha.smi (121/283). Average speed: 0.15 s/mol.\n"", + ""Processing CHEMBL345000 in Test_gra_ER_alpha.smi (122/283). Average speed: 0.15 s/mol.\n"", + ""Processing CHEMBL537887 in Test_gra_ER_alpha.smi (123/283). Average speed: 0.15 s/mol.\n"", + ""Processing CHEMBL196577 in Test_gra_ER_alpha.smi (124/283). Average speed: 0.15 s/mol.\n"", + ""Processing CHEMBL1334570 in Test_gra_ER_alpha.smi (125/283). Average speed: 0.15 s/mol.\n"", + ""Processing CHEMBL1318598 in Test_gra_ER_alpha.smi (126/283). Average speed: 0.15 s/mol.\n"", + ""Processing CHEMBL1539136 in Test_gra_ER_alpha.smi (127/283). Average speed: 0.15 s/mol.\n"", + ""Processing CHEMBL1556511 in Test_gra_ER_alpha.smi (128/283). Average speed: 0.15 s/mol.\n"", + ""Processing CHEMBL1449807 in Test_gra_ER_alpha.smi (129/283). Average speed: 0.15 s/mol.\n"", + ""Processing CHEMBL1486906 in Test_gra_ER_alpha.smi (130/283). Average speed: 0.15 s/mol.\n"", + ""Processing CHEMBL1505040 in Test_gra_ER_alpha.smi (131/283). Average speed: 0.15 s/mol.\n"", + ""Processing CHEMBL1585598 in Test_gra_ER_alpha.smi (132/283). Average speed: 0.15 s/mol.\n"", + ""Processing CHEMBL1301796 in Test_gra_ER_alpha.smi (133/283). Average speed: 0.15 s/mol.\n"", + ""Processing CHEMBL1333501 in Test_gra_ER_alpha.smi (134/283). Average speed: 0.15 s/mol.\n"", + ""Processing CHEMBL1365444 in Test_gra_ER_alpha.smi (135/283). Average speed: 0.15 s/mol.\n"", + ""Processing CHEMBL1586766 in Test_gra_ER_alpha.smi (136/283). Average speed: 0.15 s/mol.\n"", + ""Processing CHEMBL1581440 in Test_gra_ER_alpha.smi (137/283). Average speed: 0.15 s/mol.\n"", + ""Processing CHEMBL1505994 in Test_gra_ER_alpha.smi (138/283). Average speed: 0.15 s/mol.\n"", + ""Processing CHEMBL1545617 in Test_gra_ER_alpha.smi (139/283). Average speed: 0.15 s/mol.\n"", + ""Processing CHEMBL1391679 in Test_gra_ER_alpha.smi (140/283). Average speed: 0.15 s/mol.\n"", + ""Processing CHEMBL1442547 in Test_gra_ER_alpha.smi (141/283). Average speed: 0.15 s/mol.\n"", + ""Processing CHEMBL1331281 in Test_gra_ER_alpha.smi (142/283). Average speed: 0.15 s/mol.\n"", + ""Processing CHEMBL1573688 in Test_gra_ER_alpha.smi (143/283). Average speed: 0.15 s/mol.\n"", + ""Processing CHEMBL1393628 in Test_gra_ER_alpha.smi (144/283). Average speed: 0.14 s/mol.\n"", + ""Processing CHEMBL1321083 in Test_gra_ER_alpha.smi (145/283). Average speed: 0.15 s/mol.\n"", + ""Processing CHEMBL1306474 in Test_gra_ER_alpha.smi (146/283). Average speed: 0.15 s/mol.\n"", + ""Processing CHEMBL65868 in Test_gra_ER_alpha.smi (147/283). Average speed: 0.14 s/mol.\n"", + ""Processing CHEMBL189541 in Test_gra_ER_alpha.smi (148/283). Average speed: 0.14 s/mol.\n"", + ""Processing CHEMBL1350947 in Test_gra_ER_alpha.smi (149/283). Average speed: 0.14 s/mol.\n"", + ""Processing CHEMBL65221 in Test_gra_ER_alpha.smi (150/283). Average speed: 0.14 s/mol.\n"", + ""Processing CHEMBL1405097 in Test_gra_ER_alpha.smi (151/283). Average speed: 0.14 s/mol.\n"", + ""Processing CHEMBL1595235 in Test_gra_ER_alpha.smi (152/283). Average speed: 0.14 s/mol.\n"", + ""Processing CHEMBL382102 in Test_gra_ER_alpha.smi (153/283). Average speed: 0.14 s/mol.\n"", + ""Processing CHEMBL192326 in Test_gra_ER_alpha.smi (154/283). Average speed: 0.14 s/mol.\n"", + ""Processing CHEMBL195058 in Test_gra_ER_alpha.smi (155/283). Average speed: 0.14 s/mol.\n"", + ""Processing CHEMBL1322881 in Test_gra_ER_alpha.smi (156/283). Average speed: 0.14 s/mol.\n"", + ""Processing CHEMBL1367559 in Test_gra_ER_alpha.smi (157/283). Average speed: 0.14 s/mol.\n"", + ""Processing CHEMBL1343134 in Test_gra_ER_alpha.smi (158/283). Average speed: 0.14 s/mol.\n"", + ""Processing CHEMBL1582458 in Test_gra_ER_alpha.smi (159/283). Average speed: 0.14 s/mol.\n"", + ""Processing CHEMBL1387920 in Test_gra_ER_alpha.smi (160/283). Average speed: 0.14 s/mol.\n"", + ""Processing CHEMBL1344624 in Test_gra_ER_alpha.smi (161/283). Average speed: 0.14 s/mol.\n"", + ""Processing CHEMBL434183 in Test_gra_ER_alpha.smi (163/283). Average speed: 0.14 s/mol.\n"", + ""Processing CHEMBL1329411 in Test_gra_ER_alpha.smi (162/283). Average speed: 0.14 s/mol.\n"", + ""Processing CHEMBL294209 in Test_gra_ER_alpha.smi (164/283). Average speed: 0.14 s/mol.\n"", + ""Processing CHEMBL1342736 in Test_gra_ER_alpha.smi (165/283). Average speed: 0.14 s/mol.\n"", + ""Processing CHEMBL1594663 in Test_gra_ER_alpha.smi (166/283). Average speed: 0.14 s/mol.\n"", + ""Processing CHEMBL1422039 in Test_gra_ER_alpha.smi (167/283). Average speed: 0.14 s/mol.\n"", + ""Processing CHEMBL188951 in Test_gra_ER_alpha.smi (168/283). Average speed: 0.14 s/mol.\n"", + ""Processing CHEMBL65657 in Test_gra_ER_alpha.smi (170/283). Average speed: 0.14 s/mol.\n"", + ""Processing CHEMBL381489 in Test_gra_ER_alpha.smi (171/283). Average speed: 0.14 s/mol.\n"", + ""Processing CHEMBL392000 in Test_gra_ER_alpha.smi (169/283). Average speed: 0.14 s/mol.\n"", + ""Processing CHEMBL457241 in Test_gra_ER_alpha.smi (172/283). Average speed: 0.14 s/mol.\n"", + ""Processing CHEMBL205009 in Test_gra_ER_alpha.smi (173/283). Average speed: 0.14 s/mol.\n"", + ""Processing CHEMBL204341 in Test_gra_ER_alpha.smi (174/283). Average speed: 0.14 s/mol.\n"", + ""Processing CHEMBL381622 in Test_gra_ER_alpha.smi (175/283). Average speed: 0.14 s/mol.\n"", + ""Processing CHEMBL1070 in Test_gra_ER_alpha.smi (176/283). Average speed: 0.14 s/mol.\n"", + ""Processing CHEMBL232468 in Test_gra_ER_alpha.smi (177/283). Average speed: 0.14 s/mol.\n"", + ""Processing CHEMBL1082723 in Test_gra_ER_alpha.smi (178/283). Average speed: 0.14 s/mol.\n"", + ""Processing CHEMBL1359279 in Test_gra_ER_alpha.smi (179/283). Average speed: 0.14 s/mol.\n"", + ""Processing CHEMBL1433155 in Test_gra_ER_alpha.smi (180/283). Average speed: 0.14 s/mol.\n"", + ""Processing CHEMBL1365404 in Test_gra_ER_alpha.smi (181/283). Average speed: 0.14 s/mol.\n"", + ""Processing CHEMBL1438484 in Test_gra_ER_alpha.smi (182/283). Average speed: 0.14 s/mol.\n"", + ""Processing CHEMBL1455614 in Test_gra_ER_alpha.smi (183/283). Average speed: 0.14 s/mol.\n"", + ""Processing CHEMBL1582133 in Test_gra_ER_alpha.smi (184/283). Average speed: 0.14 s/mol.\n"", + ""Processing CHEMBL1504200 in Test_gra_ER_alpha.smi (185/283). Average speed: 0.14 s/mol.\n"", + ""Processing CHEMBL1333846 in Test_gra_ER_alpha.smi (187/283). Average speed: 0.14 s/mol.\n"", + ""Processing CHEMBL1583952 in Test_gra_ER_alpha.smi (186/283). Average speed: 0.14 s/mol.\n"", + ""Processing CHEMBL1352844 in Test_gra_ER_alpha.smi (188/283). Average speed: 0.13 s/mol.\n"", + ""Processing CHEMBL1500059 in Test_gra_ER_alpha.smi (189/283). Average speed: 0.14 s/mol.\n"" + ] + }, + { + ""name"": ""stdout"", + ""output_type"": ""stream"", + ""text"": [ + ""Processing CHEMBL1300705 in Test_gra_ER_alpha.smi (190/283). Average speed: 0.14 s/mol.\n"", + ""Processing CHEMBL1450059 in Test_gra_ER_alpha.smi (191/283). Average speed: 0.13 s/mol.\n"", + ""Processing CHEMBL1432915 in Test_gra_ER_alpha.smi (192/283). Average speed: 0.13 s/mol.\n"", + ""Processing CHEMBL1539668 in Test_gra_ER_alpha.smi (193/283). Average speed: 0.13 s/mol.\n"", + ""Processing CHEMBL1391501 in Test_gra_ER_alpha.smi (194/283). Average speed: 0.13 s/mol.\n"", + ""Processing CHEMBL1331278 in Test_gra_ER_alpha.smi (195/283). Average speed: 0.13 s/mol.\n"", + ""Processing CHEMBL101 in Test_gra_ER_alpha.smi (196/283). Average speed: 0.13 s/mol.\n"", + ""Processing CHEMBL1565816 in Test_gra_ER_alpha.smi (197/283). Average speed: 0.13 s/mol.\n"", + ""Processing CHEMBL1564026 in Test_gra_ER_alpha.smi (198/283). Average speed: 0.13 s/mol.\n"", + ""Processing CHEMBL1334162 in Test_gra_ER_alpha.smi (199/283). Average speed: 0.13 s/mol.\n"", + ""Processing CHEMBL1572214 in Test_gra_ER_alpha.smi (200/283). Average speed: 0.13 s/mol.\n"", + ""Processing CHEMBL1586116 in Test_gra_ER_alpha.smi (201/283). Average speed: 0.13 s/mol.\n"", + ""Processing CHEMBL1478743 in Test_gra_ER_alpha.smi (202/283). Average speed: 0.13 s/mol.\n"", + ""Processing CHEMBL1583911 in Test_gra_ER_alpha.smi (203/283). Average speed: 0.13 s/mol.\n"", + ""Processing CHEMBL1589718 in Test_gra_ER_alpha.smi (204/283). Average speed: 0.13 s/mol.\n"", + ""Processing CHEMBL1299808 in Test_gra_ER_alpha.smi (205/283). Average speed: 0.13 s/mol.\n"", + ""Processing CHEMBL1579818 in Test_gra_ER_alpha.smi (206/283). Average speed: 0.13 s/mol.\n"", + ""Processing CHEMBL377616 in Test_gra_ER_alpha.smi (207/283). Average speed: 0.13 s/mol.\n"", + ""Processing CHEMBL1095097 in Test_gra_ER_alpha.smi (208/283). Average speed: 0.13 s/mol.\n"", + ""Processing CHEMBL213457 in Test_gra_ER_alpha.smi (209/283). Average speed: 0.13 s/mol.\n"", + ""Processing CHEMBL214291 in Test_gra_ER_alpha.smi (210/283). Average speed: 0.13 s/mol.\n"", + ""Processing CHEMBL104367 in Test_gra_ER_alpha.smi (211/283). Average speed: 0.13 s/mol.\n"", + ""Processing CHEMBL362507 in Test_gra_ER_alpha.smi (212/283). Average speed: 0.13 s/mol.\n"", + ""Processing CHEMBL190238 in Test_gra_ER_alpha.smi (213/283). Average speed: 0.13 s/mol.\n"", + ""Processing CHEMBL195515 in Test_gra_ER_alpha.smi (214/283). Average speed: 0.13 s/mol.\n"", + ""Processing CHEMBL79335 in Test_gra_ER_alpha.smi (215/283). Average speed: 0.13 s/mol.\n"", + ""Processing CHEMBL103 in Test_gra_ER_alpha.smi (216/283). Average speed: 0.13 s/mol.\n"", + ""Processing CHEMBL380978 in Test_gra_ER_alpha.smi (217/283). Average speed: 0.13 s/mol.\n"", + ""Processing CHEMBL311034 in Test_gra_ER_alpha.smi (218/283). Average speed: 0.13 s/mol.\n"", + ""Processing CHEMBL379024 in Test_gra_ER_alpha.smi (219/283). Average speed: 0.13 s/mol.\n"", + ""Processing CHEMBL245132 in Test_gra_ER_alpha.smi (221/283). Average speed: 0.13 s/mol.\n"", + ""Processing CHEMBL244971 in Test_gra_ER_alpha.smi (220/283). Average speed: 0.13 s/mol.\n"", + ""Processing CHEMBL204389 in Test_gra_ER_alpha.smi (224/283). Average speed: 0.13 s/mol.\n"", + ""Processing CHEMBL1390728 in Test_gra_ER_alpha.smi (223/283). Average speed: 0.13 s/mol.\n"", + ""Processing CHEMBL1450825 in Test_gra_ER_alpha.smi (222/283). Average speed: 0.13 s/mol.\n"", + ""Processing CHEMBL377565 in Test_gra_ER_alpha.smi (225/283). Average speed: 0.13 s/mol.\n"", + ""Processing CHEMBL245576 in Test_gra_ER_alpha.smi (226/283). Average speed: 0.13 s/mol.\n"", + ""Processing CHEMBL245771 in Test_gra_ER_alpha.smi (227/283). Average speed: 0.13 s/mol.\n"", + ""Processing CHEMBL1402012 in Test_gra_ER_alpha.smi (228/283). Average speed: 0.13 s/mol.\n"", + ""Processing CHEMBL1492286 in Test_gra_ER_alpha.smi (229/283). Average speed: 0.13 s/mol.\n"", + ""Processing CHEMBL1349449 in Test_gra_ER_alpha.smi (230/283). Average speed: 0.13 s/mol.\n"", + ""Processing CHEMBL1340515 in Test_gra_ER_alpha.smi (232/283). Average speed: 0.13 s/mol.\n"", + ""Processing CHEMBL1441028 in Test_gra_ER_alpha.smi (231/283). Average speed: 0.13 s/mol.\n"", + ""Processing CHEMBL1366918 in Test_gra_ER_alpha.smi (233/283). Average speed: 0.13 s/mol.\n"", + ""Processing CHEMBL1351935 in Test_gra_ER_alpha.smi (234/283). Average speed: 0.13 s/mol.\n"", + ""Processing CHEMBL1519846 in Test_gra_ER_alpha.smi (235/283). Average speed: 0.13 s/mol.\n"", + ""Processing CHEMBL1464700 in Test_gra_ER_alpha.smi (236/283). Average speed: 0.13 s/mol.\n"", + ""Processing CHEMBL1539548 in Test_gra_ER_alpha.smi (238/283). Average speed: 0.13 s/mol.\n"", + ""Processing CHEMBL1463596 in Test_gra_ER_alpha.smi (237/283). Average speed: 0.13 s/mol.\n"", + ""Processing CHEMBL1603680 in Test_gra_ER_alpha.smi (239/283). Average speed: 0.13 s/mol.\n"", + ""Processing CHEMBL246140 in Test_gra_ER_alpha.smi (240/283). Average speed: 0.13 s/mol.\n"", + ""Processing CHEMBL1392987 in Test_gra_ER_alpha.smi (241/283). Average speed: 0.13 s/mol.\n"", + ""Processing CHEMBL1547093 in Test_gra_ER_alpha.smi (242/283). Average speed: 0.13 s/mol.\n"", + ""Processing CHEMBL1390198 in Test_gra_ER_alpha.smi (243/283). Average speed: 0.13 s/mol.\n"", + ""Processing CHEMBL515671 in Test_gra_ER_alpha.smi (244/283). Average speed: 0.13 s/mol.\n"", + ""Processing CHEMBL1985765 in Test_gra_ER_alpha.smi (245/283). Average speed: 0.13 s/mol.\n"", + ""Processing CHEMBL1971815 in Test_gra_ER_alpha.smi (246/283). Average speed: 0.13 s/mol.\n"", + ""Processing CHEMBL1965461 in Test_gra_ER_alpha.smi (247/283). Average speed: 0.13 s/mol.\n"", + ""Processing CHEMBL2000120 in Test_gra_ER_alpha.smi (248/283). Average speed: 0.13 s/mol.\n"", + ""Processing CHEMBL3261407 in Test_gra_ER_alpha.smi (249/283). Average speed: 0.13 s/mol.\n"", + ""Processing CHEMBL3191473 in Test_gra_ER_alpha.smi (250/283). Average speed: 0.13 s/mol.\n"", + ""Processing CHEMBL3211537 in Test_gra_ER_alpha.smi (251/283). Average speed: 0.13 s/mol.\n"", + ""Processing CHEMBL3234858 in Test_gra_ER_alpha.smi (252/283). Average speed: 0.13 s/mol.\n"", + ""Processing CHEMBL3234863 in Test_gra_ER_alpha.smi (253/283). Average speed: 0.12 s/mol.\n"", + ""Processing CHEMBL3207950 in Test_gra_ER_alpha.smi (254/283). Average speed: 0.12 s/mol.\n"", + ""Processing CHEMBL3213194 in Test_gra_ER_alpha.smi (255/283). Average speed: 0.12 s/mol.\n"", + ""Processing CHEMBL3196436 in Test_gra_ER_alpha.smi (256/283). Average speed: 0.12 s/mol.\n"", + ""Processing CHEMBL3263752 in Test_gra_ER_alpha.smi (257/283). Average speed: 0.12 s/mol.\n"", + ""Processing CHEMBL3208853 in Test_gra_ER_alpha.smi (258/283). Average speed: 0.12 s/mol.\n"", + ""Processing CHEMBL3208616 in Test_gra_ER_alpha.smi (259/283). Average speed: 0.12 s/mol.\n"", + ""Processing CHEMBL3326454 in Test_gra_ER_alpha.smi (260/283). Average speed: 0.12 s/mol.\n"", + ""Processing CHEMBL3261425 in Test_gra_ER_alpha.smi (261/283). Average speed: 0.12 s/mol.\n"", + ""Processing CHEMBL3427415 in Test_gra_ER_alpha.smi (262/283). Average speed: 0.12 s/mol.\n"", + ""Processing CHEMBL3427385 in Test_gra_ER_alpha.smi (263/283). Average speed: 0.12 s/mol.\n"", + ""Processing CHEMBL3578295 in Test_gra_ER_alpha.smi (264/283). Average speed: 0.12 s/mol.\n"", + ""Processing CHEMBL3597500 in Test_gra_ER_alpha.smi (265/283). Average speed: 0.12 s/mol.\n"", + ""Processing CHEMBL3597503 in Test_gra_ER_alpha.smi (266/283). Average speed: 0.12 s/mol.\n"", + ""Processing CHEMBL3597506 in Test_gra_ER_alpha.smi (267/283). Average speed: 0.12 s/mol.\n"", + ""Processing CHEMBL3597507 in Test_gra_ER_alpha.smi (268/283). Average speed: 0.12 s/mol.\n"", + ""Processing CHEMBL3597510 in Test_gra_ER_alpha.smi (269/283). Average speed: 0.12 s/mol.\n"", + ""Processing CHEMBL3597513 in Test_gra_ER_alpha.smi (270/283). Average speed: 0.12 s/mol.\n"", + ""Processing CHEMBL3597514 in Test_gra_ER_alpha.smi (271/283). Average speed: 0.12 s/mol.\n"", + ""Processing CHEMBL3421892 in Test_gra_ER_alpha.smi (272/283). Average speed: 0.13 s/mol.\n"", + ""Processing CHEMBL3421871 in Test_gra_ER_alpha.smi (273/283). Average speed: 0.13 s/mol.\n"", + ""Processing CHEMBL3427414 in Test_gra_ER_alpha.smi (274/283). Average speed: 0.13 s/mol.\n"", + ""Processing CHEMBL3427411 in Test_gra_ER_alpha.smi (275/283). Average speed: 0.13 s/mol.\n"", + ""Processing CHEMBL3427406 in Test_gra_ER_alpha.smi (276/283). Average speed: 0.13 s/mol.\n"", + ""Processing CHEMBL3774852 in Test_gra_ER_alpha.smi (277/283). Average speed: 0.13 s/mol.\n"", + ""Processing CHEMBL3775464 in Test_gra_ER_alpha.smi (278/283). Average speed: 0.13 s/mol.\n"", + ""Processing CHEMBL3775389 in Test_gra_ER_alpha.smi (279/283). Average speed: 0.13 s/mol.\n"", + ""Processing CHEMBL3775798 in Test_gra_ER_alpha.smi (280/283). Average speed: 0.13 s/mol.\n"", + ""Processing CHEMBL3775473 in Test_gra_ER_alpha.smi (281/283). Average speed: 0.13 s/mol.\n"", + ""Processing CHEMBL3827018 in Test_gra_ER_alpha.smi (282/283). Average speed: 0.13 s/mol.\n"", + ""Processing CHEMBL3827876 in Test_gra_ER_alpha.smi (283/283). Average speed: 0.13 s/mol.\n"" + ] + }, + { + ""name"": ""stdout"", + ""output_type"": ""stream"", + ""text"": [ + ""Descriptor calculation completed in 36.482 secs . Average speed: 0.13 s/mol.\n"", + ""Test_gra_ER_alpha.smi\n"", + ""Test_gra_ER_alpha.csv\n"", + ""SubstructureFingerprinter.xml\n"", + ""SubstructureFingerprinter_\n"", + ""Processing CHEMBL192325 in Test_gra_ER_alpha.smi (1/283). \n"", + ""Processing CHEMBL1331671 in Test_gra_ER_alpha.smi (2/283). \n"", + ""Processing CHEMBL1393 in Test_gra_ER_alpha.smi (3/283). \n"", + ""Processing CHEMBL1349227 in Test_gra_ER_alpha.smi (4/283). \n"", + ""Processing CHEMBL1383468 in Test_gra_ER_alpha.smi (5/283). \n"", + ""Processing CHEMBL1481853 in Test_gra_ER_alpha.smi (6/283). Average speed: 1.38 s/mol.\n"", + ""Processing CHEMBL1528804 in Test_gra_ER_alpha.smi (7/283). Average speed: 0.72 s/mol.\n"", + ""Processing CHEMBL1491627 in Test_gra_ER_alpha.smi (8/283). Average speed: 0.54 s/mol.\n"", + ""Processing CHEMBL1468795 in Test_gra_ER_alpha.smi (10/283). Average speed: 0.35 s/mol.\n"", + ""Processing CHEMBL1565048 in Test_gra_ER_alpha.smi (9/283). Average speed: 0.44 s/mol.\n"", + ""Processing CHEMBL1408468 in Test_gra_ER_alpha.smi (11/283). Average speed: 0.31 s/mol.\n"", + ""Processing CHEMBL1610665 in Test_gra_ER_alpha.smi (12/283). Average speed: 0.25 s/mol.\n"", + ""Processing CHEMBL1441655 in Test_gra_ER_alpha.smi (13/283). Average speed: 0.27 s/mol.\n"", + ""Processing CHEMBL1353351 in Test_gra_ER_alpha.smi (14/283). Average speed: 0.25 s/mol.\n"", + ""Processing CHEMBL1500491 in Test_gra_ER_alpha.smi (16/283). Average speed: 0.20 s/mol.\n"", + ""Processing CHEMBL1379474 in Test_gra_ER_alpha.smi (15/283). Average speed: 0.22 s/mol.\n"", + ""Processing CHEMBL1557815 in Test_gra_ER_alpha.smi (17/283). Average speed: 0.20 s/mol.\n"", + ""Processing CHEMBL1405411 in Test_gra_ER_alpha.smi (18/283). Average speed: 0.19 s/mol.\n"", + ""Processing CHEMBL1341487 in Test_gra_ER_alpha.smi (19/283). Average speed: 0.18 s/mol.\n"", + ""Processing CHEMBL1543038 in Test_gra_ER_alpha.smi (20/283). Average speed: 0.17 s/mol.\n"", + ""Processing CHEMBL1492422 in Test_gra_ER_alpha.smi (21/283). Average speed: 0.16 s/mol.\n"", + ""Processing CHEMBL1599210 in Test_gra_ER_alpha.smi (22/283). Average speed: 0.16 s/mol.\n"", + ""Processing CHEMBL1579100 in Test_gra_ER_alpha.smi (23/283). Average speed: 0.16 s/mol.\n"", + ""Processing CHEMBL1412224 in Test_gra_ER_alpha.smi (24/283). Average speed: 0.15 s/mol.\n"", + ""Processing CHEMBL1510990 in Test_gra_ER_alpha.smi (25/283). Average speed: 0.15 s/mol.\n"", + ""Processing CHEMBL1304094 in Test_gra_ER_alpha.smi (26/283). Average speed: 0.14 s/mol.\n"", + ""Processing CHEMBL516241 in Test_gra_ER_alpha.smi (27/283). Average speed: 0.14 s/mol.\n"", + ""Processing CHEMBL475503 in Test_gra_ER_alpha.smi (28/283). Average speed: 0.14 s/mol.\n"", + ""Processing CHEMBL470783 in Test_gra_ER_alpha.smi (29/283). Average speed: 0.13 s/mol.\n"", + ""Processing CHEMBL397343 in Test_gra_ER_alpha.smi (30/283). Average speed: 0.13 s/mol.\n"", + ""Processing CHEMBL398840 in Test_gra_ER_alpha.smi (31/283). Average speed: 0.13 s/mol.\n"", + ""Processing CHEMBL496881 in Test_gra_ER_alpha.smi (32/283). Average speed: 0.13 s/mol.\n"", + ""Processing CHEMBL396946 in Test_gra_ER_alpha.smi (33/283). Average speed: 0.13 s/mol.\n"", + ""Processing CHEMBL378473 in Test_gra_ER_alpha.smi (34/283). Average speed: 0.12 s/mol.\n"", + ""Processing CHEMBL371311 in Test_gra_ER_alpha.smi (35/283). Average speed: 0.12 s/mol.\n"", + ""Processing CHEMBL196509 in Test_gra_ER_alpha.smi (36/283). Average speed: 0.12 s/mol.\n"", + ""Processing CHEMBL293388 in Test_gra_ER_alpha.smi (37/283). Average speed: 0.12 s/mol.\n"", + ""Processing CHEMBL204959 in Test_gra_ER_alpha.smi (38/283). Average speed: 0.12 s/mol.\n"", + ""Processing CHEMBL438977 in Test_gra_ER_alpha.smi (39/283). Average speed: 0.12 s/mol.\n"", + ""Processing CHEMBL205989 in Test_gra_ER_alpha.smi (40/283). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL388474 in Test_gra_ER_alpha.smi (41/283). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL205988 in Test_gra_ER_alpha.smi (42/283). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL206218 in Test_gra_ER_alpha.smi (43/283). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL400480 in Test_gra_ER_alpha.smi (44/283). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL66598 in Test_gra_ER_alpha.smi (45/283). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL311662 in Test_gra_ER_alpha.smi (46/283). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL201364 in Test_gra_ER_alpha.smi (47/283). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL57978 in Test_gra_ER_alpha.smi (48/283). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL200903 in Test_gra_ER_alpha.smi (49/283). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL43064 in Test_gra_ER_alpha.smi (50/283). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL65354 in Test_gra_ER_alpha.smi (51/283). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL379384 in Test_gra_ER_alpha.smi (52/283). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL212923 in Test_gra_ER_alpha.smi (53/283). Average speed: 0.09 s/mol.\n"", + ""Processing CHEMBL385358 in Test_gra_ER_alpha.smi (54/283). Average speed: 0.09 s/mol.\n"", + ""Processing CHEMBL397344 in Test_gra_ER_alpha.smi (55/283). Average speed: 0.09 s/mol.\n"", + ""Processing CHEMBL251515 in Test_gra_ER_alpha.smi (56/283). Average speed: 0.09 s/mol.\n"", + ""Processing CHEMBL65021 in Test_gra_ER_alpha.smi (57/283). Average speed: 0.09 s/mol.\n"", + ""Processing CHEMBL457242 in Test_gra_ER_alpha.smi (58/283). Average speed: 0.09 s/mol.\n"", + ""Processing CHEMBL65695 in Test_gra_ER_alpha.smi (60/283). Average speed: 0.09 s/mol.\n"", + ""Processing CHEMBL361266 in Test_gra_ER_alpha.smi (59/283). Average speed: 0.09 s/mol.\n"", + ""Processing CHEMBL313062 in Test_gra_ER_alpha.smi (61/283). Average speed: 0.09 s/mol.\n"", + ""Processing CHEMBL438863 in Test_gra_ER_alpha.smi (62/283). Average speed: 0.09 s/mol.\n"", + ""Processing CHEMBL1340716 in Test_gra_ER_alpha.smi (63/283). Average speed: 0.09 s/mol.\n"", + ""Processing CHEMBL1333664 in Test_gra_ER_alpha.smi (64/283). Average speed: 0.09 s/mol.\n"", + ""Processing CHEMBL1337538 in Test_gra_ER_alpha.smi (65/283). Average speed: 0.09 s/mol.\n"", + ""Processing CHEMBL1528600 in Test_gra_ER_alpha.smi (66/283). Average speed: 0.09 s/mol.\n"", + ""Processing CHEMBL1407108 in Test_gra_ER_alpha.smi (67/283). Average speed: 0.09 s/mol.\n"", + ""Processing CHEMBL1495161 in Test_gra_ER_alpha.smi (68/283). Average speed: 0.09 s/mol.\n"", + ""Processing CHEMBL1393680 in Test_gra_ER_alpha.smi (69/283). Average speed: 0.09 s/mol.\n"", + ""Processing CHEMBL1484399 in Test_gra_ER_alpha.smi (70/283). Average speed: 0.09 s/mol.\n"", + ""Processing CHEMBL1488579 in Test_gra_ER_alpha.smi (71/283). Average speed: 0.09 s/mol.\n"", + ""Processing CHEMBL1509842 in Test_gra_ER_alpha.smi (72/283). Average speed: 0.08 s/mol.\n"", + ""Processing CHEMBL1299324 in Test_gra_ER_alpha.smi (73/283). Average speed: 0.08 s/mol.\n"", + ""Processing CHEMBL1494151 in Test_gra_ER_alpha.smi (74/283). Average speed: 0.08 s/mol.\n"", + ""Processing CHEMBL1305162 in Test_gra_ER_alpha.smi (75/283). Average speed: 0.08 s/mol.\n"", + ""Processing CHEMBL380106 in Test_gra_ER_alpha.smi (77/283). Average speed: 0.08 s/mol.\n"", + ""Processing CHEMBL1461750 in Test_gra_ER_alpha.smi (76/283). Average speed: 0.08 s/mol.\n"", + ""Processing CHEMBL1377527 in Test_gra_ER_alpha.smi (78/283). Average speed: 0.08 s/mol.\n"", + ""Processing CHEMBL1707275 in Test_gra_ER_alpha.smi (79/283). Average speed: 0.08 s/mol.\n"", + ""Processing CHEMBL1330656 in Test_gra_ER_alpha.smi (80/283). Average speed: 0.08 s/mol.\n"", + ""Processing CHEMBL1559976 in Test_gra_ER_alpha.smi (81/283). Average speed: 0.08 s/mol.\n"", + ""Processing CHEMBL1500073 in Test_gra_ER_alpha.smi (82/283). Average speed: 0.08 s/mol.\n"", + ""Processing CHEMBL1485768 in Test_gra_ER_alpha.smi (83/283). Average speed: 0.08 s/mol.\n"", + ""Processing CHEMBL1496108 in Test_gra_ER_alpha.smi (85/283). Average speed: 0.08 s/mol.\n"", + ""Processing CHEMBL203519 in Test_gra_ER_alpha.smi (84/283). Average speed: 0.08 s/mol.\n"", + ""Processing CHEMBL457036 in Test_gra_ER_alpha.smi (86/283). Average speed: 0.08 s/mol.\n"", + ""Processing CHEMBL1575022 in Test_gra_ER_alpha.smi (87/283). Average speed: 0.08 s/mol.\n"", + ""Processing CHEMBL1307842 in Test_gra_ER_alpha.smi (88/283). Average speed: 0.08 s/mol.\n"", + ""Processing CHEMBL1342429 in Test_gra_ER_alpha.smi (89/283). Average speed: 0.08 s/mol.\n"", + ""Processing CHEMBL513735 in Test_gra_ER_alpha.smi (90/283). Average speed: 0.08 s/mol.\n"", + ""Processing CHEMBL1465900 in Test_gra_ER_alpha.smi (91/283). Average speed: 0.08 s/mol.\n"", + ""Processing CHEMBL1402732 in Test_gra_ER_alpha.smi (92/283). Average speed: 0.07 s/mol.\n"", + ""Processing CHEMBL1334452 in Test_gra_ER_alpha.smi (93/283). Average speed: 0.07 s/mol.\n"", + ""Processing CHEMBL1373202 in Test_gra_ER_alpha.smi (94/283). Average speed: 0.07 s/mol.\n"", + ""Processing CHEMBL1502446 in Test_gra_ER_alpha.smi (95/283). Average speed: 0.07 s/mol.\n"", + ""Processing CHEMBL1299346 in Test_gra_ER_alpha.smi (96/283). Average speed: 0.07 s/mol.\n"" + ] + }, + { + ""name"": ""stdout"", + ""output_type"": ""stream"", + ""text"": [ + ""Processing CHEMBL1438401 in Test_gra_ER_alpha.smi (97/283). Average speed: 0.07 s/mol.\n"", + ""Processing CHEMBL1565196 in Test_gra_ER_alpha.smi (98/283). Average speed: 0.07 s/mol.\n"", + ""Processing CHEMBL1485581 in Test_gra_ER_alpha.smi (99/283). Average speed: 0.07 s/mol.\n"", + ""Processing CHEMBL194012 in Test_gra_ER_alpha.smi (100/283). Average speed: 0.07 s/mol.\n"", + ""Processing CHEMBL1606174 in Test_gra_ER_alpha.smi (101/283). Average speed: 0.07 s/mol.\n"", + ""Processing CHEMBL1483760 in Test_gra_ER_alpha.smi (102/283). Average speed: 0.07 s/mol.\n"", + ""Processing CHEMBL81591 in Test_gra_ER_alpha.smi (104/283). Average speed: 0.07 s/mol.\n"", + ""Processing CHEMBL1606939 in Test_gra_ER_alpha.smi (103/283). Average speed: 0.07 s/mol.\n"", + ""Processing CHEMBL386630 in Test_gra_ER_alpha.smi (105/283). Average speed: 0.07 s/mol.\n"", + ""Processing CHEMBL201809 in Test_gra_ER_alpha.smi (107/283). Average speed: 0.07 s/mol.\n"", + ""Processing CHEMBL379441 in Test_gra_ER_alpha.smi (106/283). Average speed: 0.07 s/mol.\n"", + ""Processing CHEMBL212276 in Test_gra_ER_alpha.smi (108/283). Average speed: 0.07 s/mol.\n"", + ""Processing CHEMBL377622 in Test_gra_ER_alpha.smi (109/283). Average speed: 0.07 s/mol.\n"", + ""Processing CHEMBL1353787 in Test_gra_ER_alpha.smi (110/283). Average speed: 0.07 s/mol.\n"", + ""Processing CHEMBL1459339 in Test_gra_ER_alpha.smi (111/283). Average speed: 0.07 s/mol.\n"", + ""Processing CHEMBL377560 in Test_gra_ER_alpha.smi (112/283). Average speed: 0.07 s/mol.\n"", + ""Processing CHEMBL438616 in Test_gra_ER_alpha.smi (113/283). Average speed: 0.07 s/mol.\n"", + ""Processing CHEMBL377166 in Test_gra_ER_alpha.smi (114/283). Average speed: 0.07 s/mol.\n"", + ""Processing CHEMBL264349 in Test_gra_ER_alpha.smi (115/283). Average speed: 0.07 s/mol.\n"", + ""Processing CHEMBL195723 in Test_gra_ER_alpha.smi (116/283). Average speed: 0.07 s/mol.\n"", + ""Processing CHEMBL206278 in Test_gra_ER_alpha.smi (117/283). Average speed: 0.07 s/mol.\n"", + ""Processing CHEMBL379827 in Test_gra_ER_alpha.smi (118/283). Average speed: 0.07 s/mol.\n"", + ""Processing CHEMBL209650 in Test_gra_ER_alpha.smi (119/283). Average speed: 0.07 s/mol.\n"", + ""Processing CHEMBL364333 in Test_gra_ER_alpha.smi (120/283). Average speed: 0.07 s/mol.\n"", + ""Processing CHEMBL125596 in Test_gra_ER_alpha.smi (121/283). Average speed: 0.07 s/mol.\n"", + ""Processing CHEMBL345000 in Test_gra_ER_alpha.smi (122/283). Average speed: 0.07 s/mol.\n"", + ""Processing CHEMBL537887 in Test_gra_ER_alpha.smi (123/283). Average speed: 0.07 s/mol.\n"", + ""Processing CHEMBL196577 in Test_gra_ER_alpha.smi (124/283). Average speed: 0.07 s/mol.\n"", + ""Processing CHEMBL1334570 in Test_gra_ER_alpha.smi (125/283). Average speed: 0.07 s/mol.\n"", + ""Processing CHEMBL1318598 in Test_gra_ER_alpha.smi (126/283). Average speed: 0.07 s/mol.\n"", + ""Processing CHEMBL1539136 in Test_gra_ER_alpha.smi (127/283). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL1556511 in Test_gra_ER_alpha.smi (128/283). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL1449807 in Test_gra_ER_alpha.smi (129/283). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL1486906 in Test_gra_ER_alpha.smi (130/283). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL1585598 in Test_gra_ER_alpha.smi (132/283). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL1505040 in Test_gra_ER_alpha.smi (131/283). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL1301796 in Test_gra_ER_alpha.smi (133/283). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL1333501 in Test_gra_ER_alpha.smi (134/283). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL1365444 in Test_gra_ER_alpha.smi (135/283). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL1586766 in Test_gra_ER_alpha.smi (136/283). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL1581440 in Test_gra_ER_alpha.smi (137/283). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL1505994 in Test_gra_ER_alpha.smi (138/283). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL1391679 in Test_gra_ER_alpha.smi (140/283). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL1545617 in Test_gra_ER_alpha.smi (139/283). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL1442547 in Test_gra_ER_alpha.smi (141/283). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL1331281 in Test_gra_ER_alpha.smi (142/283). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL1573688 in Test_gra_ER_alpha.smi (143/283). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL1393628 in Test_gra_ER_alpha.smi (144/283). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL1321083 in Test_gra_ER_alpha.smi (145/283). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL1306474 in Test_gra_ER_alpha.smi (146/283). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL65868 in Test_gra_ER_alpha.smi (147/283). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL189541 in Test_gra_ER_alpha.smi (148/283). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL1350947 in Test_gra_ER_alpha.smi (149/283). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL1405097 in Test_gra_ER_alpha.smi (151/283). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL65221 in Test_gra_ER_alpha.smi (150/283). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL1595235 in Test_gra_ER_alpha.smi (152/283). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL382102 in Test_gra_ER_alpha.smi (153/283). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL192326 in Test_gra_ER_alpha.smi (154/283). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL195058 in Test_gra_ER_alpha.smi (155/283). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL1367559 in Test_gra_ER_alpha.smi (157/283). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL1322881 in Test_gra_ER_alpha.smi (156/283). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL1343134 in Test_gra_ER_alpha.smi (158/283). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL1582458 in Test_gra_ER_alpha.smi (159/283). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL1387920 in Test_gra_ER_alpha.smi (160/283). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL1344624 in Test_gra_ER_alpha.smi (161/283). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL1329411 in Test_gra_ER_alpha.smi (162/283). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL434183 in Test_gra_ER_alpha.smi (163/283). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL294209 in Test_gra_ER_alpha.smi (164/283). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL1594663 in Test_gra_ER_alpha.smi (166/283). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL1342736 in Test_gra_ER_alpha.smi (165/283). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL1422039 in Test_gra_ER_alpha.smi (167/283). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL188951 in Test_gra_ER_alpha.smi (168/283). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL65657 in Test_gra_ER_alpha.smi (170/283). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL381489 in Test_gra_ER_alpha.smi (171/283). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL392000 in Test_gra_ER_alpha.smi (169/283). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL457241 in Test_gra_ER_alpha.smi (172/283). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL205009 in Test_gra_ER_alpha.smi (173/283). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL204341 in Test_gra_ER_alpha.smi (174/283). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL381622 in Test_gra_ER_alpha.smi (175/283). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL1070 in Test_gra_ER_alpha.smi (176/283). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL232468 in Test_gra_ER_alpha.smi (177/283). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL1359279 in Test_gra_ER_alpha.smi (179/283). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL1082723 in Test_gra_ER_alpha.smi (178/283). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL1433155 in Test_gra_ER_alpha.smi (180/283). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL1365404 in Test_gra_ER_alpha.smi (181/283). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL1438484 in Test_gra_ER_alpha.smi (182/283). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL1455614 in Test_gra_ER_alpha.smi (183/283). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL1582133 in Test_gra_ER_alpha.smi (184/283). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL1504200 in Test_gra_ER_alpha.smi (185/283). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL1583952 in Test_gra_ER_alpha.smi (186/283). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL1333846 in Test_gra_ER_alpha.smi (187/283). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL1352844 in Test_gra_ER_alpha.smi (188/283). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL1500059 in Test_gra_ER_alpha.smi (189/283). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL1300705 in Test_gra_ER_alpha.smi (190/283). Average speed: 0.05 s/mol.\n"" + ] + }, + { + ""name"": ""stdout"", + ""output_type"": ""stream"", + ""text"": [ + ""Processing CHEMBL1450059 in Test_gra_ER_alpha.smi (191/283). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL1432915 in Test_gra_ER_alpha.smi (192/283). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL1539668 in Test_gra_ER_alpha.smi (193/283). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL1391501 in Test_gra_ER_alpha.smi (194/283). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL1331278 in Test_gra_ER_alpha.smi (195/283). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL101 in Test_gra_ER_alpha.smi (196/283). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL1565816 in Test_gra_ER_alpha.smi (197/283). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL1334162 in Test_gra_ER_alpha.smi (199/283). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL1564026 in Test_gra_ER_alpha.smi (198/283). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL1572214 in Test_gra_ER_alpha.smi (200/283). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL1586116 in Test_gra_ER_alpha.smi (201/283). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL1478743 in Test_gra_ER_alpha.smi (202/283). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL1583911 in Test_gra_ER_alpha.smi (203/283). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL1589718 in Test_gra_ER_alpha.smi (204/283). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL1299808 in Test_gra_ER_alpha.smi (205/283). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL1579818 in Test_gra_ER_alpha.smi (206/283). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL377616 in Test_gra_ER_alpha.smi (207/283). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL1095097 in Test_gra_ER_alpha.smi (208/283). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL213457 in Test_gra_ER_alpha.smi (209/283). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL214291 in Test_gra_ER_alpha.smi (210/283). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL104367 in Test_gra_ER_alpha.smi (211/283). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL362507 in Test_gra_ER_alpha.smi (212/283). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL190238 in Test_gra_ER_alpha.smi (213/283). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL79335 in Test_gra_ER_alpha.smi (215/283). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL195515 in Test_gra_ER_alpha.smi (214/283). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL103 in Test_gra_ER_alpha.smi (216/283). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL380978 in Test_gra_ER_alpha.smi (217/283). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL311034 in Test_gra_ER_alpha.smi (218/283). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL379024 in Test_gra_ER_alpha.smi (219/283). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL244971 in Test_gra_ER_alpha.smi (220/283). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL245132 in Test_gra_ER_alpha.smi (221/283). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL1450825 in Test_gra_ER_alpha.smi (222/283). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL1390728 in Test_gra_ER_alpha.smi (223/283). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL204389 in Test_gra_ER_alpha.smi (224/283). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL377565 in Test_gra_ER_alpha.smi (225/283). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL245576 in Test_gra_ER_alpha.smi (226/283). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL245771 in Test_gra_ER_alpha.smi (227/283). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL1402012 in Test_gra_ER_alpha.smi (228/283). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL1492286 in Test_gra_ER_alpha.smi (229/283). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL1349449 in Test_gra_ER_alpha.smi (230/283). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL1441028 in Test_gra_ER_alpha.smi (231/283). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL1340515 in Test_gra_ER_alpha.smi (232/283). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL1366918 in Test_gra_ER_alpha.smi (233/283). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL1351935 in Test_gra_ER_alpha.smi (234/283). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL1519846 in Test_gra_ER_alpha.smi (235/283). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL1464700 in Test_gra_ER_alpha.smi (236/283). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL1463596 in Test_gra_ER_alpha.smi (237/283). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL1539548 in Test_gra_ER_alpha.smi (238/283). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL1603680 in Test_gra_ER_alpha.smi (239/283). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL246140 in Test_gra_ER_alpha.smi (240/283). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL1392987 in Test_gra_ER_alpha.smi (241/283). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL1390198 in Test_gra_ER_alpha.smi (243/283). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL1547093 in Test_gra_ER_alpha.smi (242/283). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL515671 in Test_gra_ER_alpha.smi (244/283). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL1985765 in Test_gra_ER_alpha.smi (245/283). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL1971815 in Test_gra_ER_alpha.smi (246/283). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL1965461 in Test_gra_ER_alpha.smi (247/283). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL2000120 in Test_gra_ER_alpha.smi (248/283). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL3261407 in Test_gra_ER_alpha.smi (249/283). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL3191473 in Test_gra_ER_alpha.smi (250/283). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL3211537 in Test_gra_ER_alpha.smi (251/283). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL3234858 in Test_gra_ER_alpha.smi (252/283). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL3234863 in Test_gra_ER_alpha.smi (253/283). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL3207950 in Test_gra_ER_alpha.smi (254/283). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL3213194 in Test_gra_ER_alpha.smi (255/283). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL3196436 in Test_gra_ER_alpha.smi (256/283). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL3263752 in Test_gra_ER_alpha.smi (257/283). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL3208853 in Test_gra_ER_alpha.smi (258/283). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL3208616 in Test_gra_ER_alpha.smi (259/283). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL3326454 in Test_gra_ER_alpha.smi (260/283). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL3261425 in Test_gra_ER_alpha.smi (261/283). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL3427415 in Test_gra_ER_alpha.smi (262/283). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL3427385 in Test_gra_ER_alpha.smi (263/283). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL3578295 in Test_gra_ER_alpha.smi (264/283). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL3597500 in Test_gra_ER_alpha.smi (265/283). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL3597503 in Test_gra_ER_alpha.smi (266/283). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL3597506 in Test_gra_ER_alpha.smi (267/283). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL3597507 in Test_gra_ER_alpha.smi (268/283). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL3597510 in Test_gra_ER_alpha.smi (269/283). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL3597513 in Test_gra_ER_alpha.smi (270/283). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL3597514 in Test_gra_ER_alpha.smi (271/283). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL3421892 in Test_gra_ER_alpha.smi (272/283). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL3421871 in Test_gra_ER_alpha.smi (273/283). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL3427414 in Test_gra_ER_alpha.smi (274/283). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL3427411 in Test_gra_ER_alpha.smi (275/283). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL3427406 in Test_gra_ER_alpha.smi (276/283). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL3774852 in Test_gra_ER_alpha.smi (277/283). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL3775464 in Test_gra_ER_alpha.smi (278/283). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL3775389 in Test_gra_ER_alpha.smi (279/283). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL3775798 in Test_gra_ER_alpha.smi (280/283). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL3775473 in Test_gra_ER_alpha.smi (281/283). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL3827018 in Test_gra_ER_alpha.smi (282/283). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL3827876 in Test_gra_ER_alpha.smi (283/283). Average speed: 0.05 s/mol.\n"", + ""Descriptor calculation completed in 15.431 secs . Average speed: 0.05 s/mol.\n"" + ] + }, + { + ""name"": ""stdout"", + ""output_type"": ""stream"", + ""text"": [ + ""Test_gra_ER_alpha.smi\n"", + ""Test_gra_ER_alpha.csv\n"", + ""KlekotaRothFingerprinter.xml\n"", + ""KlekotaRothFingerprinter_\n"", + ""Processing CHEMBL192325 in Test_gra_ER_alpha.smi (1/283). \n"", + ""Processing CHEMBL1331671 in Test_gra_ER_alpha.smi (2/283). \n"", + ""Processing CHEMBL1393 in Test_gra_ER_alpha.smi (3/283). \n"", + ""Processing CHEMBL1349227 in Test_gra_ER_alpha.smi (4/283). \n"", + ""Processing CHEMBL1383468 in Test_gra_ER_alpha.smi (5/283). Average speed: 6.46 s/mol.\n"", + ""Processing CHEMBL1481853 in Test_gra_ER_alpha.smi (6/283). Average speed: 6.66 s/mol.\n"", + ""Processing CHEMBL1528804 in Test_gra_ER_alpha.smi (7/283). Average speed: 3.52 s/mol.\n"", + ""Processing CHEMBL1491627 in Test_gra_ER_alpha.smi (8/283). Average speed: 2.43 s/mol.\n"", + ""Processing CHEMBL1565048 in Test_gra_ER_alpha.smi (9/283). Average speed: 1.65 s/mol.\n"", + ""Processing CHEMBL1468795 in Test_gra_ER_alpha.smi (10/283). Average speed: 1.52 s/mol.\n"", + ""Processing CHEMBL1408468 in Test_gra_ER_alpha.smi (11/283). Average speed: 1.54 s/mol.\n"", + ""Processing CHEMBL1610665 in Test_gra_ER_alpha.smi (12/283). Average speed: 1.25 s/mol.\n"", + ""Processing CHEMBL1441655 in Test_gra_ER_alpha.smi (13/283). Average speed: 1.32 s/mol.\n"", + ""Processing CHEMBL1353351 in Test_gra_ER_alpha.smi (14/283). Average speed: 1.26 s/mol.\n"", + ""Processing CHEMBL1379474 in Test_gra_ER_alpha.smi (15/283). Average speed: 1.27 s/mol.\n"", + ""Processing CHEMBL1500491 in Test_gra_ER_alpha.smi (16/283). Average speed: 1.16 s/mol.\n"", + ""Processing CHEMBL1557815 in Test_gra_ER_alpha.smi (17/283). Average speed: 1.11 s/mol.\n"", + ""Processing CHEMBL1405411 in Test_gra_ER_alpha.smi (18/283). Average speed: 1.05 s/mol.\n"", + ""Processing CHEMBL1341487 in Test_gra_ER_alpha.smi (19/283). Average speed: 1.04 s/mol.\n"", + ""Processing CHEMBL1543038 in Test_gra_ER_alpha.smi (20/283). Average speed: 0.98 s/mol.\n"", + ""Processing CHEMBL1492422 in Test_gra_ER_alpha.smi (21/283). Average speed: 0.97 s/mol.\n"", + ""Processing CHEMBL1579100 in Test_gra_ER_alpha.smi (23/283). Average speed: 0.92 s/mol.\n"", + ""Processing CHEMBL1599210 in Test_gra_ER_alpha.smi (22/283). Average speed: 0.97 s/mol.\n"", + ""Processing CHEMBL1510990 in Test_gra_ER_alpha.smi (25/283). Average speed: 0.89 s/mol.\n"", + ""Processing CHEMBL1412224 in Test_gra_ER_alpha.smi (24/283). Average speed: 0.89 s/mol.\n"", + ""Processing CHEMBL1304094 in Test_gra_ER_alpha.smi (26/283). Average speed: 0.85 s/mol.\n"", + ""Processing CHEMBL516241 in Test_gra_ER_alpha.smi (27/283). Average speed: 0.87 s/mol.\n"", + ""Processing CHEMBL475503 in Test_gra_ER_alpha.smi (28/283). Average speed: 0.82 s/mol.\n"", + ""Processing CHEMBL470783 in Test_gra_ER_alpha.smi (29/283). Average speed: 0.83 s/mol.\n"", + ""Processing CHEMBL397343 in Test_gra_ER_alpha.smi (30/283). Average speed: 0.81 s/mol.\n"", + ""Processing CHEMBL398840 in Test_gra_ER_alpha.smi (31/283). Average speed: 0.81 s/mol.\n"", + ""Processing CHEMBL496881 in Test_gra_ER_alpha.smi (32/283). Average speed: 0.82 s/mol.\n"", + ""Processing CHEMBL396946 in Test_gra_ER_alpha.smi (33/283). Average speed: 0.80 s/mol.\n"", + ""Processing CHEMBL378473 in Test_gra_ER_alpha.smi (34/283). Average speed: 0.79 s/mol.\n"", + ""Processing CHEMBL371311 in Test_gra_ER_alpha.smi (35/283). Average speed: 0.77 s/mol.\n"", + ""Processing CHEMBL196509 in Test_gra_ER_alpha.smi (36/283). Average speed: 0.75 s/mol.\n"", + ""Processing CHEMBL293388 in Test_gra_ER_alpha.smi (37/283). Average speed: 0.77 s/mol.\n"", + ""Processing CHEMBL204959 in Test_gra_ER_alpha.smi (38/283). Average speed: 0.78 s/mol.\n"", + ""Processing CHEMBL438977 in Test_gra_ER_alpha.smi (39/283). Average speed: 0.76 s/mol.\n"", + ""Processing CHEMBL205989 in Test_gra_ER_alpha.smi (40/283). Average speed: 0.75 s/mol.\n"", + ""Processing CHEMBL388474 in Test_gra_ER_alpha.smi (41/283). Average speed: 0.73 s/mol.\n"", + ""Processing CHEMBL205988 in Test_gra_ER_alpha.smi (42/283). Average speed: 0.74 s/mol.\n"", + ""Processing CHEMBL206218 in Test_gra_ER_alpha.smi (43/283). Average speed: 0.71 s/mol.\n"", + ""Processing CHEMBL400480 in Test_gra_ER_alpha.smi (44/283). Average speed: 0.72 s/mol.\n"", + ""Processing CHEMBL66598 in Test_gra_ER_alpha.smi (45/283). Average speed: 0.70 s/mol.\n"", + ""Processing CHEMBL311662 in Test_gra_ER_alpha.smi (46/283). Average speed: 0.71 s/mol.\n"", + ""Processing CHEMBL201364 in Test_gra_ER_alpha.smi (47/283). Average speed: 0.69 s/mol.\n"", + ""Processing CHEMBL57978 in Test_gra_ER_alpha.smi (48/283). Average speed: 0.69 s/mol.\n"", + ""Processing CHEMBL200903 in Test_gra_ER_alpha.smi (49/283). Average speed: 0.68 s/mol.\n"", + ""Processing CHEMBL43064 in Test_gra_ER_alpha.smi (50/283). Average speed: 0.67 s/mol.\n"", + ""Processing CHEMBL65354 in Test_gra_ER_alpha.smi (51/283). Average speed: 0.68 s/mol.\n"", + ""Processing CHEMBL379384 in Test_gra_ER_alpha.smi (52/283). Average speed: 0.68 s/mol.\n"", + ""Processing CHEMBL212923 in Test_gra_ER_alpha.smi (53/283). Average speed: 0.68 s/mol.\n"", + ""Processing CHEMBL385358 in Test_gra_ER_alpha.smi (54/283). Average speed: 0.67 s/mol.\n"", + ""Processing CHEMBL397344 in Test_gra_ER_alpha.smi (55/283). Average speed: 0.70 s/mol.\n"", + ""Processing CHEMBL251515 in Test_gra_ER_alpha.smi (56/283). Average speed: 0.70 s/mol.\n"", + ""Processing CHEMBL65021 in Test_gra_ER_alpha.smi (57/283). Average speed: 0.68 s/mol.\n"", + ""Processing CHEMBL457242 in Test_gra_ER_alpha.smi (58/283). Average speed: 0.69 s/mol.\n"", + ""Processing CHEMBL361266 in Test_gra_ER_alpha.smi (59/283). Average speed: 0.70 s/mol.\n"", + ""Processing CHEMBL65695 in Test_gra_ER_alpha.smi (60/283). Average speed: 0.69 s/mol.\n"", + ""Processing CHEMBL313062 in Test_gra_ER_alpha.smi (61/283). Average speed: 0.68 s/mol.\n"", + ""Processing CHEMBL438863 in Test_gra_ER_alpha.smi (62/283). Average speed: 0.68 s/mol.\n"", + ""Processing CHEMBL1340716 in Test_gra_ER_alpha.smi (63/283). Average speed: 0.67 s/mol.\n"", + ""Processing CHEMBL1333664 in Test_gra_ER_alpha.smi (64/283). Average speed: 0.68 s/mol.\n"", + ""Processing CHEMBL1337538 in Test_gra_ER_alpha.smi (65/283). Average speed: 0.67 s/mol.\n"", + ""Processing CHEMBL1528600 in Test_gra_ER_alpha.smi (66/283). Average speed: 0.67 s/mol.\n"", + ""Processing CHEMBL1407108 in Test_gra_ER_alpha.smi (67/283). Average speed: 0.66 s/mol.\n"", + ""Processing CHEMBL1495161 in Test_gra_ER_alpha.smi (68/283). Average speed: 0.66 s/mol.\n"", + ""Processing CHEMBL1393680 in Test_gra_ER_alpha.smi (69/283). Average speed: 0.67 s/mol.\n"", + ""Processing CHEMBL1484399 in Test_gra_ER_alpha.smi (70/283). Average speed: 0.67 s/mol.\n"", + ""Processing CHEMBL1488579 in Test_gra_ER_alpha.smi (71/283). Average speed: 0.67 s/mol.\n"", + ""Processing CHEMBL1509842 in Test_gra_ER_alpha.smi (72/283). Average speed: 0.68 s/mol.\n"", + ""Processing CHEMBL1299324 in Test_gra_ER_alpha.smi (73/283). Average speed: 0.67 s/mol.\n"", + ""Processing CHEMBL1494151 in Test_gra_ER_alpha.smi (74/283). Average speed: 0.66 s/mol.\n"", + ""Processing CHEMBL1305162 in Test_gra_ER_alpha.smi (75/283). Average speed: 0.66 s/mol.\n"", + ""Processing CHEMBL1461750 in Test_gra_ER_alpha.smi (76/283). Average speed: 0.67 s/mol.\n"", + ""Processing CHEMBL380106 in Test_gra_ER_alpha.smi (77/283). Average speed: 0.66 s/mol.\n"", + ""Processing CHEMBL1377527 in Test_gra_ER_alpha.smi (78/283). Average speed: 0.66 s/mol.\n"", + ""Processing CHEMBL1707275 in Test_gra_ER_alpha.smi (79/283). Average speed: 0.66 s/mol.\n"", + ""Processing CHEMBL1330656 in Test_gra_ER_alpha.smi (80/283). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL1559976 in Test_gra_ER_alpha.smi (81/283). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL1500073 in Test_gra_ER_alpha.smi (82/283). Average speed: 0.64 s/mol.\n"", + ""Processing CHEMBL1485768 in Test_gra_ER_alpha.smi (83/283). Average speed: 0.64 s/mol.\n"", + ""Processing CHEMBL203519 in Test_gra_ER_alpha.smi (84/283). Average speed: 0.64 s/mol.\n"", + ""Processing CHEMBL1496108 in Test_gra_ER_alpha.smi (85/283). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL457036 in Test_gra_ER_alpha.smi (86/283). Average speed: 0.64 s/mol.\n"", + ""Processing CHEMBL1575022 in Test_gra_ER_alpha.smi (87/283). Average speed: 0.64 s/mol.\n"", + ""Processing CHEMBL1307842 in Test_gra_ER_alpha.smi (88/283). Average speed: 0.64 s/mol.\n"", + ""Processing CHEMBL1342429 in Test_gra_ER_alpha.smi (89/283). Average speed: 0.63 s/mol.\n"", + ""Processing CHEMBL513735 in Test_gra_ER_alpha.smi (90/283). Average speed: 0.63 s/mol.\n"", + ""Processing CHEMBL1465900 in Test_gra_ER_alpha.smi (91/283). Average speed: 0.63 s/mol.\n"", + ""Processing CHEMBL1402732 in Test_gra_ER_alpha.smi (92/283). Average speed: 0.63 s/mol.\n"", + ""Processing CHEMBL1334452 in Test_gra_ER_alpha.smi (93/283). Average speed: 0.62 s/mol.\n"", + ""Processing CHEMBL1373202 in Test_gra_ER_alpha.smi (94/283). Average speed: 0.62 s/mol.\n"", + ""Processing CHEMBL1502446 in Test_gra_ER_alpha.smi (95/283). Average speed: 0.62 s/mol.\n"" + ] + }, + { + ""name"": ""stdout"", + ""output_type"": ""stream"", + ""text"": [ + ""Processing CHEMBL1299346 in Test_gra_ER_alpha.smi (96/283). Average speed: 0.62 s/mol.\n"", + ""Processing CHEMBL1438401 in Test_gra_ER_alpha.smi (97/283). Average speed: 0.62 s/mol.\n"", + ""Processing CHEMBL1565196 in Test_gra_ER_alpha.smi (98/283). Average speed: 0.62 s/mol.\n"", + ""Processing CHEMBL1485581 in Test_gra_ER_alpha.smi (99/283). Average speed: 0.62 s/mol.\n"", + ""Processing CHEMBL194012 in Test_gra_ER_alpha.smi (100/283). Average speed: 0.62 s/mol.\n"", + ""Processing CHEMBL1606174 in Test_gra_ER_alpha.smi (101/283). Average speed: 0.62 s/mol.\n"", + ""Processing CHEMBL1483760 in Test_gra_ER_alpha.smi (102/283). Average speed: 0.62 s/mol.\n"", + ""Processing CHEMBL1606939 in Test_gra_ER_alpha.smi (103/283). Average speed: 0.61 s/mol.\n"", + ""Processing CHEMBL81591 in Test_gra_ER_alpha.smi (104/283). Average speed: 0.61 s/mol.\n"", + ""Processing CHEMBL386630 in Test_gra_ER_alpha.smi (105/283). Average speed: 0.61 s/mol.\n"", + ""Processing CHEMBL379441 in Test_gra_ER_alpha.smi (106/283). Average speed: 0.61 s/mol.\n"", + ""Processing CHEMBL201809 in Test_gra_ER_alpha.smi (107/283). Average speed: 0.61 s/mol.\n"", + ""Processing CHEMBL212276 in Test_gra_ER_alpha.smi (108/283). Average speed: 0.60 s/mol.\n"", + ""Processing CHEMBL377622 in Test_gra_ER_alpha.smi (109/283). Average speed: 0.60 s/mol.\n"", + ""Processing CHEMBL1353787 in Test_gra_ER_alpha.smi (110/283). Average speed: 0.60 s/mol.\n"", + ""Processing CHEMBL1459339 in Test_gra_ER_alpha.smi (111/283). Average speed: 0.60 s/mol.\n"", + ""Processing CHEMBL377560 in Test_gra_ER_alpha.smi (112/283). Average speed: 0.59 s/mol.\n"", + ""Processing CHEMBL438616 in Test_gra_ER_alpha.smi (113/283). Average speed: 0.59 s/mol.\n"", + ""Processing CHEMBL377166 in Test_gra_ER_alpha.smi (114/283). Average speed: 0.59 s/mol.\n"", + ""Processing CHEMBL264349 in Test_gra_ER_alpha.smi (115/283). Average speed: 0.59 s/mol.\n"", + ""Processing CHEMBL195723 in Test_gra_ER_alpha.smi (116/283). Average speed: 0.58 s/mol.\n"", + ""Processing CHEMBL206278 in Test_gra_ER_alpha.smi (117/283). Average speed: 0.58 s/mol.\n"", + ""Processing CHEMBL379827 in Test_gra_ER_alpha.smi (118/283). Average speed: 0.58 s/mol.\n"", + ""Processing CHEMBL209650 in Test_gra_ER_alpha.smi (119/283). Average speed: 0.58 s/mol.\n"", + ""Processing CHEMBL364333 in Test_gra_ER_alpha.smi (120/283). Average speed: 0.58 s/mol.\n"", + ""Processing CHEMBL125596 in Test_gra_ER_alpha.smi (121/283). Average speed: 0.58 s/mol.\n"", + ""Processing CHEMBL345000 in Test_gra_ER_alpha.smi (122/283). Average speed: 0.58 s/mol.\n"", + ""Processing CHEMBL537887 in Test_gra_ER_alpha.smi (123/283). Average speed: 0.57 s/mol.\n"", + ""Processing CHEMBL196577 in Test_gra_ER_alpha.smi (124/283). Average speed: 0.57 s/mol.\n"", + ""Processing CHEMBL1334570 in Test_gra_ER_alpha.smi (125/283). Average speed: 0.57 s/mol.\n"", + ""Processing CHEMBL1318598 in Test_gra_ER_alpha.smi (126/283). Average speed: 0.57 s/mol.\n"", + ""Processing CHEMBL1539136 in Test_gra_ER_alpha.smi (127/283). Average speed: 0.57 s/mol.\n"", + ""Processing CHEMBL1556511 in Test_gra_ER_alpha.smi (128/283). Average speed: 0.57 s/mol.\n"", + ""Processing CHEMBL1449807 in Test_gra_ER_alpha.smi (129/283). Average speed: 0.57 s/mol.\n"", + ""Processing CHEMBL1486906 in Test_gra_ER_alpha.smi (130/283). Average speed: 0.57 s/mol.\n"", + ""Processing CHEMBL1505040 in Test_gra_ER_alpha.smi (131/283). Average speed: 0.57 s/mol.\n"", + ""Processing CHEMBL1585598 in Test_gra_ER_alpha.smi (132/283). Average speed: 0.57 s/mol.\n"", + ""Processing CHEMBL1301796 in Test_gra_ER_alpha.smi (133/283). Average speed: 0.57 s/mol.\n"", + ""Processing CHEMBL1333501 in Test_gra_ER_alpha.smi (134/283). Average speed: 0.57 s/mol.\n"", + ""Processing CHEMBL1365444 in Test_gra_ER_alpha.smi (135/283). Average speed: 0.56 s/mol.\n"", + ""Processing CHEMBL1586766 in Test_gra_ER_alpha.smi (136/283). Average speed: 0.56 s/mol.\n"", + ""Processing CHEMBL1581440 in Test_gra_ER_alpha.smi (137/283). Average speed: 0.56 s/mol.\n"", + ""Processing CHEMBL1505994 in Test_gra_ER_alpha.smi (138/283). Average speed: 0.56 s/mol.\n"", + ""Processing CHEMBL1545617 in Test_gra_ER_alpha.smi (139/283). Average speed: 0.56 s/mol.\n"", + ""Processing CHEMBL1391679 in Test_gra_ER_alpha.smi (140/283). Average speed: 0.56 s/mol.\n"", + ""Processing CHEMBL1442547 in Test_gra_ER_alpha.smi (141/283). Average speed: 0.56 s/mol.\n"", + ""Processing CHEMBL1331281 in Test_gra_ER_alpha.smi (142/283). Average speed: 0.56 s/mol.\n"", + ""Processing CHEMBL1573688 in Test_gra_ER_alpha.smi (143/283). Average speed: 0.57 s/mol.\n"", + ""Processing CHEMBL1393628 in Test_gra_ER_alpha.smi (144/283). Average speed: 0.57 s/mol.\n"", + ""Processing CHEMBL1321083 in Test_gra_ER_alpha.smi (145/283). Average speed: 0.57 s/mol.\n"", + ""Processing CHEMBL1306474 in Test_gra_ER_alpha.smi (146/283). Average speed: 0.57 s/mol.\n"", + ""Processing CHEMBL65868 in Test_gra_ER_alpha.smi (147/283). Average speed: 0.57 s/mol.\n"", + ""Processing CHEMBL189541 in Test_gra_ER_alpha.smi (148/283). Average speed: 0.57 s/mol.\n"", + ""Processing CHEMBL65221 in Test_gra_ER_alpha.smi (150/283). Average speed: 0.57 s/mol.\n"", + ""Processing CHEMBL1350947 in Test_gra_ER_alpha.smi (149/283). Average speed: 0.57 s/mol.\n"", + ""Processing CHEMBL1405097 in Test_gra_ER_alpha.smi (151/283). Average speed: 0.57 s/mol.\n"", + ""Processing CHEMBL1595235 in Test_gra_ER_alpha.smi (152/283). Average speed: 0.57 s/mol.\n"", + ""Processing CHEMBL382102 in Test_gra_ER_alpha.smi (153/283). Average speed: 0.57 s/mol.\n"", + ""Processing CHEMBL192326 in Test_gra_ER_alpha.smi (154/283). Average speed: 0.57 s/mol.\n"", + ""Processing CHEMBL195058 in Test_gra_ER_alpha.smi (155/283). Average speed: 0.56 s/mol.\n"", + ""Processing CHEMBL1322881 in Test_gra_ER_alpha.smi (156/283). Average speed: 0.56 s/mol.\n"", + ""Processing CHEMBL1367559 in Test_gra_ER_alpha.smi (157/283). Average speed: 0.57 s/mol.\n"", + ""Processing CHEMBL1343134 in Test_gra_ER_alpha.smi (158/283). Average speed: 0.56 s/mol.\n"", + ""Processing CHEMBL1582458 in Test_gra_ER_alpha.smi (159/283). Average speed: 0.56 s/mol.\n"", + ""Processing CHEMBL1387920 in Test_gra_ER_alpha.smi (160/283). Average speed: 0.56 s/mol.\n"", + ""Processing CHEMBL1344624 in Test_gra_ER_alpha.smi (161/283). Average speed: 0.56 s/mol.\n"", + ""Processing CHEMBL1329411 in Test_gra_ER_alpha.smi (162/283). Average speed: 0.56 s/mol.\n"", + ""Processing CHEMBL434183 in Test_gra_ER_alpha.smi (163/283). Average speed: 0.56 s/mol.\n"", + ""Processing CHEMBL294209 in Test_gra_ER_alpha.smi (164/283). Average speed: 0.55 s/mol.\n"", + ""Processing CHEMBL1342736 in Test_gra_ER_alpha.smi (165/283). Average speed: 0.56 s/mol.\n"", + ""Processing CHEMBL1594663 in Test_gra_ER_alpha.smi (166/283). Average speed: 0.56 s/mol.\n"", + ""Processing CHEMBL1422039 in Test_gra_ER_alpha.smi (167/283). Average speed: 0.55 s/mol.\n"", + ""Processing CHEMBL188951 in Test_gra_ER_alpha.smi (168/283). Average speed: 0.55 s/mol.\n"", + ""Processing CHEMBL392000 in Test_gra_ER_alpha.smi (169/283). Average speed: 0.55 s/mol.\n"", + ""Processing CHEMBL65657 in Test_gra_ER_alpha.smi (170/283). Average speed: 0.55 s/mol.\n"", + ""Processing CHEMBL381489 in Test_gra_ER_alpha.smi (171/283). Average speed: 0.55 s/mol.\n"", + ""Processing CHEMBL457241 in Test_gra_ER_alpha.smi (172/283). Average speed: 0.55 s/mol.\n"", + ""Processing CHEMBL205009 in Test_gra_ER_alpha.smi (173/283). Average speed: 0.55 s/mol.\n"", + ""Processing CHEMBL204341 in Test_gra_ER_alpha.smi (174/283). Average speed: 0.55 s/mol.\n"", + ""Processing CHEMBL381622 in Test_gra_ER_alpha.smi (175/283). Average speed: 0.55 s/mol.\n"", + ""Processing CHEMBL1070 in Test_gra_ER_alpha.smi (176/283). Average speed: 0.55 s/mol.\n"", + ""Processing CHEMBL232468 in Test_gra_ER_alpha.smi (177/283). Average speed: 0.55 s/mol.\n"", + ""Processing CHEMBL1082723 in Test_gra_ER_alpha.smi (178/283). Average speed: 0.55 s/mol.\n"", + ""Processing CHEMBL1359279 in Test_gra_ER_alpha.smi (179/283). Average speed: 0.55 s/mol.\n"", + ""Processing CHEMBL1433155 in Test_gra_ER_alpha.smi (180/283). Average speed: 0.54 s/mol.\n"", + ""Processing CHEMBL1365404 in Test_gra_ER_alpha.smi (181/283). Average speed: 0.54 s/mol.\n"", + ""Processing CHEMBL1438484 in Test_gra_ER_alpha.smi (182/283). Average speed: 0.55 s/mol.\n"", + ""Processing CHEMBL1455614 in Test_gra_ER_alpha.smi (183/283). Average speed: 0.55 s/mol.\n"", + ""Processing CHEMBL1582133 in Test_gra_ER_alpha.smi (184/283). Average speed: 0.55 s/mol.\n"", + ""Processing CHEMBL1504200 in Test_gra_ER_alpha.smi (185/283). Average speed: 0.55 s/mol.\n"", + ""Processing CHEMBL1583952 in Test_gra_ER_alpha.smi (186/283). Average speed: 0.55 s/mol.\n"", + ""Processing CHEMBL1333846 in Test_gra_ER_alpha.smi (187/283). Average speed: 0.55 s/mol.\n"", + ""Processing CHEMBL1352844 in Test_gra_ER_alpha.smi (188/283). Average speed: 0.54 s/mol.\n"", + ""Processing CHEMBL1500059 in Test_gra_ER_alpha.smi (189/283). Average speed: 0.54 s/mol.\n"" + ] + }, + { + ""name"": ""stdout"", + ""output_type"": ""stream"", + ""text"": [ + ""Processing CHEMBL1300705 in Test_gra_ER_alpha.smi (190/283). Average speed: 0.54 s/mol.\n"", + ""Processing CHEMBL1450059 in Test_gra_ER_alpha.smi (191/283). Average speed: 0.55 s/mol.\n"", + ""Processing CHEMBL1432915 in Test_gra_ER_alpha.smi (192/283). Average speed: 0.55 s/mol.\n"", + ""Processing CHEMBL1539668 in Test_gra_ER_alpha.smi (193/283). Average speed: 0.55 s/mol.\n"", + ""Processing CHEMBL1391501 in Test_gra_ER_alpha.smi (194/283). Average speed: 0.55 s/mol.\n"", + ""Processing CHEMBL1331278 in Test_gra_ER_alpha.smi (195/283). Average speed: 0.55 s/mol.\n"", + ""Processing CHEMBL101 in Test_gra_ER_alpha.smi (196/283). Average speed: 0.55 s/mol.\n"", + ""Processing CHEMBL1564026 in Test_gra_ER_alpha.smi (198/283). Average speed: 0.55 s/mol.\n"", + ""Processing CHEMBL1565816 in Test_gra_ER_alpha.smi (197/283). Average speed: 0.55 s/mol.\n"", + ""Processing CHEMBL1334162 in Test_gra_ER_alpha.smi (199/283). Average speed: 0.55 s/mol.\n"", + ""Processing CHEMBL1572214 in Test_gra_ER_alpha.smi (200/283). Average speed: 0.55 s/mol.\n"", + ""Processing CHEMBL1586116 in Test_gra_ER_alpha.smi (201/283). Average speed: 0.55 s/mol.\n"", + ""Processing CHEMBL1478743 in Test_gra_ER_alpha.smi (202/283). Average speed: 0.55 s/mol.\n"", + ""Processing CHEMBL1583911 in Test_gra_ER_alpha.smi (203/283). Average speed: 0.55 s/mol.\n"", + ""Processing CHEMBL1589718 in Test_gra_ER_alpha.smi (204/283). Average speed: 0.54 s/mol.\n"", + ""Processing CHEMBL1299808 in Test_gra_ER_alpha.smi (205/283). Average speed: 0.55 s/mol.\n"", + ""Processing CHEMBL1579818 in Test_gra_ER_alpha.smi (206/283). Average speed: 0.55 s/mol.\n"", + ""Processing CHEMBL377616 in Test_gra_ER_alpha.smi (207/283). Average speed: 0.54 s/mol.\n"", + ""Processing CHEMBL1095097 in Test_gra_ER_alpha.smi (208/283). Average speed: 0.54 s/mol.\n"", + ""Processing CHEMBL213457 in Test_gra_ER_alpha.smi (209/283). Average speed: 0.54 s/mol.\n"", + ""Processing CHEMBL214291 in Test_gra_ER_alpha.smi (210/283). Average speed: 0.54 s/mol.\n"", + ""Processing CHEMBL104367 in Test_gra_ER_alpha.smi (211/283). Average speed: 0.54 s/mol.\n"", + ""Processing CHEMBL362507 in Test_gra_ER_alpha.smi (212/283). Average speed: 0.54 s/mol.\n"", + ""Processing CHEMBL190238 in Test_gra_ER_alpha.smi (213/283). Average speed: 0.54 s/mol.\n"", + ""Processing CHEMBL195515 in Test_gra_ER_alpha.smi (214/283). Average speed: 0.54 s/mol.\n"", + ""Processing CHEMBL79335 in Test_gra_ER_alpha.smi (215/283). Average speed: 0.54 s/mol.\n"", + ""Processing CHEMBL103 in Test_gra_ER_alpha.smi (216/283). Average speed: 0.54 s/mol.\n"", + ""Processing CHEMBL380978 in Test_gra_ER_alpha.smi (217/283). Average speed: 0.54 s/mol.\n"", + ""Processing CHEMBL311034 in Test_gra_ER_alpha.smi (218/283). Average speed: 0.54 s/mol.\n"", + ""Processing CHEMBL379024 in Test_gra_ER_alpha.smi (219/283). Average speed: 0.54 s/mol.\n"", + ""Processing CHEMBL244971 in Test_gra_ER_alpha.smi (220/283). Average speed: 0.54 s/mol.\n"", + ""Processing CHEMBL245132 in Test_gra_ER_alpha.smi (221/283). Average speed: 0.54 s/mol.\n"", + ""Processing CHEMBL1450825 in Test_gra_ER_alpha.smi (222/283). Average speed: 0.54 s/mol.\n"", + ""Processing CHEMBL204389 in Test_gra_ER_alpha.smi (224/283). Average speed: 0.54 s/mol.\n"", + ""Processing CHEMBL1390728 in Test_gra_ER_alpha.smi (223/283). Average speed: 0.54 s/mol.\n"", + ""Processing CHEMBL245576 in Test_gra_ER_alpha.smi (226/283). Average speed: 0.54 s/mol.\n"", + ""Processing CHEMBL377565 in Test_gra_ER_alpha.smi (225/283). Average speed: 0.54 s/mol.\n"", + ""Processing CHEMBL245771 in Test_gra_ER_alpha.smi (227/283). Average speed: 0.54 s/mol.\n"", + ""Processing CHEMBL1402012 in Test_gra_ER_alpha.smi (228/283). Average speed: 0.54 s/mol.\n"", + ""Processing CHEMBL1492286 in Test_gra_ER_alpha.smi (229/283). Average speed: 0.54 s/mol.\n"", + ""Processing CHEMBL1349449 in Test_gra_ER_alpha.smi (230/283). Average speed: 0.54 s/mol.\n"", + ""Processing CHEMBL1441028 in Test_gra_ER_alpha.smi (231/283). Average speed: 0.54 s/mol.\n"", + ""Processing CHEMBL1340515 in Test_gra_ER_alpha.smi (232/283). Average speed: 0.54 s/mol.\n"", + ""Processing CHEMBL1351935 in Test_gra_ER_alpha.smi (234/283). Average speed: 0.54 s/mol.\n"", + ""Processing CHEMBL1366918 in Test_gra_ER_alpha.smi (233/283). Average speed: 0.54 s/mol.\n"", + ""Processing CHEMBL1519846 in Test_gra_ER_alpha.smi (235/283). Average speed: 0.54 s/mol.\n"", + ""Processing CHEMBL1464700 in Test_gra_ER_alpha.smi (236/283). Average speed: 0.54 s/mol.\n"", + ""Processing CHEMBL1463596 in Test_gra_ER_alpha.smi (237/283). Average speed: 0.53 s/mol.\n"", + ""Processing CHEMBL1539548 in Test_gra_ER_alpha.smi (238/283). Average speed: 0.54 s/mol.\n"", + ""Processing CHEMBL1603680 in Test_gra_ER_alpha.smi (239/283). Average speed: 0.53 s/mol.\n"", + ""Processing CHEMBL246140 in Test_gra_ER_alpha.smi (240/283). Average speed: 0.53 s/mol.\n"", + ""Processing CHEMBL1392987 in Test_gra_ER_alpha.smi (241/283). Average speed: 0.53 s/mol.\n"", + ""Processing CHEMBL1547093 in Test_gra_ER_alpha.smi (242/283). Average speed: 0.53 s/mol.\n"", + ""Processing CHEMBL1390198 in Test_gra_ER_alpha.smi (243/283). Average speed: 0.53 s/mol.\n"", + ""Processing CHEMBL515671 in Test_gra_ER_alpha.smi (244/283). Average speed: 0.53 s/mol.\n"", + ""Processing CHEMBL1985765 in Test_gra_ER_alpha.smi (245/283). Average speed: 0.53 s/mol.\n"", + ""Processing CHEMBL1971815 in Test_gra_ER_alpha.smi (246/283). Average speed: 0.53 s/mol.\n"", + ""Processing CHEMBL1965461 in Test_gra_ER_alpha.smi (247/283). Average speed: 0.53 s/mol.\n"", + ""Processing CHEMBL2000120 in Test_gra_ER_alpha.smi (248/283). Average speed: 0.53 s/mol.\n"", + ""Processing CHEMBL3261407 in Test_gra_ER_alpha.smi (249/283). Average speed: 0.53 s/mol.\n"", + ""Processing CHEMBL3191473 in Test_gra_ER_alpha.smi (250/283). Average speed: 0.53 s/mol.\n"", + ""Processing CHEMBL3211537 in Test_gra_ER_alpha.smi (251/283). Average speed: 0.53 s/mol.\n"", + ""Processing CHEMBL3234858 in Test_gra_ER_alpha.smi (252/283). Average speed: 0.53 s/mol.\n"", + ""Processing CHEMBL3234863 in Test_gra_ER_alpha.smi (253/283). Average speed: 0.53 s/mol.\n"", + ""Processing CHEMBL3207950 in Test_gra_ER_alpha.smi (254/283). Average speed: 0.53 s/mol.\n"", + ""Processing CHEMBL3213194 in Test_gra_ER_alpha.smi (255/283). Average speed: 0.53 s/mol.\n"", + ""Processing CHEMBL3196436 in Test_gra_ER_alpha.smi (256/283). Average speed: 0.53 s/mol.\n"", + ""Processing CHEMBL3263752 in Test_gra_ER_alpha.smi (257/283). Average speed: 0.54 s/mol.\n"", + ""Processing CHEMBL3208853 in Test_gra_ER_alpha.smi (258/283). Average speed: 0.54 s/mol.\n"", + ""Processing CHEMBL3208616 in Test_gra_ER_alpha.smi (259/283). Average speed: 0.54 s/mol.\n"", + ""Processing CHEMBL3326454 in Test_gra_ER_alpha.smi (260/283). Average speed: 0.54 s/mol.\n"", + ""Processing CHEMBL3261425 in Test_gra_ER_alpha.smi (261/283). Average speed: 0.54 s/mol.\n"", + ""Processing CHEMBL3427415 in Test_gra_ER_alpha.smi (262/283). Average speed: 0.54 s/mol.\n"", + ""Processing CHEMBL3427385 in Test_gra_ER_alpha.smi (263/283). Average speed: 0.54 s/mol.\n"", + ""Processing CHEMBL3578295 in Test_gra_ER_alpha.smi (264/283). Average speed: 0.53 s/mol.\n"", + ""Processing CHEMBL3597500 in Test_gra_ER_alpha.smi (265/283). Average speed: 0.54 s/mol.\n"", + ""Processing CHEMBL3597503 in Test_gra_ER_alpha.smi (266/283). Average speed: 0.54 s/mol.\n"", + ""Processing CHEMBL3597506 in Test_gra_ER_alpha.smi (267/283). Average speed: 0.54 s/mol.\n"", + ""Processing CHEMBL3597507 in Test_gra_ER_alpha.smi (268/283). Average speed: 0.54 s/mol.\n"", + ""Processing CHEMBL3597510 in Test_gra_ER_alpha.smi (269/283). Average speed: 0.55 s/mol.\n"", + ""Processing CHEMBL3597513 in Test_gra_ER_alpha.smi (270/283). Average speed: 0.55 s/mol.\n"", + ""Processing CHEMBL3597514 in Test_gra_ER_alpha.smi (271/283). Average speed: 0.55 s/mol.\n"", + ""Processing CHEMBL3421892 in Test_gra_ER_alpha.smi (272/283). Average speed: 0.55 s/mol.\n"", + ""Processing CHEMBL3421871 in Test_gra_ER_alpha.smi (273/283). Average speed: 0.55 s/mol.\n"", + ""Processing CHEMBL3427414 in Test_gra_ER_alpha.smi (274/283). Average speed: 0.56 s/mol.\n"", + ""Processing CHEMBL3427411 in Test_gra_ER_alpha.smi (275/283). Average speed: 0.56 s/mol.\n"", + ""Processing CHEMBL3427406 in Test_gra_ER_alpha.smi (276/283). Average speed: 0.57 s/mol.\n"", + ""Processing CHEMBL3774852 in Test_gra_ER_alpha.smi (277/283). Average speed: 0.57 s/mol.\n"", + ""Processing CHEMBL3775464 in Test_gra_ER_alpha.smi (278/283). Average speed: 0.58 s/mol.\n"", + ""Processing CHEMBL3775389 in Test_gra_ER_alpha.smi (279/283). Average speed: 0.58 s/mol.\n"", + ""Processing CHEMBL3775798 in Test_gra_ER_alpha.smi (280/283). Average speed: 0.58 s/mol.\n"", + ""Processing CHEMBL3775473 in Test_gra_ER_alpha.smi (281/283). Average speed: 0.58 s/mol.\n"", + ""Processing CHEMBL3827018 in Test_gra_ER_alpha.smi (282/283). Average speed: 0.58 s/mol.\n"", + ""Processing CHEMBL3827876 in Test_gra_ER_alpha.smi (283/283). Average speed: 0.58 s/mol.\n"" + ] + }, + { + ""name"": ""stdout"", + ""output_type"": ""stream"", + ""text"": [ + ""Descriptor calculation completed in 2 mins 45.3 secs . Average speed: 0.58 s/mol.\n"", + ""Test_gra_ER_alpha.smi\n"", + ""Test_gra_ER_alpha.csv\n"", + ""AtomPairs2DFingerprinter.xml\n"", + ""AtomPairs2DFingerprinter_\n"", + ""Processing CHEMBL192325 in Test_gra_ER_alpha.smi (1/283). \n"", + ""Processing CHEMBL1383468 in Test_gra_ER_alpha.smi (5/283). \n"", + ""Processing CHEMBL1408468 in Test_gra_ER_alpha.smi (11/283). \n"", + ""Processing CHEMBL1331671 in Test_gra_ER_alpha.smi (2/283). \n"", + ""Processing CHEMBL1528804 in Test_gra_ER_alpha.smi (7/283). \n"", + ""Processing CHEMBL1468795 in Test_gra_ER_alpha.smi (10/283). \n"", + ""Processing CHEMBL1393 in Test_gra_ER_alpha.smi (3/283). \n"", + ""Processing CHEMBL1491627 in Test_gra_ER_alpha.smi (8/283). \n"", + ""Processing CHEMBL1610665 in Test_gra_ER_alpha.smi (12/283). \n"", + ""Processing CHEMBL1349227 in Test_gra_ER_alpha.smi (4/283). \n"", + ""Processing CHEMBL1481853 in Test_gra_ER_alpha.smi (6/283). \n"", + ""Processing CHEMBL1565048 in Test_gra_ER_alpha.smi (9/283). \n"", + ""Processing CHEMBL1441655 in Test_gra_ER_alpha.smi (13/283). Average speed: 0.77 s/mol.\n"", + ""Processing CHEMBL1500491 in Test_gra_ER_alpha.smi (16/283). Average speed: 0.32 s/mol.\n"", + ""Processing CHEMBL1543038 in Test_gra_ER_alpha.smi (20/283). Average speed: 0.27 s/mol.\n"", + ""Processing CHEMBL1510990 in Test_gra_ER_alpha.smi (25/283). Average speed: 0.15 s/mol.\n"", + ""Processing CHEMBL470783 in Test_gra_ER_alpha.smi (29/283). Average speed: 0.13 s/mol.\n"", + ""Processing CHEMBL396946 in Test_gra_ER_alpha.smi (33/283). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL371311 in Test_gra_ER_alpha.smi (35/283). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL388474 in Test_gra_ER_alpha.smi (41/283). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL65354 in Test_gra_ER_alpha.smi (51/283). Average speed: 0.09 s/mol.\n"", + ""Processing CHEMBL385358 in Test_gra_ER_alpha.smi (54/283). Average speed: 0.08 s/mol.\n"", + ""Processing CHEMBL65021 in Test_gra_ER_alpha.smi (57/283). Average speed: 0.08 s/mol.\n"", + ""Processing CHEMBL65695 in Test_gra_ER_alpha.smi (60/283). Average speed: 0.08 s/mol.\n"", + ""Processing CHEMBL1495161 in Test_gra_ER_alpha.smi (68/283). Average speed: 0.08 s/mol.\n"", + ""Processing CHEMBL1299324 in Test_gra_ER_alpha.smi (73/283). Average speed: 0.07 s/mol.\n"", + ""Processing CHEMBL1305162 in Test_gra_ER_alpha.smi (75/283). Average speed: 0.07 s/mol.\n"", + ""Processing CHEMBL380106 in Test_gra_ER_alpha.smi (77/283). Average speed: 0.07 s/mol.\n"", + ""Processing CHEMBL1377527 in Test_gra_ER_alpha.smi (78/283). Average speed: 0.07 s/mol.\n"", + ""Processing CHEMBL1330656 in Test_gra_ER_alpha.smi (80/283). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL1500073 in Test_gra_ER_alpha.smi (82/283). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL203519 in Test_gra_ER_alpha.smi (84/283). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL457036 in Test_gra_ER_alpha.smi (86/283). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL1373202 in Test_gra_ER_alpha.smi (94/283). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL1299346 in Test_gra_ER_alpha.smi (96/283). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL194012 in Test_gra_ER_alpha.smi (100/283). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL1606939 in Test_gra_ER_alpha.smi (103/283). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL379441 in Test_gra_ER_alpha.smi (106/283). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL201809 in Test_gra_ER_alpha.smi (107/283). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL438616 in Test_gra_ER_alpha.smi (113/283). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL264349 in Test_gra_ER_alpha.smi (115/283). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL125596 in Test_gra_ER_alpha.smi (121/283). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL196577 in Test_gra_ER_alpha.smi (124/283). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL1318598 in Test_gra_ER_alpha.smi (126/283). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL1556511 in Test_gra_ER_alpha.smi (128/283). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL1486906 in Test_gra_ER_alpha.smi (130/283). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL1585598 in Test_gra_ER_alpha.smi (132/283). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL1545617 in Test_gra_ER_alpha.smi (139/283). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL1306474 in Test_gra_ER_alpha.smi (146/283). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL189541 in Test_gra_ER_alpha.smi (148/283). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL1350947 in Test_gra_ER_alpha.smi (149/283). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL1405097 in Test_gra_ER_alpha.smi (151/283). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL1595235 in Test_gra_ER_alpha.smi (152/283). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL1387920 in Test_gra_ER_alpha.smi (160/283). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL1344624 in Test_gra_ER_alpha.smi (161/283). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL294209 in Test_gra_ER_alpha.smi (164/283). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL1594663 in Test_gra_ER_alpha.smi (166/283). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL381489 in Test_gra_ER_alpha.smi (171/283). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1500059 in Test_gra_ER_alpha.smi (189/283). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1557815 in Test_gra_ER_alpha.smi (17/283). Average speed: 0.32 s/mol.\n"", + ""Processing CHEMBL1341487 in Test_gra_ER_alpha.smi (19/283). Average speed: 0.27 s/mol.\n"", + ""Processing CHEMBL1579100 in Test_gra_ER_alpha.smi (23/283). Average speed: 0.16 s/mol.\n"", + ""Processing CHEMBL1304094 in Test_gra_ER_alpha.smi (26/283). Average speed: 0.15 s/mol.\n"", + ""Processing CHEMBL397343 in Test_gra_ER_alpha.smi (30/283). Average speed: 0.12 s/mol.\n"", + ""Processing CHEMBL378473 in Test_gra_ER_alpha.smi (34/283). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL57978 in Test_gra_ER_alpha.smi (48/283). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL200903 in Test_gra_ER_alpha.smi (49/283). Average speed: 0.09 s/mol.\n"", + ""Processing CHEMBL43064 in Test_gra_ER_alpha.smi (50/283). Average speed: 0.09 s/mol.\n"", + ""Processing CHEMBL379384 in Test_gra_ER_alpha.smi (52/283). Average speed: 0.08 s/mol.\n"", + ""Processing CHEMBL212923 in Test_gra_ER_alpha.smi (53/283). Average speed: 0.08 s/mol.\n"", + ""Processing CHEMBL397344 in Test_gra_ER_alpha.smi (55/283). Average speed: 0.08 s/mol.\n"", + ""Processing CHEMBL251515 in Test_gra_ER_alpha.smi (56/283). Average speed: 0.08 s/mol.\n"", + ""Processing CHEMBL361266 in Test_gra_ER_alpha.smi (59/283). Average speed: 0.08 s/mol.\n"", + ""Processing CHEMBL1393680 in Test_gra_ER_alpha.smi (69/283). Average speed: 0.07 s/mol.\n"", + ""Processing CHEMBL1484399 in Test_gra_ER_alpha.smi (70/283). Average speed: 0.07 s/mol.\n"", + ""Processing CHEMBL1488579 in Test_gra_ER_alpha.smi (71/283). Average speed: 0.07 s/mol.\n"", + ""Processing CHEMBL1461750 in Test_gra_ER_alpha.smi (76/283). Average speed: 0.07 s/mol.\n"", + ""Processing CHEMBL1559976 in Test_gra_ER_alpha.smi (81/283). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL1342429 in Test_gra_ER_alpha.smi (89/283). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL513735 in Test_gra_ER_alpha.smi (90/283). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL1485581 in Test_gra_ER_alpha.smi (99/283). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL81591 in Test_gra_ER_alpha.smi (104/283). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL195723 in Test_gra_ER_alpha.smi (116/283). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL364333 in Test_gra_ER_alpha.smi (120/283). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL537887 in Test_gra_ER_alpha.smi (123/283). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL1333501 in Test_gra_ER_alpha.smi (134/283). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL1586766 in Test_gra_ER_alpha.smi (136/283). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL1505994 in Test_gra_ER_alpha.smi (138/283). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL1331281 in Test_gra_ER_alpha.smi (142/283). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL1321083 in Test_gra_ER_alpha.smi (145/283). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL192326 in Test_gra_ER_alpha.smi (154/283). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL195058 in Test_gra_ER_alpha.smi (155/283). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL1367559 in Test_gra_ER_alpha.smi (157/283). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL1343134 in Test_gra_ER_alpha.smi (158/283). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL434183 in Test_gra_ER_alpha.smi (163/283). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1422039 in Test_gra_ER_alpha.smi (167/283). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL188951 in Test_gra_ER_alpha.smi (168/283). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL65657 in Test_gra_ER_alpha.smi (170/283). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL1433155 in Test_gra_ER_alpha.smi (180/283). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1438484 in Test_gra_ER_alpha.smi (182/283). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1504200 in Test_gra_ER_alpha.smi (185/283). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1300705 in Test_gra_ER_alpha.smi (190/283). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1539668 in Test_gra_ER_alpha.smi (193/283). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL1353351 in Test_gra_ER_alpha.smi (14/283). Average speed: 0.52 s/mol.\n"", + ""Processing CHEMBL1599210 in Test_gra_ER_alpha.smi (22/283). Average speed: 0.18 s/mol.\n"", + ""Processing CHEMBL516241 in Test_gra_ER_alpha.smi (27/283). Average speed: 0.15 s/mol.\n"", + ""Processing CHEMBL398840 in Test_gra_ER_alpha.smi (31/283). Average speed: 0.12 s/mol.\n"", + ""Processing CHEMBL196509 in Test_gra_ER_alpha.smi (36/283). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL204959 in Test_gra_ER_alpha.smi (38/283). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL438977 in Test_gra_ER_alpha.smi (39/283). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL205989 in Test_gra_ER_alpha.smi (40/283). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL206218 in Test_gra_ER_alpha.smi (43/283). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL66598 in Test_gra_ER_alpha.smi (45/283). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL201364 in Test_gra_ER_alpha.smi (47/283). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL438863 in Test_gra_ER_alpha.smi (62/283). Average speed: 0.08 s/mol.\n"", + ""Processing CHEMBL1340716 in Test_gra_ER_alpha.smi (63/283). Average speed: 0.08 s/mol.\n"", + ""Processing CHEMBL1528600 in Test_gra_ER_alpha.smi (66/283). Average speed: 0.08 s/mol.\n"", + ""Processing CHEMBL1509842 in Test_gra_ER_alpha.smi (72/283). Average speed: 0.07 s/mol.\n"", + ""Processing CHEMBL1494151 in Test_gra_ER_alpha.smi (74/283). Average speed: 0.07 s/mol.\n"", + ""Processing CHEMBL1707275 in Test_gra_ER_alpha.smi (79/283). Average speed: 0.07 s/mol.\n"", + ""Processing CHEMBL1485768 in Test_gra_ER_alpha.smi (83/283). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL1465900 in Test_gra_ER_alpha.smi (91/283). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL1502446 in Test_gra_ER_alpha.smi (95/283). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL1606174 in Test_gra_ER_alpha.smi (101/283). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL386630 in Test_gra_ER_alpha.smi (105/283). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL1353787 in Test_gra_ER_alpha.smi (110/283). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL377560 in Test_gra_ER_alpha.smi (112/283). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL377166 in Test_gra_ER_alpha.smi (114/283). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL379827 in Test_gra_ER_alpha.smi (118/283). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL1301796 in Test_gra_ER_alpha.smi (133/283). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL1365444 in Test_gra_ER_alpha.smi (135/283). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL1581440 in Test_gra_ER_alpha.smi (137/283). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL1442547 in Test_gra_ER_alpha.smi (141/283). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL1393628 in Test_gra_ER_alpha.smi (144/283). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL65221 in Test_gra_ER_alpha.smi (150/283). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL1322881 in Test_gra_ER_alpha.smi (156/283). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL392000 in Test_gra_ER_alpha.smi (169/283). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL457241 in Test_gra_ER_alpha.smi (172/283). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL205009 in Test_gra_ER_alpha.smi (173/283). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL381622 in Test_gra_ER_alpha.smi (175/283). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL232468 in Test_gra_ER_alpha.smi (177/283). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1359279 in Test_gra_ER_alpha.smi (179/283). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1582133 in Test_gra_ER_alpha.smi (184/283). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1352844 in Test_gra_ER_alpha.smi (188/283). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1450059 in Test_gra_ER_alpha.smi (191/283). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL1379474 in Test_gra_ER_alpha.smi (15/283). Average speed: 0.52 s/mol.\n"", + ""Processing CHEMBL1405411 in Test_gra_ER_alpha.smi (18/283). Average speed: 0.32 s/mol.\n"", + ""Processing CHEMBL1492422 in Test_gra_ER_alpha.smi (21/283). Average speed: 0.21 s/mol.\n"", + ""Processing CHEMBL1412224 in Test_gra_ER_alpha.smi (24/283). Average speed: 0.16 s/mol.\n"", + ""Processing CHEMBL475503 in Test_gra_ER_alpha.smi (28/283). Average speed: 0.15 s/mol.\n"", + ""Processing CHEMBL496881 in Test_gra_ER_alpha.smi (32/283). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL293388 in Test_gra_ER_alpha.smi (37/283). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL205988 in Test_gra_ER_alpha.smi (42/283). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL400480 in Test_gra_ER_alpha.smi (44/283). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL311662 in Test_gra_ER_alpha.smi (46/283). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL457242 in Test_gra_ER_alpha.smi (58/283). Average speed: 0.08 s/mol.\n"", + ""Processing CHEMBL313062 in Test_gra_ER_alpha.smi (61/283). Average speed: 0.08 s/mol.\n"", + ""Processing CHEMBL1333664 in Test_gra_ER_alpha.smi (64/283). Average speed: 0.08 s/mol.\n"", + ""Processing CHEMBL1337538 in Test_gra_ER_alpha.smi (65/283). Average speed: 0.08 s/mol.\n"", + ""Processing CHEMBL1407108 in Test_gra_ER_alpha.smi (67/283). Average speed: 0.08 s/mol.\n"", + ""Processing CHEMBL1496108 in Test_gra_ER_alpha.smi (85/283). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL1575022 in Test_gra_ER_alpha.smi (87/283). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL1307842 in Test_gra_ER_alpha.smi (88/283). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL1402732 in Test_gra_ER_alpha.smi (92/283). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL1334452 in Test_gra_ER_alpha.smi (93/283). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL1438401 in Test_gra_ER_alpha.smi (97/283). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL1565196 in Test_gra_ER_alpha.smi (98/283). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL1483760 in Test_gra_ER_alpha.smi (102/283). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL212276 in Test_gra_ER_alpha.smi (108/283). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL377622 in Test_gra_ER_alpha.smi (109/283). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL1459339 in Test_gra_ER_alpha.smi (111/283). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL206278 in Test_gra_ER_alpha.smi (117/283). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL209650 in Test_gra_ER_alpha.smi (119/283). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL345000 in Test_gra_ER_alpha.smi (122/283). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL1334570 in Test_gra_ER_alpha.smi (125/283). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL1539136 in Test_gra_ER_alpha.smi (127/283). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL1449807 in Test_gra_ER_alpha.smi (129/283). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL1505040 in Test_gra_ER_alpha.smi (131/283). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL1391679 in Test_gra_ER_alpha.smi (140/283). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL1573688 in Test_gra_ER_alpha.smi (143/283). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL65868 in Test_gra_ER_alpha.smi (147/283). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL382102 in Test_gra_ER_alpha.smi (153/283). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL1582458 in Test_gra_ER_alpha.smi (159/283). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL1329411 in Test_gra_ER_alpha.smi (162/283). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1342736 in Test_gra_ER_alpha.smi (165/283). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL204341 in Test_gra_ER_alpha.smi (174/283). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1070 in Test_gra_ER_alpha.smi (176/283). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1082723 in Test_gra_ER_alpha.smi (178/283). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1365404 in Test_gra_ER_alpha.smi (181/283). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1455614 in Test_gra_ER_alpha.smi (183/283). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1583952 in Test_gra_ER_alpha.smi (186/283). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1333846 in Test_gra_ER_alpha.smi (187/283). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1432915 in Test_gra_ER_alpha.smi (192/283). Average speed: 0.05 s/mol.\n"", + ""Descriptor calculation completed in 6.507 secs . Average speed: 0.02 s/mol.\n"" + ] + }, + { + ""name"": ""stdout"", + ""output_type"": ""stream"", + ""text"": [ + ""Train_ER_alpha.smi\n"", + ""Train_ER_alpha.csv\n"", + ""Fingerprinter.xml\n"", + ""Fingerprinter_\n"", + ""Processing CHEMBL370037 in Train_ER_alpha.smi (1/1238). \n"", + ""Processing CHEMBL189073 in Train_ER_alpha.smi (2/1238). \n"", + ""Processing CHEMBL365290 in Train_ER_alpha.smi (3/1238). \n"", + ""Processing CHEMBL191648 in Train_ER_alpha.smi (4/1238). \n"", + ""Processing CHEMBL184431 in Train_ER_alpha.smi (5/1238). \n"", + ""Processing CHEMBL364551 in Train_ER_alpha.smi (6/1238). Average speed: 1.30 s/mol.\n"", + ""Processing CHEMBL1496978 in Train_ER_alpha.smi (9/1238). Average speed: 0.37 s/mol.\n"", + ""Processing CHEMBL198410 in Train_ER_alpha.smi (8/1238). Average speed: 1.31 s/mol.\n"", + ""Processing CHEMBL1305485 in Train_ER_alpha.smi (10/1238). Average speed: 0.29 s/mol.\n"", + ""Processing CHEMBL124482 in Train_ER_alpha.smi (7/1238). Average speed: 1.30 s/mol.\n"", + ""Processing CHEMBL1570659 in Train_ER_alpha.smi (11/1238). Average speed: 0.25 s/mol.\n"", + ""Processing CHEMBL1331084 in Train_ER_alpha.smi (12/1238). Average speed: 0.22 s/mol.\n"", + ""Processing CHEMBL184448 in Train_ER_alpha.smi (13/1238). Average speed: 0.19 s/mol.\n"", + ""Processing CHEMBL45068 in Train_ER_alpha.smi (14/1238). Average speed: 0.18 s/mol.\n"", + ""Processing CHEMBL434502 in Train_ER_alpha.smi (15/1238). Average speed: 0.16 s/mol.\n"", + ""Processing CHEMBL1628199 in Train_ER_alpha.smi (16/1238). Average speed: 0.15 s/mol.\n"", + ""Processing CHEMBL123025 in Train_ER_alpha.smi (17/1238). Average speed: 0.14 s/mol.\n"", + ""Processing CHEMBL153062 in Train_ER_alpha.smi (18/1238). Average speed: 0.14 s/mol.\n"", + ""Processing CHEMBL1468708 in Train_ER_alpha.smi (19/1238). Average speed: 0.13 s/mol.\n"", + ""Processing CHEMBL1388271 in Train_ER_alpha.smi (21/1238). Average speed: 0.13 s/mol.\n"", + ""Processing CHEMBL1442416 in Train_ER_alpha.smi (20/1238). Average speed: 0.13 s/mol.\n"", + ""Processing CHEMBL1331872 in Train_ER_alpha.smi (22/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL1460126 in Train_ER_alpha.smi (24/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL1401227 in Train_ER_alpha.smi (23/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL1499638 in Train_ER_alpha.smi (25/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL1413557 in Train_ER_alpha.smi (26/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL1608989 in Train_ER_alpha.smi (28/1238). Average speed: 0.09 s/mol.\n"", + ""Processing CHEMBL1484998 in Train_ER_alpha.smi (27/1238). Average speed: 0.09 s/mol.\n"", + ""Processing CHEMBL1469743 in Train_ER_alpha.smi (29/1238). Average speed: 0.09 s/mol.\n"", + ""Processing CHEMBL1538325 in Train_ER_alpha.smi (30/1238). Average speed: 0.09 s/mol.\n"", + ""Processing CHEMBL1605393 in Train_ER_alpha.smi (31/1238). Average speed: 0.09 s/mol.\n"", + ""Processing CHEMBL1384049 in Train_ER_alpha.smi (32/1238). Average speed: 0.08 s/mol.\n"", + ""Processing CHEMBL188049 in Train_ER_alpha.smi (33/1238). Average speed: 0.08 s/mol.\n"", + ""Processing CHEMBL1489063 in Train_ER_alpha.smi (35/1238). Average speed: 0.08 s/mol.\n"", + ""Processing CHEMBL1361641 in Train_ER_alpha.smi (34/1238). Average speed: 0.08 s/mol.\n"", + ""Processing CHEMBL1381495 in Train_ER_alpha.smi (36/1238). Average speed: 0.08 s/mol.\n"", + ""Processing CHEMBL1343011 in Train_ER_alpha.smi (37/1238). Average speed: 0.08 s/mol.\n"", + ""Processing CHEMBL1613068 in Train_ER_alpha.smi (38/1238). Average speed: 0.08 s/mol.\n"", + ""Processing CHEMBL1464837 in Train_ER_alpha.smi (39/1238). Average speed: 0.08 s/mol.\n"", + ""Processing CHEMBL1349347 in Train_ER_alpha.smi (40/1238). Average speed: 0.07 s/mol.\n"", + ""Processing CHEMBL1573735 in Train_ER_alpha.smi (42/1238). Average speed: 0.07 s/mol.\n"", + ""Processing CHEMBL1455258 in Train_ER_alpha.smi (43/1238). Average speed: 0.07 s/mol.\n"", + ""Processing CHEMBL1431555 in Train_ER_alpha.smi (44/1238). Average speed: 0.07 s/mol.\n"", + ""Processing CHEMBL1385373 in Train_ER_alpha.smi (41/1238). Average speed: 0.07 s/mol.\n"", + ""Processing CHEMBL1597933 in Train_ER_alpha.smi (46/1238). Average speed: 0.07 s/mol.\n"", + ""Processing CHEMBL1565187 in Train_ER_alpha.smi (45/1238). Average speed: 0.07 s/mol.\n"", + ""Processing CHEMBL1466882 in Train_ER_alpha.smi (47/1238). Average speed: 0.07 s/mol.\n"", + ""Processing CHEMBL1313979 in Train_ER_alpha.smi (48/1238). Average speed: 0.07 s/mol.\n"", + ""Processing CHEMBL1322939 in Train_ER_alpha.smi (50/1238). Average speed: 0.07 s/mol.\n"", + ""Processing CHEMBL1329455 in Train_ER_alpha.smi (49/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL1480843 in Train_ER_alpha.smi (51/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL1341057 in Train_ER_alpha.smi (54/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL442037 in Train_ER_alpha.smi (57/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL124474 in Train_ER_alpha.smi (59/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL1081361 in Train_ER_alpha.smi (66/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL361762 in Train_ER_alpha.smi (69/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL188458 in Train_ER_alpha.smi (71/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL396118 in Train_ER_alpha.smi (74/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL469512 in Train_ER_alpha.smi (82/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL377171 in Train_ER_alpha.smi (86/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL380362 in Train_ER_alpha.smi (90/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL196836 in Train_ER_alpha.smi (94/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL206336 in Train_ER_alpha.smi (100/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL191300 in Train_ER_alpha.smi (105/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL447837 in Train_ER_alpha.smi (106/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL191710 in Train_ER_alpha.smi (109/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL212419 in Train_ER_alpha.smi (112/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL185231 in Train_ER_alpha.smi (117/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL197495 in Train_ER_alpha.smi (124/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL182402 in Train_ER_alpha.smi (129/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL360315 in Train_ER_alpha.smi (132/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL204222 in Train_ER_alpha.smi (138/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL384709 in Train_ER_alpha.smi (141/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL234733 in Train_ER_alpha.smi (146/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL395308 in Train_ER_alpha.smi (151/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL394788 in Train_ER_alpha.smi (152/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL195208 in Train_ER_alpha.smi (155/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL366928 in Train_ER_alpha.smi (160/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL225349 in Train_ER_alpha.smi (169/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL373743 in Train_ER_alpha.smi (171/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL418327 in Train_ER_alpha.smi (175/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL439806 in Train_ER_alpha.smi (179/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL389683 in Train_ER_alpha.smi (185/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL360802 in Train_ER_alpha.smi (189/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL381298 in Train_ER_alpha.smi (195/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL385279 in Train_ER_alpha.smi (199/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL85826 in Train_ER_alpha.smi (201/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL441499 in Train_ER_alpha.smi (207/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL206584 in Train_ER_alpha.smi (212/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL383533 in Train_ER_alpha.smi (215/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL496 in Train_ER_alpha.smi (221/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL434525 in Train_ER_alpha.smi (222/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL198411 in Train_ER_alpha.smi (224/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL197667 in Train_ER_alpha.smi (226/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL206440 in Train_ER_alpha.smi (228/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL380844 in Train_ER_alpha.smi (229/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL206406 in Train_ER_alpha.smi (231/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL204922 in Train_ER_alpha.smi (234/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL211816 in Train_ER_alpha.smi (237/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL188792 in Train_ER_alpha.smi (242/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL381337 in Train_ER_alpha.smi (246/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL203351 in Train_ER_alpha.smi (249/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL1420411 in Train_ER_alpha.smi (55/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL427496 in Train_ER_alpha.smi (63/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL184371 in Train_ER_alpha.smi (70/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL365625 in Train_ER_alpha.smi (77/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL470993 in Train_ER_alpha.smi (80/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL470785 in Train_ER_alpha.smi (83/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL385680 in Train_ER_alpha.smi (87/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL471626 in Train_ER_alpha.smi (92/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL194326 in Train_ER_alpha.smi (96/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL425231 in Train_ER_alpha.smi (99/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL206499 in Train_ER_alpha.smi (103/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL187314 in Train_ER_alpha.smi (107/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL200719 in Train_ER_alpha.smi (111/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL204024 in Train_ER_alpha.smi (115/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL382734 in Train_ER_alpha.smi (119/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL372808 in Train_ER_alpha.smi (123/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL241304 in Train_ER_alpha.smi (126/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL188391 in Train_ER_alpha.smi (130/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL371777 in Train_ER_alpha.smi (133/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL195786 in Train_ER_alpha.smi (135/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL204119 in Train_ER_alpha.smi (137/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL441366 in Train_ER_alpha.smi (140/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL180707 in Train_ER_alpha.smi (143/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL245133 in Train_ER_alpha.smi (153/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL234527 in Train_ER_alpha.smi (154/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL435090 in Train_ER_alpha.smi (158/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL195878 in Train_ER_alpha.smi (163/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL365293 in Train_ER_alpha.smi (165/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL183086 in Train_ER_alpha.smi (166/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL451496 in Train_ER_alpha.smi (172/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL186643 in Train_ER_alpha.smi (173/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL181248 in Train_ER_alpha.smi (177/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL363705 in Train_ER_alpha.smi (182/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL373095 in Train_ER_alpha.smi (183/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL225022 in Train_ER_alpha.smi (187/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL389393 in Train_ER_alpha.smi (188/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL192154 in Train_ER_alpha.smi (192/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL377342 in Train_ER_alpha.smi (194/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL202615 in Train_ER_alpha.smi (197/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL187915 in Train_ER_alpha.smi (203/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL364092 in Train_ER_alpha.smi (204/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL183084 in Train_ER_alpha.smi (208/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL381299 in Train_ER_alpha.smi (211/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL385358 in Train_ER_alpha.smi (214/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL240227 in Train_ER_alpha.smi (218/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL381697 in Train_ER_alpha.smi (233/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL206227 in Train_ER_alpha.smi (236/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL209623 in Train_ER_alpha.smi (239/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL203442 in Train_ER_alpha.smi (247/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL1535728 in Train_ER_alpha.smi (53/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL512579 in Train_ER_alpha.smi (58/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL231811 in Train_ER_alpha.smi (60/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL447111 in Train_ER_alpha.smi (62/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL226269 in Train_ER_alpha.smi (65/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL235497 in Train_ER_alpha.smi (68/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL59438 in Train_ER_alpha.smi (75/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL150279 in Train_ER_alpha.smi (76/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL365016 in Train_ER_alpha.smi (78/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL511269 in Train_ER_alpha.smi (81/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL577938 in Train_ER_alpha.smi (84/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL204902 in Train_ER_alpha.smi (88/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL206547 in Train_ER_alpha.smi (91/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL25228 in Train_ER_alpha.smi (95/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL203330 in Train_ER_alpha.smi (97/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL205137 in Train_ER_alpha.smi (101/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL429446 in Train_ER_alpha.smi (104/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL426085 in Train_ER_alpha.smi (110/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL211493 in Train_ER_alpha.smi (113/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL183333 in Train_ER_alpha.smi (116/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL195807 in Train_ER_alpha.smi (120/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL198914 in Train_ER_alpha.smi (122/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL437190 in Train_ER_alpha.smi (128/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL126164 in Train_ER_alpha.smi (134/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL212392 in Train_ER_alpha.smi (139/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL438516 in Train_ER_alpha.smi (145/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL379188 in Train_ER_alpha.smi (148/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL192102 in Train_ER_alpha.smi (150/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL8145 in Train_ER_alpha.smi (156/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL192068 in Train_ER_alpha.smi (161/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL360651 in Train_ER_alpha.smi (162/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL182237 in Train_ER_alpha.smi (164/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL187207 in Train_ER_alpha.smi (176/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL183371 in Train_ER_alpha.smi (178/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL202996 in Train_ER_alpha.smi (181/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL185480 in Train_ER_alpha.smi (184/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL332625 in Train_ER_alpha.smi (190/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL204025 in Train_ER_alpha.smi (196/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL219387 in Train_ER_alpha.smi (200/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL193726 in Train_ER_alpha.smi (205/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL192218 in Train_ER_alpha.smi (206/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL187000 in Train_ER_alpha.smi (209/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL369979 in Train_ER_alpha.smi (210/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL241303 in Train_ER_alpha.smi (217/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL362428 in Train_ER_alpha.smi (220/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL365999 in Train_ER_alpha.smi (223/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL186945 in Train_ER_alpha.smi (225/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL382632 in Train_ER_alpha.smi (227/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL204056 in Train_ER_alpha.smi (232/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL206600 in Train_ER_alpha.smi (235/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL211887 in Train_ER_alpha.smi (240/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL206546 in Train_ER_alpha.smi (244/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL206814 in Train_ER_alpha.smi (248/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL1310614 in Train_ER_alpha.smi (52/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL184372 in Train_ER_alpha.smi (56/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL385801 in Train_ER_alpha.smi (61/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL225404 in Train_ER_alpha.smi (64/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL235496 in Train_ER_alpha.smi (67/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL361272 in Train_ER_alpha.smi (72/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL241257 in Train_ER_alpha.smi (73/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL179929 in Train_ER_alpha.smi (79/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL185114 in Train_ER_alpha.smi (85/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL206727 in Train_ER_alpha.smi (89/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL511492 in Train_ER_alpha.smi (93/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL206648 in Train_ER_alpha.smi (98/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL425941 in Train_ER_alpha.smi (102/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL371308 in Train_ER_alpha.smi (108/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL377475 in Train_ER_alpha.smi (114/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL378473 in Train_ER_alpha.smi (118/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL212185 in Train_ER_alpha.smi (121/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL191901 in Train_ER_alpha.smi (125/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL241485 in Train_ER_alpha.smi (127/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL184598 in Train_ER_alpha.smi (131/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL219134 in Train_ER_alpha.smi (136/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL189080 in Train_ER_alpha.smi (142/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL362272 in Train_ER_alpha.smi (144/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL206441 in Train_ER_alpha.smi (147/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL425439 in Train_ER_alpha.smi (149/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL195437 in Train_ER_alpha.smi (157/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1201151 in Train_ER_alpha.smi (159/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL426641 in Train_ER_alpha.smi (167/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL374799 in Train_ER_alpha.smi (168/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL222434 in Train_ER_alpha.smi (170/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL362718 in Train_ER_alpha.smi (174/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL121879 in Train_ER_alpha.smi (180/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL449038 in Train_ER_alpha.smi (186/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL194936 in Train_ER_alpha.smi (191/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL369991 in Train_ER_alpha.smi (193/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL201013 in Train_ER_alpha.smi (198/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL195371 in Train_ER_alpha.smi (202/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL212923 in Train_ER_alpha.smi (213/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL219577 in Train_ER_alpha.smi (216/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL393278 in Train_ER_alpha.smi (219/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL383753 in Train_ER_alpha.smi (230/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL208929 in Train_ER_alpha.smi (238/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL191714 in Train_ER_alpha.smi (241/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL204819 in Train_ER_alpha.smi (243/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL205934 in Train_ER_alpha.smi (245/1238). Average speed: 0.03 s/mol.\n"" + ] + }, + { + ""name"": ""stdout"", + ""output_type"": ""stream"", + ""text"": [ + ""Processing CHEMBL191238 in Train_ER_alpha.smi (251/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL185083 in Train_ER_alpha.smi (256/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL239938 in Train_ER_alpha.smi (262/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL361751 in Train_ER_alpha.smi (268/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL196530 in Train_ER_alpha.smi (273/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1491936 in Train_ER_alpha.smi (275/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1414254 in Train_ER_alpha.smi (280/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1627356 in Train_ER_alpha.smi (282/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1328083 in Train_ER_alpha.smi (290/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1383151 in Train_ER_alpha.smi (295/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1390963 in Train_ER_alpha.smi (301/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL294889 in Train_ER_alpha.smi (304/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1469865 in Train_ER_alpha.smi (309/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL363498 in Train_ER_alpha.smi (314/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1392188 in Train_ER_alpha.smi (315/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL215409 in Train_ER_alpha.smi (318/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL203348 in Train_ER_alpha.smi (322/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL426626 in Train_ER_alpha.smi (325/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL225081 in Train_ER_alpha.smi (327/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1368345 in Train_ER_alpha.smi (330/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL362966 in Train_ER_alpha.smi (332/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL380359 in Train_ER_alpha.smi (334/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL183367 in Train_ER_alpha.smi (339/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL364345 in Train_ER_alpha.smi (343/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL472468 in Train_ER_alpha.smi (348/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL204970 in Train_ER_alpha.smi (353/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL425092 in Train_ER_alpha.smi (357/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL377644 in Train_ER_alpha.smi (361/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL232023 in Train_ER_alpha.smi (364/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL511812 in Train_ER_alpha.smi (366/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL25580 in Train_ER_alpha.smi (376/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1509453 in Train_ER_alpha.smi (378/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL221515 in Train_ER_alpha.smi (381/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL374277 in Train_ER_alpha.smi (383/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL203724 in Train_ER_alpha.smi (385/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL365941 in Train_ER_alpha.smi (390/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL360978 in Train_ER_alpha.smi (392/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL241300 in Train_ER_alpha.smi (400/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL393179 in Train_ER_alpha.smi (402/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1464457 in Train_ER_alpha.smi (408/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1531555 in Train_ER_alpha.smi (412/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1538223 in Train_ER_alpha.smi (414/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL327320 in Train_ER_alpha.smi (416/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL363634 in Train_ER_alpha.smi (254/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL362793 in Train_ER_alpha.smi (257/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL373987 in Train_ER_alpha.smi (259/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL389907 in Train_ER_alpha.smi (264/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL360410 in Train_ER_alpha.smi (272/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1570364 in Train_ER_alpha.smi (274/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1487772 in Train_ER_alpha.smi (276/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL463804 in Train_ER_alpha.smi (279/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1403999 in Train_ER_alpha.smi (281/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1587920 in Train_ER_alpha.smi (287/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL150924 in Train_ER_alpha.smi (291/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1465370 in Train_ER_alpha.smi (294/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL192876 in Train_ER_alpha.smi (298/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL363017 in Train_ER_alpha.smi (299/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL184151 in Train_ER_alpha.smi (305/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL63768 in Train_ER_alpha.smi (307/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL196283 in Train_ER_alpha.smi (312/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL183368 in Train_ER_alpha.smi (317/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL379566 in Train_ER_alpha.smi (320/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL180873 in Train_ER_alpha.smi (324/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL379213 in Train_ER_alpha.smi (333/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL207101 in Train_ER_alpha.smi (335/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL193727 in Train_ER_alpha.smi (338/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1395826 in Train_ER_alpha.smi (340/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1334930 in Train_ER_alpha.smi (346/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL209402 in Train_ER_alpha.smi (352/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1303886 in Train_ER_alpha.smi (355/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL380469 in Train_ER_alpha.smi (358/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1340020 in Train_ER_alpha.smi (365/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1606765 in Train_ER_alpha.smi (369/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL347560 in Train_ER_alpha.smi (373/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1326210 in Train_ER_alpha.smi (380/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL379889 in Train_ER_alpha.smi (384/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL368449 in Train_ER_alpha.smi (388/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL200232 in Train_ER_alpha.smi (396/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL202673 in Train_ER_alpha.smi (398/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL442249 in Train_ER_alpha.smi (403/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1359893 in Train_ER_alpha.smi (406/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1575187 in Train_ER_alpha.smi (410/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL153405 in Train_ER_alpha.smi (415/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL180973 in Train_ER_alpha.smi (418/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL364502 in Train_ER_alpha.smi (252/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL185890 in Train_ER_alpha.smi (255/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL187473 in Train_ER_alpha.smi (258/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL213950 in Train_ER_alpha.smi (260/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL187115 in Train_ER_alpha.smi (265/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL182850 in Train_ER_alpha.smi (267/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL188189 in Train_ER_alpha.smi (269/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL316003 in Train_ER_alpha.smi (271/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL359633 in Train_ER_alpha.smi (278/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1541779 in Train_ER_alpha.smi (285/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1399039 in Train_ER_alpha.smi (288/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL184003 in Train_ER_alpha.smi (292/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1511067 in Train_ER_alpha.smi (297/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL340111 in Train_ER_alpha.smi (302/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1504315 in Train_ER_alpha.smi (306/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL380188 in Train_ER_alpha.smi (310/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1577029 in Train_ER_alpha.smi (316/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL384268 in Train_ER_alpha.smi (321/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL191711 in Train_ER_alpha.smi (326/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL188900 in Train_ER_alpha.smi (331/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL385693 in Train_ER_alpha.smi (336/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1386105 in Train_ER_alpha.smi (342/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL371126 in Train_ER_alpha.smi (344/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL363902 in Train_ER_alpha.smi (347/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL194718 in Train_ER_alpha.smi (350/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL203472 in Train_ER_alpha.smi (354/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL376986 in Train_ER_alpha.smi (359/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL208978 in Train_ER_alpha.smi (360/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL249903 in Train_ER_alpha.smi (362/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL189356 in Train_ER_alpha.smi (368/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1426947 in Train_ER_alpha.smi (371/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1453259 in Train_ER_alpha.smi (372/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL364110 in Train_ER_alpha.smi (374/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1482201 in Train_ER_alpha.smi (379/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1318094 in Train_ER_alpha.smi (387/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL191759 in Train_ER_alpha.smi (389/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL392029 in Train_ER_alpha.smi (393/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL199599 in Train_ER_alpha.smi (395/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL203072 in Train_ER_alpha.smi (399/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL238959 in Train_ER_alpha.smi (404/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1403448 in Train_ER_alpha.smi (409/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1521410 in Train_ER_alpha.smi (413/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL382698 in Train_ER_alpha.smi (250/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL331996 in Train_ER_alpha.smi (253/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL290106 in Train_ER_alpha.smi (261/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL391619 in Train_ER_alpha.smi (263/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL363633 in Train_ER_alpha.smi (266/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL196742 in Train_ER_alpha.smi (270/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL51483 in Train_ER_alpha.smi (277/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL319244 in Train_ER_alpha.smi (283/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1572403 in Train_ER_alpha.smi (284/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1458486 in Train_ER_alpha.smi (286/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1332402 in Train_ER_alpha.smi (289/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL182751 in Train_ER_alpha.smi (293/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1407483 in Train_ER_alpha.smi (296/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1428161 in Train_ER_alpha.smi (300/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL185783 in Train_ER_alpha.smi (303/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1564209 in Train_ER_alpha.smi (308/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL214693 in Train_ER_alpha.smi (311/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL195154 in Train_ER_alpha.smi (313/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL211837 in Train_ER_alpha.smi (319/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL371072 in Train_ER_alpha.smi (323/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL231812 in Train_ER_alpha.smi (328/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL182887 in Train_ER_alpha.smi (329/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL192765 in Train_ER_alpha.smi (337/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1434189 in Train_ER_alpha.smi (341/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1324414 in Train_ER_alpha.smi (345/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL189024 in Train_ER_alpha.smi (349/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL209803 in Train_ER_alpha.smi (351/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL246341 in Train_ER_alpha.smi (356/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL398634 in Train_ER_alpha.smi (363/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL191836 in Train_ER_alpha.smi (367/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1348377 in Train_ER_alpha.smi (370/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL189025 in Train_ER_alpha.smi (375/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1604500 in Train_ER_alpha.smi (377/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL389394 in Train_ER_alpha.smi (382/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL226379 in Train_ER_alpha.smi (386/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL191808 in Train_ER_alpha.smi (391/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL200483 in Train_ER_alpha.smi (394/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL201517 in Train_ER_alpha.smi (397/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL428732 in Train_ER_alpha.smi (401/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1410824 in Train_ER_alpha.smi (405/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1427643 in Train_ER_alpha.smi (407/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1548231 in Train_ER_alpha.smi (411/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL178559 in Train_ER_alpha.smi (417/1238). Average speed: 0.03 s/mol.\r\n"" + ] + }, + { + ""name"": ""stdout"", + ""output_type"": ""stream"", + ""text"": [ + ""Processing CHEMBL1386676 in Train_ER_alpha.smi (421/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1580036 in Train_ER_alpha.smi (423/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL188619 in Train_ER_alpha.smi (425/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL186513 in Train_ER_alpha.smi (429/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1495194 in Train_ER_alpha.smi (430/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL2079587 in Train_ER_alpha.smi (433/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1501269 in Train_ER_alpha.smi (440/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1544241 in Train_ER_alpha.smi (443/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL145576 in Train_ER_alpha.smi (446/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL371624 in Train_ER_alpha.smi (452/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL187087 in Train_ER_alpha.smi (457/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL450940 in Train_ER_alpha.smi (461/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL380876 in Train_ER_alpha.smi (465/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL183152 in Train_ER_alpha.smi (470/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL370849 in Train_ER_alpha.smi (472/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1337826 in Train_ER_alpha.smi (477/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL198679 in Train_ER_alpha.smi (480/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL334096 in Train_ER_alpha.smi (483/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL213051 in Train_ER_alpha.smi (488/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL378675 in Train_ER_alpha.smi (491/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL216279 in Train_ER_alpha.smi (494/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL209429 in Train_ER_alpha.smi (497/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL363425 in Train_ER_alpha.smi (504/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1116 in Train_ER_alpha.smi (506/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL211870 in Train_ER_alpha.smi (512/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL187937 in Train_ER_alpha.smi (513/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL363955 in Train_ER_alpha.smi (520/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL381720 in Train_ER_alpha.smi (523/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL202894 in Train_ER_alpha.smi (524/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL238960 in Train_ER_alpha.smi (530/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL437695 in Train_ER_alpha.smi (534/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL388418 in Train_ER_alpha.smi (539/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1475062 in Train_ER_alpha.smi (542/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1362841 in Train_ER_alpha.smi (545/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1548361 in Train_ER_alpha.smi (547/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1449885 in Train_ER_alpha.smi (551/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1472407 in Train_ER_alpha.smi (554/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1587260 in Train_ER_alpha.smi (559/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL250109 in Train_ER_alpha.smi (563/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL361226 in Train_ER_alpha.smi (569/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL189822 in Train_ER_alpha.smi (572/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL192782 in Train_ER_alpha.smi (573/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL362307 in Train_ER_alpha.smi (575/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL190038 in Train_ER_alpha.smi (580/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL189303 in Train_ER_alpha.smi (581/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL178665 in Train_ER_alpha.smi (583/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL362349 in Train_ER_alpha.smi (587/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1627353 in Train_ER_alpha.smi (590/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL427180 in Train_ER_alpha.smi (594/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL369545 in Train_ER_alpha.smi (598/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL418971 in Train_ER_alpha.smi (605/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL180702 in Train_ER_alpha.smi (608/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL189658 in Train_ER_alpha.smi (424/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1522300 in Train_ER_alpha.smi (427/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL180253 in Train_ER_alpha.smi (434/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1560367 in Train_ER_alpha.smi (441/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL186597 in Train_ER_alpha.smi (445/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL181369 in Train_ER_alpha.smi (449/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL189200 in Train_ER_alpha.smi (455/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL200021 in Train_ER_alpha.smi (458/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL201569 in Train_ER_alpha.smi (464/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL185918 in Train_ER_alpha.smi (475/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1481770 in Train_ER_alpha.smi (479/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL186598 in Train_ER_alpha.smi (485/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL180146 in Train_ER_alpha.smi (486/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1402 in Train_ER_alpha.smi (492/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL381566 in Train_ER_alpha.smi (496/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL187069 in Train_ER_alpha.smi (503/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL597498 in Train_ER_alpha.smi (505/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL379595 in Train_ER_alpha.smi (511/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL205350 in Train_ER_alpha.smi (515/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL206582 in Train_ER_alpha.smi (517/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL206764 in Train_ER_alpha.smi (521/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL379008 in Train_ER_alpha.smi (527/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL180176 in Train_ER_alpha.smi (533/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1485005 in Train_ER_alpha.smi (536/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1327977 in Train_ER_alpha.smi (538/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1433745 in Train_ER_alpha.smi (541/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1429150 in Train_ER_alpha.smi (546/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1584646 in Train_ER_alpha.smi (552/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1448466 in Train_ER_alpha.smi (555/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1367590 in Train_ER_alpha.smi (558/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1328182 in Train_ER_alpha.smi (562/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL364329 in Train_ER_alpha.smi (568/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL361063 in Train_ER_alpha.smi (571/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL192901 in Train_ER_alpha.smi (574/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1479 in Train_ER_alpha.smi (577/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL435696 in Train_ER_alpha.smi (584/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL184974 in Train_ER_alpha.smi (589/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL195788 in Train_ER_alpha.smi (596/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL361078 in Train_ER_alpha.smi (599/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1200623 in Train_ER_alpha.smi (601/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL188051 in Train_ER_alpha.smi (603/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL103650 in Train_ER_alpha.smi (609/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1528123 in Train_ER_alpha.smi (419/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL144431 in Train_ER_alpha.smi (422/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1525824 in Train_ER_alpha.smi (426/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1548186 in Train_ER_alpha.smi (432/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL190104 in Train_ER_alpha.smi (436/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1548834 in Train_ER_alpha.smi (437/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL360498 in Train_ER_alpha.smi (439/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL361258 in Train_ER_alpha.smi (447/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL57150 in Train_ER_alpha.smi (450/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL188613 in Train_ER_alpha.smi (453/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL186073 in Train_ER_alpha.smi (456/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL363963 in Train_ER_alpha.smi (460/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL188410 in Train_ER_alpha.smi (463/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL377694 in Train_ER_alpha.smi (467/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL362020 in Train_ER_alpha.smi (469/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL185126 in Train_ER_alpha.smi (474/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL264216 in Train_ER_alpha.smi (476/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL371009 in Train_ER_alpha.smi (482/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL192539 in Train_ER_alpha.smi (484/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL377848 in Train_ER_alpha.smi (489/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL369935 in Train_ER_alpha.smi (493/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL379109 in Train_ER_alpha.smi (498/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL426854 in Train_ER_alpha.smi (500/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL179518 in Train_ER_alpha.smi (502/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL211800 in Train_ER_alpha.smi (509/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL204768 in Train_ER_alpha.smi (514/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL362355 in Train_ER_alpha.smi (518/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL206131 in Train_ER_alpha.smi (522/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL206773 in Train_ER_alpha.smi (526/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL191841 in Train_ER_alpha.smi (529/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL238961 in Train_ER_alpha.smi (531/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1521111 in Train_ER_alpha.smi (537/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL396948 in Train_ER_alpha.smi (540/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL471627 in Train_ER_alpha.smi (544/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL375334 in Train_ER_alpha.smi (548/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL222580 in Train_ER_alpha.smi (550/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1454402 in Train_ER_alpha.smi (556/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1461574 in Train_ER_alpha.smi (561/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL194772 in Train_ER_alpha.smi (565/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL362425 in Train_ER_alpha.smi (566/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL104416 in Train_ER_alpha.smi (578/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1627354 in Train_ER_alpha.smi (582/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL954 in Train_ER_alpha.smi (588/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL372304 in Train_ER_alpha.smi (593/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL194265 in Train_ER_alpha.smi (595/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL183344 in Train_ER_alpha.smi (597/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL184367 in Train_ER_alpha.smi (606/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1536373 in Train_ER_alpha.smi (420/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL193064 in Train_ER_alpha.smi (428/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1606490 in Train_ER_alpha.smi (431/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL93705 in Train_ER_alpha.smi (435/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL188527 in Train_ER_alpha.smi (438/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1427879 in Train_ER_alpha.smi (442/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL577758 in Train_ER_alpha.smi (444/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL363367 in Train_ER_alpha.smi (448/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL433944 in Train_ER_alpha.smi (451/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL371788 in Train_ER_alpha.smi (454/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL193010 in Train_ER_alpha.smi (459/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL365045 in Train_ER_alpha.smi (462/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL441537 in Train_ER_alpha.smi (466/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL192386 in Train_ER_alpha.smi (468/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL192587 in Train_ER_alpha.smi (471/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL178679 in Train_ER_alpha.smi (473/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1480378 in Train_ER_alpha.smi (478/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL121978 in Train_ER_alpha.smi (481/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL377224 in Train_ER_alpha.smi (487/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL264349 in Train_ER_alpha.smi (490/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL219126 in Train_ER_alpha.smi (495/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL379821 in Train_ER_alpha.smi (499/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL202896 in Train_ER_alpha.smi (501/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL206226 in Train_ER_alpha.smi (507/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL378990 in Train_ER_alpha.smi (508/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL379567 in Train_ER_alpha.smi (510/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL380717 in Train_ER_alpha.smi (516/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL221676 in Train_ER_alpha.smi (519/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL206349 in Train_ER_alpha.smi (525/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL192016 in Train_ER_alpha.smi (528/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL184360 in Train_ER_alpha.smi (532/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1276308 in Train_ER_alpha.smi (535/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1597831 in Train_ER_alpha.smi (543/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL222522 in Train_ER_alpha.smi (549/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1506716 in Train_ER_alpha.smi (553/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1431650 in Train_ER_alpha.smi (557/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1522044 in Train_ER_alpha.smi (560/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL191716 in Train_ER_alpha.smi (564/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL363177 in Train_ER_alpha.smi (567/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL379543 in Train_ER_alpha.smi (570/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL190487 in Train_ER_alpha.smi (576/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL195991 in Train_ER_alpha.smi (579/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL371564 in Train_ER_alpha.smi (585/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1628170 in Train_ER_alpha.smi (586/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL188846 in Train_ER_alpha.smi (591/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL196131 in Train_ER_alpha.smi (592/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL187393 in Train_ER_alpha.smi (600/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL194762 in Train_ER_alpha.smi (602/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL187392 in Train_ER_alpha.smi (604/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL370282 in Train_ER_alpha.smi (607/1238). Average speed: 0.03 s/mol.\r\n"" + ] + }, + { + ""name"": ""stdout"", + ""output_type"": ""stream"", + ""text"": [ + ""Processing CHEMBL1521414 in Train_ER_alpha.smi (613/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1541552 in Train_ER_alpha.smi (619/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL183782 in Train_ER_alpha.smi (621/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1310171 in Train_ER_alpha.smi (625/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1411209 in Train_ER_alpha.smi (626/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL187673 in Train_ER_alpha.smi (632/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL153395 in Train_ER_alpha.smi (636/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1593653 in Train_ER_alpha.smi (644/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL371305 in Train_ER_alpha.smi (648/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1299357 in Train_ER_alpha.smi (653/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1433062 in Train_ER_alpha.smi (655/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL482116 in Train_ER_alpha.smi (658/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL363872 in Train_ER_alpha.smi (660/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL205920 in Train_ER_alpha.smi (662/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL200799 in Train_ER_alpha.smi (665/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL371012 in Train_ER_alpha.smi (666/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL511971 in Train_ER_alpha.smi (670/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL183044 in Train_ER_alpha.smi (675/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL203561 in Train_ER_alpha.smi (680/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL187219 in Train_ER_alpha.smi (685/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL192000 in Train_ER_alpha.smi (690/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL432454 in Train_ER_alpha.smi (693/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL232022 in Train_ER_alpha.smi (699/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL210074 in Train_ER_alpha.smi (701/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL377254 in Train_ER_alpha.smi (703/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL267431 in Train_ER_alpha.smi (707/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL414859 in Train_ER_alpha.smi (713/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL378798 in Train_ER_alpha.smi (714/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL203370 in Train_ER_alpha.smi (718/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1324290 in Train_ER_alpha.smi (723/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1498999 in Train_ER_alpha.smi (729/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1386537 in Train_ER_alpha.smi (730/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1505467 in Train_ER_alpha.smi (739/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1482875 in Train_ER_alpha.smi (742/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1370120 in Train_ER_alpha.smi (746/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1559731 in Train_ER_alpha.smi (749/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1405182 in Train_ER_alpha.smi (751/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1597805 in Train_ER_alpha.smi (755/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL386502 in Train_ER_alpha.smi (759/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL179852 in Train_ER_alpha.smi (763/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL195217 in Train_ER_alpha.smi (773/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1434556 in Train_ER_alpha.smi (775/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL361056 in Train_ER_alpha.smi (779/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL187311 in Train_ER_alpha.smi (786/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1300662 in Train_ER_alpha.smi (789/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL181936 in Train_ER_alpha.smi (610/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1322601 in Train_ER_alpha.smi (616/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1393131 in Train_ER_alpha.smi (618/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1423880 in Train_ER_alpha.smi (622/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1505283 in Train_ER_alpha.smi (629/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL189039 in Train_ER_alpha.smi (631/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL191715 in Train_ER_alpha.smi (634/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1381306 in Train_ER_alpha.smi (638/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1475434 in Train_ER_alpha.smi (641/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1449110 in Train_ER_alpha.smi (645/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL193482 in Train_ER_alpha.smi (650/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1581690 in Train_ER_alpha.smi (654/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL184421 in Train_ER_alpha.smi (661/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL362356 in Train_ER_alpha.smi (667/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL364419 in Train_ER_alpha.smi (671/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL391795 in Train_ER_alpha.smi (672/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL430395 in Train_ER_alpha.smi (674/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL211739 in Train_ER_alpha.smi (681/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL187978 in Train_ER_alpha.smi (684/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL242818 in Train_ER_alpha.smi (688/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL198445 in Train_ER_alpha.smi (692/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL393982 in Train_ER_alpha.smi (695/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL346455 in Train_ER_alpha.smi (698/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL213829 in Train_ER_alpha.smi (704/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL368688 in Train_ER_alpha.smi (709/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL46903 in Train_ER_alpha.smi (715/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL206137 in Train_ER_alpha.smi (720/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1453264 in Train_ER_alpha.smi (725/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1533166 in Train_ER_alpha.smi (726/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1452952 in Train_ER_alpha.smi (732/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1439181 in Train_ER_alpha.smi (736/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1525366 in Train_ER_alpha.smi (741/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1374573 in Train_ER_alpha.smi (744/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1548616 in Train_ER_alpha.smi (748/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1455849 in Train_ER_alpha.smi (752/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1084746 in Train_ER_alpha.smi (756/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL223026 in Train_ER_alpha.smi (760/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL433769 in Train_ER_alpha.smi (762/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL369724 in Train_ER_alpha.smi (771/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1414407 in Train_ER_alpha.smi (776/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1326483 in Train_ER_alpha.smi (781/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL188516 in Train_ER_alpha.smi (782/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL198204 in Train_ER_alpha.smi (785/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1580991 in Train_ER_alpha.smi (788/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1411687 in Train_ER_alpha.smi (611/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1491602 in Train_ER_alpha.smi (614/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1604803 in Train_ER_alpha.smi (617/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1493034 in Train_ER_alpha.smi (623/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL196583 in Train_ER_alpha.smi (628/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1532659 in Train_ER_alpha.smi (630/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL122208 in Train_ER_alpha.smi (635/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1421864 in Train_ER_alpha.smi (639/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1434961 in Train_ER_alpha.smi (642/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL340606 in Train_ER_alpha.smi (647/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1443090 in Train_ER_alpha.smi (651/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL361871 in Train_ER_alpha.smi (657/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL190682 in Train_ER_alpha.smi (659/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL226329 in Train_ER_alpha.smi (664/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL240157 in Train_ER_alpha.smi (669/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL398455 in Train_ER_alpha.smi (673/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL438256 in Train_ER_alpha.smi (677/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL213529 in Train_ER_alpha.smi (682/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL394357 in Train_ER_alpha.smi (686/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL388473 in Train_ER_alpha.smi (687/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL106773 in Train_ER_alpha.smi (689/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL202781 in Train_ER_alpha.smi (691/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL240438 in Train_ER_alpha.smi (696/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL455215 in Train_ER_alpha.smi (705/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL469510 in Train_ER_alpha.smi (708/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL125119 in Train_ER_alpha.smi (712/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1389780 in Train_ER_alpha.smi (721/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1312693 in Train_ER_alpha.smi (722/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1329686 in Train_ER_alpha.smi (728/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1393976 in Train_ER_alpha.smi (733/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1531320 in Train_ER_alpha.smi (735/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1577757 in Train_ER_alpha.smi (738/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1345810 in Train_ER_alpha.smi (743/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1356953 in Train_ER_alpha.smi (753/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL426897 in Train_ER_alpha.smi (758/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL183033 in Train_ER_alpha.smi (764/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL190025 in Train_ER_alpha.smi (765/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL198088 in Train_ER_alpha.smi (766/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1388431 in Train_ER_alpha.smi (768/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1522446 in Train_ER_alpha.smi (774/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL282575 in Train_ER_alpha.smi (778/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL9352 in Train_ER_alpha.smi (784/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL425194 in Train_ER_alpha.smi (787/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1539865 in Train_ER_alpha.smi (791/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1302802 in Train_ER_alpha.smi (612/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1341814 in Train_ER_alpha.smi (615/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1350028 in Train_ER_alpha.smi (620/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1347829 in Train_ER_alpha.smi (624/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1507788 in Train_ER_alpha.smi (627/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL197757 in Train_ER_alpha.smi (633/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL195289 in Train_ER_alpha.smi (637/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1560682 in Train_ER_alpha.smi (640/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1552932 in Train_ER_alpha.smi (643/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL192390 in Train_ER_alpha.smi (646/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL195280 in Train_ER_alpha.smi (649/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1519669 in Train_ER_alpha.smi (652/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL125362 in Train_ER_alpha.smi (656/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL185383 in Train_ER_alpha.smi (663/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL238890 in Train_ER_alpha.smi (668/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL189002 in Train_ER_alpha.smi (676/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL188957 in Train_ER_alpha.smi (678/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL204697 in Train_ER_alpha.smi (679/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL214200 in Train_ER_alpha.smi (683/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL184491 in Train_ER_alpha.smi (694/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL188246 in Train_ER_alpha.smi (697/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL206120 in Train_ER_alpha.smi (700/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL209738 in Train_ER_alpha.smi (702/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL522990 in Train_ER_alpha.smi (706/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1076306 in Train_ER_alpha.smi (710/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL198680 in Train_ER_alpha.smi (711/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL195311 in Train_ER_alpha.smi (716/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1450791 in Train_ER_alpha.smi (717/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL378572 in Train_ER_alpha.smi (719/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1377973 in Train_ER_alpha.smi (724/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1304227 in Train_ER_alpha.smi (727/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1520373 in Train_ER_alpha.smi (731/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1419810 in Train_ER_alpha.smi (734/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1484547 in Train_ER_alpha.smi (737/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1372307 in Train_ER_alpha.smi (740/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1577661 in Train_ER_alpha.smi (745/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1407054 in Train_ER_alpha.smi (747/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1555033 in Train_ER_alpha.smi (750/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1326803 in Train_ER_alpha.smi (754/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1083326 in Train_ER_alpha.smi (757/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL144897 in Train_ER_alpha.smi (761/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1576748 in Train_ER_alpha.smi (767/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1509933 in Train_ER_alpha.smi (769/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL360817 in Train_ER_alpha.smi (770/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL52 in Train_ER_alpha.smi (772/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1397845 in Train_ER_alpha.smi (777/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1491959 in Train_ER_alpha.smi (780/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL178334 in Train_ER_alpha.smi (783/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1442450 in Train_ER_alpha.smi (790/1238). Average speed: 0.03 s/mol.\r\n"" + ] + }, + { + ""name"": ""stdout"", + ""output_type"": ""stream"", + ""text"": [ + ""Processing CHEMBL182157 in Train_ER_alpha.smi (793/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL187313 in Train_ER_alpha.smi (798/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1572777 in Train_ER_alpha.smi (801/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1447311 in Train_ER_alpha.smi (804/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL204376 in Train_ER_alpha.smi (809/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1430316 in Train_ER_alpha.smi (814/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL215428 in Train_ER_alpha.smi (819/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL377457 in Train_ER_alpha.smi (822/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL398456 in Train_ER_alpha.smi (825/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL380358 in Train_ER_alpha.smi (829/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL380838 in Train_ER_alpha.smi (831/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1531512 in Train_ER_alpha.smi (836/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL214034 in Train_ER_alpha.smi (840/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL206439 in Train_ER_alpha.smi (843/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1320651 in Train_ER_alpha.smi (848/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1442697 in Train_ER_alpha.smi (852/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL206426 in Train_ER_alpha.smi (856/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL208324 in Train_ER_alpha.smi (859/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL192621 in Train_ER_alpha.smi (861/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL372411 in Train_ER_alpha.smi (868/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL199766 in Train_ER_alpha.smi (870/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL425603 in Train_ER_alpha.smi (873/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL180995 in Train_ER_alpha.smi (875/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL193146 in Train_ER_alpha.smi (882/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL182794 in Train_ER_alpha.smi (884/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL185012 in Train_ER_alpha.smi (893/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL184679 in Train_ER_alpha.smi (896/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL195290 in Train_ER_alpha.smi (902/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL186874 in Train_ER_alpha.smi (904/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL365903 in Train_ER_alpha.smi (906/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL360189 in Train_ER_alpha.smi (908/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL189077 in Train_ER_alpha.smi (911/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL178857 in Train_ER_alpha.smi (915/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL183162 in Train_ER_alpha.smi (921/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL185837 in Train_ER_alpha.smi (925/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL425246 in Train_ER_alpha.smi (927/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL377988 in Train_ER_alpha.smi (930/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL204272 in Train_ER_alpha.smi (936/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL203340 in Train_ER_alpha.smi (937/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL204118 in Train_ER_alpha.smi (940/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL205398 in Train_ER_alpha.smi (945/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL222454 in Train_ER_alpha.smi (950/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL241256 in Train_ER_alpha.smi (953/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL226268 in Train_ER_alpha.smi (960/1238). 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Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1470395 in Train_ER_alpha.smi (847/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1429596 in Train_ER_alpha.smi (850/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1603599 in Train_ER_alpha.smi (853/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL192586 in Train_ER_alpha.smi (862/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL465879 in Train_ER_alpha.smi (864/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL384708 in Train_ER_alpha.smi (865/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL204317 in Train_ER_alpha.smi (871/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL203123 in Train_ER_alpha.smi (876/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL240230 in Train_ER_alpha.smi (880/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1732228 in Train_ER_alpha.smi (888/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL189706 in Train_ER_alpha.smi (891/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1424468 in Train_ER_alpha.smi (897/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL187105 in Train_ER_alpha.smi (898/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL188528 in Train_ER_alpha.smi (900/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL364176 in Train_ER_alpha.smi (905/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL195389 in Train_ER_alpha.smi (907/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL365484 in Train_ER_alpha.smi (909/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL440757 in Train_ER_alpha.smi (914/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL182980 in Train_ER_alpha.smi (918/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1596554 in Train_ER_alpha.smi (923/1238). 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Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL222452 in Train_ER_alpha.smi (867/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL201365 in Train_ER_alpha.smi (869/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL212675 in Train_ER_alpha.smi (874/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL211599 in Train_ER_alpha.smi (877/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL214291 in Train_ER_alpha.smi (879/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL193108 in Train_ER_alpha.smi (883/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL194873 in Train_ER_alpha.smi (885/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL187671 in Train_ER_alpha.smi (886/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL188230 in Train_ER_alpha.smi (892/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL361005 in Train_ER_alpha.smi (895/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL276030 in Train_ER_alpha.smi (899/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL363412 in Train_ER_alpha.smi (903/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1627351 in Train_ER_alpha.smi (912/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL195039 in Train_ER_alpha.smi (919/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL180085 in Train_ER_alpha.smi (920/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1301994 in Train_ER_alpha.smi (924/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL234524 in Train_ER_alpha.smi (932/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL396947 in Train_ER_alpha.smi (933/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL383518 in Train_ER_alpha.smi (935/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL380720 in Train_ER_alpha.smi (938/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL577709 in Train_ER_alpha.smi (942/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL183889 in Train_ER_alpha.smi (944/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL203122 in Train_ER_alpha.smi (947/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL215593 in Train_ER_alpha.smi (951/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL396119 in Train_ER_alpha.smi (955/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL190467 in Train_ER_alpha.smi (963/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL210143 in Train_ER_alpha.smi (966/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL213070 in Train_ER_alpha.smi (970/1238). Average speed: 0.03 s/mol.\r\n"" + ] + }, + { + ""name"": ""stdout"", + ""output_type"": ""stream"", + ""text"": [ + ""Processing CHEMBL426154 in Train_ER_alpha.smi (972/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL188517 in Train_ER_alpha.smi (975/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL121720 in Train_ER_alpha.smi (978/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL192260 in Train_ER_alpha.smi (795/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL1303477 in Train_ER_alpha.smi (796/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL1320937 in Train_ER_alpha.smi (802/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL189556 in Train_ER_alpha.smi (805/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL1231453 in Train_ER_alpha.smi (808/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL460647 in Train_ER_alpha.smi (810/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL1325233 in Train_ER_alpha.smi (813/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL385949 in Train_ER_alpha.smi (817/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL214692 in Train_ER_alpha.smi (823/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL250110 in Train_ER_alpha.smi (827/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL438443 in Train_ER_alpha.smi (830/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL1300008 in Train_ER_alpha.smi (835/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL211953 in Train_ER_alpha.smi (839/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL1307301 in Train_ER_alpha.smi (844/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL1417890 in Train_ER_alpha.smi (849/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL203283 in Train_ER_alpha.smi (854/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL202738 in Train_ER_alpha.smi (857/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL434577 in Train_ER_alpha.smi (863/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL221626 in Train_ER_alpha.smi (866/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL383253 in Train_ER_alpha.smi (872/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL213457 in Train_ER_alpha.smi (878/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL394213 in Train_ER_alpha.smi (881/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL189287 in Train_ER_alpha.smi (887/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL187663 in Train_ER_alpha.smi (889/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL198089 in Train_ER_alpha.smi (890/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL122237 in Train_ER_alpha.smi (894/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL181368 in Train_ER_alpha.smi (901/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL193207 in Train_ER_alpha.smi (910/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL360338 in Train_ER_alpha.smi (913/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL188882 in Train_ER_alpha.smi (916/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL360385 in Train_ER_alpha.smi (917/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL427324 in Train_ER_alpha.smi (922/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL1466913 in Train_ER_alpha.smi (926/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL380032 in Train_ER_alpha.smi (928/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL1323006 in Train_ER_alpha.smi (931/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL205614 in Train_ER_alpha.smi (939/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL1566266 in Train_ER_alpha.smi (943/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL206286 in Train_ER_alpha.smi (948/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL391909 in Train_ER_alpha.smi (952/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL372030 in Train_ER_alpha.smi (956/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL202400 in Train_ER_alpha.smi (957/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL226327 in Train_ER_alpha.smi (961/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL208085 in Train_ER_alpha.smi (967/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL1628029 in Train_ER_alpha.smi (974/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL1377389 in Train_ER_alpha.smi (979/1238). Average speed: 0.03 s/mol.\n"", + ""Descriptor calculation completed in 25.317 secs . Average speed: 0.02 s/mol.\n"", + ""Train_ER_alpha.smi\n"", + ""Train_ER_alpha.csv\n"", + ""ExtendedFingerprinter.xml\n"", + ""ExtendedFingerprinter_\n"", + ""Processing CHEMBL370037 in Train_ER_alpha.smi (1/1238). \n"", + ""Processing CHEMBL184431 in Train_ER_alpha.smi (5/1238). \n"", + ""Processing CHEMBL189073 in Train_ER_alpha.smi (2/1238). \n"", + ""Processing CHEMBL198410 in Train_ER_alpha.smi (8/1238). \n"", + ""Processing CHEMBL1305485 in Train_ER_alpha.smi (10/1238). \n"", + ""Processing CHEMBL1570659 in Train_ER_alpha.smi (11/1238). \n"", + ""Processing CHEMBL365290 in Train_ER_alpha.smi (3/1238). \n"", + ""Processing CHEMBL124482 in Train_ER_alpha.smi (7/1238). \n"", + ""Processing CHEMBL191648 in Train_ER_alpha.smi (4/1238). \n"", + ""Processing CHEMBL364551 in Train_ER_alpha.smi (6/1238). \n"", + ""Processing CHEMBL1496978 in Train_ER_alpha.smi (9/1238). \n"", + ""Processing CHEMBL1331084 in Train_ER_alpha.smi (12/1238). Average speed: 0.58 s/mol.\n"", + ""Processing CHEMBL45068 in Train_ER_alpha.smi (14/1238). Average speed: 0.58 s/mol.\n"", + ""Processing CHEMBL1628199 in Train_ER_alpha.smi (16/1238). Average speed: 0.30 s/mol.\n"", + ""Processing CHEMBL153062 in Train_ER_alpha.smi (18/1238). Average speed: 0.16 s/mol.\n"", + ""Processing CHEMBL1468708 in Train_ER_alpha.smi (19/1238). Average speed: 0.16 s/mol.\n"", + ""Processing CHEMBL1388271 in Train_ER_alpha.smi (21/1238). Average speed: 0.15 s/mol.\n"", + ""Processing CHEMBL184448 in Train_ER_alpha.smi (13/1238). Average speed: 0.58 s/mol.\n"", + ""Processing CHEMBL434502 in Train_ER_alpha.smi (15/1238). Average speed: 0.36 s/mol.\n"", + ""Processing CHEMBL123025 in Train_ER_alpha.smi (17/1238). Average speed: 0.23 s/mol.\n"", + ""Processing CHEMBL1442416 in Train_ER_alpha.smi (20/1238). Average speed: 0.15 s/mol.\n"", + ""Processing CHEMBL1331872 in Train_ER_alpha.smi (22/1238). Average speed: 0.15 s/mol.\n"", + ""Processing CHEMBL1401227 in Train_ER_alpha.smi (23/1238). Average speed: 0.14 s/mol.\n"", + ""Processing CHEMBL1499638 in Train_ER_alpha.smi (25/1238). Average speed: 0.13 s/mol.\n"", + ""Processing CHEMBL1460126 in Train_ER_alpha.smi (24/1238). Average speed: 0.13 s/mol.\n"", + ""Processing CHEMBL1413557 in Train_ER_alpha.smi (26/1238). Average speed: 0.13 s/mol.\n"", + ""Processing CHEMBL1484998 in Train_ER_alpha.smi (27/1238). Average speed: 0.12 s/mol.\n"", + ""Processing CHEMBL1608989 in Train_ER_alpha.smi (28/1238). Average speed: 0.12 s/mol.\n"", + ""Processing CHEMBL1605393 in Train_ER_alpha.smi (31/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL1538325 in Train_ER_alpha.smi (30/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL1469743 in Train_ER_alpha.smi (29/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL1384049 in Train_ER_alpha.smi (32/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL188049 in Train_ER_alpha.smi (33/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL1489063 in Train_ER_alpha.smi (35/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL1361641 in Train_ER_alpha.smi (34/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL1343011 in Train_ER_alpha.smi (37/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL1381495 in Train_ER_alpha.smi (36/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL1613068 in Train_ER_alpha.smi (38/1238). Average speed: 0.09 s/mol.\n"", + ""Processing CHEMBL1464837 in Train_ER_alpha.smi (39/1238). Average speed: 0.09 s/mol.\n"", + ""Processing CHEMBL1349347 in Train_ER_alpha.smi (40/1238). Average speed: 0.09 s/mol.\n"", + ""Processing CHEMBL1385373 in Train_ER_alpha.smi (41/1238). Average speed: 0.09 s/mol.\n"", + ""Processing CHEMBL1573735 in Train_ER_alpha.smi (42/1238). Average speed: 0.09 s/mol.\n"", + ""Processing CHEMBL1431555 in Train_ER_alpha.smi (44/1238). Average speed: 0.08 s/mol.\n"", + ""Processing CHEMBL1565187 in Train_ER_alpha.smi (45/1238). Average speed: 0.08 s/mol.\n"", + ""Processing CHEMBL1597933 in Train_ER_alpha.smi (46/1238). Average speed: 0.08 s/mol.\n"", + ""Processing CHEMBL1455258 in Train_ER_alpha.smi (43/1238). Average speed: 0.09 s/mol.\n"", + ""Processing CHEMBL1466882 in Train_ER_alpha.smi (47/1238). Average speed: 0.08 s/mol.\n"", + ""Processing CHEMBL1313979 in Train_ER_alpha.smi (48/1238). Average speed: 0.08 s/mol.\n"", + ""Processing CHEMBL1329455 in Train_ER_alpha.smi (49/1238). Average speed: 0.08 s/mol.\n"", + ""Processing CHEMBL1322939 in Train_ER_alpha.smi (50/1238). Average speed: 0.08 s/mol.\n"" + ] + }, + { + ""name"": ""stdout"", + ""output_type"": ""stream"", + ""text"": [ + ""Processing CHEMBL1310614 in Train_ER_alpha.smi (52/1238). Average speed: 0.07 s/mol.\n"", + ""Processing CHEMBL1480843 in Train_ER_alpha.smi (51/1238). Average speed: 0.08 s/mol.\n"", + ""Processing CHEMBL1535728 in Train_ER_alpha.smi (53/1238). Average speed: 0.07 s/mol.\n"", + ""Processing CHEMBL1341057 in Train_ER_alpha.smi (54/1238). Average speed: 0.07 s/mol.\n"", + ""Processing CHEMBL184372 in Train_ER_alpha.smi (56/1238). Average speed: 0.07 s/mol.\n"", + ""Processing CHEMBL1420411 in Train_ER_alpha.smi (55/1238). Average speed: 0.07 s/mol.\n"", + ""Processing CHEMBL442037 in Train_ER_alpha.smi (57/1238). Average speed: 0.07 s/mol.\n"", + ""Processing CHEMBL512579 in Train_ER_alpha.smi (58/1238). Average speed: 0.07 s/mol.\n"", + ""Processing CHEMBL231811 in Train_ER_alpha.smi (60/1238). Average speed: 0.07 s/mol.\n"", + ""Processing CHEMBL124474 in Train_ER_alpha.smi (59/1238). Average speed: 0.07 s/mol.\n"", + ""Processing CHEMBL385801 in Train_ER_alpha.smi (61/1238). Average speed: 0.07 s/mol.\n"", + ""Processing CHEMBL447111 in Train_ER_alpha.smi (62/1238). Average speed: 0.07 s/mol.\n"", + ""Processing CHEMBL427496 in Train_ER_alpha.smi (63/1238). Average speed: 0.07 s/mol.\n"", + ""Processing CHEMBL225404 in Train_ER_alpha.smi (64/1238). Average speed: 0.07 s/mol.\n"", + ""Processing CHEMBL226269 in Train_ER_alpha.smi (65/1238). Average speed: 0.07 s/mol.\n"", + ""Processing CHEMBL1081361 in Train_ER_alpha.smi (66/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL235496 in Train_ER_alpha.smi (67/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL235497 in Train_ER_alpha.smi (68/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL361762 in Train_ER_alpha.smi (69/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL184371 in Train_ER_alpha.smi (70/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL188458 in Train_ER_alpha.smi (71/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL361272 in Train_ER_alpha.smi (72/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL241257 in Train_ER_alpha.smi (73/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL396118 in Train_ER_alpha.smi (74/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL59438 in Train_ER_alpha.smi (75/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL150279 in Train_ER_alpha.smi (76/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL365625 in Train_ER_alpha.smi (77/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL365016 in Train_ER_alpha.smi (78/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL179929 in Train_ER_alpha.smi (79/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL470993 in Train_ER_alpha.smi (80/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL511269 in Train_ER_alpha.smi (81/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL469512 in Train_ER_alpha.smi (82/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL577938 in Train_ER_alpha.smi (84/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL470785 in Train_ER_alpha.smi (83/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL385680 in Train_ER_alpha.smi (87/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL377171 in Train_ER_alpha.smi (86/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL185114 in Train_ER_alpha.smi (85/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL204902 in Train_ER_alpha.smi (88/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL206727 in Train_ER_alpha.smi (89/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL380362 in Train_ER_alpha.smi (90/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL206547 in Train_ER_alpha.smi (91/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL471626 in Train_ER_alpha.smi (92/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL511492 in Train_ER_alpha.smi (93/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL196836 in Train_ER_alpha.smi (94/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL25228 in Train_ER_alpha.smi (95/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL194326 in Train_ER_alpha.smi (96/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL203330 in Train_ER_alpha.smi (97/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL206648 in Train_ER_alpha.smi (98/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL425231 in Train_ER_alpha.smi (99/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL206336 in Train_ER_alpha.smi (100/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL205137 in Train_ER_alpha.smi (101/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL425941 in Train_ER_alpha.smi (102/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL206499 in Train_ER_alpha.smi (103/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL429446 in Train_ER_alpha.smi (104/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL191300 in Train_ER_alpha.smi (105/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL447837 in Train_ER_alpha.smi (106/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL371308 in Train_ER_alpha.smi (108/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL426085 in Train_ER_alpha.smi (110/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL187314 in Train_ER_alpha.smi (107/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL191710 in Train_ER_alpha.smi (109/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL211493 in Train_ER_alpha.smi (113/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL212419 in Train_ER_alpha.smi (112/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL200719 in Train_ER_alpha.smi (111/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL377475 in Train_ER_alpha.smi (114/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL183333 in Train_ER_alpha.smi (116/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL195807 in Train_ER_alpha.smi (120/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL185231 in Train_ER_alpha.smi (117/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL204024 in Train_ER_alpha.smi (115/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL382734 in Train_ER_alpha.smi (119/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL378473 in Train_ER_alpha.smi (118/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL212185 in Train_ER_alpha.smi (121/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL372808 in Train_ER_alpha.smi (123/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL198914 in Train_ER_alpha.smi (122/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL197495 in Train_ER_alpha.smi (124/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL191901 in Train_ER_alpha.smi (125/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL241304 in Train_ER_alpha.smi (126/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL437190 in Train_ER_alpha.smi (128/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL241485 in Train_ER_alpha.smi (127/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL182402 in Train_ER_alpha.smi (129/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL188391 in Train_ER_alpha.smi (130/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL184598 in Train_ER_alpha.smi (131/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL360315 in Train_ER_alpha.smi (132/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL371777 in Train_ER_alpha.smi (133/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL195786 in Train_ER_alpha.smi (135/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL126164 in Train_ER_alpha.smi (134/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL219134 in Train_ER_alpha.smi (136/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL204222 in Train_ER_alpha.smi (138/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL204119 in Train_ER_alpha.smi (137/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL441366 in Train_ER_alpha.smi (140/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL212392 in Train_ER_alpha.smi (139/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL362272 in Train_ER_alpha.smi (144/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL189080 in Train_ER_alpha.smi (142/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL180707 in Train_ER_alpha.smi (143/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL384709 in Train_ER_alpha.smi (141/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL234733 in Train_ER_alpha.smi (146/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL438516 in Train_ER_alpha.smi (145/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL425439 in Train_ER_alpha.smi (149/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL234527 in Train_ER_alpha.smi (154/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL435090 in Train_ER_alpha.smi (158/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL360651 in Train_ER_alpha.smi (162/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL365293 in Train_ER_alpha.smi (165/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL426641 in Train_ER_alpha.smi (167/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL225349 in Train_ER_alpha.smi (169/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL373743 in Train_ER_alpha.smi (171/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL451496 in Train_ER_alpha.smi (172/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL186643 in Train_ER_alpha.smi (173/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL439806 in Train_ER_alpha.smi (179/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL202996 in Train_ER_alpha.smi (181/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL363705 in Train_ER_alpha.smi (182/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL373095 in Train_ER_alpha.smi (183/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL225022 in Train_ER_alpha.smi (187/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL360802 in Train_ER_alpha.smi (189/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL381298 in Train_ER_alpha.smi (195/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL219387 in Train_ER_alpha.smi (200/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL195371 in Train_ER_alpha.smi (202/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL381299 in Train_ER_alpha.smi (211/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL206584 in Train_ER_alpha.smi (212/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL383533 in Train_ER_alpha.smi (215/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL362428 in Train_ER_alpha.smi (220/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL365999 in Train_ER_alpha.smi (223/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL382632 in Train_ER_alpha.smi (227/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL204922 in Train_ER_alpha.smi (234/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL209623 in Train_ER_alpha.smi (239/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL206546 in Train_ER_alpha.smi (244/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL203442 in Train_ER_alpha.smi (247/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL191238 in Train_ER_alpha.smi (251/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL185890 in Train_ER_alpha.smi (255/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL187473 in Train_ER_alpha.smi (258/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL213950 in Train_ER_alpha.smi (260/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL363633 in Train_ER_alpha.smi (266/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL188189 in Train_ER_alpha.smi (269/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL316003 in Train_ER_alpha.smi (271/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL463804 in Train_ER_alpha.smi (279/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1627356 in Train_ER_alpha.smi (282/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1332402 in Train_ER_alpha.smi (289/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1465370 in Train_ER_alpha.smi (294/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL192876 in Train_ER_alpha.smi (298/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1428161 in Train_ER_alpha.smi (300/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL294889 in Train_ER_alpha.smi (304/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1564209 in Train_ER_alpha.smi (308/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL195154 in Train_ER_alpha.smi (313/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1577029 in Train_ER_alpha.smi (316/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL384268 in Train_ER_alpha.smi (321/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL191711 in Train_ER_alpha.smi (326/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL362966 in Train_ER_alpha.smi (332/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL394788 in Train_ER_alpha.smi (152/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL195208 in Train_ER_alpha.smi (155/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL366928 in Train_ER_alpha.smi (160/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL374799 in Train_ER_alpha.smi (168/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL222434 in Train_ER_alpha.smi (170/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL418327 in Train_ER_alpha.smi (175/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL183371 in Train_ER_alpha.smi (178/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL449038 in Train_ER_alpha.smi (186/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL194936 in Train_ER_alpha.smi (191/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL192154 in Train_ER_alpha.smi (192/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL377342 in Train_ER_alpha.smi (194/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL201013 in Train_ER_alpha.smi (198/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL187915 in Train_ER_alpha.smi (203/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL193726 in Train_ER_alpha.smi (205/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL183084 in Train_ER_alpha.smi (208/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL212923 in Train_ER_alpha.smi (213/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL241303 in Train_ER_alpha.smi (217/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL434525 in Train_ER_alpha.smi (222/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL383753 in Train_ER_alpha.smi (230/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL381697 in Train_ER_alpha.smi (233/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL211816 in Train_ER_alpha.smi (237/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL204819 in Train_ER_alpha.smi (243/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL206814 in Train_ER_alpha.smi (248/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL364502 in Train_ER_alpha.smi (252/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL185083 in Train_ER_alpha.smi (256/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL391619 in Train_ER_alpha.smi (263/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL182850 in Train_ER_alpha.smi (267/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL196742 in Train_ER_alpha.smi (270/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL360410 in Train_ER_alpha.smi (272/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1570364 in Train_ER_alpha.smi (274/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL51483 in Train_ER_alpha.smi (277/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1572403 in Train_ER_alpha.smi (284/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1399039 in Train_ER_alpha.smi (288/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL150924 in Train_ER_alpha.smi (291/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL182751 in Train_ER_alpha.smi (293/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL363017 in Train_ER_alpha.smi (299/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL340111 in Train_ER_alpha.smi (302/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1469865 in Train_ER_alpha.smi (309/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL196283 in Train_ER_alpha.smi (312/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL183368 in Train_ER_alpha.smi (317/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL203348 in Train_ER_alpha.smi (322/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL180873 in Train_ER_alpha.smi (324/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL188900 in Train_ER_alpha.smi (331/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL380359 in Train_ER_alpha.smi (334/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL206441 in Train_ER_alpha.smi (147/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL395308 in Train_ER_alpha.smi (151/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL245133 in Train_ER_alpha.smi (153/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL195437 in Train_ER_alpha.smi (157/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL1201151 in Train_ER_alpha.smi (159/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL183086 in Train_ER_alpha.smi (166/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL362718 in Train_ER_alpha.smi (174/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL121879 in Train_ER_alpha.smi (180/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL389683 in Train_ER_alpha.smi (185/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL389393 in Train_ER_alpha.smi (188/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL369991 in Train_ER_alpha.smi (193/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL385279 in Train_ER_alpha.smi (199/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL85826 in Train_ER_alpha.smi (201/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL192218 in Train_ER_alpha.smi (206/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL187000 in Train_ER_alpha.smi (209/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL369979 in Train_ER_alpha.smi (210/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL219577 in Train_ER_alpha.smi (216/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL393278 in Train_ER_alpha.smi (219/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL496 in Train_ER_alpha.smi (221/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL198411 in Train_ER_alpha.smi (224/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL197667 in Train_ER_alpha.smi (226/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL380844 in Train_ER_alpha.smi (229/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL206406 in Train_ER_alpha.smi (231/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL206600 in Train_ER_alpha.smi (235/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL208929 in Train_ER_alpha.smi (238/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL191714 in Train_ER_alpha.smi (241/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL205934 in Train_ER_alpha.smi (245/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL203351 in Train_ER_alpha.smi (249/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL331996 in Train_ER_alpha.smi (253/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL373987 in Train_ER_alpha.smi (259/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL389907 in Train_ER_alpha.smi (264/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL196530 in Train_ER_alpha.smi (273/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1487772 in Train_ER_alpha.smi (276/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL359633 in Train_ER_alpha.smi (278/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL319244 in Train_ER_alpha.smi (283/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1541779 in Train_ER_alpha.smi (285/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1328083 in Train_ER_alpha.smi (290/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1383151 in Train_ER_alpha.smi (295/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1390963 in Train_ER_alpha.smi (301/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL184151 in Train_ER_alpha.smi (305/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1504315 in Train_ER_alpha.smi (306/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL380188 in Train_ER_alpha.smi (310/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL363498 in Train_ER_alpha.smi (314/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL215409 in Train_ER_alpha.smi (318/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL379566 in Train_ER_alpha.smi (320/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL426626 in Train_ER_alpha.smi (325/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL225081 in Train_ER_alpha.smi (327/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1368345 in Train_ER_alpha.smi (330/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL379213 in Train_ER_alpha.smi (333/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL379188 in Train_ER_alpha.smi (148/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL192102 in Train_ER_alpha.smi (150/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL8145 in Train_ER_alpha.smi (156/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL192068 in Train_ER_alpha.smi (161/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL195878 in Train_ER_alpha.smi (163/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL182237 in Train_ER_alpha.smi (164/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL187207 in Train_ER_alpha.smi (176/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL181248 in Train_ER_alpha.smi (177/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL185480 in Train_ER_alpha.smi (184/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL332625 in Train_ER_alpha.smi (190/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL204025 in Train_ER_alpha.smi (196/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL202615 in Train_ER_alpha.smi (197/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL364092 in Train_ER_alpha.smi (204/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL441499 in Train_ER_alpha.smi (207/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL385358 in Train_ER_alpha.smi (214/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL240227 in Train_ER_alpha.smi (218/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL186945 in Train_ER_alpha.smi (225/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL206440 in Train_ER_alpha.smi (228/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL204056 in Train_ER_alpha.smi (232/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL206227 in Train_ER_alpha.smi (236/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL211887 in Train_ER_alpha.smi (240/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL188792 in Train_ER_alpha.smi (242/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL381337 in Train_ER_alpha.smi (246/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL382698 in Train_ER_alpha.smi (250/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL363634 in Train_ER_alpha.smi (254/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL362793 in Train_ER_alpha.smi (257/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL290106 in Train_ER_alpha.smi (261/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL239938 in Train_ER_alpha.smi (262/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL187115 in Train_ER_alpha.smi (265/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL361751 in Train_ER_alpha.smi (268/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1491936 in Train_ER_alpha.smi (275/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1414254 in Train_ER_alpha.smi (280/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1403999 in Train_ER_alpha.smi (281/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1458486 in Train_ER_alpha.smi (286/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1587920 in Train_ER_alpha.smi (287/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL184003 in Train_ER_alpha.smi (292/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1407483 in Train_ER_alpha.smi (296/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1511067 in Train_ER_alpha.smi (297/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL185783 in Train_ER_alpha.smi (303/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL63768 in Train_ER_alpha.smi (307/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL214693 in Train_ER_alpha.smi (311/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1392188 in Train_ER_alpha.smi (315/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL211837 in Train_ER_alpha.smi (319/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL371072 in Train_ER_alpha.smi (323/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL231812 in Train_ER_alpha.smi (328/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL182887 in Train_ER_alpha.smi (329/1238). Average speed: 0.04 s/mol.\n"" + ] + }, + { + ""name"": ""stdout"", + ""output_type"": ""stream"", + ""text"": [ + ""Processing CHEMBL385693 in Train_ER_alpha.smi (336/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL1434189 in Train_ER_alpha.smi (341/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL371126 in Train_ER_alpha.smi (344/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL472468 in Train_ER_alpha.smi (348/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL203472 in Train_ER_alpha.smi (354/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL376986 in Train_ER_alpha.smi (359/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL249903 in Train_ER_alpha.smi (362/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL511812 in Train_ER_alpha.smi (366/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL347560 in Train_ER_alpha.smi (373/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL1326210 in Train_ER_alpha.smi (380/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL368449 in Train_ER_alpha.smi (388/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL392029 in Train_ER_alpha.smi (393/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL200232 in Train_ER_alpha.smi (396/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL203072 in Train_ER_alpha.smi (399/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL442249 in Train_ER_alpha.smi (403/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1427643 in Train_ER_alpha.smi (407/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1531555 in Train_ER_alpha.smi (412/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1521410 in Train_ER_alpha.smi (413/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1536373 in Train_ER_alpha.smi (420/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL189658 in Train_ER_alpha.smi (424/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1525824 in Train_ER_alpha.smi (426/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1548834 in Train_ER_alpha.smi (437/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL360498 in Train_ER_alpha.smi (439/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL186597 in Train_ER_alpha.smi (445/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL363367 in Train_ER_alpha.smi (448/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL371624 in Train_ER_alpha.smi (452/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL189200 in Train_ER_alpha.smi (455/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL365045 in Train_ER_alpha.smi (462/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL380876 in Train_ER_alpha.smi (465/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL370849 in Train_ER_alpha.smi (472/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1480378 in Train_ER_alpha.smi (478/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL198679 in Train_ER_alpha.smi (480/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL371009 in Train_ER_alpha.smi (482/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL186598 in Train_ER_alpha.smi (485/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL213051 in Train_ER_alpha.smi (488/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1402 in Train_ER_alpha.smi (492/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL209429 in Train_ER_alpha.smi (497/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL379821 in Train_ER_alpha.smi (499/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL597498 in Train_ER_alpha.smi (505/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL379595 in Train_ER_alpha.smi (511/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL187937 in Train_ER_alpha.smi (513/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL192765 in Train_ER_alpha.smi (337/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL1395826 in Train_ER_alpha.smi (340/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL363902 in Train_ER_alpha.smi (347/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL194718 in Train_ER_alpha.smi (350/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL204970 in Train_ER_alpha.smi (353/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL425092 in Train_ER_alpha.smi (357/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL377644 in Train_ER_alpha.smi (361/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL232023 in Train_ER_alpha.smi (364/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL1340020 in Train_ER_alpha.smi (365/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL1453259 in Train_ER_alpha.smi (372/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL25580 in Train_ER_alpha.smi (376/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL221515 in Train_ER_alpha.smi (381/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL379889 in Train_ER_alpha.smi (384/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1318094 in Train_ER_alpha.smi (387/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL191808 in Train_ER_alpha.smi (391/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL199599 in Train_ER_alpha.smi (395/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL202673 in Train_ER_alpha.smi (398/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL241300 in Train_ER_alpha.smi (400/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL238959 in Train_ER_alpha.smi (404/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1575187 in Train_ER_alpha.smi (410/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL153405 in Train_ER_alpha.smi (415/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL180973 in Train_ER_alpha.smi (418/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL188619 in Train_ER_alpha.smi (425/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL186513 in Train_ER_alpha.smi (429/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1495194 in Train_ER_alpha.smi (430/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL2079587 in Train_ER_alpha.smi (433/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL188527 in Train_ER_alpha.smi (438/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1501269 in Train_ER_alpha.smi (440/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL145576 in Train_ER_alpha.smi (446/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL57150 in Train_ER_alpha.smi (450/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL186073 in Train_ER_alpha.smi (456/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL363963 in Train_ER_alpha.smi (460/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL377694 in Train_ER_alpha.smi (467/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL192386 in Train_ER_alpha.smi (468/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL192587 in Train_ER_alpha.smi (471/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL185126 in Train_ER_alpha.smi (474/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL185918 in Train_ER_alpha.smi (475/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1337826 in Train_ER_alpha.smi (477/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL121978 in Train_ER_alpha.smi (481/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL180146 in Train_ER_alpha.smi (486/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL378675 in Train_ER_alpha.smi (491/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL381566 in Train_ER_alpha.smi (496/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL202896 in Train_ER_alpha.smi (501/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL179518 in Train_ER_alpha.smi (502/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL379567 in Train_ER_alpha.smi (510/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL204768 in Train_ER_alpha.smi (514/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL207101 in Train_ER_alpha.smi (335/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL183367 in Train_ER_alpha.smi (339/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL364345 in Train_ER_alpha.smi (343/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL1324414 in Train_ER_alpha.smi (345/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL189024 in Train_ER_alpha.smi (349/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL209402 in Train_ER_alpha.smi (352/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL1303886 in Train_ER_alpha.smi (355/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL380469 in Train_ER_alpha.smi (358/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL398634 in Train_ER_alpha.smi (363/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL191836 in Train_ER_alpha.smi (367/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL1426947 in Train_ER_alpha.smi (371/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL189025 in Train_ER_alpha.smi (375/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL1509453 in Train_ER_alpha.smi (378/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1482201 in Train_ER_alpha.smi (379/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL374277 in Train_ER_alpha.smi (383/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL226379 in Train_ER_alpha.smi (386/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL200483 in Train_ER_alpha.smi (394/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL201517 in Train_ER_alpha.smi (397/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL393179 in Train_ER_alpha.smi (402/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1464457 in Train_ER_alpha.smi (408/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1548231 in Train_ER_alpha.smi (411/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL327320 in Train_ER_alpha.smi (416/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1528123 in Train_ER_alpha.smi (419/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL144431 in Train_ER_alpha.smi (422/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL193064 in Train_ER_alpha.smi (428/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1606490 in Train_ER_alpha.smi (431/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL93705 in Train_ER_alpha.smi (435/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL190104 in Train_ER_alpha.smi (436/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1560367 in Train_ER_alpha.smi (441/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL361258 in Train_ER_alpha.smi (447/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL433944 in Train_ER_alpha.smi (451/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL188613 in Train_ER_alpha.smi (453/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL187087 in Train_ER_alpha.smi (457/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL193010 in Train_ER_alpha.smi (459/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL450940 in Train_ER_alpha.smi (461/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL201569 in Train_ER_alpha.smi (464/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL183152 in Train_ER_alpha.smi (470/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL178679 in Train_ER_alpha.smi (473/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1481770 in Train_ER_alpha.smi (479/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL192539 in Train_ER_alpha.smi (484/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL377848 in Train_ER_alpha.smi (489/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL369935 in Train_ER_alpha.smi (493/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL219126 in Train_ER_alpha.smi (495/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL426854 in Train_ER_alpha.smi (500/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL187069 in Train_ER_alpha.smi (503/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1116 in Train_ER_alpha.smi (506/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL211870 in Train_ER_alpha.smi (512/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL380717 in Train_ER_alpha.smi (516/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL193727 in Train_ER_alpha.smi (338/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL1386105 in Train_ER_alpha.smi (342/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL1334930 in Train_ER_alpha.smi (346/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL209803 in Train_ER_alpha.smi (351/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL246341 in Train_ER_alpha.smi (356/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL208978 in Train_ER_alpha.smi (360/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL189356 in Train_ER_alpha.smi (368/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1606765 in Train_ER_alpha.smi (369/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL1348377 in Train_ER_alpha.smi (370/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL364110 in Train_ER_alpha.smi (374/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL1604500 in Train_ER_alpha.smi (377/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL389394 in Train_ER_alpha.smi (382/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL203724 in Train_ER_alpha.smi (385/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL191759 in Train_ER_alpha.smi (389/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL365941 in Train_ER_alpha.smi (390/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL360978 in Train_ER_alpha.smi (392/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL428732 in Train_ER_alpha.smi (401/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1410824 in Train_ER_alpha.smi (405/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1359893 in Train_ER_alpha.smi (406/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1403448 in Train_ER_alpha.smi (409/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1538223 in Train_ER_alpha.smi (414/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL178559 in Train_ER_alpha.smi (417/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1386676 in Train_ER_alpha.smi (421/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1580036 in Train_ER_alpha.smi (423/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1522300 in Train_ER_alpha.smi (427/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1548186 in Train_ER_alpha.smi (432/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL180253 in Train_ER_alpha.smi (434/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1427879 in Train_ER_alpha.smi (442/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1544241 in Train_ER_alpha.smi (443/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL577758 in Train_ER_alpha.smi (444/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL181369 in Train_ER_alpha.smi (449/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL371788 in Train_ER_alpha.smi (454/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL200021 in Train_ER_alpha.smi (458/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL188410 in Train_ER_alpha.smi (463/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL441537 in Train_ER_alpha.smi (466/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL362020 in Train_ER_alpha.smi (469/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL264216 in Train_ER_alpha.smi (476/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL334096 in Train_ER_alpha.smi (483/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL377224 in Train_ER_alpha.smi (487/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL264349 in Train_ER_alpha.smi (490/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL216279 in Train_ER_alpha.smi (494/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL379109 in Train_ER_alpha.smi (498/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL363425 in Train_ER_alpha.smi (504/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL206226 in Train_ER_alpha.smi (507/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL378990 in Train_ER_alpha.smi (508/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL211800 in Train_ER_alpha.smi (509/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL205350 in Train_ER_alpha.smi (515/1238). Average speed: 0.03 s/mol.\r\n"" + ] + }, + { + ""name"": ""stdout"", + ""output_type"": ""stream"", + ""text"": [ + ""Processing CHEMBL362355 in Train_ER_alpha.smi (518/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL206131 in Train_ER_alpha.smi (522/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL206773 in Train_ER_alpha.smi (526/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL191841 in Train_ER_alpha.smi (529/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL184360 in Train_ER_alpha.smi (532/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1485005 in Train_ER_alpha.smi (536/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL388418 in Train_ER_alpha.smi (539/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL396948 in Train_ER_alpha.smi (540/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1475062 in Train_ER_alpha.smi (542/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1362841 in Train_ER_alpha.smi (545/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1548361 in Train_ER_alpha.smi (547/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1506716 in Train_ER_alpha.smi (553/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1472407 in Train_ER_alpha.smi (554/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1587260 in Train_ER_alpha.smi (559/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1328182 in Train_ER_alpha.smi (562/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL364329 in Train_ER_alpha.smi (568/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL361063 in Train_ER_alpha.smi (571/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL192782 in Train_ER_alpha.smi (573/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL190487 in Train_ER_alpha.smi (576/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL190038 in Train_ER_alpha.smi (580/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL178665 in Train_ER_alpha.smi (583/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL184974 in Train_ER_alpha.smi (589/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL194265 in Train_ER_alpha.smi (595/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL361078 in Train_ER_alpha.smi (599/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1200623 in Train_ER_alpha.smi (601/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL187392 in Train_ER_alpha.smi (604/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL370282 in Train_ER_alpha.smi (607/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1302802 in Train_ER_alpha.smi (612/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1341814 in Train_ER_alpha.smi (615/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1350028 in Train_ER_alpha.smi (620/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1310171 in Train_ER_alpha.smi (625/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1507788 in Train_ER_alpha.smi (627/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL189039 in Train_ER_alpha.smi (631/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL195289 in Train_ER_alpha.smi (637/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1421864 in Train_ER_alpha.smi (639/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1560682 in Train_ER_alpha.smi (640/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1434961 in Train_ER_alpha.smi (642/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL371305 in Train_ER_alpha.smi (648/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1519669 in Train_ER_alpha.smi (652/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1433062 in Train_ER_alpha.smi (655/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL482116 in Train_ER_alpha.smi (658/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL363872 in Train_ER_alpha.smi (660/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL185383 in Train_ER_alpha.smi (663/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL511971 in Train_ER_alpha.smi (670/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL183044 in Train_ER_alpha.smi (675/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL203561 in Train_ER_alpha.smi (680/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL187219 in Train_ER_alpha.smi (685/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL192000 in Train_ER_alpha.smi (690/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL432454 in Train_ER_alpha.smi (693/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL346455 in Train_ER_alpha.smi (698/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL209738 in Train_ER_alpha.smi (702/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL221676 in Train_ER_alpha.smi (519/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL206349 in Train_ER_alpha.smi (525/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL192016 in Train_ER_alpha.smi (528/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL238961 in Train_ER_alpha.smi (531/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1521111 in Train_ER_alpha.smi (537/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1433745 in Train_ER_alpha.smi (541/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1429150 in Train_ER_alpha.smi (546/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1584646 in Train_ER_alpha.smi (552/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1454402 in Train_ER_alpha.smi (556/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1461574 in Train_ER_alpha.smi (561/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL191716 in Train_ER_alpha.smi (564/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL362425 in Train_ER_alpha.smi (566/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL104416 in Train_ER_alpha.smi (578/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1627354 in Train_ER_alpha.smi (582/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL362349 in Train_ER_alpha.smi (587/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL954 in Train_ER_alpha.smi (588/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL196131 in Train_ER_alpha.smi (592/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL369545 in Train_ER_alpha.smi (598/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL184367 in Train_ER_alpha.smi (606/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1521414 in Train_ER_alpha.smi (613/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1604803 in Train_ER_alpha.smi (617/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1541552 in Train_ER_alpha.smi (619/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1347829 in Train_ER_alpha.smi (624/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1505283 in Train_ER_alpha.smi (629/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL187673 in Train_ER_alpha.smi (632/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL122208 in Train_ER_alpha.smi (635/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1475434 in Train_ER_alpha.smi (641/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1593653 in Train_ER_alpha.smi (644/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL340606 in Train_ER_alpha.smi (647/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1299357 in Train_ER_alpha.smi (653/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL361871 in Train_ER_alpha.smi (657/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL190682 in Train_ER_alpha.smi (659/1238). Average speed: 0.03 s/mol.\r"", + ""\r\n"", + ""Processing CHEMBL205920 in Train_ER_alpha.smi (662/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL200799 in Train_ER_alpha.smi (665/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL371012 in Train_ER_alpha.smi (666/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL364419 in Train_ER_alpha.smi (671/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL398455 in Train_ER_alpha.smi (673/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL438256 in Train_ER_alpha.smi (677/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL213529 in Train_ER_alpha.smi (682/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL388473 in Train_ER_alpha.smi (687/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL202781 in Train_ER_alpha.smi (691/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL198445 in Train_ER_alpha.smi (692/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL393982 in Train_ER_alpha.smi (695/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL210074 in Train_ER_alpha.smi (701/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL363955 in Train_ER_alpha.smi (520/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL381720 in Train_ER_alpha.smi (523/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL379008 in Train_ER_alpha.smi (527/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL180176 in Train_ER_alpha.smi (533/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1276308 in Train_ER_alpha.smi (535/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1597831 in Train_ER_alpha.smi (543/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL375334 in Train_ER_alpha.smi (548/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL222580 in Train_ER_alpha.smi (550/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1448466 in Train_ER_alpha.smi (555/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1522044 in Train_ER_alpha.smi (560/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL194772 in Train_ER_alpha.smi (565/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL363177 in Train_ER_alpha.smi (567/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL379543 in Train_ER_alpha.smi (570/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL362307 in Train_ER_alpha.smi (575/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL195991 in Train_ER_alpha.smi (579/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL435696 in Train_ER_alpha.smi (584/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1627353 in Train_ER_alpha.smi (590/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL427180 in Train_ER_alpha.smi (594/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL195788 in Train_ER_alpha.smi (596/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL187393 in Train_ER_alpha.smi (600/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL194762 in Train_ER_alpha.smi (602/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL418971 in Train_ER_alpha.smi (605/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL103650 in Train_ER_alpha.smi (609/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL181936 in Train_ER_alpha.smi (610/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1491602 in Train_ER_alpha.smi (614/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1393131 in Train_ER_alpha.smi (618/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1423880 in Train_ER_alpha.smi (622/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1411209 in Train_ER_alpha.smi (626/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL191715 in Train_ER_alpha.smi (634/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1381306 in Train_ER_alpha.smi (638/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1552932 in Train_ER_alpha.smi (643/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL195280 in Train_ER_alpha.smi (649/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1443090 in Train_ER_alpha.smi (651/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL125362 in Train_ER_alpha.smi (656/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL226329 in Train_ER_alpha.smi (664/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL238890 in Train_ER_alpha.smi (668/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL189002 in Train_ER_alpha.smi (676/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL188957 in Train_ER_alpha.smi (678/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL211739 in Train_ER_alpha.smi (681/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL187978 in Train_ER_alpha.smi (684/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL394357 in Train_ER_alpha.smi (686/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL106773 in Train_ER_alpha.smi (689/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL240438 in Train_ER_alpha.smi (696/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL206120 in Train_ER_alpha.smi (700/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL206582 in Train_ER_alpha.smi (517/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL206764 in Train_ER_alpha.smi (521/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL202894 in Train_ER_alpha.smi (524/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL238960 in Train_ER_alpha.smi (530/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL437695 in Train_ER_alpha.smi (534/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1327977 in Train_ER_alpha.smi (538/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL471627 in Train_ER_alpha.smi (544/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL222522 in Train_ER_alpha.smi (549/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1449885 in Train_ER_alpha.smi (551/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1431650 in Train_ER_alpha.smi (557/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1367590 in Train_ER_alpha.smi (558/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL250109 in Train_ER_alpha.smi (563/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL361226 in Train_ER_alpha.smi (569/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL189822 in Train_ER_alpha.smi (572/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL192901 in Train_ER_alpha.smi (574/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1479 in Train_ER_alpha.smi (577/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL189303 in Train_ER_alpha.smi (581/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL371564 in Train_ER_alpha.smi (585/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1628170 in Train_ER_alpha.smi (586/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL188846 in Train_ER_alpha.smi (591/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL372304 in Train_ER_alpha.smi (593/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL183344 in Train_ER_alpha.smi (597/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL188051 in Train_ER_alpha.smi (603/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL180702 in Train_ER_alpha.smi (608/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1411687 in Train_ER_alpha.smi (611/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1322601 in Train_ER_alpha.smi (616/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL183782 in Train_ER_alpha.smi (621/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1493034 in Train_ER_alpha.smi (623/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL196583 in Train_ER_alpha.smi (628/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1532659 in Train_ER_alpha.smi (630/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL197757 in Train_ER_alpha.smi (633/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL153395 in Train_ER_alpha.smi (636/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1449110 in Train_ER_alpha.smi (645/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL192390 in Train_ER_alpha.smi (646/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL193482 in Train_ER_alpha.smi (650/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1581690 in Train_ER_alpha.smi (654/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL184421 in Train_ER_alpha.smi (661/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL362356 in Train_ER_alpha.smi (667/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL240157 in Train_ER_alpha.smi (669/1238). Average speed: 0.03 s/mol.\r"", + ""\r\n"", + ""Processing CHEMBL391795 in Train_ER_alpha.smi (672/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL430395 in Train_ER_alpha.smi (674/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL204697 in Train_ER_alpha.smi (679/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL214200 in Train_ER_alpha.smi (683/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL242818 in Train_ER_alpha.smi (688/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL184491 in Train_ER_alpha.smi (694/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL188246 in Train_ER_alpha.smi (697/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL232022 in Train_ER_alpha.smi (699/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL377254 in Train_ER_alpha.smi (703/1238). Average speed: 0.03 s/mol.\r\n"" + ] + }, + { + ""name"": ""stdout"", + ""output_type"": ""stream"", + ""text"": [ + ""Processing CHEMBL469510 in Train_ER_alpha.smi (708/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL198680 in Train_ER_alpha.smi (711/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL195311 in Train_ER_alpha.smi (716/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL378572 in Train_ER_alpha.smi (719/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1324290 in Train_ER_alpha.smi (723/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1386537 in Train_ER_alpha.smi (730/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1439181 in Train_ER_alpha.smi (736/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1525366 in Train_ER_alpha.smi (741/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1374573 in Train_ER_alpha.smi (744/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1548616 in Train_ER_alpha.smi (748/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1356953 in Train_ER_alpha.smi (753/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL386502 in Train_ER_alpha.smi (759/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL183033 in Train_ER_alpha.smi (764/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL190025 in Train_ER_alpha.smi (765/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1576748 in Train_ER_alpha.smi (767/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1509933 in Train_ER_alpha.smi (769/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1522446 in Train_ER_alpha.smi (774/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL188516 in Train_ER_alpha.smi (782/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL178334 in Train_ER_alpha.smi (783/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1539865 in Train_ER_alpha.smi (791/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1467399 in Train_ER_alpha.smi (794/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL192260 in Train_ER_alpha.smi (795/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1391547 in Train_ER_alpha.smi (799/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1582403 in Train_ER_alpha.smi (803/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1431081 in Train_ER_alpha.smi (806/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1231453 in Train_ER_alpha.smi (808/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1325233 in Train_ER_alpha.smi (813/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL378883 in Train_ER_alpha.smi (818/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL372783 in Train_ER_alpha.smi (824/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL250110 in Train_ER_alpha.smi (827/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL394614 in Train_ER_alpha.smi (833/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1531512 in Train_ER_alpha.smi (836/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL211953 in Train_ER_alpha.smi (839/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1307301 in Train_ER_alpha.smi (844/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1417890 in Train_ER_alpha.smi (849/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1442697 in Train_ER_alpha.smi (852/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL205577 in Train_ER_alpha.smi (858/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL465879 in Train_ER_alpha.smi (864/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL222452 in Train_ER_alpha.smi (867/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL199766 in Train_ER_alpha.smi (870/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL425603 in Train_ER_alpha.smi (873/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL180995 in Train_ER_alpha.smi (875/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL394213 in Train_ER_alpha.smi (881/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL455215 in Train_ER_alpha.smi (705/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL522990 in Train_ER_alpha.smi (706/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL368688 in Train_ER_alpha.smi (709/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL46903 in Train_ER_alpha.smi (715/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL206137 in Train_ER_alpha.smi (720/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1377973 in Train_ER_alpha.smi (724/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1329686 in Train_ER_alpha.smi (728/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1393976 in Train_ER_alpha.smi (733/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1531320 in Train_ER_alpha.smi (735/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1505467 in Train_ER_alpha.smi (739/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1345810 in Train_ER_alpha.smi (743/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1555033 in Train_ER_alpha.smi (750/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1326803 in Train_ER_alpha.smi (754/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1084746 in Train_ER_alpha.smi (756/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL223026 in Train_ER_alpha.smi (760/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL433769 in Train_ER_alpha.smi (762/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL369724 in Train_ER_alpha.smi (771/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1397845 in Train_ER_alpha.smi (777/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL361056 in Train_ER_alpha.smi (779/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL187311 in Train_ER_alpha.smi (786/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1580991 in Train_ER_alpha.smi (788/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1442450 in Train_ER_alpha.smi (790/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1350069 in Train_ER_alpha.smi (797/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1447311 in Train_ER_alpha.smi (804/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1421887 in Train_ER_alpha.smi (807/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL377295 in Train_ER_alpha.smi (812/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL385949 in Train_ER_alpha.smi (817/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL215428 in Train_ER_alpha.smi (819/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL212391 in Train_ER_alpha.smi (821/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL214692 in Train_ER_alpha.smi (823/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL380452 in Train_ER_alpha.smi (828/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL438443 in Train_ER_alpha.smi (830/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL380838 in Train_ER_alpha.smi (831/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1300008 in Train_ER_alpha.smi (835/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL211666 in Train_ER_alpha.smi (841/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1470395 in Train_ER_alpha.smi (847/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1320651 in Train_ER_alpha.smi (848/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1603599 in Train_ER_alpha.smi (853/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL377083 in Train_ER_alpha.smi (855/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1471823 in Train_ER_alpha.smi (860/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL384708 in Train_ER_alpha.smi (865/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL201365 in Train_ER_alpha.smi (869/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL212675 in Train_ER_alpha.smi (874/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL213457 in Train_ER_alpha.smi (878/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL193146 in Train_ER_alpha.smi (882/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL193108 in Train_ER_alpha.smi (883/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL213829 in Train_ER_alpha.smi (704/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1076306 in Train_ER_alpha.smi (710/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL414859 in Train_ER_alpha.smi (713/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL378798 in Train_ER_alpha.smi (714/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1450791 in Train_ER_alpha.smi (717/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1312693 in Train_ER_alpha.smi (722/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1533166 in Train_ER_alpha.smi (726/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1498999 in Train_ER_alpha.smi (729/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1520373 in Train_ER_alpha.smi (731/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1419810 in Train_ER_alpha.smi (734/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1484547 in Train_ER_alpha.smi (737/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1372307 in Train_ER_alpha.smi (740/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1370120 in Train_ER_alpha.smi (746/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1559731 in Train_ER_alpha.smi (749/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1405182 in Train_ER_alpha.smi (751/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1597805 in Train_ER_alpha.smi (755/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL426897 in Train_ER_alpha.smi (758/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL179852 in Train_ER_alpha.smi (763/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL360817 in Train_ER_alpha.smi (770/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL195217 in Train_ER_alpha.smi (773/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1414407 in Train_ER_alpha.smi (776/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL282575 in Train_ER_alpha.smi (778/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1326483 in Train_ER_alpha.smi (781/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL9352 in Train_ER_alpha.smi (784/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL425194 in Train_ER_alpha.smi (787/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1466928 in Train_ER_alpha.smi (792/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1303477 in Train_ER_alpha.smi (796/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1572777 in Train_ER_alpha.smi (801/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1320937 in Train_ER_alpha.smi (802/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL189556 in Train_ER_alpha.smi (805/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL204376 in Train_ER_alpha.smi (809/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1430316 in Train_ER_alpha.smi (814/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL379314 in Train_ER_alpha.smi (816/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL377457 in Train_ER_alpha.smi (822/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL249904 in Train_ER_alpha.smi (826/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL188299 in Train_ER_alpha.smi (832/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1404523 in Train_ER_alpha.smi (837/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL214034 in Train_ER_alpha.smi (840/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL206439 in Train_ER_alpha.smi (843/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL10560 in Train_ER_alpha.smi (845/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1429596 in Train_ER_alpha.smi (850/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL206426 in Train_ER_alpha.smi (856/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL202738 in Train_ER_alpha.smi (857/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL192586 in Train_ER_alpha.smi (862/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL221626 in Train_ER_alpha.smi (866/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL204317 in Train_ER_alpha.smi (871/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL203123 in Train_ER_alpha.smi (876/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL214291 in Train_ER_alpha.smi (879/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL182794 in Train_ER_alpha.smi (884/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL267431 in Train_ER_alpha.smi (707/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL125119 in Train_ER_alpha.smi (712/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL203370 in Train_ER_alpha.smi (718/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1389780 in Train_ER_alpha.smi (721/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1453264 in Train_ER_alpha.smi (725/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1304227 in Train_ER_alpha.smi (727/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1452952 in Train_ER_alpha.smi (732/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1577757 in Train_ER_alpha.smi (738/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1482875 in Train_ER_alpha.smi (742/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1577661 in Train_ER_alpha.smi (745/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1407054 in Train_ER_alpha.smi (747/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1455849 in Train_ER_alpha.smi (752/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1083326 in Train_ER_alpha.smi (757/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL144897 in Train_ER_alpha.smi (761/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL198088 in Train_ER_alpha.smi (766/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1388431 in Train_ER_alpha.smi (768/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL52 in Train_ER_alpha.smi (772/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1434556 in Train_ER_alpha.smi (775/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1491959 in Train_ER_alpha.smi (780/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL198204 in Train_ER_alpha.smi (785/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1300662 in Train_ER_alpha.smi (789/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL182157 in Train_ER_alpha.smi (793/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL187313 in Train_ER_alpha.smi (798/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1526157 in Train_ER_alpha.smi (800/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL460647 in Train_ER_alpha.smi (810/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL216334 in Train_ER_alpha.smi (811/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1574512 in Train_ER_alpha.smi (815/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL385028 in Train_ER_alpha.smi (820/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL398456 in Train_ER_alpha.smi (825/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL380358 in Train_ER_alpha.smi (829/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL469511 in Train_ER_alpha.smi (834/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1386091 in Train_ER_alpha.smi (838/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL206500 in Train_ER_alpha.smi (842/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL231810 in Train_ER_alpha.smi (846/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1404155 in Train_ER_alpha.smi (851/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL203283 in Train_ER_alpha.smi (854/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL208324 in Train_ER_alpha.smi (859/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL192621 in Train_ER_alpha.smi (861/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL434577 in Train_ER_alpha.smi (863/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL372411 in Train_ER_alpha.smi (868/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL383253 in Train_ER_alpha.smi (872/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL211599 in Train_ER_alpha.smi (877/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL240230 in Train_ER_alpha.smi (880/1238). Average speed: 0.03 s/mol.\r\n"" + ] + }, + { + ""name"": ""stdout"", + ""output_type"": ""stream"", + ""text"": [ + ""Descriptor calculation completed in 26.330 secs . Average speed: 0.02 s/mol.\n"", + ""Train_ER_alpha.smi\n"", + ""Train_ER_alpha.csv\n"", + ""EStateFingerprinter.xml\n"", + ""EStateFingerprinter_\n"", + ""Processing CHEMBL370037 in Train_ER_alpha.smi (1/1238). \n"", + ""Processing CHEMBL189073 in Train_ER_alpha.smi (2/1238). \n"", + ""Processing CHEMBL365290 in Train_ER_alpha.smi (3/1238). \n"", + ""Processing CHEMBL191648 in Train_ER_alpha.smi (4/1238). \n"", + ""Processing CHEMBL184431 in Train_ER_alpha.smi (5/1238). \n"", + ""Processing CHEMBL364551 in Train_ER_alpha.smi (6/1238). Average speed: 1.55 s/mol.\n"", + ""Processing CHEMBL198410 in Train_ER_alpha.smi (8/1238). Average speed: 0.53 s/mol.\n"", + ""Processing CHEMBL124482 in Train_ER_alpha.smi (7/1238). Average speed: 0.79 s/mol.\n"", + ""Processing CHEMBL1496978 in Train_ER_alpha.smi (9/1238). Average speed: 0.42 s/mol.\n"", + ""Processing CHEMBL1305485 in Train_ER_alpha.smi (10/1238). Average speed: 0.34 s/mol.\n"", + ""Processing CHEMBL1570659 in Train_ER_alpha.smi (11/1238). Average speed: 0.29 s/mol.\n"", + ""Processing CHEMBL1331084 in Train_ER_alpha.smi (12/1238). Average speed: 0.26 s/mol.\n"", + ""Processing CHEMBL184448 in Train_ER_alpha.smi (13/1238). Average speed: 0.23 s/mol.\n"", + ""Processing CHEMBL45068 in Train_ER_alpha.smi (14/1238). Average speed: 0.21 s/mol.\n"", + ""Processing CHEMBL434502 in Train_ER_alpha.smi (15/1238). Average speed: 0.19 s/mol.\n"", + ""Processing CHEMBL1628199 in Train_ER_alpha.smi (16/1238). Average speed: 0.17 s/mol.\n"", + ""Processing CHEMBL123025 in Train_ER_alpha.smi (17/1238). Average speed: 0.16 s/mol.\n"", + ""Processing CHEMBL153062 in Train_ER_alpha.smi (18/1238). Average speed: 0.16 s/mol.\n"", + ""Processing CHEMBL1468708 in Train_ER_alpha.smi (19/1238). Average speed: 0.15 s/mol.\n"", + ""Processing CHEMBL1442416 in Train_ER_alpha.smi (20/1238). Average speed: 0.15 s/mol.\n"", + ""Processing CHEMBL1388271 in Train_ER_alpha.smi (21/1238). Average speed: 0.14 s/mol.\n"", + ""Processing CHEMBL1331872 in Train_ER_alpha.smi (22/1238). Average speed: 0.13 s/mol.\n"", + ""Processing CHEMBL1401227 in Train_ER_alpha.smi (23/1238). Average speed: 0.12 s/mol.\n"", + ""Processing CHEMBL1460126 in Train_ER_alpha.smi (24/1238). Average speed: 0.12 s/mol.\n"", + ""Processing CHEMBL1499638 in Train_ER_alpha.smi (25/1238). Average speed: 0.12 s/mol.\n"", + ""Processing CHEMBL1413557 in Train_ER_alpha.smi (26/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL1484998 in Train_ER_alpha.smi (27/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL1608989 in Train_ER_alpha.smi (28/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL1469743 in Train_ER_alpha.smi (29/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL1538325 in Train_ER_alpha.smi (30/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL1605393 in Train_ER_alpha.smi (31/1238). Average speed: 0.09 s/mol.\n"", + ""Processing CHEMBL1384049 in Train_ER_alpha.smi (32/1238). Average speed: 0.09 s/mol.\n"", + ""Processing CHEMBL188049 in Train_ER_alpha.smi (33/1238). Average speed: 0.09 s/mol.\n"", + ""Processing CHEMBL1361641 in Train_ER_alpha.smi (34/1238). Average speed: 0.09 s/mol.\n"", + ""Processing CHEMBL1489063 in Train_ER_alpha.smi (35/1238). Average speed: 0.09 s/mol.\n"", + ""Processing CHEMBL1381495 in Train_ER_alpha.smi (36/1238). Average speed: 0.08 s/mol.\n"", + ""Processing CHEMBL1343011 in Train_ER_alpha.smi (37/1238). Average speed: 0.08 s/mol.\n"", + ""Processing CHEMBL1613068 in Train_ER_alpha.smi (38/1238). Average speed: 0.08 s/mol.\n"", + ""Processing CHEMBL1464837 in Train_ER_alpha.smi (39/1238). Average speed: 0.08 s/mol.\n"", + ""Processing CHEMBL1349347 in Train_ER_alpha.smi (40/1238). Average speed: 0.08 s/mol.\n"", + ""Processing CHEMBL1385373 in Train_ER_alpha.smi (41/1238). Average speed: 0.08 s/mol.\n"", + ""Processing CHEMBL1573735 in Train_ER_alpha.smi (42/1238). Average speed: 0.07 s/mol.\n"", + ""Processing CHEMBL1455258 in Train_ER_alpha.smi (43/1238). Average speed: 0.07 s/mol.\n"", + ""Processing CHEMBL1431555 in Train_ER_alpha.smi (44/1238). Average speed: 0.07 s/mol.\n"", + ""Processing CHEMBL1565187 in Train_ER_alpha.smi (45/1238). Average speed: 0.07 s/mol.\n"", + ""Processing CHEMBL1466882 in Train_ER_alpha.smi (47/1238). Average speed: 0.07 s/mol.\n"", + ""Processing CHEMBL1597933 in Train_ER_alpha.smi (46/1238). Average speed: 0.07 s/mol.\n"", + ""Processing CHEMBL1313979 in Train_ER_alpha.smi (48/1238). Average speed: 0.07 s/mol.\n"", + ""Processing CHEMBL1329455 in Train_ER_alpha.smi (49/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL1322939 in Train_ER_alpha.smi (50/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL1480843 in Train_ER_alpha.smi (51/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL1310614 in Train_ER_alpha.smi (52/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL1535728 in Train_ER_alpha.smi (53/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL1420411 in Train_ER_alpha.smi (55/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL1341057 in Train_ER_alpha.smi (54/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL184372 in Train_ER_alpha.smi (56/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL442037 in Train_ER_alpha.smi (57/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL512579 in Train_ER_alpha.smi (58/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL124474 in Train_ER_alpha.smi (59/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL231811 in Train_ER_alpha.smi (60/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL385801 in Train_ER_alpha.smi (61/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL447111 in Train_ER_alpha.smi (62/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL427496 in Train_ER_alpha.smi (63/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL225404 in Train_ER_alpha.smi (64/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL226269 in Train_ER_alpha.smi (65/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL1081361 in Train_ER_alpha.smi (66/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL235496 in Train_ER_alpha.smi (67/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL235497 in Train_ER_alpha.smi (68/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL361762 in Train_ER_alpha.smi (69/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL184371 in Train_ER_alpha.smi (70/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL188458 in Train_ER_alpha.smi (71/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL361272 in Train_ER_alpha.smi (72/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL241257 in Train_ER_alpha.smi (73/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL396118 in Train_ER_alpha.smi (74/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL59438 in Train_ER_alpha.smi (75/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL150279 in Train_ER_alpha.smi (76/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL365625 in Train_ER_alpha.smi (77/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL365016 in Train_ER_alpha.smi (78/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL179929 in Train_ER_alpha.smi (79/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL470993 in Train_ER_alpha.smi (80/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL511269 in Train_ER_alpha.smi (81/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL469512 in Train_ER_alpha.smi (82/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL470785 in Train_ER_alpha.smi (83/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL577938 in Train_ER_alpha.smi (84/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL185114 in Train_ER_alpha.smi (85/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL377171 in Train_ER_alpha.smi (86/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL385680 in Train_ER_alpha.smi (87/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL204902 in Train_ER_alpha.smi (88/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL206727 in Train_ER_alpha.smi (89/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL380362 in Train_ER_alpha.smi (90/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL206547 in Train_ER_alpha.smi (91/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL471626 in Train_ER_alpha.smi (92/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL511492 in Train_ER_alpha.smi (93/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL196836 in Train_ER_alpha.smi (94/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL25228 in Train_ER_alpha.smi (95/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL194326 in Train_ER_alpha.smi (96/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL203330 in Train_ER_alpha.smi (97/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL206648 in Train_ER_alpha.smi (98/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL425231 in Train_ER_alpha.smi (99/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL206336 in Train_ER_alpha.smi (100/1238). Average speed: 0.04 s/mol.\n"" + ] + }, + { + ""name"": ""stdout"", + ""output_type"": ""stream"", + ""text"": [ + ""Processing CHEMBL205137 in Train_ER_alpha.smi (101/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL425941 in Train_ER_alpha.smi (102/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL206499 in Train_ER_alpha.smi (103/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL429446 in Train_ER_alpha.smi (104/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL191300 in Train_ER_alpha.smi (105/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL447837 in Train_ER_alpha.smi (106/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL187314 in Train_ER_alpha.smi (107/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL371308 in Train_ER_alpha.smi (108/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL191710 in Train_ER_alpha.smi (109/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL426085 in Train_ER_alpha.smi (110/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL200719 in Train_ER_alpha.smi (111/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL212419 in Train_ER_alpha.smi (112/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL211493 in Train_ER_alpha.smi (113/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL377475 in Train_ER_alpha.smi (114/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL204024 in Train_ER_alpha.smi (115/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL183333 in Train_ER_alpha.smi (116/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL185231 in Train_ER_alpha.smi (117/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL378473 in Train_ER_alpha.smi (118/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL382734 in Train_ER_alpha.smi (119/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL195807 in Train_ER_alpha.smi (120/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL212185 in Train_ER_alpha.smi (121/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL372808 in Train_ER_alpha.smi (123/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL198914 in Train_ER_alpha.smi (122/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL197495 in Train_ER_alpha.smi (124/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL191901 in Train_ER_alpha.smi (125/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL241304 in Train_ER_alpha.smi (126/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL241485 in Train_ER_alpha.smi (127/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL437190 in Train_ER_alpha.smi (128/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL182402 in Train_ER_alpha.smi (129/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL188391 in Train_ER_alpha.smi (130/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL360315 in Train_ER_alpha.smi (132/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL184598 in Train_ER_alpha.smi (131/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL371777 in Train_ER_alpha.smi (133/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL126164 in Train_ER_alpha.smi (134/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL195786 in Train_ER_alpha.smi (135/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL219134 in Train_ER_alpha.smi (136/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL204222 in Train_ER_alpha.smi (138/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL204119 in Train_ER_alpha.smi (137/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL212392 in Train_ER_alpha.smi (139/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL441366 in Train_ER_alpha.smi (140/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL384709 in Train_ER_alpha.smi (141/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL189080 in Train_ER_alpha.smi (142/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL180707 in Train_ER_alpha.smi (143/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL362272 in Train_ER_alpha.smi (144/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL438516 in Train_ER_alpha.smi (145/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL234733 in Train_ER_alpha.smi (146/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL206441 in Train_ER_alpha.smi (147/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL379188 in Train_ER_alpha.smi (148/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL425439 in Train_ER_alpha.smi (149/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL192102 in Train_ER_alpha.smi (150/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL395308 in Train_ER_alpha.smi (151/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL394788 in Train_ER_alpha.smi (152/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL245133 in Train_ER_alpha.smi (153/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL234527 in Train_ER_alpha.smi (154/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL195208 in Train_ER_alpha.smi (155/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL8145 in Train_ER_alpha.smi (156/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL195437 in Train_ER_alpha.smi (157/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL435090 in Train_ER_alpha.smi (158/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1201151 in Train_ER_alpha.smi (159/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL366928 in Train_ER_alpha.smi (160/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL192068 in Train_ER_alpha.smi (161/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL360651 in Train_ER_alpha.smi (162/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL195878 in Train_ER_alpha.smi (163/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL182237 in Train_ER_alpha.smi (164/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL365293 in Train_ER_alpha.smi (165/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL183086 in Train_ER_alpha.smi (166/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL426641 in Train_ER_alpha.smi (167/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL374799 in Train_ER_alpha.smi (168/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL225349 in Train_ER_alpha.smi (169/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL222434 in Train_ER_alpha.smi (170/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL373743 in Train_ER_alpha.smi (171/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL451496 in Train_ER_alpha.smi (172/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL186643 in Train_ER_alpha.smi (173/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL362718 in Train_ER_alpha.smi (174/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL418327 in Train_ER_alpha.smi (175/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL187207 in Train_ER_alpha.smi (176/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL181248 in Train_ER_alpha.smi (177/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL183371 in Train_ER_alpha.smi (178/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL439806 in Train_ER_alpha.smi (179/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL121879 in Train_ER_alpha.smi (180/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL202996 in Train_ER_alpha.smi (181/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL363705 in Train_ER_alpha.smi (182/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL373095 in Train_ER_alpha.smi (183/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL185480 in Train_ER_alpha.smi (184/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL389683 in Train_ER_alpha.smi (185/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL449038 in Train_ER_alpha.smi (186/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL225022 in Train_ER_alpha.smi (187/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL389393 in Train_ER_alpha.smi (188/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL360802 in Train_ER_alpha.smi (189/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL332625 in Train_ER_alpha.smi (190/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL194936 in Train_ER_alpha.smi (191/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL192154 in Train_ER_alpha.smi (192/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL369991 in Train_ER_alpha.smi (193/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL381298 in Train_ER_alpha.smi (195/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL377342 in Train_ER_alpha.smi (194/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL204025 in Train_ER_alpha.smi (196/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL201013 in Train_ER_alpha.smi (198/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL202615 in Train_ER_alpha.smi (197/1238). Average speed: 0.03 s/mol.\n"" + ] + }, + { + ""name"": ""stdout"", + ""output_type"": ""stream"", + ""text"": [ + ""Processing CHEMBL219387 in Train_ER_alpha.smi (200/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL385279 in Train_ER_alpha.smi (199/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL85826 in Train_ER_alpha.smi (201/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL195371 in Train_ER_alpha.smi (202/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL187915 in Train_ER_alpha.smi (203/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL364092 in Train_ER_alpha.smi (204/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL193726 in Train_ER_alpha.smi (205/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL192218 in Train_ER_alpha.smi (206/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL441499 in Train_ER_alpha.smi (207/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL183084 in Train_ER_alpha.smi (208/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL187000 in Train_ER_alpha.smi (209/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL369979 in Train_ER_alpha.smi (210/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL381299 in Train_ER_alpha.smi (211/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL206584 in Train_ER_alpha.smi (212/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL212923 in Train_ER_alpha.smi (213/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL385358 in Train_ER_alpha.smi (214/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL383533 in Train_ER_alpha.smi (215/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL219577 in Train_ER_alpha.smi (216/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL241303 in Train_ER_alpha.smi (217/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL393278 in Train_ER_alpha.smi (219/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL240227 in Train_ER_alpha.smi (218/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL362428 in Train_ER_alpha.smi (220/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL496 in Train_ER_alpha.smi (221/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL434525 in Train_ER_alpha.smi (222/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL365999 in Train_ER_alpha.smi (223/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL198411 in Train_ER_alpha.smi (224/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL186945 in Train_ER_alpha.smi (225/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL197667 in Train_ER_alpha.smi (226/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL382632 in Train_ER_alpha.smi (227/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL206440 in Train_ER_alpha.smi (228/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL380844 in Train_ER_alpha.smi (229/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL383753 in Train_ER_alpha.smi (230/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL206406 in Train_ER_alpha.smi (231/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL204056 in Train_ER_alpha.smi (232/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL381697 in Train_ER_alpha.smi (233/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL204922 in Train_ER_alpha.smi (234/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL206600 in Train_ER_alpha.smi (235/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL206227 in Train_ER_alpha.smi (236/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL211816 in Train_ER_alpha.smi (237/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL208929 in Train_ER_alpha.smi (238/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL209623 in Train_ER_alpha.smi (239/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL211887 in Train_ER_alpha.smi (240/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL191714 in Train_ER_alpha.smi (241/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL188792 in Train_ER_alpha.smi (242/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL204819 in Train_ER_alpha.smi (243/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL206546 in Train_ER_alpha.smi (244/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL205934 in Train_ER_alpha.smi (245/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL381337 in Train_ER_alpha.smi (246/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL203442 in Train_ER_alpha.smi (247/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL206814 in Train_ER_alpha.smi (248/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL203351 in Train_ER_alpha.smi (249/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL382698 in Train_ER_alpha.smi (250/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL191238 in Train_ER_alpha.smi (251/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL364502 in Train_ER_alpha.smi (252/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL185890 in Train_ER_alpha.smi (255/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL331996 in Train_ER_alpha.smi (253/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL363634 in Train_ER_alpha.smi (254/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL185083 in Train_ER_alpha.smi (256/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL362793 in Train_ER_alpha.smi (257/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL187473 in Train_ER_alpha.smi (258/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL373987 in Train_ER_alpha.smi (259/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL213950 in Train_ER_alpha.smi (260/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL290106 in Train_ER_alpha.smi (261/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL239938 in Train_ER_alpha.smi (262/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL391619 in Train_ER_alpha.smi (263/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL187115 in Train_ER_alpha.smi (265/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL389907 in Train_ER_alpha.smi (264/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL363633 in Train_ER_alpha.smi (266/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL182850 in Train_ER_alpha.smi (267/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL361751 in Train_ER_alpha.smi (268/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL188189 in Train_ER_alpha.smi (269/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL196742 in Train_ER_alpha.smi (270/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL316003 in Train_ER_alpha.smi (271/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL360410 in Train_ER_alpha.smi (272/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL196530 in Train_ER_alpha.smi (273/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL1570364 in Train_ER_alpha.smi (274/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL1491936 in Train_ER_alpha.smi (275/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL1487772 in Train_ER_alpha.smi (276/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL51483 in Train_ER_alpha.smi (277/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL463804 in Train_ER_alpha.smi (279/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL359633 in Train_ER_alpha.smi (278/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL1414254 in Train_ER_alpha.smi (280/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL1403999 in Train_ER_alpha.smi (281/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL1627356 in Train_ER_alpha.smi (282/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL319244 in Train_ER_alpha.smi (283/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL1572403 in Train_ER_alpha.smi (284/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL1541779 in Train_ER_alpha.smi (285/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL1458486 in Train_ER_alpha.smi (286/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL1587920 in Train_ER_alpha.smi (287/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL1399039 in Train_ER_alpha.smi (288/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL1332402 in Train_ER_alpha.smi (289/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL1328083 in Train_ER_alpha.smi (290/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL150924 in Train_ER_alpha.smi (291/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL184003 in Train_ER_alpha.smi (292/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL182751 in Train_ER_alpha.smi (293/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL1465370 in Train_ER_alpha.smi (294/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL1383151 in Train_ER_alpha.smi (295/1238). Average speed: 0.03 s/mol.\n"" + ] + }, + { + ""name"": ""stdout"", + ""output_type"": ""stream"", + ""text"": [ + ""Processing CHEMBL1407483 in Train_ER_alpha.smi (296/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL1511067 in Train_ER_alpha.smi (297/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL192876 in Train_ER_alpha.smi (298/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL363017 in Train_ER_alpha.smi (299/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL1428161 in Train_ER_alpha.smi (300/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL1390963 in Train_ER_alpha.smi (301/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL340111 in Train_ER_alpha.smi (302/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL185783 in Train_ER_alpha.smi (303/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL294889 in Train_ER_alpha.smi (304/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL184151 in Train_ER_alpha.smi (305/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL1504315 in Train_ER_alpha.smi (306/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL63768 in Train_ER_alpha.smi (307/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL1564209 in Train_ER_alpha.smi (308/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL1469865 in Train_ER_alpha.smi (309/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL380188 in Train_ER_alpha.smi (310/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL214693 in Train_ER_alpha.smi (311/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL196283 in Train_ER_alpha.smi (312/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL195154 in Train_ER_alpha.smi (313/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL363498 in Train_ER_alpha.smi (314/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL1392188 in Train_ER_alpha.smi (315/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL1577029 in Train_ER_alpha.smi (316/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL183368 in Train_ER_alpha.smi (317/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL215409 in Train_ER_alpha.smi (318/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL211837 in Train_ER_alpha.smi (319/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL379566 in Train_ER_alpha.smi (320/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL384268 in Train_ER_alpha.smi (321/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL203348 in Train_ER_alpha.smi (322/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL371072 in Train_ER_alpha.smi (323/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL180873 in Train_ER_alpha.smi (324/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL426626 in Train_ER_alpha.smi (325/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL191711 in Train_ER_alpha.smi (326/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL225081 in Train_ER_alpha.smi (327/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL231812 in Train_ER_alpha.smi (328/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL1368345 in Train_ER_alpha.smi (330/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL182887 in Train_ER_alpha.smi (329/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL188900 in Train_ER_alpha.smi (331/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL362966 in Train_ER_alpha.smi (332/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL379213 in Train_ER_alpha.smi (333/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL380359 in Train_ER_alpha.smi (334/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL207101 in Train_ER_alpha.smi (335/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL385693 in Train_ER_alpha.smi (336/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL192765 in Train_ER_alpha.smi (337/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL193727 in Train_ER_alpha.smi (338/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL183367 in Train_ER_alpha.smi (339/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL1395826 in Train_ER_alpha.smi (340/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL1434189 in Train_ER_alpha.smi (341/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL1386105 in Train_ER_alpha.smi (342/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL364345 in Train_ER_alpha.smi (343/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL371126 in Train_ER_alpha.smi (344/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL1334930 in Train_ER_alpha.smi (346/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL1324414 in Train_ER_alpha.smi (345/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL363902 in Train_ER_alpha.smi (347/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL472468 in Train_ER_alpha.smi (348/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL189024 in Train_ER_alpha.smi (349/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL194718 in Train_ER_alpha.smi (350/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL209803 in Train_ER_alpha.smi (351/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL209402 in Train_ER_alpha.smi (352/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL204970 in Train_ER_alpha.smi (353/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL203472 in Train_ER_alpha.smi (354/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL1303886 in Train_ER_alpha.smi (355/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL246341 in Train_ER_alpha.smi (356/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL425092 in Train_ER_alpha.smi (357/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL380469 in Train_ER_alpha.smi (358/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL376986 in Train_ER_alpha.smi (359/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL208978 in Train_ER_alpha.smi (360/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL249903 in Train_ER_alpha.smi (362/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL377644 in Train_ER_alpha.smi (361/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL398634 in Train_ER_alpha.smi (363/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL232023 in Train_ER_alpha.smi (364/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL1340020 in Train_ER_alpha.smi (365/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL511812 in Train_ER_alpha.smi (366/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL191836 in Train_ER_alpha.smi (367/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL189356 in Train_ER_alpha.smi (368/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL1606765 in Train_ER_alpha.smi (369/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL1348377 in Train_ER_alpha.smi (370/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL1426947 in Train_ER_alpha.smi (371/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL1453259 in Train_ER_alpha.smi (372/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL347560 in Train_ER_alpha.smi (373/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL364110 in Train_ER_alpha.smi (374/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL189025 in Train_ER_alpha.smi (375/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL25580 in Train_ER_alpha.smi (376/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL1604500 in Train_ER_alpha.smi (377/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL1509453 in Train_ER_alpha.smi (378/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL1482201 in Train_ER_alpha.smi (379/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL1326210 in Train_ER_alpha.smi (380/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL389394 in Train_ER_alpha.smi (382/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL221515 in Train_ER_alpha.smi (381/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL374277 in Train_ER_alpha.smi (383/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL379889 in Train_ER_alpha.smi (384/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL203724 in Train_ER_alpha.smi (385/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL226379 in Train_ER_alpha.smi (386/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL1318094 in Train_ER_alpha.smi (387/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL368449 in Train_ER_alpha.smi (388/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL191759 in Train_ER_alpha.smi (389/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL365941 in Train_ER_alpha.smi (390/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL191808 in Train_ER_alpha.smi (391/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL360978 in Train_ER_alpha.smi (392/1238). Average speed: 0.03 s/mol.\n"" + ] + }, + { + ""name"": ""stdout"", + ""output_type"": ""stream"", + ""text"": [ + ""Processing CHEMBL200483 in Train_ER_alpha.smi (394/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL392029 in Train_ER_alpha.smi (393/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL199599 in Train_ER_alpha.smi (395/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL200232 in Train_ER_alpha.smi (396/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL201517 in Train_ER_alpha.smi (397/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL202673 in Train_ER_alpha.smi (398/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL203072 in Train_ER_alpha.smi (399/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL241300 in Train_ER_alpha.smi (400/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL428732 in Train_ER_alpha.smi (401/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL393179 in Train_ER_alpha.smi (402/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL442249 in Train_ER_alpha.smi (403/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL238959 in Train_ER_alpha.smi (404/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL1410824 in Train_ER_alpha.smi (405/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL1359893 in Train_ER_alpha.smi (406/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL1427643 in Train_ER_alpha.smi (407/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL1464457 in Train_ER_alpha.smi (408/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL1403448 in Train_ER_alpha.smi (409/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL1548231 in Train_ER_alpha.smi (411/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL1575187 in Train_ER_alpha.smi (410/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL1531555 in Train_ER_alpha.smi (412/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL1521410 in Train_ER_alpha.smi (413/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL1538223 in Train_ER_alpha.smi (414/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL153405 in Train_ER_alpha.smi (415/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL327320 in Train_ER_alpha.smi (416/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL178559 in Train_ER_alpha.smi (417/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL180973 in Train_ER_alpha.smi (418/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL1528123 in Train_ER_alpha.smi (419/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL1536373 in Train_ER_alpha.smi (420/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL1386676 in Train_ER_alpha.smi (421/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL144431 in Train_ER_alpha.smi (422/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL1580036 in Train_ER_alpha.smi (423/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL189658 in Train_ER_alpha.smi (424/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL188619 in Train_ER_alpha.smi (425/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL1525824 in Train_ER_alpha.smi (426/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL1522300 in Train_ER_alpha.smi (427/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL193064 in Train_ER_alpha.smi (428/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL186513 in Train_ER_alpha.smi (429/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL1495194 in Train_ER_alpha.smi (430/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL1606490 in Train_ER_alpha.smi (431/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL1548186 in Train_ER_alpha.smi (432/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL2079587 in Train_ER_alpha.smi (433/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL93705 in Train_ER_alpha.smi (435/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL180253 in Train_ER_alpha.smi (434/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL190104 in Train_ER_alpha.smi (436/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL1548834 in Train_ER_alpha.smi (437/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL188527 in Train_ER_alpha.smi (438/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL360498 in Train_ER_alpha.smi (439/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL1501269 in Train_ER_alpha.smi (440/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL1560367 in Train_ER_alpha.smi (441/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL1427879 in Train_ER_alpha.smi (442/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL1544241 in Train_ER_alpha.smi (443/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL577758 in Train_ER_alpha.smi (444/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL186597 in Train_ER_alpha.smi (445/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL145576 in Train_ER_alpha.smi (446/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL361258 in Train_ER_alpha.smi (447/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL363367 in Train_ER_alpha.smi (448/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL181369 in Train_ER_alpha.smi (449/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL57150 in Train_ER_alpha.smi (450/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL433944 in Train_ER_alpha.smi (451/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL371624 in Train_ER_alpha.smi (452/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL188613 in Train_ER_alpha.smi (453/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL371788 in Train_ER_alpha.smi (454/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL189200 in Train_ER_alpha.smi (455/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL186073 in Train_ER_alpha.smi (456/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL187087 in Train_ER_alpha.smi (457/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL200021 in Train_ER_alpha.smi (458/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL193010 in Train_ER_alpha.smi (459/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL363963 in Train_ER_alpha.smi (460/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL450940 in Train_ER_alpha.smi (461/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL365045 in Train_ER_alpha.smi (462/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL188410 in Train_ER_alpha.smi (463/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL201569 in Train_ER_alpha.smi (464/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL380876 in Train_ER_alpha.smi (465/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL441537 in Train_ER_alpha.smi (466/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL377694 in Train_ER_alpha.smi (467/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL192386 in Train_ER_alpha.smi (468/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL362020 in Train_ER_alpha.smi (469/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL183152 in Train_ER_alpha.smi (470/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL192587 in Train_ER_alpha.smi (471/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL370849 in Train_ER_alpha.smi (472/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL178679 in Train_ER_alpha.smi (473/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL185126 in Train_ER_alpha.smi (474/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL185918 in Train_ER_alpha.smi (475/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL264216 in Train_ER_alpha.smi (476/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL1337826 in Train_ER_alpha.smi (477/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL1480378 in Train_ER_alpha.smi (478/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL1481770 in Train_ER_alpha.smi (479/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL198679 in Train_ER_alpha.smi (480/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL121978 in Train_ER_alpha.smi (481/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL371009 in Train_ER_alpha.smi (482/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL334096 in Train_ER_alpha.smi (483/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL192539 in Train_ER_alpha.smi (484/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL186598 in Train_ER_alpha.smi (485/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL180146 in Train_ER_alpha.smi (486/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL377224 in Train_ER_alpha.smi (487/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL377848 in Train_ER_alpha.smi (489/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL213051 in Train_ER_alpha.smi (488/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL264349 in Train_ER_alpha.smi (490/1238). Average speed: 0.03 s/mol.\n"" + ] + }, + { + ""name"": ""stdout"", + ""output_type"": ""stream"", + ""text"": [ + ""Processing CHEMBL378675 in Train_ER_alpha.smi (491/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL1402 in Train_ER_alpha.smi (492/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL369935 in Train_ER_alpha.smi (493/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL216279 in Train_ER_alpha.smi (494/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL219126 in Train_ER_alpha.smi (495/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL381566 in Train_ER_alpha.smi (496/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL209429 in Train_ER_alpha.smi (497/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL379109 in Train_ER_alpha.smi (498/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL379821 in Train_ER_alpha.smi (499/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL426854 in Train_ER_alpha.smi (500/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL202896 in Train_ER_alpha.smi (501/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL179518 in Train_ER_alpha.smi (502/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL187069 in Train_ER_alpha.smi (503/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL363425 in Train_ER_alpha.smi (504/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL597498 in Train_ER_alpha.smi (505/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL1116 in Train_ER_alpha.smi (506/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL206226 in Train_ER_alpha.smi (507/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL378990 in Train_ER_alpha.smi (508/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL211800 in Train_ER_alpha.smi (509/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL379567 in Train_ER_alpha.smi (510/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL379595 in Train_ER_alpha.smi (511/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL211870 in Train_ER_alpha.smi (512/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL187937 in Train_ER_alpha.smi (513/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL204768 in Train_ER_alpha.smi (514/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL205350 in Train_ER_alpha.smi (515/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL380717 in Train_ER_alpha.smi (516/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL206582 in Train_ER_alpha.smi (517/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL362355 in Train_ER_alpha.smi (518/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL221676 in Train_ER_alpha.smi (519/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL363955 in Train_ER_alpha.smi (520/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL206764 in Train_ER_alpha.smi (521/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL206131 in Train_ER_alpha.smi (522/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL202894 in Train_ER_alpha.smi (524/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL381720 in Train_ER_alpha.smi (523/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL206349 in Train_ER_alpha.smi (525/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL206773 in Train_ER_alpha.smi (526/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL379008 in Train_ER_alpha.smi (527/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL192016 in Train_ER_alpha.smi (528/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL191841 in Train_ER_alpha.smi (529/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL238960 in Train_ER_alpha.smi (530/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL238961 in Train_ER_alpha.smi (531/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL184360 in Train_ER_alpha.smi (532/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL180176 in Train_ER_alpha.smi (533/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL437695 in Train_ER_alpha.smi (534/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL1276308 in Train_ER_alpha.smi (535/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL1485005 in Train_ER_alpha.smi (536/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL1521111 in Train_ER_alpha.smi (537/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL1327977 in Train_ER_alpha.smi (538/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL388418 in Train_ER_alpha.smi (539/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL396948 in Train_ER_alpha.smi (540/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL1433745 in Train_ER_alpha.smi (541/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL1475062 in Train_ER_alpha.smi (542/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL1597831 in Train_ER_alpha.smi (543/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL471627 in Train_ER_alpha.smi (544/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL1362841 in Train_ER_alpha.smi (545/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL1429150 in Train_ER_alpha.smi (546/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL1548361 in Train_ER_alpha.smi (547/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL375334 in Train_ER_alpha.smi (548/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL222522 in Train_ER_alpha.smi (549/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL222580 in Train_ER_alpha.smi (550/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL1449885 in Train_ER_alpha.smi (551/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL1584646 in Train_ER_alpha.smi (552/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL1506716 in Train_ER_alpha.smi (553/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL1472407 in Train_ER_alpha.smi (554/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL1448466 in Train_ER_alpha.smi (555/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL1454402 in Train_ER_alpha.smi (556/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL1431650 in Train_ER_alpha.smi (557/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL1367590 in Train_ER_alpha.smi (558/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL1587260 in Train_ER_alpha.smi (559/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL1522044 in Train_ER_alpha.smi (560/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL1461574 in Train_ER_alpha.smi (561/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL1328182 in Train_ER_alpha.smi (562/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL250109 in Train_ER_alpha.smi (563/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL191716 in Train_ER_alpha.smi (564/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL194772 in Train_ER_alpha.smi (565/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL362425 in Train_ER_alpha.smi (566/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL363177 in Train_ER_alpha.smi (567/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL364329 in Train_ER_alpha.smi (568/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL361226 in Train_ER_alpha.smi (569/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL379543 in Train_ER_alpha.smi (570/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL361063 in Train_ER_alpha.smi (571/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL189822 in Train_ER_alpha.smi (572/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL192782 in Train_ER_alpha.smi (573/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL192901 in Train_ER_alpha.smi (574/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL362307 in Train_ER_alpha.smi (575/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL190487 in Train_ER_alpha.smi (576/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL1479 in Train_ER_alpha.smi (577/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL104416 in Train_ER_alpha.smi (578/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL195991 in Train_ER_alpha.smi (579/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL190038 in Train_ER_alpha.smi (580/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL1627354 in Train_ER_alpha.smi (582/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL189303 in Train_ER_alpha.smi (581/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL178665 in Train_ER_alpha.smi (583/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL435696 in Train_ER_alpha.smi (584/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL371564 in Train_ER_alpha.smi (585/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL1628170 in Train_ER_alpha.smi (586/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL362349 in Train_ER_alpha.smi (587/1238). Average speed: 0.02 s/mol.\n"" + ] + }, + { + ""name"": ""stdout"", + ""output_type"": ""stream"", + ""text"": [ + ""Processing CHEMBL954 in Train_ER_alpha.smi (588/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL184974 in Train_ER_alpha.smi (589/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL1627353 in Train_ER_alpha.smi (590/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL188846 in Train_ER_alpha.smi (591/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL372304 in Train_ER_alpha.smi (593/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL196131 in Train_ER_alpha.smi (592/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL427180 in Train_ER_alpha.smi (594/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL194265 in Train_ER_alpha.smi (595/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL195788 in Train_ER_alpha.smi (596/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL369545 in Train_ER_alpha.smi (598/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL183344 in Train_ER_alpha.smi (597/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL361078 in Train_ER_alpha.smi (599/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL187393 in Train_ER_alpha.smi (600/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL194762 in Train_ER_alpha.smi (602/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL1200623 in Train_ER_alpha.smi (601/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL187392 in Train_ER_alpha.smi (604/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL188051 in Train_ER_alpha.smi (603/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL418971 in Train_ER_alpha.smi (605/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL184367 in Train_ER_alpha.smi (606/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL370282 in Train_ER_alpha.smi (607/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL180702 in Train_ER_alpha.smi (608/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL103650 in Train_ER_alpha.smi (609/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL181936 in Train_ER_alpha.smi (610/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL1411687 in Train_ER_alpha.smi (611/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL1302802 in Train_ER_alpha.smi (612/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL1521414 in Train_ER_alpha.smi (613/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL1491602 in Train_ER_alpha.smi (614/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL1341814 in Train_ER_alpha.smi (615/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL1322601 in Train_ER_alpha.smi (616/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL1604803 in Train_ER_alpha.smi (617/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL1541552 in Train_ER_alpha.smi (619/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL1393131 in Train_ER_alpha.smi (618/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL1350028 in Train_ER_alpha.smi (620/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL183782 in Train_ER_alpha.smi (621/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL1423880 in Train_ER_alpha.smi (622/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL1493034 in Train_ER_alpha.smi (623/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL1347829 in Train_ER_alpha.smi (624/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL1310171 in Train_ER_alpha.smi (625/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL1411209 in Train_ER_alpha.smi (626/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL1507788 in Train_ER_alpha.smi (627/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL196583 in Train_ER_alpha.smi (628/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL1505283 in Train_ER_alpha.smi (629/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL1532659 in Train_ER_alpha.smi (630/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL189039 in Train_ER_alpha.smi (631/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL187673 in Train_ER_alpha.smi (632/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL197757 in Train_ER_alpha.smi (633/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL191715 in Train_ER_alpha.smi (634/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL153395 in Train_ER_alpha.smi (636/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL195289 in Train_ER_alpha.smi (637/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL122208 in Train_ER_alpha.smi (635/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL1381306 in Train_ER_alpha.smi (638/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL1421864 in Train_ER_alpha.smi (639/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL1560682 in Train_ER_alpha.smi (640/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL1475434 in Train_ER_alpha.smi (641/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL1434961 in Train_ER_alpha.smi (642/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL1552932 in Train_ER_alpha.smi (643/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL1593653 in Train_ER_alpha.smi (644/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL1449110 in Train_ER_alpha.smi (645/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL192390 in Train_ER_alpha.smi (646/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL340606 in Train_ER_alpha.smi (647/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL371305 in Train_ER_alpha.smi (648/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL195280 in Train_ER_alpha.smi (649/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL193482 in Train_ER_alpha.smi (650/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL1519669 in Train_ER_alpha.smi (652/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL1443090 in Train_ER_alpha.smi (651/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL1299357 in Train_ER_alpha.smi (653/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL1581690 in Train_ER_alpha.smi (654/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL1433062 in Train_ER_alpha.smi (655/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL125362 in Train_ER_alpha.smi (656/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL361871 in Train_ER_alpha.smi (657/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL482116 in Train_ER_alpha.smi (658/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL190682 in Train_ER_alpha.smi (659/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL363872 in Train_ER_alpha.smi (660/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL184421 in Train_ER_alpha.smi (661/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL205920 in Train_ER_alpha.smi (662/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL185383 in Train_ER_alpha.smi (663/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL226329 in Train_ER_alpha.smi (664/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL200799 in Train_ER_alpha.smi (665/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL371012 in Train_ER_alpha.smi (666/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL362356 in Train_ER_alpha.smi (667/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL238890 in Train_ER_alpha.smi (668/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL240157 in Train_ER_alpha.smi (669/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL511971 in Train_ER_alpha.smi (670/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL364419 in Train_ER_alpha.smi (671/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL391795 in Train_ER_alpha.smi (672/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL398455 in Train_ER_alpha.smi (673/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL430395 in Train_ER_alpha.smi (674/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL183044 in Train_ER_alpha.smi (675/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL189002 in Train_ER_alpha.smi (676/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL438256 in Train_ER_alpha.smi (677/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL188957 in Train_ER_alpha.smi (678/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL204697 in Train_ER_alpha.smi (679/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL203561 in Train_ER_alpha.smi (680/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL211739 in Train_ER_alpha.smi (681/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL213529 in Train_ER_alpha.smi (682/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL187978 in Train_ER_alpha.smi (684/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL214200 in Train_ER_alpha.smi (683/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL187219 in Train_ER_alpha.smi (685/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL388473 in Train_ER_alpha.smi (687/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL394357 in Train_ER_alpha.smi (686/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL242818 in Train_ER_alpha.smi (688/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL106773 in Train_ER_alpha.smi (689/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL192000 in Train_ER_alpha.smi (690/1238). Average speed: 0.02 s/mol.\n"" + ] + }, + { + ""name"": ""stdout"", + ""output_type"": ""stream"", + ""text"": [ + ""Processing CHEMBL202781 in Train_ER_alpha.smi (691/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL198445 in Train_ER_alpha.smi (692/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL184491 in Train_ER_alpha.smi (694/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL432454 in Train_ER_alpha.smi (693/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL393982 in Train_ER_alpha.smi (695/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL240438 in Train_ER_alpha.smi (696/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL346455 in Train_ER_alpha.smi (698/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL188246 in Train_ER_alpha.smi (697/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL232022 in Train_ER_alpha.smi (699/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL206120 in Train_ER_alpha.smi (700/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL210074 in Train_ER_alpha.smi (701/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL209738 in Train_ER_alpha.smi (702/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL377254 in Train_ER_alpha.smi (703/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL213829 in Train_ER_alpha.smi (704/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL455215 in Train_ER_alpha.smi (705/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL522990 in Train_ER_alpha.smi (706/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL267431 in Train_ER_alpha.smi (707/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL469510 in Train_ER_alpha.smi (708/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL368688 in Train_ER_alpha.smi (709/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL1076306 in Train_ER_alpha.smi (710/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL198680 in Train_ER_alpha.smi (711/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL125119 in Train_ER_alpha.smi (712/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL414859 in Train_ER_alpha.smi (713/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL378798 in Train_ER_alpha.smi (714/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL46903 in Train_ER_alpha.smi (715/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL195311 in Train_ER_alpha.smi (716/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL1450791 in Train_ER_alpha.smi (717/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL203370 in Train_ER_alpha.smi (718/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL378572 in Train_ER_alpha.smi (719/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL206137 in Train_ER_alpha.smi (720/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL1389780 in Train_ER_alpha.smi (721/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL1312693 in Train_ER_alpha.smi (722/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL1324290 in Train_ER_alpha.smi (723/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL1377973 in Train_ER_alpha.smi (724/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL1453264 in Train_ER_alpha.smi (725/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL1533166 in Train_ER_alpha.smi (726/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL1304227 in Train_ER_alpha.smi (727/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL1329686 in Train_ER_alpha.smi (728/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL1498999 in Train_ER_alpha.smi (729/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL1386537 in Train_ER_alpha.smi (730/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL1520373 in Train_ER_alpha.smi (731/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL1452952 in Train_ER_alpha.smi (732/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL1393976 in Train_ER_alpha.smi (733/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL1419810 in Train_ER_alpha.smi (734/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL1439181 in Train_ER_alpha.smi (736/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL1531320 in Train_ER_alpha.smi (735/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL1484547 in Train_ER_alpha.smi (737/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL1577757 in Train_ER_alpha.smi (738/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL1505467 in Train_ER_alpha.smi (739/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL1372307 in Train_ER_alpha.smi (740/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL1525366 in Train_ER_alpha.smi (741/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL1482875 in Train_ER_alpha.smi (742/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL1345810 in Train_ER_alpha.smi (743/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL1374573 in Train_ER_alpha.smi (744/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL1577661 in Train_ER_alpha.smi (745/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL1370120 in Train_ER_alpha.smi (746/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL1407054 in Train_ER_alpha.smi (747/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL1548616 in Train_ER_alpha.smi (748/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL1559731 in Train_ER_alpha.smi (749/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL1555033 in Train_ER_alpha.smi (750/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL1405182 in Train_ER_alpha.smi (751/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL1455849 in Train_ER_alpha.smi (752/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL1356953 in Train_ER_alpha.smi (753/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL1326803 in Train_ER_alpha.smi (754/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL1597805 in Train_ER_alpha.smi (755/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL1084746 in Train_ER_alpha.smi (756/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL1083326 in Train_ER_alpha.smi (757/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL426897 in Train_ER_alpha.smi (758/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL386502 in Train_ER_alpha.smi (759/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL223026 in Train_ER_alpha.smi (760/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL144897 in Train_ER_alpha.smi (761/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL433769 in Train_ER_alpha.smi (762/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL179852 in Train_ER_alpha.smi (763/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL183033 in Train_ER_alpha.smi (764/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL190025 in Train_ER_alpha.smi (765/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL198088 in Train_ER_alpha.smi (766/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL1576748 in Train_ER_alpha.smi (767/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL1388431 in Train_ER_alpha.smi (768/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL1509933 in Train_ER_alpha.smi (769/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL360817 in Train_ER_alpha.smi (770/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL369724 in Train_ER_alpha.smi (771/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL52 in Train_ER_alpha.smi (772/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL195217 in Train_ER_alpha.smi (773/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL1522446 in Train_ER_alpha.smi (774/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL1434556 in Train_ER_alpha.smi (775/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL1414407 in Train_ER_alpha.smi (776/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL1397845 in Train_ER_alpha.smi (777/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL282575 in Train_ER_alpha.smi (778/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL361056 in Train_ER_alpha.smi (779/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL1491959 in Train_ER_alpha.smi (780/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL1326483 in Train_ER_alpha.smi (781/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL188516 in Train_ER_alpha.smi (782/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL178334 in Train_ER_alpha.smi (783/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL9352 in Train_ER_alpha.smi (784/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL198204 in Train_ER_alpha.smi (785/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL187311 in Train_ER_alpha.smi (786/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL425194 in Train_ER_alpha.smi (787/1238). Average speed: 0.02 s/mol.\n"" + ] + }, + { + ""name"": ""stdout"", + ""output_type"": ""stream"", + ""text"": [ + ""Processing CHEMBL1580991 in Train_ER_alpha.smi (788/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL1300662 in Train_ER_alpha.smi (789/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL1442450 in Train_ER_alpha.smi (790/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL1539865 in Train_ER_alpha.smi (791/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL1466928 in Train_ER_alpha.smi (792/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL182157 in Train_ER_alpha.smi (793/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL1467399 in Train_ER_alpha.smi (794/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL192260 in Train_ER_alpha.smi (795/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL1303477 in Train_ER_alpha.smi (796/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL1350069 in Train_ER_alpha.smi (797/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL187313 in Train_ER_alpha.smi (798/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL1391547 in Train_ER_alpha.smi (799/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL1526157 in Train_ER_alpha.smi (800/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL1572777 in Train_ER_alpha.smi (801/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL1320937 in Train_ER_alpha.smi (802/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL1582403 in Train_ER_alpha.smi (803/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL1447311 in Train_ER_alpha.smi (804/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL189556 in Train_ER_alpha.smi (805/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL1431081 in Train_ER_alpha.smi (806/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL1421887 in Train_ER_alpha.smi (807/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL1231453 in Train_ER_alpha.smi (808/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL204376 in Train_ER_alpha.smi (809/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL460647 in Train_ER_alpha.smi (810/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL216334 in Train_ER_alpha.smi (811/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL377295 in Train_ER_alpha.smi (812/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL1325233 in Train_ER_alpha.smi (813/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL1574512 in Train_ER_alpha.smi (815/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL1430316 in Train_ER_alpha.smi (814/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL379314 in Train_ER_alpha.smi (816/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL385949 in Train_ER_alpha.smi (817/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL378883 in Train_ER_alpha.smi (818/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL215428 in Train_ER_alpha.smi (819/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL385028 in Train_ER_alpha.smi (820/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL212391 in Train_ER_alpha.smi (821/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL377457 in Train_ER_alpha.smi (822/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL214692 in Train_ER_alpha.smi (823/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL372783 in Train_ER_alpha.smi (824/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL398456 in Train_ER_alpha.smi (825/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL249904 in Train_ER_alpha.smi (826/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL250110 in Train_ER_alpha.smi (827/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL380452 in Train_ER_alpha.smi (828/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL380358 in Train_ER_alpha.smi (829/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL438443 in Train_ER_alpha.smi (830/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL380838 in Train_ER_alpha.smi (831/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL188299 in Train_ER_alpha.smi (832/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL394614 in Train_ER_alpha.smi (833/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL469511 in Train_ER_alpha.smi (834/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL1300008 in Train_ER_alpha.smi (835/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL1531512 in Train_ER_alpha.smi (836/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL1404523 in Train_ER_alpha.smi (837/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL1386091 in Train_ER_alpha.smi (838/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL211953 in Train_ER_alpha.smi (839/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL214034 in Train_ER_alpha.smi (840/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL211666 in Train_ER_alpha.smi (841/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL206500 in Train_ER_alpha.smi (842/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL206439 in Train_ER_alpha.smi (843/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL1307301 in Train_ER_alpha.smi (844/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL231810 in Train_ER_alpha.smi (846/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL10560 in Train_ER_alpha.smi (845/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL1470395 in Train_ER_alpha.smi (847/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL1320651 in Train_ER_alpha.smi (848/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL1417890 in Train_ER_alpha.smi (849/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL1429596 in Train_ER_alpha.smi (850/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL1404155 in Train_ER_alpha.smi (851/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL1442697 in Train_ER_alpha.smi (852/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL1603599 in Train_ER_alpha.smi (853/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL203283 in Train_ER_alpha.smi (854/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL206426 in Train_ER_alpha.smi (856/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL377083 in Train_ER_alpha.smi (855/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL202738 in Train_ER_alpha.smi (857/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL205577 in Train_ER_alpha.smi (858/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL208324 in Train_ER_alpha.smi (859/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL1471823 in Train_ER_alpha.smi (860/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL192621 in Train_ER_alpha.smi (861/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL192586 in Train_ER_alpha.smi (862/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL434577 in Train_ER_alpha.smi (863/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL465879 in Train_ER_alpha.smi (864/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL384708 in Train_ER_alpha.smi (865/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL221626 in Train_ER_alpha.smi (866/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL222452 in Train_ER_alpha.smi (867/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL372411 in Train_ER_alpha.smi (868/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL201365 in Train_ER_alpha.smi (869/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL199766 in Train_ER_alpha.smi (870/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL204317 in Train_ER_alpha.smi (871/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL383253 in Train_ER_alpha.smi (872/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL425603 in Train_ER_alpha.smi (873/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL180995 in Train_ER_alpha.smi (875/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL203123 in Train_ER_alpha.smi (876/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL212675 in Train_ER_alpha.smi (874/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL211599 in Train_ER_alpha.smi (877/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL213457 in Train_ER_alpha.smi (878/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL214291 in Train_ER_alpha.smi (879/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL240230 in Train_ER_alpha.smi (880/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL394213 in Train_ER_alpha.smi (881/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL193108 in Train_ER_alpha.smi (883/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL193146 in Train_ER_alpha.smi (882/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL182794 in Train_ER_alpha.smi (884/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL194873 in Train_ER_alpha.smi (885/1238). Average speed: 0.02 s/mol.\n"" + ] + }, + { + ""name"": ""stdout"", + ""output_type"": ""stream"", + ""text"": [ + ""Processing CHEMBL187671 in Train_ER_alpha.smi (886/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL189287 in Train_ER_alpha.smi (887/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL1732228 in Train_ER_alpha.smi (888/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL187663 in Train_ER_alpha.smi (889/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL198089 in Train_ER_alpha.smi (890/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL189706 in Train_ER_alpha.smi (891/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL188230 in Train_ER_alpha.smi (892/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL185012 in Train_ER_alpha.smi (893/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL122237 in Train_ER_alpha.smi (894/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL361005 in Train_ER_alpha.smi (895/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL184679 in Train_ER_alpha.smi (896/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL1424468 in Train_ER_alpha.smi (897/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL187105 in Train_ER_alpha.smi (898/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL276030 in Train_ER_alpha.smi (899/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL188528 in Train_ER_alpha.smi (900/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL181368 in Train_ER_alpha.smi (901/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL195290 in Train_ER_alpha.smi (902/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL363412 in Train_ER_alpha.smi (903/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL186874 in Train_ER_alpha.smi (904/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL364176 in Train_ER_alpha.smi (905/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL365903 in Train_ER_alpha.smi (906/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL195389 in Train_ER_alpha.smi (907/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL360189 in Train_ER_alpha.smi (908/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL365484 in Train_ER_alpha.smi (909/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL193207 in Train_ER_alpha.smi (910/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL189077 in Train_ER_alpha.smi (911/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL360338 in Train_ER_alpha.smi (913/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL1627351 in Train_ER_alpha.smi (912/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL440757 in Train_ER_alpha.smi (914/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL178857 in Train_ER_alpha.smi (915/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL188882 in Train_ER_alpha.smi (916/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL360385 in Train_ER_alpha.smi (917/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL182980 in Train_ER_alpha.smi (918/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL195039 in Train_ER_alpha.smi (919/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL180085 in Train_ER_alpha.smi (920/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL183162 in Train_ER_alpha.smi (921/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL427324 in Train_ER_alpha.smi (922/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL1596554 in Train_ER_alpha.smi (923/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL1301994 in Train_ER_alpha.smi (924/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL185837 in Train_ER_alpha.smi (925/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL1466913 in Train_ER_alpha.smi (926/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL425246 in Train_ER_alpha.smi (927/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL380032 in Train_ER_alpha.smi (928/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL211349 in Train_ER_alpha.smi (929/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL377988 in Train_ER_alpha.smi (930/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL1323006 in Train_ER_alpha.smi (931/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL234524 in Train_ER_alpha.smi (932/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL396947 in Train_ER_alpha.smi (933/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL212468 in Train_ER_alpha.smi (934/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL383518 in Train_ER_alpha.smi (935/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL203340 in Train_ER_alpha.smi (937/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL204272 in Train_ER_alpha.smi (936/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL380720 in Train_ER_alpha.smi (938/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL205614 in Train_ER_alpha.smi (939/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL204118 in Train_ER_alpha.smi (940/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL222453 in Train_ER_alpha.smi (941/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL577709 in Train_ER_alpha.smi (942/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL1566266 in Train_ER_alpha.smi (943/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL183889 in Train_ER_alpha.smi (944/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL205398 in Train_ER_alpha.smi (945/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL379252 in Train_ER_alpha.smi (946/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL203122 in Train_ER_alpha.smi (947/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL206286 in Train_ER_alpha.smi (948/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL222454 in Train_ER_alpha.smi (950/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL225514 in Train_ER_alpha.smi (949/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL215593 in Train_ER_alpha.smi (951/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL391909 in Train_ER_alpha.smi (952/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL241256 in Train_ER_alpha.smi (953/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL241484 in Train_ER_alpha.smi (954/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL396119 in Train_ER_alpha.smi (955/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL372030 in Train_ER_alpha.smi (956/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL202400 in Train_ER_alpha.smi (957/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL437065 in Train_ER_alpha.smi (958/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL377847 in Train_ER_alpha.smi (959/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL226268 in Train_ER_alpha.smi (960/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL226327 in Train_ER_alpha.smi (961/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL239723 in Train_ER_alpha.smi (962/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL190467 in Train_ER_alpha.smi (963/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL192849 in Train_ER_alpha.smi (964/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL205119 in Train_ER_alpha.smi (965/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL208085 in Train_ER_alpha.smi (967/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL210143 in Train_ER_alpha.smi (966/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL383112 in Train_ER_alpha.smi (968/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL209978 in Train_ER_alpha.smi (969/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL213070 in Train_ER_alpha.smi (970/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL341367 in Train_ER_alpha.smi (971/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL426154 in Train_ER_alpha.smi (972/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL216596 in Train_ER_alpha.smi (973/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL1628029 in Train_ER_alpha.smi (974/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL188517 in Train_ER_alpha.smi (975/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL197889 in Train_ER_alpha.smi (976/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL121720 in Train_ER_alpha.smi (978/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL513397 in Train_ER_alpha.smi (977/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL1377389 in Train_ER_alpha.smi (979/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL1516948 in Train_ER_alpha.smi (980/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL1519384 in Train_ER_alpha.smi (981/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL1330161 in Train_ER_alpha.smi (982/1238). Average speed: 0.02 s/mol.\n"" + ] + }, + { + ""name"": ""stdout"", + ""output_type"": ""stream"", + ""text"": [ + ""Processing CHEMBL1309506 in Train_ER_alpha.smi (983/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL1162 in Train_ER_alpha.smi (984/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL1507012 in Train_ER_alpha.smi (985/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL1468124 in Train_ER_alpha.smi (986/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL1476347 in Train_ER_alpha.smi (987/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL1612880 in Train_ER_alpha.smi (988/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL1585431 in Train_ER_alpha.smi (990/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL1359354 in Train_ER_alpha.smi (989/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL1501342 in Train_ER_alpha.smi (991/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL1580543 in Train_ER_alpha.smi (992/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL1448272 in Train_ER_alpha.smi (993/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL1406018 in Train_ER_alpha.smi (994/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL245378 in Train_ER_alpha.smi (995/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL1532234 in Train_ER_alpha.smi (996/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL1312859 in Train_ER_alpha.smi (997/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL249700 in Train_ER_alpha.smi (998/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL1584299 in Train_ER_alpha.smi (999/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL457035 in Train_ER_alpha.smi (1000/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL1983345 in Train_ER_alpha.smi (1001/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL1996233 in Train_ER_alpha.smi (1002/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL1976499 in Train_ER_alpha.smi (1003/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL1984703 in Train_ER_alpha.smi (1004/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL2103774 in Train_ER_alpha.smi (1005/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL1999723 in Train_ER_alpha.smi (1006/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL1972915 in Train_ER_alpha.smi (1007/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL2024377 in Train_ER_alpha.smi (1008/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL2024376 in Train_ER_alpha.smi (1009/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL2113002 in Train_ER_alpha.smi (1010/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL2112471 in Train_ER_alpha.smi (1011/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL2112469 in Train_ER_alpha.smi (1012/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL2112470 in Train_ER_alpha.smi (1013/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL2113027 in Train_ER_alpha.smi (1014/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL2113001 in Train_ER_alpha.smi (1015/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL2403351 in Train_ER_alpha.smi (1017/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL2071180 in Train_ER_alpha.smi (1016/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL1208076 in Train_ER_alpha.smi (1018/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL2403361 in Train_ER_alpha.smi (1019/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL2403363 in Train_ER_alpha.smi (1020/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL2403346 in Train_ER_alpha.smi (1021/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL2403359 in Train_ER_alpha.smi (1022/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL2403358 in Train_ER_alpha.smi (1023/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL2403360 in Train_ER_alpha.smi (1024/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL2401849 in Train_ER_alpha.smi (1025/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL2403362 in Train_ER_alpha.smi (1026/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL2401848 in Train_ER_alpha.smi (1027/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL2403348 in Train_ER_alpha.smi (1028/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL2407651 in Train_ER_alpha.smi (1029/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL2407647 in Train_ER_alpha.smi (1030/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL2403347 in Train_ER_alpha.smi (1031/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL2392777 in Train_ER_alpha.smi (1032/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL2441817 in Train_ER_alpha.smi (1033/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL2441818 in Train_ER_alpha.smi (1034/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL2441819 in Train_ER_alpha.smi (1035/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL3099431 in Train_ER_alpha.smi (1036/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL3099432 in Train_ER_alpha.smi (1037/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL3098274 in Train_ER_alpha.smi (1038/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL3099429 in Train_ER_alpha.smi (1040/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL3145004 in Train_ER_alpha.smi (1041/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL3098273 in Train_ER_alpha.smi (1039/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL3098275 in Train_ER_alpha.smi (1042/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL3208438 in Train_ER_alpha.smi (1043/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL3208556 in Train_ER_alpha.smi (1044/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL3189964 in Train_ER_alpha.smi (1045/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL3199523 in Train_ER_alpha.smi (1047/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL3190827 in Train_ER_alpha.smi (1046/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL3189836 in Train_ER_alpha.smi (1048/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL3213380 in Train_ER_alpha.smi (1049/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL3109601 in Train_ER_alpha.smi (1050/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL3109240 in Train_ER_alpha.smi (1051/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL3109156 in Train_ER_alpha.smi (1052/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL3109154 in Train_ER_alpha.smi (1053/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL3189442 in Train_ER_alpha.smi (1054/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL3189821 in Train_ER_alpha.smi (1055/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL3198515 in Train_ER_alpha.smi (1056/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL3193600 in Train_ER_alpha.smi (1057/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL3214324 in Train_ER_alpha.smi (1059/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL3207314 in Train_ER_alpha.smi (1058/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL3212870 in Train_ER_alpha.smi (1060/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL3208784 in Train_ER_alpha.smi (1061/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL3235685 in Train_ER_alpha.smi (1062/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL3195623 in Train_ER_alpha.smi (1064/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL3235686 in Train_ER_alpha.smi (1063/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL3360841 in Train_ER_alpha.smi (1066/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL3196534 in Train_ER_alpha.smi (1065/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL3290287 in Train_ER_alpha.smi (1067/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL3290288 in Train_ER_alpha.smi (1068/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL3288736 in Train_ER_alpha.smi (1069/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL3427502 in Train_ER_alpha.smi (1070/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL3360850 in Train_ER_alpha.smi (1071/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL3581693 in Train_ER_alpha.smi (1072/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL3586190 in Train_ER_alpha.smi (1073/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL3586194 in Train_ER_alpha.smi (1074/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL3586191 in Train_ER_alpha.smi (1075/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL3360845 in Train_ER_alpha.smi (1076/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL3586192 in Train_ER_alpha.smi (1077/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL3586193 in Train_ER_alpha.smi (1078/1238). Average speed: 0.02 s/mol.\n"" + ] + }, + { + ""name"": ""stdout"", + ""output_type"": ""stream"", + ""text"": [ + ""Processing CHEMBL3586195 in Train_ER_alpha.smi (1079/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL3360843 in Train_ER_alpha.smi (1080/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL3360846 in Train_ER_alpha.smi (1081/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL3360848 in Train_ER_alpha.smi (1082/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL3589347 in Train_ER_alpha.smi (1083/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL3589365 in Train_ER_alpha.smi (1084/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL3589701 in Train_ER_alpha.smi (1085/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL3589349 in Train_ER_alpha.smi (1086/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL3589367 in Train_ER_alpha.smi (1087/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL3578274 in Train_ER_alpha.smi (1088/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL3578276 in Train_ER_alpha.smi (1089/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL3578271 in Train_ER_alpha.smi (1090/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL3589346 in Train_ER_alpha.smi (1091/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL3589361 in Train_ER_alpha.smi (1092/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL3589363 in Train_ER_alpha.smi (1093/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL3589364 in Train_ER_alpha.smi (1094/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL3588865 in Train_ER_alpha.smi (1095/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL3589356 in Train_ER_alpha.smi (1097/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL3589352 in Train_ER_alpha.smi (1096/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL3597498 in Train_ER_alpha.smi (1098/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL3597499 in Train_ER_alpha.smi (1099/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL3597505 in Train_ER_alpha.smi (1101/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL3597501 in Train_ER_alpha.smi (1100/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL3597508 in Train_ER_alpha.smi (1102/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL3589354 in Train_ER_alpha.smi (1104/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL3597509 in Train_ER_alpha.smi (1103/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL3589368 in Train_ER_alpha.smi (1105/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL3597502 in Train_ER_alpha.smi (1106/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL3360847 in Train_ER_alpha.smi (1107/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL3360849 in Train_ER_alpha.smi (1108/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL3427411 in Train_ER_alpha.smi (1109/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL3427406 in Train_ER_alpha.smi (1110/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL3578285 in Train_ER_alpha.smi (1111/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL3578286 in Train_ER_alpha.smi (1112/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL3576885 in Train_ER_alpha.smi (1113/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL3578284 in Train_ER_alpha.smi (1114/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL3622364 in Train_ER_alpha.smi (1115/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL3622365 in Train_ER_alpha.smi (1116/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL3657137 in Train_ER_alpha.smi (1117/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL3657148 in Train_ER_alpha.smi (1118/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL3657159 in Train_ER_alpha.smi (1119/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL3657170 in Train_ER_alpha.smi (1120/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL3657181 in Train_ER_alpha.smi (1121/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL3667040 in Train_ER_alpha.smi (1122/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL3657142 in Train_ER_alpha.smi (1123/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL3657143 in Train_ER_alpha.smi (1124/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL3657144 in Train_ER_alpha.smi (1125/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL3657145 in Train_ER_alpha.smi (1126/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL3657146 in Train_ER_alpha.smi (1127/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL3657172 in Train_ER_alpha.smi (1128/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL3657173 in Train_ER_alpha.smi (1129/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL3657174 in Train_ER_alpha.smi (1130/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL3657175 in Train_ER_alpha.smi (1131/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL3657147 in Train_ER_alpha.smi (1132/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL3657154 in Train_ER_alpha.smi (1133/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL3657155 in Train_ER_alpha.smi (1134/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL3657149 in Train_ER_alpha.smi (1135/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL3657150 in Train_ER_alpha.smi (1136/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL3657156 in Train_ER_alpha.smi (1137/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL3657157 in Train_ER_alpha.smi (1138/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL3657158 in Train_ER_alpha.smi (1139/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL3657151 in Train_ER_alpha.smi (1140/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL3657152 in Train_ER_alpha.smi (1141/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL3657153 in Train_ER_alpha.smi (1142/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL3657176 in Train_ER_alpha.smi (1144/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL3657135 in Train_ER_alpha.smi (1143/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL3657177 in Train_ER_alpha.smi (1145/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL3657185 in Train_ER_alpha.smi (1147/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL3657184 in Train_ER_alpha.smi (1146/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL3657164 in Train_ER_alpha.smi (1148/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL3657165 in Train_ER_alpha.smi (1149/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL3648370 in Train_ER_alpha.smi (1150/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL3657178 in Train_ER_alpha.smi (1151/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL3657179 in Train_ER_alpha.smi (1152/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL3657160 in Train_ER_alpha.smi (1153/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL3657161 in Train_ER_alpha.smi (1154/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL3657162 in Train_ER_alpha.smi (1155/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL3657163 in Train_ER_alpha.smi (1156/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL3657180 in Train_ER_alpha.smi (1157/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL3657182 in Train_ER_alpha.smi (1158/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL3657183 in Train_ER_alpha.smi (1159/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL3657166 in Train_ER_alpha.smi (1160/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL3657167 in Train_ER_alpha.smi (1161/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL3657168 in Train_ER_alpha.smi (1162/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL3657136 in Train_ER_alpha.smi (1163/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL3657138 in Train_ER_alpha.smi (1164/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL3657139 in Train_ER_alpha.smi (1165/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL3657140 in Train_ER_alpha.smi (1166/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL3657141 in Train_ER_alpha.smi (1167/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL3657169 in Train_ER_alpha.smi (1168/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL3657171 in Train_ER_alpha.smi (1169/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL3775116 in Train_ER_alpha.smi (1170/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL3774660 in Train_ER_alpha.smi (1171/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL3775016 in Train_ER_alpha.smi (1172/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL3774647 in Train_ER_alpha.smi (1173/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL3775129 in Train_ER_alpha.smi (1174/1238). Average speed: 0.02 s/mol.\n"" + ] + }, + { + ""name"": ""stdout"", + ""output_type"": ""stream"", + ""text"": [ + ""Processing CHEMBL3775006 in Train_ER_alpha.smi (1175/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL3775410 in Train_ER_alpha.smi (1176/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL3775434 in Train_ER_alpha.smi (1177/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL3775224 in Train_ER_alpha.smi (1178/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL3775283 in Train_ER_alpha.smi (1180/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL3775978 in Train_ER_alpha.smi (1179/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL3775375 in Train_ER_alpha.smi (1181/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL3774642 in Train_ER_alpha.smi (1182/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL3805921 in Train_ER_alpha.smi (1183/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL3805170 in Train_ER_alpha.smi (1184/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL3804907 in Train_ER_alpha.smi (1185/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL3805025 in Train_ER_alpha.smi (1186/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL3805385 in Train_ER_alpha.smi (1187/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL3823498 in Train_ER_alpha.smi (1188/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL3815076 in Train_ER_alpha.smi (1189/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL3823426 in Train_ER_alpha.smi (1190/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL3827870 in Train_ER_alpha.smi (1191/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL3827137 in Train_ER_alpha.smi (1192/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL3827692 in Train_ER_alpha.smi (1193/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL3827034 in Train_ER_alpha.smi (1194/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL3827784 in Train_ER_alpha.smi (1195/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL3827200 in Train_ER_alpha.smi (1196/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL3828208 in Train_ER_alpha.smi (1197/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL3827633 in Train_ER_alpha.smi (1198/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL3827098 in Train_ER_alpha.smi (1199/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL3828416 in Train_ER_alpha.smi (1200/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL3827045 in Train_ER_alpha.smi (1201/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL3827152 in Train_ER_alpha.smi (1202/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL3828422 in Train_ER_alpha.smi (1203/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL3827193 in Train_ER_alpha.smi (1204/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL3827286 in Train_ER_alpha.smi (1205/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL3828117 in Train_ER_alpha.smi (1206/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL101382 in Train_ER_alpha.smi (1208/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL3823192 in Train_ER_alpha.smi (1207/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL180792 in Train_ER_alpha.smi (1209/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL183584 in Train_ER_alpha.smi (1210/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL184202 in Train_ER_alpha.smi (1211/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL191974 in Train_ER_alpha.smi (1212/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL192548 in Train_ER_alpha.smi (1213/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL193518 in Train_ER_alpha.smi (1214/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL209269 in Train_ER_alpha.smi (1215/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL209851 in Train_ER_alpha.smi (1216/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL2403356 in Train_ER_alpha.smi (1217/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL2403357 in Train_ER_alpha.smi (1218/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL246138 in Train_ER_alpha.smi (1219/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL267385 in Train_ER_alpha.smi (1220/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL281499 in Train_ER_alpha.smi (1221/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL304552 in Train_ER_alpha.smi (1222/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL30707 in Train_ER_alpha.smi (1223/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL328190 in Train_ER_alpha.smi (1224/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL3427402 in Train_ER_alpha.smi (1225/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL3623002 in Train_ER_alpha.smi (1226/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL3623003 in Train_ER_alpha.smi (1227/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL3623004 in Train_ER_alpha.smi (1228/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL362623 in Train_ER_alpha.smi (1229/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL367588 in Train_ER_alpha.smi (1230/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL370039 in Train_ER_alpha.smi (1231/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL3774510 in Train_ER_alpha.smi (1232/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL3774584 in Train_ER_alpha.smi (1233/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL3774737 in Train_ER_alpha.smi (1234/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL3774944 in Train_ER_alpha.smi (1235/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL3775766 in Train_ER_alpha.smi (1236/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL691 in Train_ER_alpha.smi (1238/1238). Average speed: 0.02 s/mol.\n"", + ""Processing CHEMBL3775908 in Train_ER_alpha.smi (1237/1238). Average speed: 0.02 s/mol.\n"", + ""Descriptor calculation completed in 25.654 secs . Average speed: 0.02 s/mol.\n"", + ""Train_ER_alpha.smi\n"", + ""Train_ER_alpha.csv\n"", + ""GraphOnlyFingerprinter.xml\n"", + ""GraphOnlyFingerprinter_\n"", + ""Processing CHEMBL370037 in Train_ER_alpha.smi (1/1238). \n"", + ""Processing CHEMBL184431 in Train_ER_alpha.smi (5/1238). \n"", + ""Processing CHEMBL189073 in Train_ER_alpha.smi (2/1238). \n"", + ""Processing CHEMBL365290 in Train_ER_alpha.smi (3/1238). \n"", + ""Processing CHEMBL364551 in Train_ER_alpha.smi (6/1238). \n"", + ""Processing CHEMBL191648 in Train_ER_alpha.smi (4/1238). \n"", + ""Processing CHEMBL198410 in Train_ER_alpha.smi (8/1238). Average speed: 0.61 s/mol.\n"", + ""Processing CHEMBL1496978 in Train_ER_alpha.smi (9/1238). Average speed: 0.62 s/mol.\n"", + ""Processing CHEMBL124482 in Train_ER_alpha.smi (7/1238). Average speed: 0.59 s/mol.\n"", + ""Processing CHEMBL1331084 in Train_ER_alpha.smi (12/1238). Average speed: 0.28 s/mol.\n"", + ""Processing CHEMBL1305485 in Train_ER_alpha.smi (10/1238). Average speed: 0.47 s/mol.\n"", + ""Processing CHEMBL1570659 in Train_ER_alpha.smi (11/1238). Average speed: 0.38 s/mol.\n"", + ""Processing CHEMBL434502 in Train_ER_alpha.smi (15/1238). Average speed: 0.22 s/mol.\n"", + ""Processing CHEMBL184448 in Train_ER_alpha.smi (13/1238). Average speed: 0.25 s/mol.\n"", + ""Processing CHEMBL123025 in Train_ER_alpha.smi (17/1238). Average speed: 0.19 s/mol.\n"", + ""Processing CHEMBL153062 in Train_ER_alpha.smi (18/1238). Average speed: 0.17 s/mol.\n"", + ""Processing CHEMBL1468708 in Train_ER_alpha.smi (19/1238). Average speed: 0.16 s/mol.\n"", + ""Processing CHEMBL45068 in Train_ER_alpha.smi (14/1238). Average speed: 0.25 s/mol.\n"", + ""Processing CHEMBL1628199 in Train_ER_alpha.smi (16/1238). Average speed: 0.21 s/mol.\n"", + ""Processing CHEMBL1442416 in Train_ER_alpha.smi (20/1238). Average speed: 0.15 s/mol.\n"", + ""Processing CHEMBL1388271 in Train_ER_alpha.smi (21/1238). Average speed: 0.13 s/mol.\n"", + ""Processing CHEMBL1331872 in Train_ER_alpha.smi (22/1238). Average speed: 0.14 s/mol.\n"", + ""Processing CHEMBL1401227 in Train_ER_alpha.smi (23/1238). Average speed: 0.13 s/mol.\n"", + ""Processing CHEMBL1608989 in Train_ER_alpha.smi (28/1238). Average speed: 0.12 s/mol.\n"", + ""Processing CHEMBL1460126 in Train_ER_alpha.smi (24/1238). Average speed: 0.12 s/mol.\n"", + ""Processing CHEMBL1484998 in Train_ER_alpha.smi (27/1238). Average speed: 0.12 s/mol.\n"", + ""Processing CHEMBL1499638 in Train_ER_alpha.smi (25/1238). Average speed: 0.12 s/mol.\n"", + ""Processing CHEMBL1413557 in Train_ER_alpha.smi (26/1238). Average speed: 0.12 s/mol.\n"", + ""Processing CHEMBL1469743 in Train_ER_alpha.smi (29/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL1384049 in Train_ER_alpha.smi (32/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL1538325 in Train_ER_alpha.smi (30/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL1605393 in Train_ER_alpha.smi (31/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL188049 in Train_ER_alpha.smi (33/1238). Average speed: 0.10 s/mol.\n"" + ] + }, + { + ""name"": ""stdout"", + ""output_type"": ""stream"", + ""text"": [ + ""Processing CHEMBL1381495 in Train_ER_alpha.smi (36/1238). Average speed: 0.09 s/mol.\n"", + ""Processing CHEMBL1385373 in Train_ER_alpha.smi (41/1238). Average speed: 0.08 s/mol.\n"", + ""Processing CHEMBL1565187 in Train_ER_alpha.smi (45/1238). Average speed: 0.07 s/mol.\n"", + ""Processing CHEMBL1480843 in Train_ER_alpha.smi (51/1238). Average speed: 0.07 s/mol.\n"", + ""Processing CHEMBL184372 in Train_ER_alpha.smi (56/1238). Average speed: 0.07 s/mol.\n"", + ""Processing CHEMBL231811 in Train_ER_alpha.smi (60/1238). Average speed: 0.07 s/mol.\n"", + ""Processing CHEMBL225404 in Train_ER_alpha.smi (64/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL1081361 in Train_ER_alpha.smi (66/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL235497 in Train_ER_alpha.smi (68/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL241257 in Train_ER_alpha.smi (73/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL179929 in Train_ER_alpha.smi (79/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL470785 in Train_ER_alpha.smi (83/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL377171 in Train_ER_alpha.smi (86/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL380362 in Train_ER_alpha.smi (90/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL196836 in Train_ER_alpha.smi (94/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL205137 in Train_ER_alpha.smi (101/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL447837 in Train_ER_alpha.smi (106/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL191710 in Train_ER_alpha.smi (109/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL211493 in Train_ER_alpha.smi (113/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL204024 in Train_ER_alpha.smi (115/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL382734 in Train_ER_alpha.smi (119/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL197495 in Train_ER_alpha.smi (124/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL437190 in Train_ER_alpha.smi (128/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL195786 in Train_ER_alpha.smi (135/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL204222 in Train_ER_alpha.smi (138/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL441366 in Train_ER_alpha.smi (140/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL234733 in Train_ER_alpha.smi (146/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL425439 in Train_ER_alpha.smi (149/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL245133 in Train_ER_alpha.smi (153/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL192068 in Train_ER_alpha.smi (161/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL195878 in Train_ER_alpha.smi (163/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL365293 in Train_ER_alpha.smi (165/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL426641 in Train_ER_alpha.smi (167/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL225349 in Train_ER_alpha.smi (169/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL451496 in Train_ER_alpha.smi (172/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL186643 in Train_ER_alpha.smi (173/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL181248 in Train_ER_alpha.smi (177/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL202996 in Train_ER_alpha.smi (181/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL185480 in Train_ER_alpha.smi (184/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL192154 in Train_ER_alpha.smi (192/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL369991 in Train_ER_alpha.smi (193/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL219387 in Train_ER_alpha.smi (200/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL195371 in Train_ER_alpha.smi (202/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL381299 in Train_ER_alpha.smi (211/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL206584 in Train_ER_alpha.smi (212/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL383533 in Train_ER_alpha.smi (215/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL362428 in Train_ER_alpha.smi (220/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL1343011 in Train_ER_alpha.smi (37/1238). Average speed: 0.09 s/mol.\n"", + ""Processing CHEMBL1613068 in Train_ER_alpha.smi (38/1238). Average speed: 0.08 s/mol.\n"", + ""Processing CHEMBL1455258 in Train_ER_alpha.smi (43/1238). Average speed: 0.08 s/mol.\n"", + ""Processing CHEMBL1313979 in Train_ER_alpha.smi (48/1238). Average speed: 0.07 s/mol.\n"", + ""Processing CHEMBL1535728 in Train_ER_alpha.smi (53/1238). Average speed: 0.07 s/mol.\n"", + ""Processing CHEMBL512579 in Train_ER_alpha.smi (58/1238). Average speed: 0.07 s/mol.\n"", + ""Processing CHEMBL385801 in Train_ER_alpha.smi (61/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL235496 in Train_ER_alpha.smi (67/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL361762 in Train_ER_alpha.smi (69/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL188458 in Train_ER_alpha.smi (71/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL59438 in Train_ER_alpha.smi (75/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL150279 in Train_ER_alpha.smi (76/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL470993 in Train_ER_alpha.smi (80/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL577938 in Train_ER_alpha.smi (84/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL385680 in Train_ER_alpha.smi (87/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL206547 in Train_ER_alpha.smi (91/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL25228 in Train_ER_alpha.smi (95/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL425231 in Train_ER_alpha.smi (99/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL429446 in Train_ER_alpha.smi (104/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL426085 in Train_ER_alpha.smi (110/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL212419 in Train_ER_alpha.smi (112/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL377475 in Train_ER_alpha.smi (114/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL378473 in Train_ER_alpha.smi (118/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL198914 in Train_ER_alpha.smi (122/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL241485 in Train_ER_alpha.smi (127/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL184598 in Train_ER_alpha.smi (131/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL204119 in Train_ER_alpha.smi (137/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL384709 in Train_ER_alpha.smi (141/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL438516 in Train_ER_alpha.smi (145/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL379188 in Train_ER_alpha.smi (148/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL192102 in Train_ER_alpha.smi (150/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL234527 in Train_ER_alpha.smi (154/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL366928 in Train_ER_alpha.smi (160/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL374799 in Train_ER_alpha.smi (168/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL222434 in Train_ER_alpha.smi (170/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL362718 in Train_ER_alpha.smi (174/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL121879 in Train_ER_alpha.smi (180/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL449038 in Train_ER_alpha.smi (186/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL332625 in Train_ER_alpha.smi (190/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL377342 in Train_ER_alpha.smi (194/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL202615 in Train_ER_alpha.smi (197/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL364092 in Train_ER_alpha.smi (204/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL441499 in Train_ER_alpha.smi (207/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL212923 in Train_ER_alpha.smi (213/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL241303 in Train_ER_alpha.smi (217/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL496 in Train_ER_alpha.smi (221/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL434525 in Train_ER_alpha.smi (222/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL1361641 in Train_ER_alpha.smi (34/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL1349347 in Train_ER_alpha.smi (40/1238). Average speed: 0.08 s/mol.\n"", + ""Processing CHEMBL1573735 in Train_ER_alpha.smi (42/1238). Average speed: 0.08 s/mol.\n"", + ""Processing CHEMBL1466882 in Train_ER_alpha.smi (47/1238). Average speed: 0.07 s/mol.\n"", + ""Processing CHEMBL1322939 in Train_ER_alpha.smi (50/1238). Average speed: 0.07 s/mol.\n"", + ""Processing CHEMBL1341057 in Train_ER_alpha.smi (54/1238). Average speed: 0.07 s/mol.\n"", + ""Processing CHEMBL442037 in Train_ER_alpha.smi (57/1238). Average speed: 0.07 s/mol.\n"", + ""Processing CHEMBL124474 in Train_ER_alpha.smi (59/1238). Average speed: 0.07 s/mol.\n"", + ""Processing CHEMBL226269 in Train_ER_alpha.smi (65/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL184371 in Train_ER_alpha.smi (70/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL365625 in Train_ER_alpha.smi (77/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL365016 in Train_ER_alpha.smi (78/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL511269 in Train_ER_alpha.smi (81/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL204902 in Train_ER_alpha.smi (88/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL206727 in Train_ER_alpha.smi (89/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL471626 in Train_ER_alpha.smi (92/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL194326 in Train_ER_alpha.smi (96/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL206648 in Train_ER_alpha.smi (98/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL425941 in Train_ER_alpha.smi (102/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL191300 in Train_ER_alpha.smi (105/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL371308 in Train_ER_alpha.smi (108/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL185231 in Train_ER_alpha.smi (117/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL372808 in Train_ER_alpha.smi (123/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL182402 in Train_ER_alpha.smi (129/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL371777 in Train_ER_alpha.smi (133/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL126164 in Train_ER_alpha.smi (134/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL212392 in Train_ER_alpha.smi (139/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL180707 in Train_ER_alpha.smi (143/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL395308 in Train_ER_alpha.smi (151/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL8145 in Train_ER_alpha.smi (156/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL435090 in Train_ER_alpha.smi (158/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL360651 in Train_ER_alpha.smi (162/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL183086 in Train_ER_alpha.smi (166/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL373743 in Train_ER_alpha.smi (171/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL418327 in Train_ER_alpha.smi (175/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL439806 in Train_ER_alpha.smi (179/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL373095 in Train_ER_alpha.smi (183/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL225022 in Train_ER_alpha.smi (187/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL389393 in Train_ER_alpha.smi (188/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL194936 in Train_ER_alpha.smi (191/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL381298 in Train_ER_alpha.smi (195/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL385279 in Train_ER_alpha.smi (199/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL85826 in Train_ER_alpha.smi (201/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL193726 in Train_ER_alpha.smi (205/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL183084 in Train_ER_alpha.smi (208/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL385358 in Train_ER_alpha.smi (214/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL393278 in Train_ER_alpha.smi (219/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL365999 in Train_ER_alpha.smi (223/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1489063 in Train_ER_alpha.smi (35/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL1464837 in Train_ER_alpha.smi (39/1238). Average speed: 0.08 s/mol.\n"", + ""Processing CHEMBL1431555 in Train_ER_alpha.smi (44/1238). Average speed: 0.08 s/mol.\n"", + ""Processing CHEMBL1597933 in Train_ER_alpha.smi (46/1238). Average speed: 0.07 s/mol.\n"", + ""Processing CHEMBL1329455 in Train_ER_alpha.smi (49/1238). Average speed: 0.07 s/mol.\n"", + ""Processing CHEMBL1310614 in Train_ER_alpha.smi (52/1238). Average speed: 0.07 s/mol.\n"", + ""Processing CHEMBL1420411 in Train_ER_alpha.smi (55/1238). Average speed: 0.07 s/mol.\n"", + ""Processing CHEMBL447111 in Train_ER_alpha.smi (62/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL427496 in Train_ER_alpha.smi (63/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL361272 in Train_ER_alpha.smi (72/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL396118 in Train_ER_alpha.smi (74/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL469512 in Train_ER_alpha.smi (82/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL185114 in Train_ER_alpha.smi (85/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL511492 in Train_ER_alpha.smi (93/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL203330 in Train_ER_alpha.smi (97/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL206336 in Train_ER_alpha.smi (100/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL206499 in Train_ER_alpha.smi (103/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL187314 in Train_ER_alpha.smi (107/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL200719 in Train_ER_alpha.smi (111/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL183333 in Train_ER_alpha.smi (116/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL195807 in Train_ER_alpha.smi (120/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL212185 in Train_ER_alpha.smi (121/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL191901 in Train_ER_alpha.smi (125/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL241304 in Train_ER_alpha.smi (126/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL188391 in Train_ER_alpha.smi (130/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL360315 in Train_ER_alpha.smi (132/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL219134 in Train_ER_alpha.smi (136/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL189080 in Train_ER_alpha.smi (142/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL362272 in Train_ER_alpha.smi (144/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL206441 in Train_ER_alpha.smi (147/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL394788 in Train_ER_alpha.smi (152/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL195208 in Train_ER_alpha.smi (155/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL195437 in Train_ER_alpha.smi (157/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1201151 in Train_ER_alpha.smi (159/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL182237 in Train_ER_alpha.smi (164/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL187207 in Train_ER_alpha.smi (176/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL183371 in Train_ER_alpha.smi (178/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL363705 in Train_ER_alpha.smi (182/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL389683 in Train_ER_alpha.smi (185/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL360802 in Train_ER_alpha.smi (189/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL204025 in Train_ER_alpha.smi (196/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL201013 in Train_ER_alpha.smi (198/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL187915 in Train_ER_alpha.smi (203/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL192218 in Train_ER_alpha.smi (206/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL187000 in Train_ER_alpha.smi (209/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL369979 in Train_ER_alpha.smi (210/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL219577 in Train_ER_alpha.smi (216/1238). Average speed: 0.03 s/mol.\n"", + ""Processing CHEMBL240227 in Train_ER_alpha.smi (218/1238). Average speed: 0.03 s/mol.\n"" + ] + }, + { + ""name"": ""stdout"", + ""output_type"": ""stream"", + ""text"": [ + ""Processing CHEMBL186945 in Train_ER_alpha.smi (225/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL197667 in Train_ER_alpha.smi (226/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL383753 in Train_ER_alpha.smi (230/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL206600 in Train_ER_alpha.smi (235/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL211887 in Train_ER_alpha.smi (240/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL191714 in Train_ER_alpha.smi (241/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL381337 in Train_ER_alpha.smi (246/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL382698 in Train_ER_alpha.smi (250/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL331996 in Train_ER_alpha.smi (253/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL239938 in Train_ER_alpha.smi (262/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL187115 in Train_ER_alpha.smi (265/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL188189 in Train_ER_alpha.smi (269/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL196742 in Train_ER_alpha.smi (270/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL196530 in Train_ER_alpha.smi (273/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1487772 in Train_ER_alpha.smi (276/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1414254 in Train_ER_alpha.smi (280/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1403999 in Train_ER_alpha.smi (281/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1572403 in Train_ER_alpha.smi (284/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1399039 in Train_ER_alpha.smi (288/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL150924 in Train_ER_alpha.smi (291/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1383151 in Train_ER_alpha.smi (295/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1428161 in Train_ER_alpha.smi (300/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL294889 in Train_ER_alpha.smi (304/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1469865 in Train_ER_alpha.smi (309/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL196283 in Train_ER_alpha.smi (312/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL215409 in Train_ER_alpha.smi (318/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL180873 in Train_ER_alpha.smi (324/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1368345 in Train_ER_alpha.smi (330/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL379213 in Train_ER_alpha.smi (333/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL385693 in Train_ER_alpha.smi (336/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL364345 in Train_ER_alpha.smi (343/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1324414 in Train_ER_alpha.smi (345/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL472468 in Train_ER_alpha.smi (348/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL203472 in Train_ER_alpha.smi (354/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL376986 in Train_ER_alpha.smi (359/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL249903 in Train_ER_alpha.smi (362/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL191836 in Train_ER_alpha.smi (367/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL189356 in Train_ER_alpha.smi (368/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL347560 in Train_ER_alpha.smi (373/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL389394 in Train_ER_alpha.smi (382/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1318094 in Train_ER_alpha.smi (387/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL191808 in Train_ER_alpha.smi (391/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL199599 in Train_ER_alpha.smi (395/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL202673 in Train_ER_alpha.smi (398/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL428732 in Train_ER_alpha.smi (401/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL380844 in Train_ER_alpha.smi (229/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL204922 in Train_ER_alpha.smi (234/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL211816 in Train_ER_alpha.smi (237/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL188792 in Train_ER_alpha.smi (242/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL206814 in Train_ER_alpha.smi (248/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL203351 in Train_ER_alpha.smi (249/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL363634 in Train_ER_alpha.smi (254/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL362793 in Train_ER_alpha.smi (257/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL213950 in Train_ER_alpha.smi (260/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL389907 in Train_ER_alpha.smi (264/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL360410 in Train_ER_alpha.smi (272/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1570364 in Train_ER_alpha.smi (274/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL51483 in Train_ER_alpha.smi (277/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1541779 in Train_ER_alpha.smi (285/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1328083 in Train_ER_alpha.smi (290/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1465370 in Train_ER_alpha.smi (294/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL363017 in Train_ER_alpha.smi (299/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL185783 in Train_ER_alpha.smi (303/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1504315 in Train_ER_alpha.smi (306/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL380188 in Train_ER_alpha.smi (310/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL363498 in Train_ER_alpha.smi (314/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1392188 in Train_ER_alpha.smi (315/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL211837 in Train_ER_alpha.smi (319/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL371072 in Train_ER_alpha.smi (323/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL182887 in Train_ER_alpha.smi (329/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL192765 in Train_ER_alpha.smi (337/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL193727 in Train_ER_alpha.smi (338/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1434189 in Train_ER_alpha.smi (341/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1334930 in Train_ER_alpha.smi (346/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL209402 in Train_ER_alpha.smi (352/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL246341 in Train_ER_alpha.smi (356/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL377644 in Train_ER_alpha.smi (361/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1340020 in Train_ER_alpha.smi (365/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1348377 in Train_ER_alpha.smi (370/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL364110 in Train_ER_alpha.smi (374/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1509453 in Train_ER_alpha.smi (378/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1482201 in Train_ER_alpha.smi (379/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL374277 in Train_ER_alpha.smi (383/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL368449 in Train_ER_alpha.smi (388/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL200483 in Train_ER_alpha.smi (394/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL201517 in Train_ER_alpha.smi (397/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL442249 in Train_ER_alpha.smi (403/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL198411 in Train_ER_alpha.smi (224/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL206440 in Train_ER_alpha.smi (228/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL206406 in Train_ER_alpha.smi (231/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL204056 in Train_ER_alpha.smi (232/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL208929 in Train_ER_alpha.smi (238/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL204819 in Train_ER_alpha.smi (243/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL203442 in Train_ER_alpha.smi (247/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL191238 in Train_ER_alpha.smi (251/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL185890 in Train_ER_alpha.smi (255/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL185083 in Train_ER_alpha.smi (256/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL182850 in Train_ER_alpha.smi (267/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL361751 in Train_ER_alpha.smi (268/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1491936 in Train_ER_alpha.smi (275/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL463804 in Train_ER_alpha.smi (279/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1627356 in Train_ER_alpha.smi (282/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1332402 in Train_ER_alpha.smi (289/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL182751 in Train_ER_alpha.smi (293/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1511067 in Train_ER_alpha.smi (297/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL340111 in Train_ER_alpha.smi (302/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1564209 in Train_ER_alpha.smi (308/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL195154 in Train_ER_alpha.smi (313/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1577029 in Train_ER_alpha.smi (316/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL384268 in Train_ER_alpha.smi (321/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL426626 in Train_ER_alpha.smi (325/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL225081 in Train_ER_alpha.smi (327/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL231812 in Train_ER_alpha.smi (328/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL188900 in Train_ER_alpha.smi (331/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL207101 in Train_ER_alpha.smi (335/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL183367 in Train_ER_alpha.smi (339/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1386105 in Train_ER_alpha.smi (342/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL363902 in Train_ER_alpha.smi (347/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL209803 in Train_ER_alpha.smi (351/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1303886 in Train_ER_alpha.smi (355/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL380469 in Train_ER_alpha.smi (358/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL232023 in Train_ER_alpha.smi (364/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL511812 in Train_ER_alpha.smi (366/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1453259 in Train_ER_alpha.smi (372/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL25580 in Train_ER_alpha.smi (376/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1326210 in Train_ER_alpha.smi (380/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL203724 in Train_ER_alpha.smi (385/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL191759 in Train_ER_alpha.smi (389/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL365941 in Train_ER_alpha.smi (390/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL360978 in Train_ER_alpha.smi (392/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL203072 in Train_ER_alpha.smi (399/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL393179 in Train_ER_alpha.smi (402/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL382632 in Train_ER_alpha.smi (227/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL381697 in Train_ER_alpha.smi (233/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL206227 in Train_ER_alpha.smi (236/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL209623 in Train_ER_alpha.smi (239/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL206546 in Train_ER_alpha.smi (244/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL205934 in Train_ER_alpha.smi (245/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL364502 in Train_ER_alpha.smi (252/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL187473 in Train_ER_alpha.smi (258/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL373987 in Train_ER_alpha.smi (259/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL290106 in Train_ER_alpha.smi (261/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL391619 in Train_ER_alpha.smi (263/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL363633 in Train_ER_alpha.smi (266/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL316003 in Train_ER_alpha.smi (271/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL359633 in Train_ER_alpha.smi (278/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL319244 in Train_ER_alpha.smi (283/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1458486 in Train_ER_alpha.smi (286/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1587920 in Train_ER_alpha.smi (287/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL184003 in Train_ER_alpha.smi (292/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1407483 in Train_ER_alpha.smi (296/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL192876 in Train_ER_alpha.smi (298/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1390963 in Train_ER_alpha.smi (301/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL184151 in Train_ER_alpha.smi (305/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL63768 in Train_ER_alpha.smi (307/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL214693 in Train_ER_alpha.smi (311/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL183368 in Train_ER_alpha.smi (317/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL379566 in Train_ER_alpha.smi (320/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL203348 in Train_ER_alpha.smi (322/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL191711 in Train_ER_alpha.smi (326/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL362966 in Train_ER_alpha.smi (332/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL380359 in Train_ER_alpha.smi (334/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1395826 in Train_ER_alpha.smi (340/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL371126 in Train_ER_alpha.smi (344/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL189024 in Train_ER_alpha.smi (349/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL194718 in Train_ER_alpha.smi (350/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL204970 in Train_ER_alpha.smi (353/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL425092 in Train_ER_alpha.smi (357/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL208978 in Train_ER_alpha.smi (360/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL398634 in Train_ER_alpha.smi (363/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1606765 in Train_ER_alpha.smi (369/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1426947 in Train_ER_alpha.smi (371/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL189025 in Train_ER_alpha.smi (375/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1604500 in Train_ER_alpha.smi (377/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL221515 in Train_ER_alpha.smi (381/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL379889 in Train_ER_alpha.smi (384/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL226379 in Train_ER_alpha.smi (386/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL392029 in Train_ER_alpha.smi (393/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL200232 in Train_ER_alpha.smi (396/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL241300 in Train_ER_alpha.smi (400/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL238959 in Train_ER_alpha.smi (404/1238). Average speed: 0.03 s/mol.\r\n"" + ] + }, + { + ""name"": ""stdout"", + ""output_type"": ""stream"", + ""text"": [ + ""Processing CHEMBL1410824 in Train_ER_alpha.smi (405/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1427643 in Train_ER_alpha.smi (407/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1575187 in Train_ER_alpha.smi (410/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL153405 in Train_ER_alpha.smi (415/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1536373 in Train_ER_alpha.smi (420/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL188619 in Train_ER_alpha.smi (425/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL186513 in Train_ER_alpha.smi (429/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1606490 in Train_ER_alpha.smi (431/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL190104 in Train_ER_alpha.smi (436/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1548834 in Train_ER_alpha.smi (437/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL360498 in Train_ER_alpha.smi (439/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL181369 in Train_ER_alpha.smi (449/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL200021 in Train_ER_alpha.smi (458/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL188410 in Train_ER_alpha.smi (463/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL441537 in Train_ER_alpha.smi (466/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL192386 in Train_ER_alpha.smi (468/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL370849 in Train_ER_alpha.smi (472/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL185918 in Train_ER_alpha.smi (475/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1480378 in Train_ER_alpha.smi (478/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1481770 in Train_ER_alpha.smi (479/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL186598 in Train_ER_alpha.smi (485/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL377848 in Train_ER_alpha.smi (489/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL369935 in Train_ER_alpha.smi (493/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL209429 in Train_ER_alpha.smi (497/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL426854 in Train_ER_alpha.smi (500/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL363425 in Train_ER_alpha.smi (504/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL206226 in Train_ER_alpha.smi (507/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL211800 in Train_ER_alpha.smi (509/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL211870 in Train_ER_alpha.smi (512/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL221676 in Train_ER_alpha.smi (519/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL206349 in Train_ER_alpha.smi (525/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL192016 in Train_ER_alpha.smi (528/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL238961 in Train_ER_alpha.smi (531/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1521111 in Train_ER_alpha.smi (537/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1597831 in Train_ER_alpha.smi (543/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL375334 in Train_ER_alpha.smi (548/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1449885 in Train_ER_alpha.smi (551/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1587260 in Train_ER_alpha.smi (559/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1328182 in Train_ER_alpha.smi (562/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL363177 in Train_ER_alpha.smi (567/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL361226 in Train_ER_alpha.smi (569/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL189822 in Train_ER_alpha.smi (572/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL362307 in Train_ER_alpha.smi (575/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL104416 in Train_ER_alpha.smi (578/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL189303 in Train_ER_alpha.smi (581/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL435696 in Train_ER_alpha.smi (584/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1464457 in Train_ER_alpha.smi (408/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1531555 in Train_ER_alpha.smi (412/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1538223 in Train_ER_alpha.smi (414/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL178559 in Train_ER_alpha.smi (417/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1386676 in Train_ER_alpha.smi (421/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL189658 in Train_ER_alpha.smi (424/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1522300 in Train_ER_alpha.smi (427/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL180253 in Train_ER_alpha.smi (434/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL188527 in Train_ER_alpha.smi (438/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1427879 in Train_ER_alpha.smi (442/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL186597 in Train_ER_alpha.smi (445/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL361258 in Train_ER_alpha.smi (447/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL57150 in Train_ER_alpha.smi (450/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL188613 in Train_ER_alpha.smi (453/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL187087 in Train_ER_alpha.smi (457/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL450940 in Train_ER_alpha.smi (461/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL201569 in Train_ER_alpha.smi (464/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL183152 in Train_ER_alpha.smi (470/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL178679 in Train_ER_alpha.smi (473/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL198679 in Train_ER_alpha.smi (480/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL334096 in Train_ER_alpha.smi (483/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL180146 in Train_ER_alpha.smi (486/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1402 in Train_ER_alpha.smi (492/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL381566 in Train_ER_alpha.smi (496/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL202896 in Train_ER_alpha.smi (501/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1116 in Train_ER_alpha.smi (506/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL379567 in Train_ER_alpha.smi (510/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL204768 in Train_ER_alpha.smi (514/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL380717 in Train_ER_alpha.smi (516/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL206764 in Train_ER_alpha.smi (521/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL202894 in Train_ER_alpha.smi (524/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL238960 in Train_ER_alpha.smi (530/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL437695 in Train_ER_alpha.smi (534/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL388418 in Train_ER_alpha.smi (539/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1433745 in Train_ER_alpha.smi (541/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1362841 in Train_ER_alpha.smi (545/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1548361 in Train_ER_alpha.smi (547/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1584646 in Train_ER_alpha.smi (552/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1448466 in Train_ER_alpha.smi (555/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1367590 in Train_ER_alpha.smi (558/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL191716 in Train_ER_alpha.smi (564/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL362425 in Train_ER_alpha.smi (566/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL192782 in Train_ER_alpha.smi (573/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL190487 in Train_ER_alpha.smi (576/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL195991 in Train_ER_alpha.smi (579/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL371564 in Train_ER_alpha.smi (585/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1628170 in Train_ER_alpha.smi (586/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1359893 in Train_ER_alpha.smi (406/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1403448 in Train_ER_alpha.smi (409/1238). Average speed: 0.03 s/mol.\r"", + ""\r\n"", + ""Processing CHEMBL1521410 in Train_ER_alpha.smi (413/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1528123 in Train_ER_alpha.smi (419/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL144431 in Train_ER_alpha.smi (422/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL193064 in Train_ER_alpha.smi (428/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1495194 in Train_ER_alpha.smi (430/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL2079587 in Train_ER_alpha.smi (433/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1560367 in Train_ER_alpha.smi (441/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL577758 in Train_ER_alpha.smi (444/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL145576 in Train_ER_alpha.smi (446/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL371788 in Train_ER_alpha.smi (454/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL186073 in Train_ER_alpha.smi (456/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL363963 in Train_ER_alpha.smi (460/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL380876 in Train_ER_alpha.smi (465/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL192587 in Train_ER_alpha.smi (471/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL185126 in Train_ER_alpha.smi (474/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL264216 in Train_ER_alpha.smi (476/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL121978 in Train_ER_alpha.smi (481/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL377224 in Train_ER_alpha.smi (487/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL264349 in Train_ER_alpha.smi (490/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL216279 in Train_ER_alpha.smi (494/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL379109 in Train_ER_alpha.smi (498/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL179518 in Train_ER_alpha.smi (502/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL378990 in Train_ER_alpha.smi (508/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL379595 in Train_ER_alpha.smi (511/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL206582 in Train_ER_alpha.smi (517/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL363955 in Train_ER_alpha.smi (520/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL381720 in Train_ER_alpha.smi (523/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL379008 in Train_ER_alpha.smi (527/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL180176 in Train_ER_alpha.smi (533/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1485005 in Train_ER_alpha.smi (536/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1327977 in Train_ER_alpha.smi (538/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1475062 in Train_ER_alpha.smi (542/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1429150 in Train_ER_alpha.smi (546/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL222580 in Train_ER_alpha.smi (550/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1472407 in Train_ER_alpha.smi (554/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1431650 in Train_ER_alpha.smi (557/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1461574 in Train_ER_alpha.smi (561/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL250109 in Train_ER_alpha.smi (563/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL361063 in Train_ER_alpha.smi (571/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL192901 in Train_ER_alpha.smi (574/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1479 in Train_ER_alpha.smi (577/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL178665 in Train_ER_alpha.smi (583/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1548231 in Train_ER_alpha.smi (411/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL327320 in Train_ER_alpha.smi (416/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL180973 in Train_ER_alpha.smi (418/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1580036 in Train_ER_alpha.smi (423/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1525824 in Train_ER_alpha.smi (426/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1548186 in Train_ER_alpha.smi (432/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL93705 in Train_ER_alpha.smi (435/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1501269 in Train_ER_alpha.smi (440/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1544241 in Train_ER_alpha.smi (443/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL363367 in Train_ER_alpha.smi (448/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL433944 in Train_ER_alpha.smi (451/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL371624 in Train_ER_alpha.smi (452/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL189200 in Train_ER_alpha.smi (455/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL193010 in Train_ER_alpha.smi (459/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL365045 in Train_ER_alpha.smi (462/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL377694 in Train_ER_alpha.smi (467/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL362020 in Train_ER_alpha.smi (469/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1337826 in Train_ER_alpha.smi (477/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL371009 in Train_ER_alpha.smi (482/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL192539 in Train_ER_alpha.smi (484/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL213051 in Train_ER_alpha.smi (488/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL378675 in Train_ER_alpha.smi (491/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL219126 in Train_ER_alpha.smi (495/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL379821 in Train_ER_alpha.smi (499/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL187069 in Train_ER_alpha.smi (503/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL597498 in Train_ER_alpha.smi (505/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL187937 in Train_ER_alpha.smi (513/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL205350 in Train_ER_alpha.smi (515/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL362355 in Train_ER_alpha.smi (518/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL206131 in Train_ER_alpha.smi (522/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL206773 in Train_ER_alpha.smi (526/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL191841 in Train_ER_alpha.smi (529/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL184360 in Train_ER_alpha.smi (532/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1276308 in Train_ER_alpha.smi (535/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL396948 in Train_ER_alpha.smi (540/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL471627 in Train_ER_alpha.smi (544/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL222522 in Train_ER_alpha.smi (549/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1506716 in Train_ER_alpha.smi (553/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1454402 in Train_ER_alpha.smi (556/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1522044 in Train_ER_alpha.smi (560/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL194772 in Train_ER_alpha.smi (565/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL364329 in Train_ER_alpha.smi (568/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL379543 in Train_ER_alpha.smi (570/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL190038 in Train_ER_alpha.smi (580/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1627354 in Train_ER_alpha.smi (582/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL362349 in Train_ER_alpha.smi (587/1238). Average speed: 0.03 s/mol.\r\n"" + ] + }, + { + ""name"": ""stdout"", + ""output_type"": ""stream"", + ""text"": [ + ""Processing CHEMBL954 in Train_ER_alpha.smi (588/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL188846 in Train_ER_alpha.smi (591/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL372304 in Train_ER_alpha.smi (593/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL194265 in Train_ER_alpha.smi (595/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL183344 in Train_ER_alpha.smi (597/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL187392 in Train_ER_alpha.smi (604/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL370282 in Train_ER_alpha.smi (607/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1411687 in Train_ER_alpha.smi (611/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1322601 in Train_ER_alpha.smi (616/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1350028 in Train_ER_alpha.smi (620/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1347829 in Train_ER_alpha.smi (624/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1411209 in Train_ER_alpha.smi (626/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL187673 in Train_ER_alpha.smi (632/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL153395 in Train_ER_alpha.smi (636/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1475434 in Train_ER_alpha.smi (641/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1449110 in Train_ER_alpha.smi (645/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL340606 in Train_ER_alpha.smi (647/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1299357 in Train_ER_alpha.smi (653/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL482116 in Train_ER_alpha.smi (658/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL190682 in Train_ER_alpha.smi (659/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL226329 in Train_ER_alpha.smi (664/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL240157 in Train_ER_alpha.smi (669/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL391795 in Train_ER_alpha.smi (672/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL438256 in Train_ER_alpha.smi (677/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL213529 in Train_ER_alpha.smi (682/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL192000 in Train_ER_alpha.smi (690/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL432454 in Train_ER_alpha.smi (693/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL188246 in Train_ER_alpha.smi (697/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL346455 in Train_ER_alpha.smi (698/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL209738 in Train_ER_alpha.smi (702/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL455215 in Train_ER_alpha.smi (705/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL522990 in Train_ER_alpha.smi (706/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1076306 in Train_ER_alpha.smi (710/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL198680 in Train_ER_alpha.smi (711/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL414859 in Train_ER_alpha.smi (713/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL203370 in Train_ER_alpha.smi (718/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1389780 in Train_ER_alpha.smi (721/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1453264 in Train_ER_alpha.smi (725/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1329686 in Train_ER_alpha.smi (728/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1419810 in Train_ER_alpha.smi (734/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1439181 in Train_ER_alpha.smi (736/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1372307 in Train_ER_alpha.smi (740/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1577661 in Train_ER_alpha.smi (745/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1407054 in Train_ER_alpha.smi (747/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1555033 in Train_ER_alpha.smi (750/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1326803 in Train_ER_alpha.smi (754/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1083326 in Train_ER_alpha.smi (757/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL433769 in Train_ER_alpha.smi (762/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL360817 in Train_ER_alpha.smi (770/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL196131 in Train_ER_alpha.smi (592/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL361078 in Train_ER_alpha.smi (599/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1200623 in Train_ER_alpha.smi (601/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL184367 in Train_ER_alpha.smi (606/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1302802 in Train_ER_alpha.smi (612/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1341814 in Train_ER_alpha.smi (615/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1541552 in Train_ER_alpha.smi (619/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1310171 in Train_ER_alpha.smi (625/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL196583 in Train_ER_alpha.smi (628/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1505283 in Train_ER_alpha.smi (629/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL191715 in Train_ER_alpha.smi (634/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL195289 in Train_ER_alpha.smi (637/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1381306 in Train_ER_alpha.smi (638/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1552932 in Train_ER_alpha.smi (643/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL371305 in Train_ER_alpha.smi (648/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1443090 in Train_ER_alpha.smi (651/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL125362 in Train_ER_alpha.smi (656/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL185383 in Train_ER_alpha.smi (663/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL511971 in Train_ER_alpha.smi (670/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL430395 in Train_ER_alpha.smi (674/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL204697 in Train_ER_alpha.smi (679/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL214200 in Train_ER_alpha.smi (683/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL106773 in Train_ER_alpha.smi (689/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL393982 in Train_ER_alpha.smi (695/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL213829 in Train_ER_alpha.smi (704/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL368688 in Train_ER_alpha.smi (709/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL46903 in Train_ER_alpha.smi (715/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1312693 in Train_ER_alpha.smi (722/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1533166 in Train_ER_alpha.smi (726/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1498999 in Train_ER_alpha.smi (729/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1393976 in Train_ER_alpha.smi (733/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1531320 in Train_ER_alpha.smi (735/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1505467 in Train_ER_alpha.smi (739/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1482875 in Train_ER_alpha.smi (742/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1374573 in Train_ER_alpha.smi (744/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1548616 in Train_ER_alpha.smi (748/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1405182 in Train_ER_alpha.smi (751/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1084746 in Train_ER_alpha.smi (756/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL223026 in Train_ER_alpha.smi (760/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL144897 in Train_ER_alpha.smi (761/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL190025 in Train_ER_alpha.smi (765/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1576748 in Train_ER_alpha.smi (767/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1388431 in Train_ER_alpha.smi (768/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL184974 in Train_ER_alpha.smi (589/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL369545 in Train_ER_alpha.smi (598/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL418971 in Train_ER_alpha.smi (605/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL180702 in Train_ER_alpha.smi (608/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1521414 in Train_ER_alpha.smi (613/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1604803 in Train_ER_alpha.smi (617/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL183782 in Train_ER_alpha.smi (621/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1493034 in Train_ER_alpha.smi (623/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1532659 in Train_ER_alpha.smi (630/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL189039 in Train_ER_alpha.smi (631/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL197757 in Train_ER_alpha.smi (633/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL122208 in Train_ER_alpha.smi (635/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1421864 in Train_ER_alpha.smi (639/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1560682 in Train_ER_alpha.smi (640/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1593653 in Train_ER_alpha.smi (644/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL192390 in Train_ER_alpha.smi (646/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL193482 in Train_ER_alpha.smi (650/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1581690 in Train_ER_alpha.smi (654/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL184421 in Train_ER_alpha.smi (661/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL362356 in Train_ER_alpha.smi (667/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL238890 in Train_ER_alpha.smi (668/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL189002 in Train_ER_alpha.smi (676/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL188957 in Train_ER_alpha.smi (678/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL203561 in Train_ER_alpha.smi (680/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL187219 in Train_ER_alpha.smi (685/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL394357 in Train_ER_alpha.smi (686/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL242818 in Train_ER_alpha.smi (688/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL198445 in Train_ER_alpha.smi (692/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL184491 in Train_ER_alpha.smi (694/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL232022 in Train_ER_alpha.smi (699/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL206120 in Train_ER_alpha.smi (700/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL210074 in Train_ER_alpha.smi (701/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL377254 in Train_ER_alpha.smi (703/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL267431 in Train_ER_alpha.smi (707/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL378798 in Train_ER_alpha.smi (714/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL195311 in Train_ER_alpha.smi (716/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL206137 in Train_ER_alpha.smi (720/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1377973 in Train_ER_alpha.smi (724/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1304227 in Train_ER_alpha.smi (727/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1386537 in Train_ER_alpha.smi (730/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1577757 in Train_ER_alpha.smi (738/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1345810 in Train_ER_alpha.smi (743/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1356953 in Train_ER_alpha.smi (753/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL426897 in Train_ER_alpha.smi (758/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL179852 in Train_ER_alpha.smi (763/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL369724 in Train_ER_alpha.smi (771/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1627353 in Train_ER_alpha.smi (590/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL427180 in Train_ER_alpha.smi (594/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL195788 in Train_ER_alpha.smi (596/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL187393 in Train_ER_alpha.smi (600/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL194762 in Train_ER_alpha.smi (602/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL188051 in Train_ER_alpha.smi (603/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL103650 in Train_ER_alpha.smi (609/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL181936 in Train_ER_alpha.smi (610/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1491602 in Train_ER_alpha.smi (614/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1393131 in Train_ER_alpha.smi (618/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1423880 in Train_ER_alpha.smi (622/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1507788 in Train_ER_alpha.smi (627/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1434961 in Train_ER_alpha.smi (642/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL195280 in Train_ER_alpha.smi (649/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1519669 in Train_ER_alpha.smi (652/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1433062 in Train_ER_alpha.smi (655/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL361871 in Train_ER_alpha.smi (657/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL363872 in Train_ER_alpha.smi (660/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL205920 in Train_ER_alpha.smi (662/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL200799 in Train_ER_alpha.smi (665/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL371012 in Train_ER_alpha.smi (666/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL364419 in Train_ER_alpha.smi (671/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL398455 in Train_ER_alpha.smi (673/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL183044 in Train_ER_alpha.smi (675/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL211739 in Train_ER_alpha.smi (681/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL187978 in Train_ER_alpha.smi (684/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL388473 in Train_ER_alpha.smi (687/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL202781 in Train_ER_alpha.smi (691/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL240438 in Train_ER_alpha.smi (696/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL469510 in Train_ER_alpha.smi (708/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL125119 in Train_ER_alpha.smi (712/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1450791 in Train_ER_alpha.smi (717/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL378572 in Train_ER_alpha.smi (719/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1324290 in Train_ER_alpha.smi (723/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1520373 in Train_ER_alpha.smi (731/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1452952 in Train_ER_alpha.smi (732/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1484547 in Train_ER_alpha.smi (737/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1525366 in Train_ER_alpha.smi (741/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1370120 in Train_ER_alpha.smi (746/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1559731 in Train_ER_alpha.smi (749/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1455849 in Train_ER_alpha.smi (752/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1597805 in Train_ER_alpha.smi (755/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL386502 in Train_ER_alpha.smi (759/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL183033 in Train_ER_alpha.smi (764/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL198088 in Train_ER_alpha.smi (766/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1509933 in Train_ER_alpha.smi (769/1238). Average speed: 0.03 s/mol.\r\n"" + ] + }, + { + ""name"": ""stdout"", + ""output_type"": ""stream"", + ""text"": [ + ""Processing CHEMBL195217 in Train_ER_alpha.smi (773/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1414407 in Train_ER_alpha.smi (776/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1491959 in Train_ER_alpha.smi (780/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL188516 in Train_ER_alpha.smi (782/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL198204 in Train_ER_alpha.smi (785/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL425194 in Train_ER_alpha.smi (787/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL182157 in Train_ER_alpha.smi (793/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1391547 in Train_ER_alpha.smi (799/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1447311 in Train_ER_alpha.smi (804/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL204376 in Train_ER_alpha.smi (809/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1430316 in Train_ER_alpha.smi (814/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL378883 in Train_ER_alpha.smi (818/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL377457 in Train_ER_alpha.smi (822/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL398456 in Train_ER_alpha.smi (825/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL188299 in Train_ER_alpha.smi (832/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL469511 in Train_ER_alpha.smi (834/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL206500 in Train_ER_alpha.smi (842/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL10560 in Train_ER_alpha.smi (845/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1404155 in Train_ER_alpha.smi (851/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1442697 in Train_ER_alpha.smi (852/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1471823 in Train_ER_alpha.smi (860/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL465879 in Train_ER_alpha.smi (864/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL221626 in Train_ER_alpha.smi (866/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL201365 in Train_ER_alpha.smi (869/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL212675 in Train_ER_alpha.smi (874/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL180995 in Train_ER_alpha.smi (875/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL193146 in Train_ER_alpha.smi (882/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL193108 in Train_ER_alpha.smi (883/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL189287 in Train_ER_alpha.smi (887/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL187663 in Train_ER_alpha.smi (889/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL185012 in Train_ER_alpha.smi (893/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL184679 in Train_ER_alpha.smi (896/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL195290 in Train_ER_alpha.smi (902/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL364176 in Train_ER_alpha.smi (905/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL195389 in Train_ER_alpha.smi (907/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL189077 in Train_ER_alpha.smi (911/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL440757 in Train_ER_alpha.smi (914/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL188882 in Train_ER_alpha.smi (916/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL360385 in Train_ER_alpha.smi (917/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL195039 in Train_ER_alpha.smi (919/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL183162 in Train_ER_alpha.smi (921/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL185837 in Train_ER_alpha.smi (925/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL425246 in Train_ER_alpha.smi (927/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1323006 in Train_ER_alpha.smi (931/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL383518 in Train_ER_alpha.smi (935/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL222453 in Train_ER_alpha.smi (941/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1566266 in Train_ER_alpha.smi (943/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL241484 in Train_ER_alpha.smi (954/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL202400 in Train_ER_alpha.smi (957/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL226268 in Train_ER_alpha.smi (960/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1522446 in Train_ER_alpha.smi (774/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL361056 in Train_ER_alpha.smi (779/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL187311 in Train_ER_alpha.smi (786/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1300662 in Train_ER_alpha.smi (789/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1442450 in Train_ER_alpha.smi (790/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1467399 in Train_ER_alpha.smi (794/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1350069 in Train_ER_alpha.smi (797/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1582403 in Train_ER_alpha.smi (803/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1431081 in Train_ER_alpha.smi (806/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL460647 in Train_ER_alpha.smi (810/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL216334 in Train_ER_alpha.smi (811/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL379314 in Train_ER_alpha.smi (816/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL215428 in Train_ER_alpha.smi (819/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL372783 in Train_ER_alpha.smi (824/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL250110 in Train_ER_alpha.smi (827/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL438443 in Train_ER_alpha.smi (830/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1404523 in Train_ER_alpha.smi (837/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1386091 in Train_ER_alpha.smi (838/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL211666 in Train_ER_alpha.smi (841/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1307301 in Train_ER_alpha.smi (844/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1320651 in Train_ER_alpha.smi (848/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL377083 in Train_ER_alpha.smi (855/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL206426 in Train_ER_alpha.smi (856/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL202738 in Train_ER_alpha.smi (857/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL383253 in Train_ER_alpha.smi (872/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL211599 in Train_ER_alpha.smi (877/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL213457 in Train_ER_alpha.smi (878/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL394213 in Train_ER_alpha.smi (881/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL194873 in Train_ER_alpha.smi (885/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL187671 in Train_ER_alpha.smi (886/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL189706 in Train_ER_alpha.smi (891/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL361005 in Train_ER_alpha.smi (895/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL187105 in Train_ER_alpha.smi (898/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL363412 in Train_ER_alpha.smi (903/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1627351 in Train_ER_alpha.smi (912/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL180085 in Train_ER_alpha.smi (920/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1301994 in Train_ER_alpha.smi (924/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL377988 in Train_ER_alpha.smi (930/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL212468 in Train_ER_alpha.smi (934/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL380720 in Train_ER_alpha.smi (938/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL204118 in Train_ER_alpha.smi (940/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL183889 in Train_ER_alpha.smi (944/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL203122 in Train_ER_alpha.smi (947/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL222454 in Train_ER_alpha.smi (950/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL396119 in Train_ER_alpha.smi (955/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1397845 in Train_ER_alpha.smi (777/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1326483 in Train_ER_alpha.smi (781/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL178334 in Train_ER_alpha.smi (783/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1539865 in Train_ER_alpha.smi (791/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL192260 in Train_ER_alpha.smi (795/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL187313 in Train_ER_alpha.smi (798/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1526157 in Train_ER_alpha.smi (800/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1231453 in Train_ER_alpha.smi (808/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL377295 in Train_ER_alpha.smi (812/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1574512 in Train_ER_alpha.smi (815/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL385028 in Train_ER_alpha.smi (820/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL214692 in Train_ER_alpha.smi (823/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL380452 in Train_ER_alpha.smi (828/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL380838 in Train_ER_alpha.smi (831/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1300008 in Train_ER_alpha.smi (835/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL214034 in Train_ER_alpha.smi (840/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL231810 in Train_ER_alpha.smi (846/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1470395 in Train_ER_alpha.smi (847/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1429596 in Train_ER_alpha.smi (850/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1603599 in Train_ER_alpha.smi (853/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL205577 in Train_ER_alpha.smi (858/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL192621 in Train_ER_alpha.smi (861/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL434577 in Train_ER_alpha.smi (863/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL222452 in Train_ER_alpha.smi (867/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL199766 in Train_ER_alpha.smi (870/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL204317 in Train_ER_alpha.smi (871/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL240230 in Train_ER_alpha.smi (880/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1732228 in Train_ER_alpha.smi (888/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL198089 in Train_ER_alpha.smi (890/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL122237 in Train_ER_alpha.smi (894/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL188528 in Train_ER_alpha.smi (900/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL186874 in Train_ER_alpha.smi (904/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL365903 in Train_ER_alpha.smi (906/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL365484 in Train_ER_alpha.smi (909/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL178857 in Train_ER_alpha.smi (915/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1596554 in Train_ER_alpha.smi (923/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL380032 in Train_ER_alpha.smi (928/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL234524 in Train_ER_alpha.smi (932/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL396947 in Train_ER_alpha.smi (933/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL203340 in Train_ER_alpha.smi (937/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL205614 in Train_ER_alpha.smi (939/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL205398 in Train_ER_alpha.smi (945/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL215593 in Train_ER_alpha.smi (951/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL241256 in Train_ER_alpha.smi (953/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL437065 in Train_ER_alpha.smi (958/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL239723 in Train_ER_alpha.smi (962/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL52 in Train_ER_alpha.smi (772/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1434556 in Train_ER_alpha.smi (775/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL282575 in Train_ER_alpha.smi (778/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL9352 in Train_ER_alpha.smi (784/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1580991 in Train_ER_alpha.smi (788/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1466928 in Train_ER_alpha.smi (792/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1303477 in Train_ER_alpha.smi (796/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1572777 in Train_ER_alpha.smi (801/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1320937 in Train_ER_alpha.smi (802/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL189556 in Train_ER_alpha.smi (805/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1421887 in Train_ER_alpha.smi (807/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1325233 in Train_ER_alpha.smi (813/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL385949 in Train_ER_alpha.smi (817/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL212391 in Train_ER_alpha.smi (821/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL249904 in Train_ER_alpha.smi (826/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL380358 in Train_ER_alpha.smi (829/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL394614 in Train_ER_alpha.smi (833/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1531512 in Train_ER_alpha.smi (836/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL211953 in Train_ER_alpha.smi (839/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL206439 in Train_ER_alpha.smi (843/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1417890 in Train_ER_alpha.smi (849/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL203283 in Train_ER_alpha.smi (854/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL208324 in Train_ER_alpha.smi (859/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL192586 in Train_ER_alpha.smi (862/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL384708 in Train_ER_alpha.smi (865/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL372411 in Train_ER_alpha.smi (868/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL425603 in Train_ER_alpha.smi (873/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL203123 in Train_ER_alpha.smi (876/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL214291 in Train_ER_alpha.smi (879/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL182794 in Train_ER_alpha.smi (884/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL188230 in Train_ER_alpha.smi (892/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1424468 in Train_ER_alpha.smi (897/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL276030 in Train_ER_alpha.smi (899/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL181368 in Train_ER_alpha.smi (901/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL360189 in Train_ER_alpha.smi (908/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL193207 in Train_ER_alpha.smi (910/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL360338 in Train_ER_alpha.smi (913/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL182980 in Train_ER_alpha.smi (918/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL427324 in Train_ER_alpha.smi (922/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL1466913 in Train_ER_alpha.smi (926/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL211349 in Train_ER_alpha.smi (929/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL204272 in Train_ER_alpha.smi (936/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL577709 in Train_ER_alpha.smi (942/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL379252 in Train_ER_alpha.smi (946/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL206286 in Train_ER_alpha.smi (948/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL225514 in Train_ER_alpha.smi (949/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL391909 in Train_ER_alpha.smi (952/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL372030 in Train_ER_alpha.smi (956/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL377847 in Train_ER_alpha.smi (959/1238). Average speed: 0.03 s/mol.\r\n"", + ""Processing CHEMBL226327 in Train_ER_alpha.smi (961/1238). Average speed: 0.03 s/mol.\r\n"" + ] + }, + { + ""name"": ""stdout"", + ""output_type"": ""stream"", + ""text"": [ + ""Descriptor calculation completed in 25.570 secs . Average speed: 0.02 s/mol.\n"", + ""Train_ER_alpha.smi\n"", + ""Train_ER_alpha.csv\n"", + ""MACCSFingerprinter.xml\n"", + ""MACCSFingerprinter_\n"", + ""Processing CHEMBL370037 in Train_ER_alpha.smi (1/1238). \n"", + ""Processing CHEMBL189073 in Train_ER_alpha.smi (2/1238). \n"", + ""Processing CHEMBL365290 in Train_ER_alpha.smi (3/1238). \n"", + ""Processing CHEMBL191648 in Train_ER_alpha.smi (4/1238). \n"", + ""Processing CHEMBL184431 in Train_ER_alpha.smi (5/1238). Average speed: 1.75 s/mol.\n"", + ""Processing CHEMBL364551 in Train_ER_alpha.smi (6/1238). Average speed: 0.89 s/mol.\n"", + ""Processing CHEMBL124482 in Train_ER_alpha.smi (7/1238). Average speed: 0.62 s/mol.\n"", + ""Processing CHEMBL198410 in Train_ER_alpha.smi (8/1238). Average speed: 0.48 s/mol.\n"", + ""Processing CHEMBL1496978 in Train_ER_alpha.smi (9/1238). Average speed: 0.54 s/mol.\n"", + ""Processing CHEMBL1305485 in Train_ER_alpha.smi (10/1238). Average speed: 0.44 s/mol.\n"", + ""Processing CHEMBL1570659 in Train_ER_alpha.smi (11/1238). Average speed: 0.34 s/mol.\n"", + ""Processing CHEMBL1331084 in Train_ER_alpha.smi (12/1238). Average speed: 0.33 s/mol.\n"", + ""Processing CHEMBL184448 in Train_ER_alpha.smi (13/1238). Average speed: 0.29 s/mol.\n"", + ""Processing CHEMBL45068 in Train_ER_alpha.smi (14/1238). Average speed: 0.27 s/mol.\n"", + ""Processing CHEMBL434502 in Train_ER_alpha.smi (15/1238). Average speed: 0.24 s/mol.\n"", + ""Processing CHEMBL1628199 in Train_ER_alpha.smi (16/1238). Average speed: 0.24 s/mol.\n"", + ""Processing CHEMBL123025 in Train_ER_alpha.smi (17/1238). Average speed: 0.22 s/mol.\n"", + ""Processing CHEMBL153062 in Train_ER_alpha.smi (18/1238). Average speed: 0.24 s/mol.\n"", + ""Processing CHEMBL1468708 in Train_ER_alpha.smi (19/1238). Average speed: 0.23 s/mol.\n"", + ""Processing CHEMBL1442416 in Train_ER_alpha.smi (20/1238). Average speed: 0.23 s/mol.\n"", + ""Processing CHEMBL1388271 in Train_ER_alpha.smi (21/1238). Average speed: 0.21 s/mol.\n"", + ""Processing CHEMBL1331872 in Train_ER_alpha.smi (22/1238). Average speed: 0.20 s/mol.\n"", + ""Processing CHEMBL1401227 in Train_ER_alpha.smi (23/1238). Average speed: 0.19 s/mol.\n"", + ""Processing CHEMBL1460126 in Train_ER_alpha.smi (24/1238). Average speed: 0.19 s/mol.\n"", + ""Processing CHEMBL1499638 in Train_ER_alpha.smi (25/1238). Average speed: 0.19 s/mol.\n"", + ""Processing CHEMBL1413557 in Train_ER_alpha.smi (26/1238). Average speed: 0.18 s/mol.\n"", + ""Processing CHEMBL1484998 in Train_ER_alpha.smi (27/1238). Average speed: 0.17 s/mol.\n"", + ""Processing CHEMBL1469743 in Train_ER_alpha.smi (29/1238). Average speed: 0.17 s/mol.\n"", + ""Processing CHEMBL1608989 in Train_ER_alpha.smi (28/1238). Average speed: 0.17 s/mol.\n"", + ""Processing CHEMBL1538325 in Train_ER_alpha.smi (30/1238). Average speed: 0.16 s/mol.\n"", + ""Processing CHEMBL1605393 in Train_ER_alpha.smi (31/1238). Average speed: 0.16 s/mol.\n"", + ""Processing CHEMBL1384049 in Train_ER_alpha.smi (32/1238). Average speed: 0.16 s/mol.\n"", + ""Processing CHEMBL188049 in Train_ER_alpha.smi (33/1238). Average speed: 0.15 s/mol.\n"", + ""Processing CHEMBL1361641 in Train_ER_alpha.smi (34/1238). Average speed: 0.15 s/mol.\n"", + ""Processing CHEMBL1489063 in Train_ER_alpha.smi (35/1238). Average speed: 0.15 s/mol.\n"", + ""Processing CHEMBL1381495 in Train_ER_alpha.smi (36/1238). Average speed: 0.15 s/mol.\n"", + ""Processing CHEMBL1343011 in Train_ER_alpha.smi (37/1238). Average speed: 0.14 s/mol.\n"", + ""Processing CHEMBL1613068 in Train_ER_alpha.smi (38/1238). Average speed: 0.15 s/mol.\n"", + ""Processing CHEMBL1464837 in Train_ER_alpha.smi (39/1238). Average speed: 0.14 s/mol.\n"", + ""Processing CHEMBL1349347 in Train_ER_alpha.smi (40/1238). Average speed: 0.14 s/mol.\n"", + ""Processing CHEMBL1385373 in Train_ER_alpha.smi (41/1238). Average speed: 0.14 s/mol.\n"", + ""Processing CHEMBL1573735 in Train_ER_alpha.smi (42/1238). Average speed: 0.14 s/mol.\n"", + ""Processing CHEMBL1455258 in Train_ER_alpha.smi (43/1238). Average speed: 0.14 s/mol.\n"", + ""Processing CHEMBL1431555 in Train_ER_alpha.smi (44/1238). Average speed: 0.14 s/mol.\n"", + ""Processing CHEMBL1565187 in Train_ER_alpha.smi (45/1238). Average speed: 0.14 s/mol.\n"", + ""Processing CHEMBL1466882 in Train_ER_alpha.smi (47/1238). Average speed: 0.13 s/mol.\n"", + ""Processing CHEMBL1597933 in Train_ER_alpha.smi (46/1238). Average speed: 0.14 s/mol.\n"", + ""Processing CHEMBL1313979 in Train_ER_alpha.smi (48/1238). Average speed: 0.13 s/mol.\n"", + ""Processing CHEMBL1329455 in Train_ER_alpha.smi (49/1238). Average speed: 0.13 s/mol.\n"", + ""Processing CHEMBL1322939 in Train_ER_alpha.smi (50/1238). Average speed: 0.13 s/mol.\n"", + ""Processing CHEMBL1480843 in Train_ER_alpha.smi (51/1238). Average speed: 0.13 s/mol.\n"", + ""Processing CHEMBL1535728 in Train_ER_alpha.smi (53/1238). Average speed: 0.13 s/mol.\n"", + ""Processing CHEMBL1310614 in Train_ER_alpha.smi (52/1238). Average speed: 0.13 s/mol.\n"", + ""Processing CHEMBL1420411 in Train_ER_alpha.smi (55/1238). Average speed: 0.13 s/mol.\n"", + ""Processing CHEMBL184372 in Train_ER_alpha.smi (56/1238). Average speed: 0.13 s/mol.\n"", + ""Processing CHEMBL1341057 in Train_ER_alpha.smi (54/1238). Average speed: 0.13 s/mol.\n"", + ""Processing CHEMBL442037 in Train_ER_alpha.smi (57/1238). Average speed: 0.13 s/mol.\n"", + ""Processing CHEMBL512579 in Train_ER_alpha.smi (58/1238). Average speed: 0.12 s/mol.\n"", + ""Processing CHEMBL124474 in Train_ER_alpha.smi (59/1238). Average speed: 0.13 s/mol.\n"", + ""Processing CHEMBL231811 in Train_ER_alpha.smi (60/1238). Average speed: 0.13 s/mol.\n"", + ""Processing CHEMBL385801 in Train_ER_alpha.smi (61/1238). Average speed: 0.13 s/mol.\n"", + ""Processing CHEMBL447111 in Train_ER_alpha.smi (62/1238). Average speed: 0.12 s/mol.\n"", + ""Processing CHEMBL427496 in Train_ER_alpha.smi (63/1238). Average speed: 0.12 s/mol.\n"", + ""Processing CHEMBL225404 in Train_ER_alpha.smi (64/1238). Average speed: 0.12 s/mol.\n"", + ""Processing CHEMBL226269 in Train_ER_alpha.smi (65/1238). Average speed: 0.12 s/mol.\n"", + ""Processing CHEMBL1081361 in Train_ER_alpha.smi (66/1238). Average speed: 0.12 s/mol.\n"", + ""Processing CHEMBL235496 in Train_ER_alpha.smi (67/1238). Average speed: 0.12 s/mol.\n"", + ""Processing CHEMBL235497 in Train_ER_alpha.smi (68/1238). Average speed: 0.12 s/mol.\n"", + ""Processing CHEMBL361762 in Train_ER_alpha.smi (69/1238). Average speed: 0.12 s/mol.\n"", + ""Processing CHEMBL184371 in Train_ER_alpha.smi (70/1238). Average speed: 0.12 s/mol.\n"", + ""Processing CHEMBL188458 in Train_ER_alpha.smi (71/1238). Average speed: 0.12 s/mol.\n"", + ""Processing CHEMBL361272 in Train_ER_alpha.smi (72/1238). Average speed: 0.12 s/mol.\n"", + ""Processing CHEMBL241257 in Train_ER_alpha.smi (73/1238). Average speed: 0.12 s/mol.\n"", + ""Processing CHEMBL396118 in Train_ER_alpha.smi (74/1238). Average speed: 0.12 s/mol.\n"", + ""Processing CHEMBL59438 in Train_ER_alpha.smi (75/1238). Average speed: 0.12 s/mol.\n"", + ""Processing CHEMBL150279 in Train_ER_alpha.smi (76/1238). Average speed: 0.12 s/mol.\n"", + ""Processing CHEMBL365625 in Train_ER_alpha.smi (77/1238). Average speed: 0.12 s/mol.\n"", + ""Processing CHEMBL365016 in Train_ER_alpha.smi (78/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL179929 in Train_ER_alpha.smi (79/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL470993 in Train_ER_alpha.smi (80/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL511269 in Train_ER_alpha.smi (81/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL469512 in Train_ER_alpha.smi (82/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL470785 in Train_ER_alpha.smi (83/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL577938 in Train_ER_alpha.smi (84/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL185114 in Train_ER_alpha.smi (85/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL377171 in Train_ER_alpha.smi (86/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL385680 in Train_ER_alpha.smi (87/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL204902 in Train_ER_alpha.smi (88/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL206727 in Train_ER_alpha.smi (89/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL380362 in Train_ER_alpha.smi (90/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL206547 in Train_ER_alpha.smi (91/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL196836 in Train_ER_alpha.smi (94/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL511492 in Train_ER_alpha.smi (93/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL471626 in Train_ER_alpha.smi (92/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL25228 in Train_ER_alpha.smi (95/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL203330 in Train_ER_alpha.smi (97/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL194326 in Train_ER_alpha.smi (96/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL206648 in Train_ER_alpha.smi (98/1238). Average speed: 0.10 s/mol.\n"" + ] + }, + { + ""name"": ""stdout"", + ""output_type"": ""stream"", + ""text"": [ + ""Processing CHEMBL425231 in Train_ER_alpha.smi (99/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL206336 in Train_ER_alpha.smi (100/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL205137 in Train_ER_alpha.smi (101/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL425941 in Train_ER_alpha.smi (102/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL206499 in Train_ER_alpha.smi (103/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL429446 in Train_ER_alpha.smi (104/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL191300 in Train_ER_alpha.smi (105/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL447837 in Train_ER_alpha.smi (106/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL187314 in Train_ER_alpha.smi (107/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL371308 in Train_ER_alpha.smi (108/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL426085 in Train_ER_alpha.smi (110/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL191710 in Train_ER_alpha.smi (109/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL200719 in Train_ER_alpha.smi (111/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL212419 in Train_ER_alpha.smi (112/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL211493 in Train_ER_alpha.smi (113/1238). Average speed: 0.09 s/mol.\n"", + ""Processing CHEMBL204024 in Train_ER_alpha.smi (115/1238). Average speed: 0.09 s/mol.\n"", + ""Processing CHEMBL377475 in Train_ER_alpha.smi (114/1238). Average speed: 0.09 s/mol.\n"", + ""Processing CHEMBL183333 in Train_ER_alpha.smi (116/1238). Average speed: 0.09 s/mol.\n"", + ""Processing CHEMBL185231 in Train_ER_alpha.smi (117/1238). Average speed: 0.09 s/mol.\n"", + ""Processing CHEMBL378473 in Train_ER_alpha.smi (118/1238). Average speed: 0.09 s/mol.\n"", + ""Processing CHEMBL382734 in Train_ER_alpha.smi (119/1238). Average speed: 0.09 s/mol.\n"", + ""Processing CHEMBL195807 in Train_ER_alpha.smi (120/1238). Average speed: 0.09 s/mol.\n"", + ""Processing CHEMBL212185 in Train_ER_alpha.smi (121/1238). Average speed: 0.09 s/mol.\n"", + ""Processing CHEMBL198914 in Train_ER_alpha.smi (122/1238). Average speed: 0.09 s/mol.\n"", + ""Processing CHEMBL372808 in Train_ER_alpha.smi (123/1238). Average speed: 0.09 s/mol.\n"", + ""Processing CHEMBL197495 in Train_ER_alpha.smi (124/1238). Average speed: 0.09 s/mol.\n"", + ""Processing CHEMBL191901 in Train_ER_alpha.smi (125/1238). Average speed: 0.09 s/mol.\n"", + ""Processing CHEMBL241304 in Train_ER_alpha.smi (126/1238). Average speed: 0.09 s/mol.\n"", + ""Processing CHEMBL241485 in Train_ER_alpha.smi (127/1238). Average speed: 0.09 s/mol.\n"", + ""Processing CHEMBL437190 in Train_ER_alpha.smi (128/1238). Average speed: 0.09 s/mol.\n"", + ""Processing CHEMBL182402 in Train_ER_alpha.smi (129/1238). Average speed: 0.09 s/mol.\n"", + ""Processing CHEMBL188391 in Train_ER_alpha.smi (130/1238). Average speed: 0.09 s/mol.\n"", + ""Processing CHEMBL184598 in Train_ER_alpha.smi (131/1238). Average speed: 0.09 s/mol.\n"", + ""Processing CHEMBL360315 in Train_ER_alpha.smi (132/1238). Average speed: 0.09 s/mol.\n"", + ""Processing CHEMBL371777 in Train_ER_alpha.smi (133/1238). Average speed: 0.09 s/mol.\n"", + ""Processing CHEMBL126164 in Train_ER_alpha.smi (134/1238). Average speed: 0.09 s/mol.\n"", + ""Processing CHEMBL195786 in Train_ER_alpha.smi (135/1238). Average speed: 0.09 s/mol.\n"", + ""Processing CHEMBL219134 in Train_ER_alpha.smi (136/1238). Average speed: 0.09 s/mol.\n"", + ""Processing CHEMBL204119 in Train_ER_alpha.smi (137/1238). Average speed: 0.09 s/mol.\n"", + ""Processing CHEMBL204222 in Train_ER_alpha.smi (138/1238). Average speed: 0.09 s/mol.\n"", + ""Processing CHEMBL441366 in Train_ER_alpha.smi (140/1238). Average speed: 0.09 s/mol.\n"", + ""Processing CHEMBL212392 in Train_ER_alpha.smi (139/1238). Average speed: 0.09 s/mol.\n"", + ""Processing CHEMBL384709 in Train_ER_alpha.smi (141/1238). Average speed: 0.09 s/mol.\n"", + ""Processing CHEMBL189080 in Train_ER_alpha.smi (142/1238). Average speed: 0.09 s/mol.\n"", + ""Processing CHEMBL180707 in Train_ER_alpha.smi (143/1238). Average speed: 0.09 s/mol.\n"", + ""Processing CHEMBL362272 in Train_ER_alpha.smi (144/1238). Average speed: 0.09 s/mol.\n"", + ""Processing CHEMBL438516 in Train_ER_alpha.smi (145/1238). Average speed: 0.09 s/mol.\n"", + ""Processing CHEMBL234733 in Train_ER_alpha.smi (146/1238). Average speed: 0.09 s/mol.\n"", + ""Processing CHEMBL206441 in Train_ER_alpha.smi (147/1238). Average speed: 0.09 s/mol.\n"", + ""Processing CHEMBL379188 in Train_ER_alpha.smi (148/1238). Average speed: 0.09 s/mol.\n"", + ""Processing CHEMBL425439 in Train_ER_alpha.smi (149/1238). Average speed: 0.08 s/mol.\n"", + ""Processing CHEMBL192102 in Train_ER_alpha.smi (150/1238). Average speed: 0.08 s/mol.\n"", + ""Processing CHEMBL395308 in Train_ER_alpha.smi (151/1238). Average speed: 0.08 s/mol.\n"", + ""Processing CHEMBL394788 in Train_ER_alpha.smi (152/1238). Average speed: 0.08 s/mol.\n"", + ""Processing CHEMBL245133 in Train_ER_alpha.smi (153/1238). Average speed: 0.08 s/mol.\n"", + ""Processing CHEMBL234527 in Train_ER_alpha.smi (154/1238). Average speed: 0.08 s/mol.\n"", + ""Processing CHEMBL195208 in Train_ER_alpha.smi (155/1238). Average speed: 0.08 s/mol.\n"", + ""Processing CHEMBL8145 in Train_ER_alpha.smi (156/1238). Average speed: 0.08 s/mol.\n"", + ""Processing CHEMBL195437 in Train_ER_alpha.smi (157/1238). Average speed: 0.08 s/mol.\n"", + ""Processing CHEMBL435090 in Train_ER_alpha.smi (158/1238). Average speed: 0.08 s/mol.\n"", + ""Processing CHEMBL1201151 in Train_ER_alpha.smi (159/1238). Average speed: 0.08 s/mol.\n"", + ""Processing CHEMBL192068 in Train_ER_alpha.smi (161/1238). Average speed: 0.08 s/mol.\n"", + ""Processing CHEMBL366928 in Train_ER_alpha.smi (160/1238). Average speed: 0.08 s/mol.\n"", + ""Processing CHEMBL360651 in Train_ER_alpha.smi (162/1238). Average speed: 0.08 s/mol.\n"", + ""Processing CHEMBL195878 in Train_ER_alpha.smi (163/1238). Average speed: 0.08 s/mol.\n"", + ""Processing CHEMBL182237 in Train_ER_alpha.smi (164/1238). Average speed: 0.08 s/mol.\n"", + ""Processing CHEMBL365293 in Train_ER_alpha.smi (165/1238). Average speed: 0.08 s/mol.\n"", + ""Processing CHEMBL183086 in Train_ER_alpha.smi (166/1238). Average speed: 0.08 s/mol.\n"", + ""Processing CHEMBL426641 in Train_ER_alpha.smi (167/1238). Average speed: 0.08 s/mol.\n"", + ""Processing CHEMBL374799 in Train_ER_alpha.smi (168/1238). Average speed: 0.08 s/mol.\n"", + ""Processing CHEMBL225349 in Train_ER_alpha.smi (169/1238). Average speed: 0.08 s/mol.\n"", + ""Processing CHEMBL222434 in Train_ER_alpha.smi (170/1238). Average speed: 0.08 s/mol.\n"", + ""Processing CHEMBL373743 in Train_ER_alpha.smi (171/1238). Average speed: 0.08 s/mol.\n"", + ""Processing CHEMBL451496 in Train_ER_alpha.smi (172/1238). Average speed: 0.08 s/mol.\n"", + ""Processing CHEMBL186643 in Train_ER_alpha.smi (173/1238). Average speed: 0.08 s/mol.\n"", + ""Processing CHEMBL362718 in Train_ER_alpha.smi (174/1238). Average speed: 0.08 s/mol.\n"", + ""Processing CHEMBL418327 in Train_ER_alpha.smi (175/1238). Average speed: 0.08 s/mol.\n"", + ""Processing CHEMBL187207 in Train_ER_alpha.smi (176/1238). Average speed: 0.08 s/mol.\n"", + ""Processing CHEMBL181248 in Train_ER_alpha.smi (177/1238). Average speed: 0.08 s/mol.\n"", + ""Processing CHEMBL183371 in Train_ER_alpha.smi (178/1238). Average speed: 0.08 s/mol.\n"", + ""Processing CHEMBL439806 in Train_ER_alpha.smi (179/1238). Average speed: 0.08 s/mol.\n"", + ""Processing CHEMBL121879 in Train_ER_alpha.smi (180/1238). Average speed: 0.08 s/mol.\n"", + ""Processing CHEMBL202996 in Train_ER_alpha.smi (181/1238). Average speed: 0.08 s/mol.\n"", + ""Processing CHEMBL363705 in Train_ER_alpha.smi (182/1238). Average speed: 0.08 s/mol.\n"", + ""Processing CHEMBL373095 in Train_ER_alpha.smi (183/1238). Average speed: 0.08 s/mol.\n"", + ""Processing CHEMBL185480 in Train_ER_alpha.smi (184/1238). Average speed: 0.08 s/mol.\n"", + ""Processing CHEMBL389683 in Train_ER_alpha.smi (185/1238). Average speed: 0.08 s/mol.\n"", + ""Processing CHEMBL449038 in Train_ER_alpha.smi (186/1238). Average speed: 0.08 s/mol.\n"", + ""Processing CHEMBL225022 in Train_ER_alpha.smi (187/1238). Average speed: 0.08 s/mol.\n"", + ""Processing CHEMBL360802 in Train_ER_alpha.smi (189/1238). Average speed: 0.08 s/mol.\n"", + ""Processing CHEMBL389393 in Train_ER_alpha.smi (188/1238). Average speed: 0.08 s/mol.\n"", + ""Processing CHEMBL332625 in Train_ER_alpha.smi (190/1238). Average speed: 0.08 s/mol.\n"", + ""Processing CHEMBL194936 in Train_ER_alpha.smi (191/1238). Average speed: 0.08 s/mol.\n"", + ""Processing CHEMBL192154 in Train_ER_alpha.smi (192/1238). Average speed: 0.08 s/mol.\n"", + ""Processing CHEMBL369991 in Train_ER_alpha.smi (193/1238). Average speed: 0.08 s/mol.\n"", + ""Processing CHEMBL377342 in Train_ER_alpha.smi (194/1238). Average speed: 0.08 s/mol.\n"", + ""Processing CHEMBL381298 in Train_ER_alpha.smi (195/1238). Average speed: 0.08 s/mol.\n"" + ] + }, + { + ""name"": ""stdout"", + ""output_type"": ""stream"", + ""text"": [ + ""Processing CHEMBL204025 in Train_ER_alpha.smi (196/1238). Average speed: 0.08 s/mol.\n"", + ""Processing CHEMBL202615 in Train_ER_alpha.smi (197/1238). Average speed: 0.08 s/mol.\n"", + ""Processing CHEMBL201013 in Train_ER_alpha.smi (198/1238). Average speed: 0.08 s/mol.\n"", + ""Processing CHEMBL385279 in Train_ER_alpha.smi (199/1238). Average speed: 0.08 s/mol.\n"", + ""Processing CHEMBL85826 in Train_ER_alpha.smi (201/1238). Average speed: 0.07 s/mol.\n"", + ""Processing CHEMBL219387 in Train_ER_alpha.smi (200/1238). Average speed: 0.07 s/mol.\n"", + ""Processing CHEMBL195371 in Train_ER_alpha.smi (202/1238). Average speed: 0.07 s/mol.\n"", + ""Processing CHEMBL187915 in Train_ER_alpha.smi (203/1238). Average speed: 0.07 s/mol.\n"", + ""Processing CHEMBL364092 in Train_ER_alpha.smi (204/1238). Average speed: 0.07 s/mol.\n"", + ""Processing CHEMBL193726 in Train_ER_alpha.smi (205/1238). Average speed: 0.07 s/mol.\n"", + ""Processing CHEMBL192218 in Train_ER_alpha.smi (206/1238). Average speed: 0.07 s/mol.\n"", + ""Processing CHEMBL441499 in Train_ER_alpha.smi (207/1238). Average speed: 0.07 s/mol.\n"", + ""Processing CHEMBL183084 in Train_ER_alpha.smi (208/1238). Average speed: 0.07 s/mol.\n"", + ""Processing CHEMBL187000 in Train_ER_alpha.smi (209/1238). Average speed: 0.07 s/mol.\n"", + ""Processing CHEMBL369979 in Train_ER_alpha.smi (210/1238). Average speed: 0.07 s/mol.\n"", + ""Processing CHEMBL381299 in Train_ER_alpha.smi (211/1238). Average speed: 0.07 s/mol.\n"", + ""Processing CHEMBL206584 in Train_ER_alpha.smi (212/1238). Average speed: 0.07 s/mol.\n"", + ""Processing CHEMBL212923 in Train_ER_alpha.smi (213/1238). Average speed: 0.07 s/mol.\n"", + ""Processing CHEMBL385358 in Train_ER_alpha.smi (214/1238). Average speed: 0.07 s/mol.\n"", + ""Processing CHEMBL383533 in Train_ER_alpha.smi (215/1238). Average speed: 0.07 s/mol.\n"", + ""Processing CHEMBL219577 in Train_ER_alpha.smi (216/1238). Average speed: 0.07 s/mol.\n"", + ""Processing CHEMBL241303 in Train_ER_alpha.smi (217/1238). Average speed: 0.07 s/mol.\n"", + ""Processing CHEMBL240227 in Train_ER_alpha.smi (218/1238). Average speed: 0.07 s/mol.\n"", + ""Processing CHEMBL393278 in Train_ER_alpha.smi (219/1238). Average speed: 0.07 s/mol.\n"", + ""Processing CHEMBL362428 in Train_ER_alpha.smi (220/1238). Average speed: 0.07 s/mol.\n"", + ""Processing CHEMBL496 in Train_ER_alpha.smi (221/1238). Average speed: 0.07 s/mol.\n"", + ""Processing CHEMBL434525 in Train_ER_alpha.smi (222/1238). Average speed: 0.07 s/mol.\n"", + ""Processing CHEMBL365999 in Train_ER_alpha.smi (223/1238). Average speed: 0.07 s/mol.\n"", + ""Processing CHEMBL198411 in Train_ER_alpha.smi (224/1238). Average speed: 0.07 s/mol.\n"", + ""Processing CHEMBL186945 in Train_ER_alpha.smi (225/1238). Average speed: 0.07 s/mol.\n"", + ""Processing CHEMBL382632 in Train_ER_alpha.smi (227/1238). Average speed: 0.07 s/mol.\n"", + ""Processing CHEMBL197667 in Train_ER_alpha.smi (226/1238). Average speed: 0.07 s/mol.\n"", + ""Processing CHEMBL380844 in Train_ER_alpha.smi (229/1238). Average speed: 0.07 s/mol.\n"", + ""Processing CHEMBL206440 in Train_ER_alpha.smi (228/1238). Average speed: 0.07 s/mol.\n"", + ""Processing CHEMBL383753 in Train_ER_alpha.smi (230/1238). Average speed: 0.07 s/mol.\n"", + ""Processing CHEMBL206406 in Train_ER_alpha.smi (231/1238). Average speed: 0.07 s/mol.\n"", + ""Processing CHEMBL381697 in Train_ER_alpha.smi (233/1238). Average speed: 0.07 s/mol.\n"", + ""Processing CHEMBL204056 in Train_ER_alpha.smi (232/1238). Average speed: 0.07 s/mol.\n"", + ""Processing CHEMBL204922 in Train_ER_alpha.smi (234/1238). Average speed: 0.07 s/mol.\n"", + ""Processing CHEMBL206600 in Train_ER_alpha.smi (235/1238). Average speed: 0.07 s/mol.\n"", + ""Processing CHEMBL206227 in Train_ER_alpha.smi (236/1238). Average speed: 0.07 s/mol.\n"", + ""Processing CHEMBL211816 in Train_ER_alpha.smi (237/1238). Average speed: 0.07 s/mol.\n"", + ""Processing CHEMBL208929 in Train_ER_alpha.smi (238/1238). Average speed: 0.07 s/mol.\n"", + ""Processing CHEMBL209623 in Train_ER_alpha.smi (239/1238). Average speed: 0.07 s/mol.\n"", + ""Processing CHEMBL211887 in Train_ER_alpha.smi (240/1238). Average speed: 0.07 s/mol.\n"", + ""Processing CHEMBL191714 in Train_ER_alpha.smi (241/1238). Average speed: 0.07 s/mol.\n"", + ""Processing CHEMBL188792 in Train_ER_alpha.smi (242/1238). Average speed: 0.07 s/mol.\n"", + ""Processing CHEMBL204819 in Train_ER_alpha.smi (243/1238). Average speed: 0.07 s/mol.\n"", + ""Processing CHEMBL206546 in Train_ER_alpha.smi (244/1238). Average speed: 0.07 s/mol.\n"", + ""Processing CHEMBL205934 in Train_ER_alpha.smi (245/1238). Average speed: 0.07 s/mol.\n"", + ""Processing CHEMBL381337 in Train_ER_alpha.smi (246/1238). Average speed: 0.07 s/mol.\n"", + ""Processing CHEMBL203442 in Train_ER_alpha.smi (247/1238). Average speed: 0.07 s/mol.\n"", + ""Processing CHEMBL206814 in Train_ER_alpha.smi (248/1238). Average speed: 0.07 s/mol.\n"", + ""Processing CHEMBL203351 in Train_ER_alpha.smi (249/1238). Average speed: 0.07 s/mol.\n"", + ""Processing CHEMBL382698 in Train_ER_alpha.smi (250/1238). Average speed: 0.07 s/mol.\n"", + ""Processing CHEMBL364502 in Train_ER_alpha.smi (252/1238). Average speed: 0.07 s/mol.\n"", + ""Processing CHEMBL331996 in Train_ER_alpha.smi (253/1238). Average speed: 0.07 s/mol.\n"", + ""Processing CHEMBL191238 in Train_ER_alpha.smi (251/1238). Average speed: 0.07 s/mol.\n"", + ""Processing CHEMBL363634 in Train_ER_alpha.smi (254/1238). Average speed: 0.07 s/mol.\n"", + ""Processing CHEMBL185890 in Train_ER_alpha.smi (255/1238). Average speed: 0.07 s/mol.\n"", + ""Processing CHEMBL185083 in Train_ER_alpha.smi (256/1238). Average speed: 0.07 s/mol.\n"", + ""Processing CHEMBL362793 in Train_ER_alpha.smi (257/1238). Average speed: 0.07 s/mol.\n"", + ""Processing CHEMBL373987 in Train_ER_alpha.smi (259/1238). Average speed: 0.07 s/mol.\n"", + ""Processing CHEMBL187473 in Train_ER_alpha.smi (258/1238). Average speed: 0.07 s/mol.\n"", + ""Processing CHEMBL290106 in Train_ER_alpha.smi (261/1238). Average speed: 0.07 s/mol.\n"", + ""Processing CHEMBL213950 in Train_ER_alpha.smi (260/1238). Average speed: 0.07 s/mol.\n"", + ""Processing CHEMBL239938 in Train_ER_alpha.smi (262/1238). Average speed: 0.07 s/mol.\n"", + ""Processing CHEMBL391619 in Train_ER_alpha.smi (263/1238). Average speed: 0.07 s/mol.\n"", + ""Processing CHEMBL389907 in Train_ER_alpha.smi (264/1238). Average speed: 0.07 s/mol.\n"", + ""Processing CHEMBL187115 in Train_ER_alpha.smi (265/1238). Average speed: 0.07 s/mol.\n"", + ""Processing CHEMBL363633 in Train_ER_alpha.smi (266/1238). Average speed: 0.07 s/mol.\n"", + ""Processing CHEMBL182850 in Train_ER_alpha.smi (267/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL188189 in Train_ER_alpha.smi (269/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL361751 in Train_ER_alpha.smi (268/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL196742 in Train_ER_alpha.smi (270/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL316003 in Train_ER_alpha.smi (271/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL360410 in Train_ER_alpha.smi (272/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL196530 in Train_ER_alpha.smi (273/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL1570364 in Train_ER_alpha.smi (274/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL1491936 in Train_ER_alpha.smi (275/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL1487772 in Train_ER_alpha.smi (276/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL51483 in Train_ER_alpha.smi (277/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL359633 in Train_ER_alpha.smi (278/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL463804 in Train_ER_alpha.smi (279/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL1414254 in Train_ER_alpha.smi (280/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL1403999 in Train_ER_alpha.smi (281/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL1627356 in Train_ER_alpha.smi (282/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL319244 in Train_ER_alpha.smi (283/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL1541779 in Train_ER_alpha.smi (285/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL1572403 in Train_ER_alpha.smi (284/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL1458486 in Train_ER_alpha.smi (286/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL1587920 in Train_ER_alpha.smi (287/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL1399039 in Train_ER_alpha.smi (288/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL1332402 in Train_ER_alpha.smi (289/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL1328083 in Train_ER_alpha.smi (290/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL150924 in Train_ER_alpha.smi (291/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL184003 in Train_ER_alpha.smi (292/1238). Average speed: 0.06 s/mol.\n"" + ] + }, + { + ""name"": ""stdout"", + ""output_type"": ""stream"", + ""text"": [ + ""Processing CHEMBL182751 in Train_ER_alpha.smi (293/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL1465370 in Train_ER_alpha.smi (294/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL1407483 in Train_ER_alpha.smi (296/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL1383151 in Train_ER_alpha.smi (295/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL1511067 in Train_ER_alpha.smi (297/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL192876 in Train_ER_alpha.smi (298/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL363017 in Train_ER_alpha.smi (299/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL1428161 in Train_ER_alpha.smi (300/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL1390963 in Train_ER_alpha.smi (301/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL340111 in Train_ER_alpha.smi (302/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL185783 in Train_ER_alpha.smi (303/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL294889 in Train_ER_alpha.smi (304/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL184151 in Train_ER_alpha.smi (305/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL1504315 in Train_ER_alpha.smi (306/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL63768 in Train_ER_alpha.smi (307/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL1564209 in Train_ER_alpha.smi (308/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL380188 in Train_ER_alpha.smi (310/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL1469865 in Train_ER_alpha.smi (309/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL214693 in Train_ER_alpha.smi (311/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL196283 in Train_ER_alpha.smi (312/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL195154 in Train_ER_alpha.smi (313/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL363498 in Train_ER_alpha.smi (314/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL1392188 in Train_ER_alpha.smi (315/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL1577029 in Train_ER_alpha.smi (316/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL183368 in Train_ER_alpha.smi (317/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL215409 in Train_ER_alpha.smi (318/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL211837 in Train_ER_alpha.smi (319/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL379566 in Train_ER_alpha.smi (320/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL384268 in Train_ER_alpha.smi (321/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL203348 in Train_ER_alpha.smi (322/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL371072 in Train_ER_alpha.smi (323/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL180873 in Train_ER_alpha.smi (324/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL426626 in Train_ER_alpha.smi (325/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL191711 in Train_ER_alpha.smi (326/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL225081 in Train_ER_alpha.smi (327/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL182887 in Train_ER_alpha.smi (329/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL231812 in Train_ER_alpha.smi (328/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL1368345 in Train_ER_alpha.smi (330/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL188900 in Train_ER_alpha.smi (331/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL362966 in Train_ER_alpha.smi (332/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL379213 in Train_ER_alpha.smi (333/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL380359 in Train_ER_alpha.smi (334/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL207101 in Train_ER_alpha.smi (335/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL385693 in Train_ER_alpha.smi (336/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL192765 in Train_ER_alpha.smi (337/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL193727 in Train_ER_alpha.smi (338/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL183367 in Train_ER_alpha.smi (339/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL1395826 in Train_ER_alpha.smi (340/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL1434189 in Train_ER_alpha.smi (341/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL1386105 in Train_ER_alpha.smi (342/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL371126 in Train_ER_alpha.smi (344/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL364345 in Train_ER_alpha.smi (343/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL1324414 in Train_ER_alpha.smi (345/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL1334930 in Train_ER_alpha.smi (346/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL363902 in Train_ER_alpha.smi (347/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL472468 in Train_ER_alpha.smi (348/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL189024 in Train_ER_alpha.smi (349/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL194718 in Train_ER_alpha.smi (350/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL209803 in Train_ER_alpha.smi (351/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL209402 in Train_ER_alpha.smi (352/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL204970 in Train_ER_alpha.smi (353/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL1303886 in Train_ER_alpha.smi (355/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL203472 in Train_ER_alpha.smi (354/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL246341 in Train_ER_alpha.smi (356/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL425092 in Train_ER_alpha.smi (357/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL380469 in Train_ER_alpha.smi (358/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL376986 in Train_ER_alpha.smi (359/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL208978 in Train_ER_alpha.smi (360/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL377644 in Train_ER_alpha.smi (361/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL249903 in Train_ER_alpha.smi (362/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL398634 in Train_ER_alpha.smi (363/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL232023 in Train_ER_alpha.smi (364/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL1340020 in Train_ER_alpha.smi (365/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL191836 in Train_ER_alpha.smi (367/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL511812 in Train_ER_alpha.smi (366/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL189356 in Train_ER_alpha.smi (368/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL1606765 in Train_ER_alpha.smi (369/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL1426947 in Train_ER_alpha.smi (371/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL1348377 in Train_ER_alpha.smi (370/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL1453259 in Train_ER_alpha.smi (372/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL347560 in Train_ER_alpha.smi (373/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL364110 in Train_ER_alpha.smi (374/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL189025 in Train_ER_alpha.smi (375/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL25580 in Train_ER_alpha.smi (376/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL1604500 in Train_ER_alpha.smi (377/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL1509453 in Train_ER_alpha.smi (378/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL1482201 in Train_ER_alpha.smi (379/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL221515 in Train_ER_alpha.smi (381/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL1326210 in Train_ER_alpha.smi (380/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL389394 in Train_ER_alpha.smi (382/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL374277 in Train_ER_alpha.smi (383/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL379889 in Train_ER_alpha.smi (384/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL203724 in Train_ER_alpha.smi (385/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL226379 in Train_ER_alpha.smi (386/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL1318094 in Train_ER_alpha.smi (387/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL368449 in Train_ER_alpha.smi (388/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL191759 in Train_ER_alpha.smi (389/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL365941 in Train_ER_alpha.smi (390/1238). Average speed: 0.06 s/mol.\n"" + ] + }, + { + ""name"": ""stdout"", + ""output_type"": ""stream"", + ""text"": [ + ""Processing CHEMBL191808 in Train_ER_alpha.smi (391/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL360978 in Train_ER_alpha.smi (392/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL200483 in Train_ER_alpha.smi (394/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL392029 in Train_ER_alpha.smi (393/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL199599 in Train_ER_alpha.smi (395/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL200232 in Train_ER_alpha.smi (396/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL201517 in Train_ER_alpha.smi (397/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL202673 in Train_ER_alpha.smi (398/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL203072 in Train_ER_alpha.smi (399/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL241300 in Train_ER_alpha.smi (400/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL428732 in Train_ER_alpha.smi (401/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL442249 in Train_ER_alpha.smi (403/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL393179 in Train_ER_alpha.smi (402/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL238959 in Train_ER_alpha.smi (404/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL1410824 in Train_ER_alpha.smi (405/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL1359893 in Train_ER_alpha.smi (406/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL1427643 in Train_ER_alpha.smi (407/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL1464457 in Train_ER_alpha.smi (408/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL1403448 in Train_ER_alpha.smi (409/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL1575187 in Train_ER_alpha.smi (410/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL1548231 in Train_ER_alpha.smi (411/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL1531555 in Train_ER_alpha.smi (412/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL1521410 in Train_ER_alpha.smi (413/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL1538223 in Train_ER_alpha.smi (414/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL153405 in Train_ER_alpha.smi (415/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL327320 in Train_ER_alpha.smi (416/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL178559 in Train_ER_alpha.smi (417/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL180973 in Train_ER_alpha.smi (418/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL1528123 in Train_ER_alpha.smi (419/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL1536373 in Train_ER_alpha.smi (420/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL1386676 in Train_ER_alpha.smi (421/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL144431 in Train_ER_alpha.smi (422/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL1580036 in Train_ER_alpha.smi (423/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL189658 in Train_ER_alpha.smi (424/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL188619 in Train_ER_alpha.smi (425/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL1525824 in Train_ER_alpha.smi (426/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL1522300 in Train_ER_alpha.smi (427/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL193064 in Train_ER_alpha.smi (428/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL186513 in Train_ER_alpha.smi (429/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL1495194 in Train_ER_alpha.smi (430/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL1606490 in Train_ER_alpha.smi (431/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL1548186 in Train_ER_alpha.smi (432/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL2079587 in Train_ER_alpha.smi (433/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL180253 in Train_ER_alpha.smi (434/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL93705 in Train_ER_alpha.smi (435/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL190104 in Train_ER_alpha.smi (436/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL1548834 in Train_ER_alpha.smi (437/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL188527 in Train_ER_alpha.smi (438/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL360498 in Train_ER_alpha.smi (439/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL1501269 in Train_ER_alpha.smi (440/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL1560367 in Train_ER_alpha.smi (441/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL1427879 in Train_ER_alpha.smi (442/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL1544241 in Train_ER_alpha.smi (443/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL577758 in Train_ER_alpha.smi (444/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL186597 in Train_ER_alpha.smi (445/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL145576 in Train_ER_alpha.smi (446/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL361258 in Train_ER_alpha.smi (447/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL363367 in Train_ER_alpha.smi (448/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL181369 in Train_ER_alpha.smi (449/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL57150 in Train_ER_alpha.smi (450/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL433944 in Train_ER_alpha.smi (451/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL371624 in Train_ER_alpha.smi (452/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL188613 in Train_ER_alpha.smi (453/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL371788 in Train_ER_alpha.smi (454/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL189200 in Train_ER_alpha.smi (455/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL186073 in Train_ER_alpha.smi (456/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL187087 in Train_ER_alpha.smi (457/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL200021 in Train_ER_alpha.smi (458/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL193010 in Train_ER_alpha.smi (459/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL363963 in Train_ER_alpha.smi (460/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL450940 in Train_ER_alpha.smi (461/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL365045 in Train_ER_alpha.smi (462/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL188410 in Train_ER_alpha.smi (463/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL201569 in Train_ER_alpha.smi (464/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL380876 in Train_ER_alpha.smi (465/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL441537 in Train_ER_alpha.smi (466/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL377694 in Train_ER_alpha.smi (467/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL362020 in Train_ER_alpha.smi (469/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL192386 in Train_ER_alpha.smi (468/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL183152 in Train_ER_alpha.smi (470/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL192587 in Train_ER_alpha.smi (471/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL370849 in Train_ER_alpha.smi (472/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL178679 in Train_ER_alpha.smi (473/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL185126 in Train_ER_alpha.smi (474/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL185918 in Train_ER_alpha.smi (475/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL264216 in Train_ER_alpha.smi (476/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL1337826 in Train_ER_alpha.smi (477/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL1480378 in Train_ER_alpha.smi (478/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL1481770 in Train_ER_alpha.smi (479/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL198679 in Train_ER_alpha.smi (480/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL121978 in Train_ER_alpha.smi (481/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL371009 in Train_ER_alpha.smi (482/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL334096 in Train_ER_alpha.smi (483/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL192539 in Train_ER_alpha.smi (484/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL186598 in Train_ER_alpha.smi (485/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL180146 in Train_ER_alpha.smi (486/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL377224 in Train_ER_alpha.smi (487/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL213051 in Train_ER_alpha.smi (488/1238). Average speed: 0.05 s/mol.\n"" + ] + }, + { + ""name"": ""stdout"", + ""output_type"": ""stream"", + ""text"": [ + ""Processing CHEMBL377848 in Train_ER_alpha.smi (489/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL264349 in Train_ER_alpha.smi (490/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL378675 in Train_ER_alpha.smi (491/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL1402 in Train_ER_alpha.smi (492/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL369935 in Train_ER_alpha.smi (493/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL216279 in Train_ER_alpha.smi (494/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL219126 in Train_ER_alpha.smi (495/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL381566 in Train_ER_alpha.smi (496/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL209429 in Train_ER_alpha.smi (497/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL379109 in Train_ER_alpha.smi (498/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL379821 in Train_ER_alpha.smi (499/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL426854 in Train_ER_alpha.smi (500/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL202896 in Train_ER_alpha.smi (501/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL179518 in Train_ER_alpha.smi (502/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL187069 in Train_ER_alpha.smi (503/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL1116 in Train_ER_alpha.smi (506/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL363425 in Train_ER_alpha.smi (504/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL597498 in Train_ER_alpha.smi (505/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL206226 in Train_ER_alpha.smi (507/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL378990 in Train_ER_alpha.smi (508/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL211800 in Train_ER_alpha.smi (509/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL379567 in Train_ER_alpha.smi (510/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL379595 in Train_ER_alpha.smi (511/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL187937 in Train_ER_alpha.smi (513/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL211870 in Train_ER_alpha.smi (512/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL204768 in Train_ER_alpha.smi (514/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL205350 in Train_ER_alpha.smi (515/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL380717 in Train_ER_alpha.smi (516/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL206582 in Train_ER_alpha.smi (517/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL362355 in Train_ER_alpha.smi (518/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL221676 in Train_ER_alpha.smi (519/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL363955 in Train_ER_alpha.smi (520/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL206764 in Train_ER_alpha.smi (521/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL206131 in Train_ER_alpha.smi (522/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL202894 in Train_ER_alpha.smi (524/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL381720 in Train_ER_alpha.smi (523/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL206349 in Train_ER_alpha.smi (525/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL206773 in Train_ER_alpha.smi (526/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL379008 in Train_ER_alpha.smi (527/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL192016 in Train_ER_alpha.smi (528/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL191841 in Train_ER_alpha.smi (529/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL238960 in Train_ER_alpha.smi (530/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL184360 in Train_ER_alpha.smi (532/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL238961 in Train_ER_alpha.smi (531/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL180176 in Train_ER_alpha.smi (533/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL437695 in Train_ER_alpha.smi (534/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL1276308 in Train_ER_alpha.smi (535/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL1485005 in Train_ER_alpha.smi (536/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL1327977 in Train_ER_alpha.smi (538/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL1521111 in Train_ER_alpha.smi (537/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL388418 in Train_ER_alpha.smi (539/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL396948 in Train_ER_alpha.smi (540/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL1433745 in Train_ER_alpha.smi (541/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL1475062 in Train_ER_alpha.smi (542/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL1597831 in Train_ER_alpha.smi (543/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL471627 in Train_ER_alpha.smi (544/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL1362841 in Train_ER_alpha.smi (545/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL1429150 in Train_ER_alpha.smi (546/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL1548361 in Train_ER_alpha.smi (547/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL375334 in Train_ER_alpha.smi (548/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL222522 in Train_ER_alpha.smi (549/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL222580 in Train_ER_alpha.smi (550/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL1449885 in Train_ER_alpha.smi (551/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL1584646 in Train_ER_alpha.smi (552/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL1506716 in Train_ER_alpha.smi (553/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL1472407 in Train_ER_alpha.smi (554/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL1448466 in Train_ER_alpha.smi (555/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL1454402 in Train_ER_alpha.smi (556/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL1431650 in Train_ER_alpha.smi (557/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL1367590 in Train_ER_alpha.smi (558/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL1522044 in Train_ER_alpha.smi (560/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL1587260 in Train_ER_alpha.smi (559/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL1461574 in Train_ER_alpha.smi (561/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL1328182 in Train_ER_alpha.smi (562/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL250109 in Train_ER_alpha.smi (563/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL194772 in Train_ER_alpha.smi (565/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL191716 in Train_ER_alpha.smi (564/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL362425 in Train_ER_alpha.smi (566/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL363177 in Train_ER_alpha.smi (567/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL364329 in Train_ER_alpha.smi (568/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL361226 in Train_ER_alpha.smi (569/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL379543 in Train_ER_alpha.smi (570/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL361063 in Train_ER_alpha.smi (571/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL189822 in Train_ER_alpha.smi (572/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL192782 in Train_ER_alpha.smi (573/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL192901 in Train_ER_alpha.smi (574/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL362307 in Train_ER_alpha.smi (575/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL190487 in Train_ER_alpha.smi (576/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL1479 in Train_ER_alpha.smi (577/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL104416 in Train_ER_alpha.smi (578/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL195991 in Train_ER_alpha.smi (579/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL190038 in Train_ER_alpha.smi (580/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL189303 in Train_ER_alpha.smi (581/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL1627354 in Train_ER_alpha.smi (582/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL178665 in Train_ER_alpha.smi (583/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL435696 in Train_ER_alpha.smi (584/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL371564 in Train_ER_alpha.smi (585/1238). Average speed: 0.05 s/mol.\n"" + ] + }, + { + ""name"": ""stdout"", + ""output_type"": ""stream"", + ""text"": [ + ""Processing CHEMBL1628170 in Train_ER_alpha.smi (586/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL362349 in Train_ER_alpha.smi (587/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL954 in Train_ER_alpha.smi (588/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL1627353 in Train_ER_alpha.smi (590/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL184974 in Train_ER_alpha.smi (589/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL188846 in Train_ER_alpha.smi (591/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL196131 in Train_ER_alpha.smi (592/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL372304 in Train_ER_alpha.smi (593/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL427180 in Train_ER_alpha.smi (594/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL194265 in Train_ER_alpha.smi (595/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL183344 in Train_ER_alpha.smi (597/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL195788 in Train_ER_alpha.smi (596/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL187393 in Train_ER_alpha.smi (600/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL361078 in Train_ER_alpha.smi (599/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL369545 in Train_ER_alpha.smi (598/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL1200623 in Train_ER_alpha.smi (601/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL194762 in Train_ER_alpha.smi (602/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL188051 in Train_ER_alpha.smi (603/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL187392 in Train_ER_alpha.smi (604/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL418971 in Train_ER_alpha.smi (605/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL184367 in Train_ER_alpha.smi (606/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL370282 in Train_ER_alpha.smi (607/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL180702 in Train_ER_alpha.smi (608/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL103650 in Train_ER_alpha.smi (609/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL181936 in Train_ER_alpha.smi (610/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL1302802 in Train_ER_alpha.smi (612/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL1411687 in Train_ER_alpha.smi (611/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL1521414 in Train_ER_alpha.smi (613/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL1491602 in Train_ER_alpha.smi (614/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL1341814 in Train_ER_alpha.smi (615/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL1322601 in Train_ER_alpha.smi (616/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL1604803 in Train_ER_alpha.smi (617/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL1393131 in Train_ER_alpha.smi (618/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL1541552 in Train_ER_alpha.smi (619/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL1350028 in Train_ER_alpha.smi (620/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL183782 in Train_ER_alpha.smi (621/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL1423880 in Train_ER_alpha.smi (622/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL1493034 in Train_ER_alpha.smi (623/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL1347829 in Train_ER_alpha.smi (624/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL1310171 in Train_ER_alpha.smi (625/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL1411209 in Train_ER_alpha.smi (626/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL1507788 in Train_ER_alpha.smi (627/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL196583 in Train_ER_alpha.smi (628/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL1532659 in Train_ER_alpha.smi (630/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL1505283 in Train_ER_alpha.smi (629/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL189039 in Train_ER_alpha.smi (631/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL187673 in Train_ER_alpha.smi (632/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL197757 in Train_ER_alpha.smi (633/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL191715 in Train_ER_alpha.smi (634/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL122208 in Train_ER_alpha.smi (635/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL153395 in Train_ER_alpha.smi (636/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL195289 in Train_ER_alpha.smi (637/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL1381306 in Train_ER_alpha.smi (638/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL1421864 in Train_ER_alpha.smi (639/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL1560682 in Train_ER_alpha.smi (640/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL1475434 in Train_ER_alpha.smi (641/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL1434961 in Train_ER_alpha.smi (642/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL1552932 in Train_ER_alpha.smi (643/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL1593653 in Train_ER_alpha.smi (644/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL1449110 in Train_ER_alpha.smi (645/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL192390 in Train_ER_alpha.smi (646/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL340606 in Train_ER_alpha.smi (647/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL371305 in Train_ER_alpha.smi (648/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL195280 in Train_ER_alpha.smi (649/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL193482 in Train_ER_alpha.smi (650/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL1443090 in Train_ER_alpha.smi (651/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL1519669 in Train_ER_alpha.smi (652/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL1299357 in Train_ER_alpha.smi (653/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL1581690 in Train_ER_alpha.smi (654/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL1433062 in Train_ER_alpha.smi (655/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL125362 in Train_ER_alpha.smi (656/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL361871 in Train_ER_alpha.smi (657/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL482116 in Train_ER_alpha.smi (658/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL190682 in Train_ER_alpha.smi (659/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL363872 in Train_ER_alpha.smi (660/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL184421 in Train_ER_alpha.smi (661/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL205920 in Train_ER_alpha.smi (662/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL185383 in Train_ER_alpha.smi (663/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL226329 in Train_ER_alpha.smi (664/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL200799 in Train_ER_alpha.smi (665/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL371012 in Train_ER_alpha.smi (666/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL362356 in Train_ER_alpha.smi (667/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL238890 in Train_ER_alpha.smi (668/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL240157 in Train_ER_alpha.smi (669/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL511971 in Train_ER_alpha.smi (670/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL364419 in Train_ER_alpha.smi (671/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL391795 in Train_ER_alpha.smi (672/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL398455 in Train_ER_alpha.smi (673/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL430395 in Train_ER_alpha.smi (674/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL183044 in Train_ER_alpha.smi (675/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL189002 in Train_ER_alpha.smi (676/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL438256 in Train_ER_alpha.smi (677/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL188957 in Train_ER_alpha.smi (678/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL204697 in Train_ER_alpha.smi (679/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL203561 in Train_ER_alpha.smi (680/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL211739 in Train_ER_alpha.smi (681/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL213529 in Train_ER_alpha.smi (682/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL214200 in Train_ER_alpha.smi (683/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL187978 in Train_ER_alpha.smi (684/1238). Average speed: 0.05 s/mol.\n"" + ] + }, + { + ""name"": ""stdout"", + ""output_type"": ""stream"", + ""text"": [ + ""Processing CHEMBL187219 in Train_ER_alpha.smi (685/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL394357 in Train_ER_alpha.smi (686/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL388473 in Train_ER_alpha.smi (687/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL242818 in Train_ER_alpha.smi (688/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL106773 in Train_ER_alpha.smi (689/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL192000 in Train_ER_alpha.smi (690/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL202781 in Train_ER_alpha.smi (691/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL198445 in Train_ER_alpha.smi (692/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL432454 in Train_ER_alpha.smi (693/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL184491 in Train_ER_alpha.smi (694/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL393982 in Train_ER_alpha.smi (695/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL240438 in Train_ER_alpha.smi (696/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL188246 in Train_ER_alpha.smi (697/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL346455 in Train_ER_alpha.smi (698/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL232022 in Train_ER_alpha.smi (699/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL206120 in Train_ER_alpha.smi (700/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL210074 in Train_ER_alpha.smi (701/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL209738 in Train_ER_alpha.smi (702/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL377254 in Train_ER_alpha.smi (703/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL213829 in Train_ER_alpha.smi (704/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL455215 in Train_ER_alpha.smi (705/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL522990 in Train_ER_alpha.smi (706/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL267431 in Train_ER_alpha.smi (707/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL469510 in Train_ER_alpha.smi (708/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL368688 in Train_ER_alpha.smi (709/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL1076306 in Train_ER_alpha.smi (710/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL125119 in Train_ER_alpha.smi (712/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL198680 in Train_ER_alpha.smi (711/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL414859 in Train_ER_alpha.smi (713/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL378798 in Train_ER_alpha.smi (714/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL1450791 in Train_ER_alpha.smi (717/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL195311 in Train_ER_alpha.smi (716/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL46903 in Train_ER_alpha.smi (715/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL203370 in Train_ER_alpha.smi (718/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL378572 in Train_ER_alpha.smi (719/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL206137 in Train_ER_alpha.smi (720/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL1389780 in Train_ER_alpha.smi (721/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL1312693 in Train_ER_alpha.smi (722/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL1377973 in Train_ER_alpha.smi (724/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL1324290 in Train_ER_alpha.smi (723/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL1453264 in Train_ER_alpha.smi (725/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL1304227 in Train_ER_alpha.smi (727/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL1533166 in Train_ER_alpha.smi (726/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL1329686 in Train_ER_alpha.smi (728/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL1498999 in Train_ER_alpha.smi (729/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL1386537 in Train_ER_alpha.smi (730/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL1520373 in Train_ER_alpha.smi (731/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL1452952 in Train_ER_alpha.smi (732/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL1393976 in Train_ER_alpha.smi (733/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL1419810 in Train_ER_alpha.smi (734/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL1531320 in Train_ER_alpha.smi (735/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL1439181 in Train_ER_alpha.smi (736/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL1484547 in Train_ER_alpha.smi (737/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL1577757 in Train_ER_alpha.smi (738/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL1372307 in Train_ER_alpha.smi (740/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL1505467 in Train_ER_alpha.smi (739/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL1525366 in Train_ER_alpha.smi (741/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL1482875 in Train_ER_alpha.smi (742/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL1345810 in Train_ER_alpha.smi (743/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL1374573 in Train_ER_alpha.smi (744/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL1577661 in Train_ER_alpha.smi (745/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL1370120 in Train_ER_alpha.smi (746/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL1407054 in Train_ER_alpha.smi (747/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL1548616 in Train_ER_alpha.smi (748/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL1559731 in Train_ER_alpha.smi (749/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL1555033 in Train_ER_alpha.smi (750/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL1405182 in Train_ER_alpha.smi (751/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL1455849 in Train_ER_alpha.smi (752/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL1356953 in Train_ER_alpha.smi (753/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL1326803 in Train_ER_alpha.smi (754/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL1597805 in Train_ER_alpha.smi (755/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL1084746 in Train_ER_alpha.smi (756/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL1083326 in Train_ER_alpha.smi (757/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL426897 in Train_ER_alpha.smi (758/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL386502 in Train_ER_alpha.smi (759/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL223026 in Train_ER_alpha.smi (760/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL144897 in Train_ER_alpha.smi (761/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL433769 in Train_ER_alpha.smi (762/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL179852 in Train_ER_alpha.smi (763/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL183033 in Train_ER_alpha.smi (764/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL190025 in Train_ER_alpha.smi (765/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL198088 in Train_ER_alpha.smi (766/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL1576748 in Train_ER_alpha.smi (767/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL1388431 in Train_ER_alpha.smi (768/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL1509933 in Train_ER_alpha.smi (769/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL360817 in Train_ER_alpha.smi (770/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL369724 in Train_ER_alpha.smi (771/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL52 in Train_ER_alpha.smi (772/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL195217 in Train_ER_alpha.smi (773/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL1522446 in Train_ER_alpha.smi (774/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL1434556 in Train_ER_alpha.smi (775/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL1414407 in Train_ER_alpha.smi (776/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL1397845 in Train_ER_alpha.smi (777/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL282575 in Train_ER_alpha.smi (778/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL361056 in Train_ER_alpha.smi (779/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL1491959 in Train_ER_alpha.smi (780/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL1326483 in Train_ER_alpha.smi (781/1238). Average speed: 0.05 s/mol.\n"" + ] + }, + { + ""name"": ""stdout"", + ""output_type"": ""stream"", + ""text"": [ + ""Processing CHEMBL188516 in Train_ER_alpha.smi (782/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL178334 in Train_ER_alpha.smi (783/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL9352 in Train_ER_alpha.smi (784/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL198204 in Train_ER_alpha.smi (785/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL187311 in Train_ER_alpha.smi (786/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL425194 in Train_ER_alpha.smi (787/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL1300662 in Train_ER_alpha.smi (789/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL1580991 in Train_ER_alpha.smi (788/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL1539865 in Train_ER_alpha.smi (791/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1442450 in Train_ER_alpha.smi (790/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL1466928 in Train_ER_alpha.smi (792/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL182157 in Train_ER_alpha.smi (793/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1467399 in Train_ER_alpha.smi (794/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL192260 in Train_ER_alpha.smi (795/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1303477 in Train_ER_alpha.smi (796/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1350069 in Train_ER_alpha.smi (797/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL187313 in Train_ER_alpha.smi (798/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1391547 in Train_ER_alpha.smi (799/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1526157 in Train_ER_alpha.smi (800/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1572777 in Train_ER_alpha.smi (801/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1320937 in Train_ER_alpha.smi (802/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1582403 in Train_ER_alpha.smi (803/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1447311 in Train_ER_alpha.smi (804/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL189556 in Train_ER_alpha.smi (805/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1431081 in Train_ER_alpha.smi (806/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1421887 in Train_ER_alpha.smi (807/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1231453 in Train_ER_alpha.smi (808/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL204376 in Train_ER_alpha.smi (809/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL460647 in Train_ER_alpha.smi (810/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL216334 in Train_ER_alpha.smi (811/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL377295 in Train_ER_alpha.smi (812/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1325233 in Train_ER_alpha.smi (813/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1430316 in Train_ER_alpha.smi (814/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1574512 in Train_ER_alpha.smi (815/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL379314 in Train_ER_alpha.smi (816/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL385949 in Train_ER_alpha.smi (817/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL378883 in Train_ER_alpha.smi (818/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL215428 in Train_ER_alpha.smi (819/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL385028 in Train_ER_alpha.smi (820/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL212391 in Train_ER_alpha.smi (821/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL377457 in Train_ER_alpha.smi (822/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL214692 in Train_ER_alpha.smi (823/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL372783 in Train_ER_alpha.smi (824/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL398456 in Train_ER_alpha.smi (825/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL249904 in Train_ER_alpha.smi (826/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL250110 in Train_ER_alpha.smi (827/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL380452 in Train_ER_alpha.smi (828/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL380358 in Train_ER_alpha.smi (829/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL438443 in Train_ER_alpha.smi (830/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL380838 in Train_ER_alpha.smi (831/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL188299 in Train_ER_alpha.smi (832/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL394614 in Train_ER_alpha.smi (833/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL469511 in Train_ER_alpha.smi (834/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1300008 in Train_ER_alpha.smi (835/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1531512 in Train_ER_alpha.smi (836/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1404523 in Train_ER_alpha.smi (837/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1386091 in Train_ER_alpha.smi (838/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL211953 in Train_ER_alpha.smi (839/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL214034 in Train_ER_alpha.smi (840/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL211666 in Train_ER_alpha.smi (841/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL206439 in Train_ER_alpha.smi (843/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL206500 in Train_ER_alpha.smi (842/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1307301 in Train_ER_alpha.smi (844/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL10560 in Train_ER_alpha.smi (845/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL231810 in Train_ER_alpha.smi (846/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1470395 in Train_ER_alpha.smi (847/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1320651 in Train_ER_alpha.smi (848/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1417890 in Train_ER_alpha.smi (849/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1404155 in Train_ER_alpha.smi (851/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1429596 in Train_ER_alpha.smi (850/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1442697 in Train_ER_alpha.smi (852/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL203283 in Train_ER_alpha.smi (854/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1603599 in Train_ER_alpha.smi (853/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL377083 in Train_ER_alpha.smi (855/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL206426 in Train_ER_alpha.smi (856/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL202738 in Train_ER_alpha.smi (857/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL205577 in Train_ER_alpha.smi (858/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL208324 in Train_ER_alpha.smi (859/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1471823 in Train_ER_alpha.smi (860/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL192621 in Train_ER_alpha.smi (861/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL192586 in Train_ER_alpha.smi (862/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL434577 in Train_ER_alpha.smi (863/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL465879 in Train_ER_alpha.smi (864/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL384708 in Train_ER_alpha.smi (865/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL221626 in Train_ER_alpha.smi (866/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL222452 in Train_ER_alpha.smi (867/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL372411 in Train_ER_alpha.smi (868/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL201365 in Train_ER_alpha.smi (869/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL204317 in Train_ER_alpha.smi (871/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL199766 in Train_ER_alpha.smi (870/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL383253 in Train_ER_alpha.smi (872/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL425603 in Train_ER_alpha.smi (873/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL212675 in Train_ER_alpha.smi (874/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL203123 in Train_ER_alpha.smi (876/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL211599 in Train_ER_alpha.smi (877/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL180995 in Train_ER_alpha.smi (875/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL213457 in Train_ER_alpha.smi (878/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL214291 in Train_ER_alpha.smi (879/1238). Average speed: 0.04 s/mol.\n"" + ] + }, + { + ""name"": ""stdout"", + ""output_type"": ""stream"", + ""text"": [ + ""Processing CHEMBL240230 in Train_ER_alpha.smi (880/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL394213 in Train_ER_alpha.smi (881/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL193146 in Train_ER_alpha.smi (882/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL193108 in Train_ER_alpha.smi (883/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL182794 in Train_ER_alpha.smi (884/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL194873 in Train_ER_alpha.smi (885/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL187671 in Train_ER_alpha.smi (886/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL189287 in Train_ER_alpha.smi (887/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1732228 in Train_ER_alpha.smi (888/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL187663 in Train_ER_alpha.smi (889/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL198089 in Train_ER_alpha.smi (890/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL189706 in Train_ER_alpha.smi (891/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL188230 in Train_ER_alpha.smi (892/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL185012 in Train_ER_alpha.smi (893/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL122237 in Train_ER_alpha.smi (894/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL184679 in Train_ER_alpha.smi (896/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL361005 in Train_ER_alpha.smi (895/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1424468 in Train_ER_alpha.smi (897/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL187105 in Train_ER_alpha.smi (898/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL276030 in Train_ER_alpha.smi (899/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL181368 in Train_ER_alpha.smi (901/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL188528 in Train_ER_alpha.smi (900/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL195290 in Train_ER_alpha.smi (902/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL363412 in Train_ER_alpha.smi (903/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL186874 in Train_ER_alpha.smi (904/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL364176 in Train_ER_alpha.smi (905/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL365903 in Train_ER_alpha.smi (906/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL195389 in Train_ER_alpha.smi (907/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL365484 in Train_ER_alpha.smi (909/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL360189 in Train_ER_alpha.smi (908/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL189077 in Train_ER_alpha.smi (911/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL193207 in Train_ER_alpha.smi (910/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1627351 in Train_ER_alpha.smi (912/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL360338 in Train_ER_alpha.smi (913/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL440757 in Train_ER_alpha.smi (914/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL178857 in Train_ER_alpha.smi (915/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL360385 in Train_ER_alpha.smi (917/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL188882 in Train_ER_alpha.smi (916/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL182980 in Train_ER_alpha.smi (918/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL195039 in Train_ER_alpha.smi (919/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL180085 in Train_ER_alpha.smi (920/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL183162 in Train_ER_alpha.smi (921/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1596554 in Train_ER_alpha.smi (923/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL427324 in Train_ER_alpha.smi (922/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1301994 in Train_ER_alpha.smi (924/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL185837 in Train_ER_alpha.smi (925/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1466913 in Train_ER_alpha.smi (926/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL425246 in Train_ER_alpha.smi (927/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL380032 in Train_ER_alpha.smi (928/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL211349 in Train_ER_alpha.smi (929/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL377988 in Train_ER_alpha.smi (930/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL234524 in Train_ER_alpha.smi (932/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1323006 in Train_ER_alpha.smi (931/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL396947 in Train_ER_alpha.smi (933/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL212468 in Train_ER_alpha.smi (934/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL383518 in Train_ER_alpha.smi (935/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL204272 in Train_ER_alpha.smi (936/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL203340 in Train_ER_alpha.smi (937/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL380720 in Train_ER_alpha.smi (938/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL205614 in Train_ER_alpha.smi (939/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL204118 in Train_ER_alpha.smi (940/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL222453 in Train_ER_alpha.smi (941/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL577709 in Train_ER_alpha.smi (942/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1566266 in Train_ER_alpha.smi (943/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL183889 in Train_ER_alpha.smi (944/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL205398 in Train_ER_alpha.smi (945/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL379252 in Train_ER_alpha.smi (946/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL203122 in Train_ER_alpha.smi (947/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL206286 in Train_ER_alpha.smi (948/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL225514 in Train_ER_alpha.smi (949/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL222454 in Train_ER_alpha.smi (950/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL215593 in Train_ER_alpha.smi (951/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL391909 in Train_ER_alpha.smi (952/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL241256 in Train_ER_alpha.smi (953/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL241484 in Train_ER_alpha.smi (954/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL396119 in Train_ER_alpha.smi (955/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL372030 in Train_ER_alpha.smi (956/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL202400 in Train_ER_alpha.smi (957/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL437065 in Train_ER_alpha.smi (958/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL377847 in Train_ER_alpha.smi (959/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL226268 in Train_ER_alpha.smi (960/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL239723 in Train_ER_alpha.smi (962/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL190467 in Train_ER_alpha.smi (963/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL226327 in Train_ER_alpha.smi (961/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL192849 in Train_ER_alpha.smi (964/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL205119 in Train_ER_alpha.smi (965/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL210143 in Train_ER_alpha.smi (966/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL208085 in Train_ER_alpha.smi (967/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL383112 in Train_ER_alpha.smi (968/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL209978 in Train_ER_alpha.smi (969/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL213070 in Train_ER_alpha.smi (970/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL341367 in Train_ER_alpha.smi (971/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL426154 in Train_ER_alpha.smi (972/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL216596 in Train_ER_alpha.smi (973/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1628029 in Train_ER_alpha.smi (974/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL188517 in Train_ER_alpha.smi (975/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL197889 in Train_ER_alpha.smi (976/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL513397 in Train_ER_alpha.smi (977/1238). Average speed: 0.04 s/mol.\n"" + ] + }, + { + ""name"": ""stdout"", + ""output_type"": ""stream"", + ""text"": [ + ""Processing CHEMBL1516948 in Train_ER_alpha.smi (980/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL121720 in Train_ER_alpha.smi (978/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1377389 in Train_ER_alpha.smi (979/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1519384 in Train_ER_alpha.smi (981/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1330161 in Train_ER_alpha.smi (982/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1309506 in Train_ER_alpha.smi (983/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1162 in Train_ER_alpha.smi (984/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1507012 in Train_ER_alpha.smi (985/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1468124 in Train_ER_alpha.smi (986/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1476347 in Train_ER_alpha.smi (987/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1612880 in Train_ER_alpha.smi (988/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1359354 in Train_ER_alpha.smi (989/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1585431 in Train_ER_alpha.smi (990/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1501342 in Train_ER_alpha.smi (991/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1448272 in Train_ER_alpha.smi (993/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1580543 in Train_ER_alpha.smi (992/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1406018 in Train_ER_alpha.smi (994/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL245378 in Train_ER_alpha.smi (995/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1532234 in Train_ER_alpha.smi (996/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1312859 in Train_ER_alpha.smi (997/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL249700 in Train_ER_alpha.smi (998/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1584299 in Train_ER_alpha.smi (999/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL457035 in Train_ER_alpha.smi (1000/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1983345 in Train_ER_alpha.smi (1001/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1996233 in Train_ER_alpha.smi (1002/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1976499 in Train_ER_alpha.smi (1003/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1984703 in Train_ER_alpha.smi (1004/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL2103774 in Train_ER_alpha.smi (1005/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1999723 in Train_ER_alpha.smi (1006/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1972915 in Train_ER_alpha.smi (1007/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL2024376 in Train_ER_alpha.smi (1009/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL2113002 in Train_ER_alpha.smi (1010/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL2024377 in Train_ER_alpha.smi (1008/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL2112471 in Train_ER_alpha.smi (1011/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL2112470 in Train_ER_alpha.smi (1013/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL2112469 in Train_ER_alpha.smi (1012/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL2113027 in Train_ER_alpha.smi (1014/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL2113001 in Train_ER_alpha.smi (1015/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL2071180 in Train_ER_alpha.smi (1016/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL2403351 in Train_ER_alpha.smi (1017/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1208076 in Train_ER_alpha.smi (1018/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL2403363 in Train_ER_alpha.smi (1020/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL2403361 in Train_ER_alpha.smi (1019/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL2403359 in Train_ER_alpha.smi (1022/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL2403346 in Train_ER_alpha.smi (1021/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL2403358 in Train_ER_alpha.smi (1023/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL2403360 in Train_ER_alpha.smi (1024/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL2401849 in Train_ER_alpha.smi (1025/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL2403362 in Train_ER_alpha.smi (1026/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL2401848 in Train_ER_alpha.smi (1027/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL2403348 in Train_ER_alpha.smi (1028/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL2407651 in Train_ER_alpha.smi (1029/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL2407647 in Train_ER_alpha.smi (1030/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL2392777 in Train_ER_alpha.smi (1032/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL2403347 in Train_ER_alpha.smi (1031/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL2441817 in Train_ER_alpha.smi (1033/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL2441818 in Train_ER_alpha.smi (1034/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3099431 in Train_ER_alpha.smi (1036/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL2441819 in Train_ER_alpha.smi (1035/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3099432 in Train_ER_alpha.smi (1037/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3098274 in Train_ER_alpha.smi (1038/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3098273 in Train_ER_alpha.smi (1039/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3099429 in Train_ER_alpha.smi (1040/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3145004 in Train_ER_alpha.smi (1041/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3098275 in Train_ER_alpha.smi (1042/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3208438 in Train_ER_alpha.smi (1043/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3208556 in Train_ER_alpha.smi (1044/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3189964 in Train_ER_alpha.smi (1045/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3190827 in Train_ER_alpha.smi (1046/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3199523 in Train_ER_alpha.smi (1047/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3189836 in Train_ER_alpha.smi (1048/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3213380 in Train_ER_alpha.smi (1049/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3109601 in Train_ER_alpha.smi (1050/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3109240 in Train_ER_alpha.smi (1051/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3109156 in Train_ER_alpha.smi (1052/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3109154 in Train_ER_alpha.smi (1053/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3189442 in Train_ER_alpha.smi (1054/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3189821 in Train_ER_alpha.smi (1055/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3198515 in Train_ER_alpha.smi (1056/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3193600 in Train_ER_alpha.smi (1057/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3207314 in Train_ER_alpha.smi (1058/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3214324 in Train_ER_alpha.smi (1059/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3212870 in Train_ER_alpha.smi (1060/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3208784 in Train_ER_alpha.smi (1061/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3235685 in Train_ER_alpha.smi (1062/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3235686 in Train_ER_alpha.smi (1063/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3195623 in Train_ER_alpha.smi (1064/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3196534 in Train_ER_alpha.smi (1065/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3360841 in Train_ER_alpha.smi (1066/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3290288 in Train_ER_alpha.smi (1068/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3290287 in Train_ER_alpha.smi (1067/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3288736 in Train_ER_alpha.smi (1069/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3427502 in Train_ER_alpha.smi (1070/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3581693 in Train_ER_alpha.smi (1072/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3360850 in Train_ER_alpha.smi (1071/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3586190 in Train_ER_alpha.smi (1073/1238). Average speed: 0.04 s/mol.\n"" + ] + }, + { + ""name"": ""stdout"", + ""output_type"": ""stream"", + ""text"": [ + ""Processing CHEMBL3586194 in Train_ER_alpha.smi (1074/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3586191 in Train_ER_alpha.smi (1075/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3360845 in Train_ER_alpha.smi (1076/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3586192 in Train_ER_alpha.smi (1077/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3586195 in Train_ER_alpha.smi (1079/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3586193 in Train_ER_alpha.smi (1078/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3360843 in Train_ER_alpha.smi (1080/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3360846 in Train_ER_alpha.smi (1081/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3360848 in Train_ER_alpha.smi (1082/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3589347 in Train_ER_alpha.smi (1083/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3589365 in Train_ER_alpha.smi (1084/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3589701 in Train_ER_alpha.smi (1085/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3589349 in Train_ER_alpha.smi (1086/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3589367 in Train_ER_alpha.smi (1087/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3578274 in Train_ER_alpha.smi (1088/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3578276 in Train_ER_alpha.smi (1089/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3578271 in Train_ER_alpha.smi (1090/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3589346 in Train_ER_alpha.smi (1091/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3589363 in Train_ER_alpha.smi (1093/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3589361 in Train_ER_alpha.smi (1092/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3589364 in Train_ER_alpha.smi (1094/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3588865 in Train_ER_alpha.smi (1095/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3589352 in Train_ER_alpha.smi (1096/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3589356 in Train_ER_alpha.smi (1097/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3597498 in Train_ER_alpha.smi (1098/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3597499 in Train_ER_alpha.smi (1099/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3597501 in Train_ER_alpha.smi (1100/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3597505 in Train_ER_alpha.smi (1101/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3597508 in Train_ER_alpha.smi (1102/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3597509 in Train_ER_alpha.smi (1103/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3589368 in Train_ER_alpha.smi (1105/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3589354 in Train_ER_alpha.smi (1104/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3360847 in Train_ER_alpha.smi (1107/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3597502 in Train_ER_alpha.smi (1106/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3360849 in Train_ER_alpha.smi (1108/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3427411 in Train_ER_alpha.smi (1109/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3427406 in Train_ER_alpha.smi (1110/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3578285 in Train_ER_alpha.smi (1111/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3578286 in Train_ER_alpha.smi (1112/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3576885 in Train_ER_alpha.smi (1113/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3578284 in Train_ER_alpha.smi (1114/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3622364 in Train_ER_alpha.smi (1115/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3622365 in Train_ER_alpha.smi (1116/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3657137 in Train_ER_alpha.smi (1117/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3657148 in Train_ER_alpha.smi (1118/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3657159 in Train_ER_alpha.smi (1119/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3657170 in Train_ER_alpha.smi (1120/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3657181 in Train_ER_alpha.smi (1121/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3667040 in Train_ER_alpha.smi (1122/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3657142 in Train_ER_alpha.smi (1123/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3657143 in Train_ER_alpha.smi (1124/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3657144 in Train_ER_alpha.smi (1125/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3657146 in Train_ER_alpha.smi (1127/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3657145 in Train_ER_alpha.smi (1126/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3657173 in Train_ER_alpha.smi (1129/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3657172 in Train_ER_alpha.smi (1128/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3657174 in Train_ER_alpha.smi (1130/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3657175 in Train_ER_alpha.smi (1131/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3657147 in Train_ER_alpha.smi (1132/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3657154 in Train_ER_alpha.smi (1133/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3657155 in Train_ER_alpha.smi (1134/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3657149 in Train_ER_alpha.smi (1135/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3657150 in Train_ER_alpha.smi (1136/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3657156 in Train_ER_alpha.smi (1137/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3657158 in Train_ER_alpha.smi (1139/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3657157 in Train_ER_alpha.smi (1138/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3657151 in Train_ER_alpha.smi (1140/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3657152 in Train_ER_alpha.smi (1141/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3657153 in Train_ER_alpha.smi (1142/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3657135 in Train_ER_alpha.smi (1143/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3657177 in Train_ER_alpha.smi (1145/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3657176 in Train_ER_alpha.smi (1144/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3657185 in Train_ER_alpha.smi (1147/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3657164 in Train_ER_alpha.smi (1148/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3657184 in Train_ER_alpha.smi (1146/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3657165 in Train_ER_alpha.smi (1149/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3648370 in Train_ER_alpha.smi (1150/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3657178 in Train_ER_alpha.smi (1151/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3657179 in Train_ER_alpha.smi (1152/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3657160 in Train_ER_alpha.smi (1153/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3657161 in Train_ER_alpha.smi (1154/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3657162 in Train_ER_alpha.smi (1155/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3657163 in Train_ER_alpha.smi (1156/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3657180 in Train_ER_alpha.smi (1157/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3657182 in Train_ER_alpha.smi (1158/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3657183 in Train_ER_alpha.smi (1159/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3657166 in Train_ER_alpha.smi (1160/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3657167 in Train_ER_alpha.smi (1161/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3657168 in Train_ER_alpha.smi (1162/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3657136 in Train_ER_alpha.smi (1163/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3657138 in Train_ER_alpha.smi (1164/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3657139 in Train_ER_alpha.smi (1165/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3657140 in Train_ER_alpha.smi (1166/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3657141 in Train_ER_alpha.smi (1167/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3657169 in Train_ER_alpha.smi (1168/1238). Average speed: 0.04 s/mol.\n"" + ] + }, + { + ""name"": ""stdout"", + ""output_type"": ""stream"", + ""text"": [ + ""Processing CHEMBL3657171 in Train_ER_alpha.smi (1169/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3775116 in Train_ER_alpha.smi (1170/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3774660 in Train_ER_alpha.smi (1171/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3775016 in Train_ER_alpha.smi (1172/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3774647 in Train_ER_alpha.smi (1173/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3775129 in Train_ER_alpha.smi (1174/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3775006 in Train_ER_alpha.smi (1175/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3775410 in Train_ER_alpha.smi (1176/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3775434 in Train_ER_alpha.smi (1177/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3775224 in Train_ER_alpha.smi (1178/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3775978 in Train_ER_alpha.smi (1179/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3775283 in Train_ER_alpha.smi (1180/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3775375 in Train_ER_alpha.smi (1181/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3774642 in Train_ER_alpha.smi (1182/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3805170 in Train_ER_alpha.smi (1184/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3805921 in Train_ER_alpha.smi (1183/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3804907 in Train_ER_alpha.smi (1185/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3805025 in Train_ER_alpha.smi (1186/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3805385 in Train_ER_alpha.smi (1187/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3823498 in Train_ER_alpha.smi (1188/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3815076 in Train_ER_alpha.smi (1189/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3823426 in Train_ER_alpha.smi (1190/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3827870 in Train_ER_alpha.smi (1191/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3827137 in Train_ER_alpha.smi (1192/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3827692 in Train_ER_alpha.smi (1193/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3827034 in Train_ER_alpha.smi (1194/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3827784 in Train_ER_alpha.smi (1195/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3828208 in Train_ER_alpha.smi (1197/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3827200 in Train_ER_alpha.smi (1196/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3827633 in Train_ER_alpha.smi (1198/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3827098 in Train_ER_alpha.smi (1199/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3828416 in Train_ER_alpha.smi (1200/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3827152 in Train_ER_alpha.smi (1202/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3828422 in Train_ER_alpha.smi (1203/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3827045 in Train_ER_alpha.smi (1201/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3827193 in Train_ER_alpha.smi (1204/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3827286 in Train_ER_alpha.smi (1205/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3828117 in Train_ER_alpha.smi (1206/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3823192 in Train_ER_alpha.smi (1207/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL101382 in Train_ER_alpha.smi (1208/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL180792 in Train_ER_alpha.smi (1209/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL183584 in Train_ER_alpha.smi (1210/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL184202 in Train_ER_alpha.smi (1211/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL191974 in Train_ER_alpha.smi (1212/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL192548 in Train_ER_alpha.smi (1213/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL193518 in Train_ER_alpha.smi (1214/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL209269 in Train_ER_alpha.smi (1215/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL209851 in Train_ER_alpha.smi (1216/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL2403356 in Train_ER_alpha.smi (1217/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL2403357 in Train_ER_alpha.smi (1218/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL246138 in Train_ER_alpha.smi (1219/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL281499 in Train_ER_alpha.smi (1221/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL267385 in Train_ER_alpha.smi (1220/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL304552 in Train_ER_alpha.smi (1222/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL30707 in Train_ER_alpha.smi (1223/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL328190 in Train_ER_alpha.smi (1224/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3427402 in Train_ER_alpha.smi (1225/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3623002 in Train_ER_alpha.smi (1226/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3623003 in Train_ER_alpha.smi (1227/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL362623 in Train_ER_alpha.smi (1229/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3623004 in Train_ER_alpha.smi (1228/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL367588 in Train_ER_alpha.smi (1230/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL370039 in Train_ER_alpha.smi (1231/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3774510 in Train_ER_alpha.smi (1232/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3774584 in Train_ER_alpha.smi (1233/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3774944 in Train_ER_alpha.smi (1235/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3775766 in Train_ER_alpha.smi (1236/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3774737 in Train_ER_alpha.smi (1234/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3775908 in Train_ER_alpha.smi (1237/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL691 in Train_ER_alpha.smi (1238/1238). Average speed: 0.04 s/mol.\n"", + ""Descriptor calculation completed in 48.157 secs . Average speed: 0.04 s/mol.\n"", + ""Train_ER_alpha.smi\n"", + ""Train_ER_alpha.csv\n"", + ""PubchemFingerprinter.xml\n"", + ""PubchemFingerprinter_\n"", + ""Processing CHEMBL370037 in Train_ER_alpha.smi (1/1238). \n"", + ""Processing CHEMBL189073 in Train_ER_alpha.smi (2/1238). \n"", + ""Processing CHEMBL365290 in Train_ER_alpha.smi (3/1238). \n"", + ""Processing CHEMBL191648 in Train_ER_alpha.smi (4/1238). \n"", + ""Processing CHEMBL184431 in Train_ER_alpha.smi (5/1238). Average speed: 3.64 s/mol.\n"", + ""Processing CHEMBL364551 in Train_ER_alpha.smi (6/1238). Average speed: 1.96 s/mol.\n"", + ""Processing CHEMBL124482 in Train_ER_alpha.smi (7/1238). Average speed: 1.63 s/mol.\n"", + ""Processing CHEMBL1496978 in Train_ER_alpha.smi (9/1238). Average speed: 1.23 s/mol.\n"", + ""Processing CHEMBL198410 in Train_ER_alpha.smi (8/1238). Average speed: 1.23 s/mol.\n"", + ""Processing CHEMBL1305485 in Train_ER_alpha.smi (10/1238). Average speed: 0.91 s/mol.\n"", + ""Processing CHEMBL1570659 in Train_ER_alpha.smi (11/1238). Average speed: 0.78 s/mol.\n"", + ""Processing CHEMBL1331084 in Train_ER_alpha.smi (12/1238). Average speed: 0.79 s/mol.\n"", + ""Processing CHEMBL184448 in Train_ER_alpha.smi (13/1238). Average speed: 0.69 s/mol.\n"", + ""Processing CHEMBL45068 in Train_ER_alpha.smi (14/1238). Average speed: 0.59 s/mol.\n"", + ""Processing CHEMBL434502 in Train_ER_alpha.smi (15/1238). Average speed: 0.59 s/mol.\n"", + ""Processing CHEMBL1628199 in Train_ER_alpha.smi (16/1238). Average speed: 0.55 s/mol.\n"", + ""Processing CHEMBL123025 in Train_ER_alpha.smi (17/1238). Average speed: 0.54 s/mol.\n"", + ""Processing CHEMBL153062 in Train_ER_alpha.smi (18/1238). Average speed: 0.50 s/mol.\n"", + ""Processing CHEMBL1442416 in Train_ER_alpha.smi (20/1238). Average speed: 0.46 s/mol.\n"", + ""Processing CHEMBL1468708 in Train_ER_alpha.smi (19/1238). Average speed: 0.49 s/mol.\n"", + ""Processing CHEMBL1388271 in Train_ER_alpha.smi (21/1238). Average speed: 0.44 s/mol.\n"", + ""Processing CHEMBL1331872 in Train_ER_alpha.smi (22/1238). Average speed: 0.42 s/mol.\n"", + ""Processing CHEMBL1401227 in Train_ER_alpha.smi (23/1238). Average speed: 0.40 s/mol.\n"", + ""Processing CHEMBL1460126 in Train_ER_alpha.smi (24/1238). Average speed: 0.38 s/mol.\n"", + ""Processing CHEMBL1499638 in Train_ER_alpha.smi (25/1238). Average speed: 0.37 s/mol.\n"" + ] + }, + { + ""name"": ""stdout"", + ""output_type"": ""stream"", + ""text"": [ + ""Processing CHEMBL1413557 in Train_ER_alpha.smi (26/1238). Average speed: 0.35 s/mol.\n"", + ""Processing CHEMBL1484998 in Train_ER_alpha.smi (27/1238). Average speed: 0.34 s/mol.\n"", + ""Processing CHEMBL1608989 in Train_ER_alpha.smi (28/1238). Average speed: 0.33 s/mol.\n"", + ""Processing CHEMBL1469743 in Train_ER_alpha.smi (29/1238). Average speed: 0.32 s/mol.\n"", + ""Processing CHEMBL1538325 in Train_ER_alpha.smi (30/1238). Average speed: 0.30 s/mol.\n"", + ""Processing CHEMBL1605393 in Train_ER_alpha.smi (31/1238). Average speed: 0.30 s/mol.\n"", + ""Processing CHEMBL1384049 in Train_ER_alpha.smi (32/1238). Average speed: 0.29 s/mol.\n"", + ""Processing CHEMBL188049 in Train_ER_alpha.smi (33/1238). Average speed: 0.29 s/mol.\n"", + ""Processing CHEMBL1361641 in Train_ER_alpha.smi (34/1238). Average speed: 0.27 s/mol.\n"", + ""Processing CHEMBL1489063 in Train_ER_alpha.smi (35/1238). Average speed: 0.28 s/mol.\n"", + ""Processing CHEMBL1381495 in Train_ER_alpha.smi (36/1238). Average speed: 0.27 s/mol.\n"", + ""Processing CHEMBL1343011 in Train_ER_alpha.smi (37/1238). Average speed: 0.27 s/mol.\n"", + ""Processing CHEMBL1613068 in Train_ER_alpha.smi (38/1238). Average speed: 0.25 s/mol.\n"", + ""Processing CHEMBL1464837 in Train_ER_alpha.smi (39/1238). Average speed: 0.25 s/mol.\n"", + ""Processing CHEMBL1349347 in Train_ER_alpha.smi (40/1238). Average speed: 0.25 s/mol.\n"", + ""Processing CHEMBL1573735 in Train_ER_alpha.smi (42/1238). Average speed: 0.24 s/mol.\n"", + ""Processing CHEMBL1385373 in Train_ER_alpha.smi (41/1238). Average speed: 0.24 s/mol.\n"", + ""Processing CHEMBL1455258 in Train_ER_alpha.smi (43/1238). Average speed: 0.24 s/mol.\n"", + ""Processing CHEMBL1431555 in Train_ER_alpha.smi (44/1238). Average speed: 0.24 s/mol.\n"", + ""Processing CHEMBL1565187 in Train_ER_alpha.smi (45/1238). Average speed: 0.23 s/mol.\n"", + ""Processing CHEMBL1597933 in Train_ER_alpha.smi (46/1238). Average speed: 0.23 s/mol.\n"", + ""Processing CHEMBL1466882 in Train_ER_alpha.smi (47/1238). Average speed: 0.23 s/mol.\n"", + ""Processing CHEMBL1313979 in Train_ER_alpha.smi (48/1238). Average speed: 0.22 s/mol.\n"", + ""Processing CHEMBL1329455 in Train_ER_alpha.smi (49/1238). Average speed: 0.22 s/mol.\n"", + ""Processing CHEMBL1322939 in Train_ER_alpha.smi (50/1238). Average speed: 0.22 s/mol.\n"", + ""Processing CHEMBL1480843 in Train_ER_alpha.smi (51/1238). Average speed: 0.21 s/mol.\n"", + ""Processing CHEMBL1310614 in Train_ER_alpha.smi (52/1238). Average speed: 0.21 s/mol.\n"", + ""Processing CHEMBL1535728 in Train_ER_alpha.smi (53/1238). Average speed: 0.21 s/mol.\n"", + ""Processing CHEMBL1341057 in Train_ER_alpha.smi (54/1238). Average speed: 0.21 s/mol.\n"", + ""Processing CHEMBL1420411 in Train_ER_alpha.smi (55/1238). Average speed: 0.20 s/mol.\n"", + ""Processing CHEMBL184372 in Train_ER_alpha.smi (56/1238). Average speed: 0.20 s/mol.\n"", + ""Processing CHEMBL442037 in Train_ER_alpha.smi (57/1238). Average speed: 0.20 s/mol.\n"", + ""Processing CHEMBL512579 in Train_ER_alpha.smi (58/1238). Average speed: 0.20 s/mol.\n"", + ""Processing CHEMBL124474 in Train_ER_alpha.smi (59/1238). Average speed: 0.20 s/mol.\n"", + ""Processing CHEMBL231811 in Train_ER_alpha.smi (60/1238). Average speed: 0.20 s/mol.\n"", + ""Processing CHEMBL385801 in Train_ER_alpha.smi (61/1238). Average speed: 0.19 s/mol.\n"", + ""Processing CHEMBL447111 in Train_ER_alpha.smi (62/1238). Average speed: 0.19 s/mol.\n"", + ""Processing CHEMBL427496 in Train_ER_alpha.smi (63/1238). Average speed: 0.19 s/mol.\n"", + ""Processing CHEMBL225404 in Train_ER_alpha.smi (64/1238). Average speed: 0.19 s/mol.\n"", + ""Processing CHEMBL226269 in Train_ER_alpha.smi (65/1238). Average speed: 0.19 s/mol.\n"", + ""Processing CHEMBL1081361 in Train_ER_alpha.smi (66/1238). Average speed: 0.19 s/mol.\n"", + ""Processing CHEMBL235496 in Train_ER_alpha.smi (67/1238). Average speed: 0.19 s/mol.\n"", + ""Processing CHEMBL235497 in Train_ER_alpha.smi (68/1238). Average speed: 0.19 s/mol.\n"", + ""Processing CHEMBL361762 in Train_ER_alpha.smi (69/1238). Average speed: 0.19 s/mol.\n"", + ""Processing CHEMBL184371 in Train_ER_alpha.smi (70/1238). Average speed: 0.19 s/mol.\n"", + ""Processing CHEMBL188458 in Train_ER_alpha.smi (71/1238). Average speed: 0.19 s/mol.\n"", + ""Processing CHEMBL361272 in Train_ER_alpha.smi (72/1238). Average speed: 0.19 s/mol.\n"", + ""Processing CHEMBL241257 in Train_ER_alpha.smi (73/1238). Average speed: 0.19 s/mol.\n"", + ""Processing CHEMBL396118 in Train_ER_alpha.smi (74/1238). Average speed: 0.19 s/mol.\n"", + ""Processing CHEMBL59438 in Train_ER_alpha.smi (75/1238). Average speed: 0.19 s/mol.\n"", + ""Processing CHEMBL365625 in Train_ER_alpha.smi (77/1238). Average speed: 0.18 s/mol.\n"", + ""Processing CHEMBL150279 in Train_ER_alpha.smi (76/1238). Average speed: 0.19 s/mol.\n"", + ""Processing CHEMBL365016 in Train_ER_alpha.smi (78/1238). Average speed: 0.19 s/mol.\n"", + ""Processing CHEMBL179929 in Train_ER_alpha.smi (79/1238). Average speed: 0.19 s/mol.\n"", + ""Processing CHEMBL470993 in Train_ER_alpha.smi (80/1238). Average speed: 0.19 s/mol.\n"", + ""Processing CHEMBL511269 in Train_ER_alpha.smi (81/1238). Average speed: 0.19 s/mol.\n"", + ""Processing CHEMBL469512 in Train_ER_alpha.smi (82/1238). Average speed: 0.19 s/mol.\n"", + ""Processing CHEMBL470785 in Train_ER_alpha.smi (83/1238). Average speed: 0.19 s/mol.\n"", + ""Processing CHEMBL577938 in Train_ER_alpha.smi (84/1238). Average speed: 0.18 s/mol.\n"", + ""Processing CHEMBL185114 in Train_ER_alpha.smi (85/1238). Average speed: 0.18 s/mol.\n"", + ""Processing CHEMBL377171 in Train_ER_alpha.smi (86/1238). Average speed: 0.18 s/mol.\n"", + ""Processing CHEMBL385680 in Train_ER_alpha.smi (87/1238). Average speed: 0.18 s/mol.\n"", + ""Processing CHEMBL204902 in Train_ER_alpha.smi (88/1238). Average speed: 0.18 s/mol.\n"", + ""Processing CHEMBL206727 in Train_ER_alpha.smi (89/1238). Average speed: 0.18 s/mol.\n"", + ""Processing CHEMBL380362 in Train_ER_alpha.smi (90/1238). Average speed: 0.18 s/mol.\n"", + ""Processing CHEMBL206547 in Train_ER_alpha.smi (91/1238). Average speed: 0.18 s/mol.\n"", + ""Processing CHEMBL471626 in Train_ER_alpha.smi (92/1238). Average speed: 0.18 s/mol.\n"", + ""Processing CHEMBL511492 in Train_ER_alpha.smi (93/1238). Average speed: 0.18 s/mol.\n"", + ""Processing CHEMBL196836 in Train_ER_alpha.smi (94/1238). Average speed: 0.18 s/mol.\n"", + ""Processing CHEMBL25228 in Train_ER_alpha.smi (95/1238). Average speed: 0.18 s/mol.\n"", + ""Processing CHEMBL194326 in Train_ER_alpha.smi (96/1238). Average speed: 0.17 s/mol.\n"", + ""Processing CHEMBL203330 in Train_ER_alpha.smi (97/1238). Average speed: 0.18 s/mol.\n"", + ""Processing CHEMBL206648 in Train_ER_alpha.smi (98/1238). Average speed: 0.17 s/mol.\n"", + ""Processing CHEMBL425231 in Train_ER_alpha.smi (99/1238). Average speed: 0.17 s/mol.\n"", + ""Processing CHEMBL206336 in Train_ER_alpha.smi (100/1238). Average speed: 0.17 s/mol.\n"", + ""Processing CHEMBL205137 in Train_ER_alpha.smi (101/1238). Average speed: 0.17 s/mol.\n"", + ""Processing CHEMBL425941 in Train_ER_alpha.smi (102/1238). Average speed: 0.17 s/mol.\n"", + ""Processing CHEMBL206499 in Train_ER_alpha.smi (103/1238). Average speed: 0.17 s/mol.\n"", + ""Processing CHEMBL429446 in Train_ER_alpha.smi (104/1238). Average speed: 0.17 s/mol.\n"", + ""Processing CHEMBL191300 in Train_ER_alpha.smi (105/1238). Average speed: 0.17 s/mol.\n"", + ""Processing CHEMBL447837 in Train_ER_alpha.smi (106/1238). Average speed: 0.17 s/mol.\n"", + ""Processing CHEMBL187314 in Train_ER_alpha.smi (107/1238). Average speed: 0.17 s/mol.\n"", + ""Processing CHEMBL371308 in Train_ER_alpha.smi (108/1238). Average speed: 0.17 s/mol.\n"", + ""Processing CHEMBL191710 in Train_ER_alpha.smi (109/1238). Average speed: 0.17 s/mol.\n"", + ""Processing CHEMBL426085 in Train_ER_alpha.smi (110/1238). Average speed: 0.17 s/mol.\n"", + ""Processing CHEMBL200719 in Train_ER_alpha.smi (111/1238). Average speed: 0.17 s/mol.\n"", + ""Processing CHEMBL212419 in Train_ER_alpha.smi (112/1238). Average speed: 0.17 s/mol.\n"", + ""Processing CHEMBL211493 in Train_ER_alpha.smi (113/1238). Average speed: 0.16 s/mol.\n"", + ""Processing CHEMBL377475 in Train_ER_alpha.smi (114/1238). Average speed: 0.17 s/mol.\n"", + ""Processing CHEMBL204024 in Train_ER_alpha.smi (115/1238). Average speed: 0.16 s/mol.\n"", + ""Processing CHEMBL183333 in Train_ER_alpha.smi (116/1238). Average speed: 0.16 s/mol.\n"", + ""Processing CHEMBL185231 in Train_ER_alpha.smi (117/1238). Average speed: 0.16 s/mol.\n"", + ""Processing CHEMBL378473 in Train_ER_alpha.smi (118/1238). Average speed: 0.16 s/mol.\n"", + ""Processing CHEMBL382734 in Train_ER_alpha.smi (119/1238). Average speed: 0.16 s/mol.\n"", + ""Processing CHEMBL195807 in Train_ER_alpha.smi (120/1238). Average speed: 0.16 s/mol.\n"", + ""Processing CHEMBL212185 in Train_ER_alpha.smi (121/1238). Average speed: 0.16 s/mol.\n"", + ""Processing CHEMBL198914 in Train_ER_alpha.smi (122/1238). Average speed: 0.16 s/mol.\n"" + ] + }, + { + ""name"": ""stdout"", + ""output_type"": ""stream"", + ""text"": [ + ""Processing CHEMBL372808 in Train_ER_alpha.smi (123/1238). Average speed: 0.17 s/mol.\n"", + ""Processing CHEMBL197495 in Train_ER_alpha.smi (124/1238). Average speed: 0.17 s/mol.\n"", + ""Processing CHEMBL191901 in Train_ER_alpha.smi (125/1238). Average speed: 0.16 s/mol.\n"", + ""Processing CHEMBL241304 in Train_ER_alpha.smi (126/1238). Average speed: 0.16 s/mol.\n"", + ""Processing CHEMBL241485 in Train_ER_alpha.smi (127/1238). Average speed: 0.16 s/mol.\n"", + ""Processing CHEMBL182402 in Train_ER_alpha.smi (129/1238). Average speed: 0.17 s/mol.\n"", + ""Processing CHEMBL437190 in Train_ER_alpha.smi (128/1238). Average speed: 0.16 s/mol.\n"", + ""Processing CHEMBL188391 in Train_ER_alpha.smi (130/1238). Average speed: 0.17 s/mol.\n"", + ""Processing CHEMBL184598 in Train_ER_alpha.smi (131/1238). Average speed: 0.17 s/mol.\n"", + ""Processing CHEMBL360315 in Train_ER_alpha.smi (132/1238). Average speed: 0.17 s/mol.\n"", + ""Processing CHEMBL371777 in Train_ER_alpha.smi (133/1238). Average speed: 0.17 s/mol.\n"", + ""Processing CHEMBL126164 in Train_ER_alpha.smi (134/1238). Average speed: 0.17 s/mol.\n"", + ""Processing CHEMBL195786 in Train_ER_alpha.smi (135/1238). Average speed: 0.17 s/mol.\n"", + ""Processing CHEMBL219134 in Train_ER_alpha.smi (136/1238). Average speed: 0.17 s/mol.\n"", + ""Processing CHEMBL204119 in Train_ER_alpha.smi (137/1238). Average speed: 0.17 s/mol.\n"", + ""Processing CHEMBL204222 in Train_ER_alpha.smi (138/1238). Average speed: 0.17 s/mol.\n"", + ""Processing CHEMBL212392 in Train_ER_alpha.smi (139/1238). Average speed: 0.17 s/mol.\n"", + ""Processing CHEMBL441366 in Train_ER_alpha.smi (140/1238). Average speed: 0.17 s/mol.\n"", + ""Processing CHEMBL384709 in Train_ER_alpha.smi (141/1238). Average speed: 0.16 s/mol.\n"", + ""Processing CHEMBL189080 in Train_ER_alpha.smi (142/1238). Average speed: 0.17 s/mol.\n"", + ""Processing CHEMBL180707 in Train_ER_alpha.smi (143/1238). Average speed: 0.16 s/mol.\n"", + ""Processing CHEMBL438516 in Train_ER_alpha.smi (145/1238). Average speed: 0.16 s/mol.\n"", + ""Processing CHEMBL362272 in Train_ER_alpha.smi (144/1238). Average speed: 0.16 s/mol.\n"", + ""Processing CHEMBL234733 in Train_ER_alpha.smi (146/1238). Average speed: 0.16 s/mol.\n"", + ""Processing CHEMBL206441 in Train_ER_alpha.smi (147/1238). Average speed: 0.16 s/mol.\n"", + ""Processing CHEMBL379188 in Train_ER_alpha.smi (148/1238). Average speed: 0.16 s/mol.\n"", + ""Processing CHEMBL425439 in Train_ER_alpha.smi (149/1238). Average speed: 0.16 s/mol.\n"", + ""Processing CHEMBL192102 in Train_ER_alpha.smi (150/1238). Average speed: 0.16 s/mol.\n"", + ""Processing CHEMBL395308 in Train_ER_alpha.smi (151/1238). Average speed: 0.16 s/mol.\n"", + ""Processing CHEMBL394788 in Train_ER_alpha.smi (152/1238). Average speed: 0.16 s/mol.\n"", + ""Processing CHEMBL245133 in Train_ER_alpha.smi (153/1238). Average speed: 0.16 s/mol.\n"", + ""Processing CHEMBL234527 in Train_ER_alpha.smi (154/1238). Average speed: 0.16 s/mol.\n"", + ""Processing CHEMBL195208 in Train_ER_alpha.smi (155/1238). Average speed: 0.16 s/mol.\n"", + ""Processing CHEMBL8145 in Train_ER_alpha.smi (156/1238). Average speed: 0.16 s/mol.\n"", + ""Processing CHEMBL195437 in Train_ER_alpha.smi (157/1238). Average speed: 0.16 s/mol.\n"", + ""Processing CHEMBL435090 in Train_ER_alpha.smi (158/1238). Average speed: 0.16 s/mol.\n"", + ""Processing CHEMBL1201151 in Train_ER_alpha.smi (159/1238). Average speed: 0.16 s/mol.\n"", + ""Processing CHEMBL366928 in Train_ER_alpha.smi (160/1238). Average speed: 0.16 s/mol.\n"", + ""Processing CHEMBL192068 in Train_ER_alpha.smi (161/1238). Average speed: 0.16 s/mol.\n"", + ""Processing CHEMBL360651 in Train_ER_alpha.smi (162/1238). Average speed: 0.16 s/mol.\n"", + ""Processing CHEMBL195878 in Train_ER_alpha.smi (163/1238). Average speed: 0.16 s/mol.\n"", + ""Processing CHEMBL182237 in Train_ER_alpha.smi (164/1238). Average speed: 0.16 s/mol.\n"", + ""Processing CHEMBL365293 in Train_ER_alpha.smi (165/1238). Average speed: 0.16 s/mol.\n"", + ""Processing CHEMBL183086 in Train_ER_alpha.smi (166/1238). Average speed: 0.16 s/mol.\n"", + ""Processing CHEMBL426641 in Train_ER_alpha.smi (167/1238). Average speed: 0.16 s/mol.\n"", + ""Processing CHEMBL374799 in Train_ER_alpha.smi (168/1238). Average speed: 0.16 s/mol.\n"", + ""Processing CHEMBL225349 in Train_ER_alpha.smi (169/1238). Average speed: 0.16 s/mol.\n"", + ""Processing CHEMBL222434 in Train_ER_alpha.smi (170/1238). Average speed: 0.16 s/mol.\n"", + ""Processing CHEMBL373743 in Train_ER_alpha.smi (171/1238). Average speed: 0.16 s/mol.\n"", + ""Processing CHEMBL451496 in Train_ER_alpha.smi (172/1238). Average speed: 0.15 s/mol.\n"", + ""Processing CHEMBL186643 in Train_ER_alpha.smi (173/1238). Average speed: 0.15 s/mol.\n"", + ""Processing CHEMBL362718 in Train_ER_alpha.smi (174/1238). Average speed: 0.15 s/mol.\n"", + ""Processing CHEMBL418327 in Train_ER_alpha.smi (175/1238). Average speed: 0.15 s/mol.\n"", + ""Processing CHEMBL187207 in Train_ER_alpha.smi (176/1238). Average speed: 0.15 s/mol.\n"", + ""Processing CHEMBL181248 in Train_ER_alpha.smi (177/1238). Average speed: 0.15 s/mol.\n"", + ""Processing CHEMBL183371 in Train_ER_alpha.smi (178/1238). Average speed: 0.15 s/mol.\n"", + ""Processing CHEMBL439806 in Train_ER_alpha.smi (179/1238). Average speed: 0.15 s/mol.\n"", + ""Processing CHEMBL121879 in Train_ER_alpha.smi (180/1238). Average speed: 0.15 s/mol.\n"", + ""Processing CHEMBL202996 in Train_ER_alpha.smi (181/1238). Average speed: 0.15 s/mol.\n"", + ""Processing CHEMBL363705 in Train_ER_alpha.smi (182/1238). Average speed: 0.15 s/mol.\n"", + ""Processing CHEMBL373095 in Train_ER_alpha.smi (183/1238). Average speed: 0.15 s/mol.\n"", + ""Processing CHEMBL185480 in Train_ER_alpha.smi (184/1238). Average speed: 0.15 s/mol.\n"", + ""Processing CHEMBL389683 in Train_ER_alpha.smi (185/1238). Average speed: 0.15 s/mol.\n"", + ""Processing CHEMBL449038 in Train_ER_alpha.smi (186/1238). Average speed: 0.15 s/mol.\n"", + ""Processing CHEMBL225022 in Train_ER_alpha.smi (187/1238). Average speed: 0.15 s/mol.\n"", + ""Processing CHEMBL389393 in Train_ER_alpha.smi (188/1238). Average speed: 0.15 s/mol.\n"", + ""Processing CHEMBL360802 in Train_ER_alpha.smi (189/1238). Average speed: 0.15 s/mol.\n"", + ""Processing CHEMBL332625 in Train_ER_alpha.smi (190/1238). Average speed: 0.15 s/mol.\n"", + ""Processing CHEMBL194936 in Train_ER_alpha.smi (191/1238). Average speed: 0.15 s/mol.\n"", + ""Processing CHEMBL192154 in Train_ER_alpha.smi (192/1238). Average speed: 0.15 s/mol.\n"", + ""Processing CHEMBL369991 in Train_ER_alpha.smi (193/1238). Average speed: 0.15 s/mol.\n"", + ""Processing CHEMBL377342 in Train_ER_alpha.smi (194/1238). Average speed: 0.15 s/mol.\n"", + ""Processing CHEMBL381298 in Train_ER_alpha.smi (195/1238). Average speed: 0.15 s/mol.\n"", + ""Processing CHEMBL204025 in Train_ER_alpha.smi (196/1238). Average speed: 0.15 s/mol.\n"", + ""Processing CHEMBL202615 in Train_ER_alpha.smi (197/1238). Average speed: 0.15 s/mol.\n"", + ""Processing CHEMBL385279 in Train_ER_alpha.smi (199/1238). Average speed: 0.15 s/mol.\n"", + ""Processing CHEMBL201013 in Train_ER_alpha.smi (198/1238). Average speed: 0.15 s/mol.\n"", + ""Processing CHEMBL219387 in Train_ER_alpha.smi (200/1238). Average speed: 0.15 s/mol.\n"", + ""Processing CHEMBL85826 in Train_ER_alpha.smi (201/1238). Average speed: 0.15 s/mol.\n"", + ""Processing CHEMBL195371 in Train_ER_alpha.smi (202/1238). Average speed: 0.15 s/mol.\n"", + ""Processing CHEMBL187915 in Train_ER_alpha.smi (203/1238). Average speed: 0.15 s/mol.\n"", + ""Processing CHEMBL364092 in Train_ER_alpha.smi (204/1238). Average speed: 0.15 s/mol.\n"", + ""Processing CHEMBL193726 in Train_ER_alpha.smi (205/1238). Average speed: 0.15 s/mol.\n"", + ""Processing CHEMBL192218 in Train_ER_alpha.smi (206/1238). Average speed: 0.15 s/mol.\n"", + ""Processing CHEMBL441499 in Train_ER_alpha.smi (207/1238). Average speed: 0.15 s/mol.\n"", + ""Processing CHEMBL183084 in Train_ER_alpha.smi (208/1238). Average speed: 0.15 s/mol.\n"", + ""Processing CHEMBL187000 in Train_ER_alpha.smi (209/1238). Average speed: 0.15 s/mol.\n"", + ""Processing CHEMBL369979 in Train_ER_alpha.smi (210/1238). Average speed: 0.15 s/mol.\n"", + ""Processing CHEMBL381299 in Train_ER_alpha.smi (211/1238). Average speed: 0.15 s/mol.\n"", + ""Processing CHEMBL212923 in Train_ER_alpha.smi (213/1238). Average speed: 0.15 s/mol.\n"", + ""Processing CHEMBL206584 in Train_ER_alpha.smi (212/1238). Average speed: 0.15 s/mol.\n"", + ""Processing CHEMBL385358 in Train_ER_alpha.smi (214/1238). Average speed: 0.14 s/mol.\n"", + ""Processing CHEMBL383533 in Train_ER_alpha.smi (215/1238). Average speed: 0.14 s/mol.\n"", + ""Processing CHEMBL219577 in Train_ER_alpha.smi (216/1238). Average speed: 0.14 s/mol.\n"", + ""Processing CHEMBL241303 in Train_ER_alpha.smi (217/1238). Average speed: 0.14 s/mol.\n"", + ""Processing CHEMBL240227 in Train_ER_alpha.smi (218/1238). Average speed: 0.14 s/mol.\n"", + ""Processing CHEMBL393278 in Train_ER_alpha.smi (219/1238). Average speed: 0.14 s/mol.\n"" + ] + }, + { + ""name"": ""stdout"", + ""output_type"": ""stream"", + ""text"": [ + ""Processing CHEMBL362428 in Train_ER_alpha.smi (220/1238). Average speed: 0.14 s/mol.\n"", + ""Processing CHEMBL496 in Train_ER_alpha.smi (221/1238). Average speed: 0.14 s/mol.\n"", + ""Processing CHEMBL434525 in Train_ER_alpha.smi (222/1238). Average speed: 0.14 s/mol.\n"", + ""Processing CHEMBL365999 in Train_ER_alpha.smi (223/1238). Average speed: 0.14 s/mol.\n"", + ""Processing CHEMBL198411 in Train_ER_alpha.smi (224/1238). Average speed: 0.14 s/mol.\n"", + ""Processing CHEMBL186945 in Train_ER_alpha.smi (225/1238). Average speed: 0.14 s/mol.\n"", + ""Processing CHEMBL197667 in Train_ER_alpha.smi (226/1238). Average speed: 0.14 s/mol.\n"", + ""Processing CHEMBL382632 in Train_ER_alpha.smi (227/1238). Average speed: 0.14 s/mol.\n"", + ""Processing CHEMBL206440 in Train_ER_alpha.smi (228/1238). Average speed: 0.14 s/mol.\n"", + ""Processing CHEMBL380844 in Train_ER_alpha.smi (229/1238). Average speed: 0.14 s/mol.\n"", + ""Processing CHEMBL383753 in Train_ER_alpha.smi (230/1238). Average speed: 0.14 s/mol.\n"", + ""Processing CHEMBL206406 in Train_ER_alpha.smi (231/1238). Average speed: 0.14 s/mol.\n"", + ""Processing CHEMBL204056 in Train_ER_alpha.smi (232/1238). Average speed: 0.14 s/mol.\n"", + ""Processing CHEMBL381697 in Train_ER_alpha.smi (233/1238). Average speed: 0.14 s/mol.\n"", + ""Processing CHEMBL204922 in Train_ER_alpha.smi (234/1238). Average speed: 0.14 s/mol.\n"", + ""Processing CHEMBL206600 in Train_ER_alpha.smi (235/1238). Average speed: 0.14 s/mol.\n"", + ""Processing CHEMBL206227 in Train_ER_alpha.smi (236/1238). Average speed: 0.14 s/mol.\n"", + ""Processing CHEMBL211816 in Train_ER_alpha.smi (237/1238). Average speed: 0.14 s/mol.\n"", + ""Processing CHEMBL209623 in Train_ER_alpha.smi (239/1238). Average speed: 0.14 s/mol.\n"", + ""Processing CHEMBL208929 in Train_ER_alpha.smi (238/1238). Average speed: 0.14 s/mol.\n"", + ""Processing CHEMBL211887 in Train_ER_alpha.smi (240/1238). Average speed: 0.14 s/mol.\n"", + ""Processing CHEMBL191714 in Train_ER_alpha.smi (241/1238). Average speed: 0.14 s/mol.\n"", + ""Processing CHEMBL188792 in Train_ER_alpha.smi (242/1238). Average speed: 0.14 s/mol.\n"", + ""Processing CHEMBL204819 in Train_ER_alpha.smi (243/1238). Average speed: 0.14 s/mol.\n"", + ""Processing CHEMBL206546 in Train_ER_alpha.smi (244/1238). Average speed: 0.14 s/mol.\n"", + ""Processing CHEMBL205934 in Train_ER_alpha.smi (245/1238). Average speed: 0.14 s/mol.\n"", + ""Processing CHEMBL381337 in Train_ER_alpha.smi (246/1238). Average speed: 0.14 s/mol.\n"", + ""Processing CHEMBL203442 in Train_ER_alpha.smi (247/1238). Average speed: 0.14 s/mol.\n"", + ""Processing CHEMBL206814 in Train_ER_alpha.smi (248/1238). Average speed: 0.14 s/mol.\n"", + ""Processing CHEMBL203351 in Train_ER_alpha.smi (249/1238). Average speed: 0.14 s/mol.\n"", + ""Processing CHEMBL382698 in Train_ER_alpha.smi (250/1238). Average speed: 0.14 s/mol.\n"", + ""Processing CHEMBL191238 in Train_ER_alpha.smi (251/1238). Average speed: 0.14 s/mol.\n"", + ""Processing CHEMBL364502 in Train_ER_alpha.smi (252/1238). Average speed: 0.14 s/mol.\n"", + ""Processing CHEMBL331996 in Train_ER_alpha.smi (253/1238). Average speed: 0.14 s/mol.\n"", + ""Processing CHEMBL363634 in Train_ER_alpha.smi (254/1238). Average speed: 0.14 s/mol.\n"", + ""Processing CHEMBL185890 in Train_ER_alpha.smi (255/1238). Average speed: 0.14 s/mol.\n"", + ""Processing CHEMBL185083 in Train_ER_alpha.smi (256/1238). Average speed: 0.14 s/mol.\n"", + ""Processing CHEMBL362793 in Train_ER_alpha.smi (257/1238). Average speed: 0.14 s/mol.\n"", + ""Processing CHEMBL187473 in Train_ER_alpha.smi (258/1238). Average speed: 0.14 s/mol.\n"", + ""Processing CHEMBL373987 in Train_ER_alpha.smi (259/1238). Average speed: 0.13 s/mol.\n"", + ""Processing CHEMBL213950 in Train_ER_alpha.smi (260/1238). Average speed: 0.13 s/mol.\n"", + ""Processing CHEMBL290106 in Train_ER_alpha.smi (261/1238). Average speed: 0.13 s/mol.\n"", + ""Processing CHEMBL239938 in Train_ER_alpha.smi (262/1238). Average speed: 0.13 s/mol.\n"", + ""Processing CHEMBL391619 in Train_ER_alpha.smi (263/1238). Average speed: 0.13 s/mol.\n"", + ""Processing CHEMBL389907 in Train_ER_alpha.smi (264/1238). Average speed: 0.13 s/mol.\n"", + ""Processing CHEMBL187115 in Train_ER_alpha.smi (265/1238). Average speed: 0.13 s/mol.\n"", + ""Processing CHEMBL363633 in Train_ER_alpha.smi (266/1238). Average speed: 0.13 s/mol.\n"", + ""Processing CHEMBL182850 in Train_ER_alpha.smi (267/1238). Average speed: 0.13 s/mol.\n"", + ""Processing CHEMBL361751 in Train_ER_alpha.smi (268/1238). Average speed: 0.13 s/mol.\n"", + ""Processing CHEMBL188189 in Train_ER_alpha.smi (269/1238). Average speed: 0.13 s/mol.\n"", + ""Processing CHEMBL196742 in Train_ER_alpha.smi (270/1238). Average speed: 0.13 s/mol.\n"", + ""Processing CHEMBL316003 in Train_ER_alpha.smi (271/1238). Average speed: 0.13 s/mol.\n"", + ""Processing CHEMBL360410 in Train_ER_alpha.smi (272/1238). Average speed: 0.13 s/mol.\n"", + ""Processing CHEMBL196530 in Train_ER_alpha.smi (273/1238). Average speed: 0.13 s/mol.\n"", + ""Processing CHEMBL1570364 in Train_ER_alpha.smi (274/1238). Average speed: 0.13 s/mol.\n"", + ""Processing CHEMBL1491936 in Train_ER_alpha.smi (275/1238). Average speed: 0.13 s/mol.\n"", + ""Processing CHEMBL1487772 in Train_ER_alpha.smi (276/1238). Average speed: 0.13 s/mol.\n"", + ""Processing CHEMBL359633 in Train_ER_alpha.smi (278/1238). Average speed: 0.13 s/mol.\n"", + ""Processing CHEMBL51483 in Train_ER_alpha.smi (277/1238). Average speed: 0.13 s/mol.\n"", + ""Processing CHEMBL463804 in Train_ER_alpha.smi (279/1238). Average speed: 0.13 s/mol.\n"", + ""Processing CHEMBL1414254 in Train_ER_alpha.smi (280/1238). Average speed: 0.13 s/mol.\n"", + ""Processing CHEMBL1403999 in Train_ER_alpha.smi (281/1238). Average speed: 0.13 s/mol.\n"", + ""Processing CHEMBL1627356 in Train_ER_alpha.smi (282/1238). Average speed: 0.13 s/mol.\n"", + ""Processing CHEMBL319244 in Train_ER_alpha.smi (283/1238). Average speed: 0.13 s/mol.\n"", + ""Processing CHEMBL1572403 in Train_ER_alpha.smi (284/1238). Average speed: 0.13 s/mol.\n"", + ""Processing CHEMBL1541779 in Train_ER_alpha.smi (285/1238). Average speed: 0.13 s/mol.\n"", + ""Processing CHEMBL1458486 in Train_ER_alpha.smi (286/1238). Average speed: 0.13 s/mol.\n"", + ""Processing CHEMBL1587920 in Train_ER_alpha.smi (287/1238). Average speed: 0.13 s/mol.\n"", + ""Processing CHEMBL1399039 in Train_ER_alpha.smi (288/1238). Average speed: 0.13 s/mol.\n"", + ""Processing CHEMBL1332402 in Train_ER_alpha.smi (289/1238). Average speed: 0.13 s/mol.\n"", + ""Processing CHEMBL1328083 in Train_ER_alpha.smi (290/1238). Average speed: 0.13 s/mol.\n"", + ""Processing CHEMBL184003 in Train_ER_alpha.smi (292/1238). Average speed: 0.13 s/mol.\n"", + ""Processing CHEMBL150924 in Train_ER_alpha.smi (291/1238). Average speed: 0.13 s/mol.\n"", + ""Processing CHEMBL182751 in Train_ER_alpha.smi (293/1238). Average speed: 0.13 s/mol.\n"", + ""Processing CHEMBL1465370 in Train_ER_alpha.smi (294/1238). Average speed: 0.13 s/mol.\n"", + ""Processing CHEMBL1383151 in Train_ER_alpha.smi (295/1238). Average speed: 0.13 s/mol.\n"", + ""Processing CHEMBL1407483 in Train_ER_alpha.smi (296/1238). Average speed: 0.13 s/mol.\n"", + ""Processing CHEMBL1511067 in Train_ER_alpha.smi (297/1238). Average speed: 0.13 s/mol.\n"", + ""Processing CHEMBL192876 in Train_ER_alpha.smi (298/1238). Average speed: 0.13 s/mol.\n"", + ""Processing CHEMBL363017 in Train_ER_alpha.smi (299/1238). Average speed: 0.13 s/mol.\n"", + ""Processing CHEMBL1428161 in Train_ER_alpha.smi (300/1238). Average speed: 0.13 s/mol.\n"", + ""Processing CHEMBL1390963 in Train_ER_alpha.smi (301/1238). Average speed: 0.13 s/mol.\n"", + ""Processing CHEMBL185783 in Train_ER_alpha.smi (303/1238). Average speed: 0.13 s/mol.\n"", + ""Processing CHEMBL340111 in Train_ER_alpha.smi (302/1238). Average speed: 0.13 s/mol.\n"", + ""Processing CHEMBL294889 in Train_ER_alpha.smi (304/1238). Average speed: 0.13 s/mol.\n"", + ""Processing CHEMBL184151 in Train_ER_alpha.smi (305/1238). Average speed: 0.13 s/mol.\n"", + ""Processing CHEMBL1504315 in Train_ER_alpha.smi (306/1238). Average speed: 0.13 s/mol.\n"", + ""Processing CHEMBL1564209 in Train_ER_alpha.smi (308/1238). Average speed: 0.13 s/mol.\n"", + ""Processing CHEMBL1469865 in Train_ER_alpha.smi (309/1238). Average speed: 0.13 s/mol.\n"", + ""Processing CHEMBL63768 in Train_ER_alpha.smi (307/1238). Average speed: 0.13 s/mol.\n"", + ""Processing CHEMBL380188 in Train_ER_alpha.smi (310/1238). Average speed: 0.13 s/mol.\n"", + ""Processing CHEMBL214693 in Train_ER_alpha.smi (311/1238). Average speed: 0.13 s/mol.\n"", + ""Processing CHEMBL196283 in Train_ER_alpha.smi (312/1238). Average speed: 0.13 s/mol.\n"", + ""Processing CHEMBL195154 in Train_ER_alpha.smi (313/1238). Average speed: 0.13 s/mol.\n"", + ""Processing CHEMBL363498 in Train_ER_alpha.smi (314/1238). Average speed: 0.13 s/mol.\n"", + ""Processing CHEMBL1392188 in Train_ER_alpha.smi (315/1238). Average speed: 0.13 s/mol.\n"", + ""Processing CHEMBL1577029 in Train_ER_alpha.smi (316/1238). Average speed: 0.13 s/mol.\n"", + ""Processing CHEMBL183368 in Train_ER_alpha.smi (317/1238). Average speed: 0.13 s/mol.\n"", + ""Processing CHEMBL215409 in Train_ER_alpha.smi (318/1238). Average speed: 0.13 s/mol.\n"" + ] + }, + { + ""name"": ""stdout"", + ""output_type"": ""stream"", + ""text"": [ + ""Processing CHEMBL211837 in Train_ER_alpha.smi (319/1238). Average speed: 0.13 s/mol.\n"", + ""Processing CHEMBL379566 in Train_ER_alpha.smi (320/1238). Average speed: 0.13 s/mol.\n"", + ""Processing CHEMBL384268 in Train_ER_alpha.smi (321/1238). Average speed: 0.13 s/mol.\n"", + ""Processing CHEMBL203348 in Train_ER_alpha.smi (322/1238). Average speed: 0.13 s/mol.\n"", + ""Processing CHEMBL371072 in Train_ER_alpha.smi (323/1238). Average speed: 0.13 s/mol.\n"", + ""Processing CHEMBL180873 in Train_ER_alpha.smi (324/1238). Average speed: 0.12 s/mol.\n"", + ""Processing CHEMBL426626 in Train_ER_alpha.smi (325/1238). Average speed: 0.13 s/mol.\n"", + ""Processing CHEMBL191711 in Train_ER_alpha.smi (326/1238). Average speed: 0.12 s/mol.\n"", + ""Processing CHEMBL225081 in Train_ER_alpha.smi (327/1238). Average speed: 0.13 s/mol.\n"", + ""Processing CHEMBL231812 in Train_ER_alpha.smi (328/1238). Average speed: 0.12 s/mol.\n"", + ""Processing CHEMBL182887 in Train_ER_alpha.smi (329/1238). Average speed: 0.13 s/mol.\n"", + ""Processing CHEMBL1368345 in Train_ER_alpha.smi (330/1238). Average speed: 0.12 s/mol.\n"", + ""Processing CHEMBL188900 in Train_ER_alpha.smi (331/1238). Average speed: 0.12 s/mol.\n"", + ""Processing CHEMBL362966 in Train_ER_alpha.smi (332/1238). Average speed: 0.12 s/mol.\n"", + ""Processing CHEMBL379213 in Train_ER_alpha.smi (333/1238). Average speed: 0.12 s/mol.\n"", + ""Processing CHEMBL380359 in Train_ER_alpha.smi (334/1238). Average speed: 0.12 s/mol.\n"", + ""Processing CHEMBL207101 in Train_ER_alpha.smi (335/1238). Average speed: 0.12 s/mol.\n"", + ""Processing CHEMBL385693 in Train_ER_alpha.smi (336/1238). Average speed: 0.12 s/mol.\n"", + ""Processing CHEMBL192765 in Train_ER_alpha.smi (337/1238). Average speed: 0.12 s/mol.\n"", + ""Processing CHEMBL193727 in Train_ER_alpha.smi (338/1238). Average speed: 0.12 s/mol.\n"", + ""Processing CHEMBL183367 in Train_ER_alpha.smi (339/1238). Average speed: 0.12 s/mol.\n"", + ""Processing CHEMBL1395826 in Train_ER_alpha.smi (340/1238). Average speed: 0.12 s/mol.\n"", + ""Processing CHEMBL1434189 in Train_ER_alpha.smi (341/1238). Average speed: 0.12 s/mol.\n"", + ""Processing CHEMBL364345 in Train_ER_alpha.smi (343/1238). Average speed: 0.12 s/mol.\n"", + ""Processing CHEMBL1386105 in Train_ER_alpha.smi (342/1238). Average speed: 0.12 s/mol.\n"", + ""Processing CHEMBL1324414 in Train_ER_alpha.smi (345/1238). Average speed: 0.12 s/mol.\n"", + ""Processing CHEMBL371126 in Train_ER_alpha.smi (344/1238). Average speed: 0.12 s/mol.\n"", + ""Processing CHEMBL1334930 in Train_ER_alpha.smi (346/1238). Average speed: 0.12 s/mol.\n"", + ""Processing CHEMBL363902 in Train_ER_alpha.smi (347/1238). Average speed: 0.12 s/mol.\n"", + ""Processing CHEMBL472468 in Train_ER_alpha.smi (348/1238). Average speed: 0.12 s/mol.\n"", + ""Processing CHEMBL189024 in Train_ER_alpha.smi (349/1238). Average speed: 0.12 s/mol.\n"", + ""Processing CHEMBL194718 in Train_ER_alpha.smi (350/1238). Average speed: 0.12 s/mol.\n"", + ""Processing CHEMBL209402 in Train_ER_alpha.smi (352/1238). Average speed: 0.12 s/mol.\n"", + ""Processing CHEMBL209803 in Train_ER_alpha.smi (351/1238). Average speed: 0.12 s/mol.\n"", + ""Processing CHEMBL204970 in Train_ER_alpha.smi (353/1238). Average speed: 0.12 s/mol.\n"", + ""Processing CHEMBL203472 in Train_ER_alpha.smi (354/1238). Average speed: 0.12 s/mol.\n"", + ""Processing CHEMBL1303886 in Train_ER_alpha.smi (355/1238). Average speed: 0.12 s/mol.\n"", + ""Processing CHEMBL246341 in Train_ER_alpha.smi (356/1238). Average speed: 0.12 s/mol.\n"", + ""Processing CHEMBL425092 in Train_ER_alpha.smi (357/1238). Average speed: 0.12 s/mol.\n"", + ""Processing CHEMBL380469 in Train_ER_alpha.smi (358/1238). Average speed: 0.12 s/mol.\n"", + ""Processing CHEMBL376986 in Train_ER_alpha.smi (359/1238). Average speed: 0.12 s/mol.\n"", + ""Processing CHEMBL208978 in Train_ER_alpha.smi (360/1238). Average speed: 0.12 s/mol.\n"", + ""Processing CHEMBL377644 in Train_ER_alpha.smi (361/1238). Average speed: 0.12 s/mol.\n"", + ""Processing CHEMBL249903 in Train_ER_alpha.smi (362/1238). Average speed: 0.12 s/mol.\n"", + ""Processing CHEMBL398634 in Train_ER_alpha.smi (363/1238). Average speed: 0.12 s/mol.\n"", + ""Processing CHEMBL232023 in Train_ER_alpha.smi (364/1238). Average speed: 0.12 s/mol.\n"", + ""Processing CHEMBL1340020 in Train_ER_alpha.smi (365/1238). Average speed: 0.12 s/mol.\n"", + ""Processing CHEMBL511812 in Train_ER_alpha.smi (366/1238). Average speed: 0.12 s/mol.\n"", + ""Processing CHEMBL191836 in Train_ER_alpha.smi (367/1238). Average speed: 0.12 s/mol.\n"", + ""Processing CHEMBL189356 in Train_ER_alpha.smi (368/1238). Average speed: 0.12 s/mol.\n"", + ""Processing CHEMBL1606765 in Train_ER_alpha.smi (369/1238). Average speed: 0.12 s/mol.\n"", + ""Processing CHEMBL1348377 in Train_ER_alpha.smi (370/1238). Average speed: 0.12 s/mol.\n"", + ""Processing CHEMBL1426947 in Train_ER_alpha.smi (371/1238). Average speed: 0.12 s/mol.\n"", + ""Processing CHEMBL1453259 in Train_ER_alpha.smi (372/1238). Average speed: 0.12 s/mol.\n"", + ""Processing CHEMBL347560 in Train_ER_alpha.smi (373/1238). Average speed: 0.12 s/mol.\n"", + ""Processing CHEMBL364110 in Train_ER_alpha.smi (374/1238). Average speed: 0.12 s/mol.\n"", + ""Processing CHEMBL189025 in Train_ER_alpha.smi (375/1238). Average speed: 0.12 s/mol.\n"", + ""Processing CHEMBL25580 in Train_ER_alpha.smi (376/1238). Average speed: 0.12 s/mol.\n"", + ""Processing CHEMBL1604500 in Train_ER_alpha.smi (377/1238). Average speed: 0.12 s/mol.\n"", + ""Processing CHEMBL1509453 in Train_ER_alpha.smi (378/1238). Average speed: 0.12 s/mol.\n"", + ""Processing CHEMBL1482201 in Train_ER_alpha.smi (379/1238). Average speed: 0.12 s/mol.\n"", + ""Processing CHEMBL1326210 in Train_ER_alpha.smi (380/1238). Average speed: 0.12 s/mol.\n"", + ""Processing CHEMBL221515 in Train_ER_alpha.smi (381/1238). Average speed: 0.12 s/mol.\n"", + ""Processing CHEMBL389394 in Train_ER_alpha.smi (382/1238). Average speed: 0.12 s/mol.\n"", + ""Processing CHEMBL374277 in Train_ER_alpha.smi (383/1238). Average speed: 0.12 s/mol.\n"", + ""Processing CHEMBL379889 in Train_ER_alpha.smi (384/1238). Average speed: 0.12 s/mol.\n"", + ""Processing CHEMBL203724 in Train_ER_alpha.smi (385/1238). Average speed: 0.12 s/mol.\n"", + ""Processing CHEMBL226379 in Train_ER_alpha.smi (386/1238). Average speed: 0.12 s/mol.\n"", + ""Processing CHEMBL1318094 in Train_ER_alpha.smi (387/1238). Average speed: 0.12 s/mol.\n"", + ""Processing CHEMBL368449 in Train_ER_alpha.smi (388/1238). Average speed: 0.12 s/mol.\n"", + ""Processing CHEMBL191759 in Train_ER_alpha.smi (389/1238). Average speed: 0.12 s/mol.\n"", + ""Processing CHEMBL365941 in Train_ER_alpha.smi (390/1238). Average speed: 0.12 s/mol.\n"", + ""Processing CHEMBL191808 in Train_ER_alpha.smi (391/1238). Average speed: 0.12 s/mol.\n"", + ""Processing CHEMBL360978 in Train_ER_alpha.smi (392/1238). Average speed: 0.12 s/mol.\n"", + ""Processing CHEMBL392029 in Train_ER_alpha.smi (393/1238). Average speed: 0.12 s/mol.\n"", + ""Processing CHEMBL200483 in Train_ER_alpha.smi (394/1238). Average speed: 0.12 s/mol.\n"", + ""Processing CHEMBL199599 in Train_ER_alpha.smi (395/1238). Average speed: 0.12 s/mol.\n"", + ""Processing CHEMBL200232 in Train_ER_alpha.smi (396/1238). Average speed: 0.12 s/mol.\n"", + ""Processing CHEMBL201517 in Train_ER_alpha.smi (397/1238). Average speed: 0.12 s/mol.\n"", + ""Processing CHEMBL202673 in Train_ER_alpha.smi (398/1238). Average speed: 0.12 s/mol.\n"", + ""Processing CHEMBL203072 in Train_ER_alpha.smi (399/1238). Average speed: 0.12 s/mol.\n"", + ""Processing CHEMBL241300 in Train_ER_alpha.smi (400/1238). Average speed: 0.12 s/mol.\n"", + ""Processing CHEMBL428732 in Train_ER_alpha.smi (401/1238). Average speed: 0.12 s/mol.\n"", + ""Processing CHEMBL393179 in Train_ER_alpha.smi (402/1238). Average speed: 0.12 s/mol.\n"", + ""Processing CHEMBL442249 in Train_ER_alpha.smi (403/1238). Average speed: 0.12 s/mol.\n"", + ""Processing CHEMBL238959 in Train_ER_alpha.smi (404/1238). Average speed: 0.12 s/mol.\n"", + ""Processing CHEMBL1410824 in Train_ER_alpha.smi (405/1238). Average speed: 0.12 s/mol.\n"", + ""Processing CHEMBL1359893 in Train_ER_alpha.smi (406/1238). Average speed: 0.12 s/mol.\n"", + ""Processing CHEMBL1427643 in Train_ER_alpha.smi (407/1238). Average speed: 0.12 s/mol.\n"", + ""Processing CHEMBL1464457 in Train_ER_alpha.smi (408/1238). Average speed: 0.12 s/mol.\n"", + ""Processing CHEMBL1403448 in Train_ER_alpha.smi (409/1238). Average speed: 0.12 s/mol.\n"", + ""Processing CHEMBL1575187 in Train_ER_alpha.smi (410/1238). Average speed: 0.12 s/mol.\n"", + ""Processing CHEMBL1548231 in Train_ER_alpha.smi (411/1238). Average speed: 0.12 s/mol.\n"", + ""Processing CHEMBL1531555 in Train_ER_alpha.smi (412/1238). Average speed: 0.12 s/mol.\n"", + ""Processing CHEMBL1521410 in Train_ER_alpha.smi (413/1238). Average speed: 0.12 s/mol.\n"", + ""Processing CHEMBL1538223 in Train_ER_alpha.smi (414/1238). Average speed: 0.12 s/mol.\n"", + ""Processing CHEMBL153405 in Train_ER_alpha.smi (415/1238). Average speed: 0.12 s/mol.\n"" + ] + }, + { + ""name"": ""stdout"", + ""output_type"": ""stream"", + ""text"": [ + ""Processing CHEMBL327320 in Train_ER_alpha.smi (416/1238). Average speed: 0.12 s/mol.\n"", + ""Processing CHEMBL178559 in Train_ER_alpha.smi (417/1238). Average speed: 0.12 s/mol.\n"", + ""Processing CHEMBL180973 in Train_ER_alpha.smi (418/1238). Average speed: 0.12 s/mol.\n"", + ""Processing CHEMBL1528123 in Train_ER_alpha.smi (419/1238). Average speed: 0.12 s/mol.\n"", + ""Processing CHEMBL1536373 in Train_ER_alpha.smi (420/1238). Average speed: 0.12 s/mol.\n"", + ""Processing CHEMBL1386676 in Train_ER_alpha.smi (421/1238). Average speed: 0.12 s/mol.\n"", + ""Processing CHEMBL144431 in Train_ER_alpha.smi (422/1238). Average speed: 0.12 s/mol.\n"", + ""Processing CHEMBL1580036 in Train_ER_alpha.smi (423/1238). Average speed: 0.12 s/mol.\n"", + ""Processing CHEMBL189658 in Train_ER_alpha.smi (424/1238). Average speed: 0.12 s/mol.\n"", + ""Processing CHEMBL188619 in Train_ER_alpha.smi (425/1238). Average speed: 0.12 s/mol.\n"", + ""Processing CHEMBL1525824 in Train_ER_alpha.smi (426/1238). Average speed: 0.12 s/mol.\n"", + ""Processing CHEMBL193064 in Train_ER_alpha.smi (428/1238). Average speed: 0.12 s/mol.\n"", + ""Processing CHEMBL1522300 in Train_ER_alpha.smi (427/1238). Average speed: 0.12 s/mol.\n"", + ""Processing CHEMBL186513 in Train_ER_alpha.smi (429/1238). Average speed: 0.12 s/mol.\n"", + ""Processing CHEMBL1495194 in Train_ER_alpha.smi (430/1238). Average speed: 0.12 s/mol.\n"", + ""Processing CHEMBL1606490 in Train_ER_alpha.smi (431/1238). Average speed: 0.12 s/mol.\n"", + ""Processing CHEMBL1548186 in Train_ER_alpha.smi (432/1238). Average speed: 0.12 s/mol.\n"", + ""Processing CHEMBL2079587 in Train_ER_alpha.smi (433/1238). Average speed: 0.12 s/mol.\n"", + ""Processing CHEMBL180253 in Train_ER_alpha.smi (434/1238). Average speed: 0.12 s/mol.\n"", + ""Processing CHEMBL93705 in Train_ER_alpha.smi (435/1238). Average speed: 0.12 s/mol.\n"", + ""Processing CHEMBL190104 in Train_ER_alpha.smi (436/1238). Average speed: 0.12 s/mol.\n"", + ""Processing CHEMBL1548834 in Train_ER_alpha.smi (437/1238). Average speed: 0.12 s/mol.\n"", + ""Processing CHEMBL188527 in Train_ER_alpha.smi (438/1238). Average speed: 0.12 s/mol.\n"", + ""Processing CHEMBL360498 in Train_ER_alpha.smi (439/1238). Average speed: 0.12 s/mol.\n"", + ""Processing CHEMBL1501269 in Train_ER_alpha.smi (440/1238). Average speed: 0.12 s/mol.\n"", + ""Processing CHEMBL1560367 in Train_ER_alpha.smi (441/1238). Average speed: 0.12 s/mol.\n"", + ""Processing CHEMBL1427879 in Train_ER_alpha.smi (442/1238). Average speed: 0.12 s/mol.\n"", + ""Processing CHEMBL1544241 in Train_ER_alpha.smi (443/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL577758 in Train_ER_alpha.smi (444/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL186597 in Train_ER_alpha.smi (445/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL145576 in Train_ER_alpha.smi (446/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL361258 in Train_ER_alpha.smi (447/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL363367 in Train_ER_alpha.smi (448/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL181369 in Train_ER_alpha.smi (449/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL57150 in Train_ER_alpha.smi (450/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL433944 in Train_ER_alpha.smi (451/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL371624 in Train_ER_alpha.smi (452/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL188613 in Train_ER_alpha.smi (453/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL371788 in Train_ER_alpha.smi (454/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL189200 in Train_ER_alpha.smi (455/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL186073 in Train_ER_alpha.smi (456/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL187087 in Train_ER_alpha.smi (457/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL200021 in Train_ER_alpha.smi (458/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL193010 in Train_ER_alpha.smi (459/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL363963 in Train_ER_alpha.smi (460/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL450940 in Train_ER_alpha.smi (461/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL365045 in Train_ER_alpha.smi (462/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL188410 in Train_ER_alpha.smi (463/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL201569 in Train_ER_alpha.smi (464/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL380876 in Train_ER_alpha.smi (465/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL441537 in Train_ER_alpha.smi (466/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL377694 in Train_ER_alpha.smi (467/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL362020 in Train_ER_alpha.smi (469/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL192386 in Train_ER_alpha.smi (468/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL192587 in Train_ER_alpha.smi (471/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL183152 in Train_ER_alpha.smi (470/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL370849 in Train_ER_alpha.smi (472/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL178679 in Train_ER_alpha.smi (473/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL185126 in Train_ER_alpha.smi (474/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL185918 in Train_ER_alpha.smi (475/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL264216 in Train_ER_alpha.smi (476/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL1337826 in Train_ER_alpha.smi (477/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL1480378 in Train_ER_alpha.smi (478/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL1481770 in Train_ER_alpha.smi (479/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL198679 in Train_ER_alpha.smi (480/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL121978 in Train_ER_alpha.smi (481/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL371009 in Train_ER_alpha.smi (482/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL192539 in Train_ER_alpha.smi (484/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL334096 in Train_ER_alpha.smi (483/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL186598 in Train_ER_alpha.smi (485/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL180146 in Train_ER_alpha.smi (486/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL377224 in Train_ER_alpha.smi (487/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL213051 in Train_ER_alpha.smi (488/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL377848 in Train_ER_alpha.smi (489/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL264349 in Train_ER_alpha.smi (490/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL378675 in Train_ER_alpha.smi (491/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL1402 in Train_ER_alpha.smi (492/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL369935 in Train_ER_alpha.smi (493/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL216279 in Train_ER_alpha.smi (494/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL219126 in Train_ER_alpha.smi (495/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL381566 in Train_ER_alpha.smi (496/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL209429 in Train_ER_alpha.smi (497/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL379109 in Train_ER_alpha.smi (498/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL379821 in Train_ER_alpha.smi (499/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL202896 in Train_ER_alpha.smi (501/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL426854 in Train_ER_alpha.smi (500/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL179518 in Train_ER_alpha.smi (502/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL187069 in Train_ER_alpha.smi (503/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL363425 in Train_ER_alpha.smi (504/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL597498 in Train_ER_alpha.smi (505/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL1116 in Train_ER_alpha.smi (506/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL206226 in Train_ER_alpha.smi (507/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL378990 in Train_ER_alpha.smi (508/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL211800 in Train_ER_alpha.smi (509/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL379567 in Train_ER_alpha.smi (510/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL379595 in Train_ER_alpha.smi (511/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL211870 in Train_ER_alpha.smi (512/1238). Average speed: 0.11 s/mol.\n"" + ] + }, + { + ""name"": ""stdout"", + ""output_type"": ""stream"", + ""text"": [ + ""Processing CHEMBL187937 in Train_ER_alpha.smi (513/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL205350 in Train_ER_alpha.smi (515/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL204768 in Train_ER_alpha.smi (514/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL380717 in Train_ER_alpha.smi (516/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL206582 in Train_ER_alpha.smi (517/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL362355 in Train_ER_alpha.smi (518/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL221676 in Train_ER_alpha.smi (519/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL363955 in Train_ER_alpha.smi (520/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL206764 in Train_ER_alpha.smi (521/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL206131 in Train_ER_alpha.smi (522/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL381720 in Train_ER_alpha.smi (523/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL202894 in Train_ER_alpha.smi (524/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL206349 in Train_ER_alpha.smi (525/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL206773 in Train_ER_alpha.smi (526/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL379008 in Train_ER_alpha.smi (527/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL192016 in Train_ER_alpha.smi (528/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL191841 in Train_ER_alpha.smi (529/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL238960 in Train_ER_alpha.smi (530/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL238961 in Train_ER_alpha.smi (531/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL184360 in Train_ER_alpha.smi (532/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL180176 in Train_ER_alpha.smi (533/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL437695 in Train_ER_alpha.smi (534/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL1276308 in Train_ER_alpha.smi (535/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL1485005 in Train_ER_alpha.smi (536/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL1521111 in Train_ER_alpha.smi (537/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL1327977 in Train_ER_alpha.smi (538/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL388418 in Train_ER_alpha.smi (539/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL396948 in Train_ER_alpha.smi (540/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL1433745 in Train_ER_alpha.smi (541/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL1475062 in Train_ER_alpha.smi (542/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL1597831 in Train_ER_alpha.smi (543/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL471627 in Train_ER_alpha.smi (544/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL1362841 in Train_ER_alpha.smi (545/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL1429150 in Train_ER_alpha.smi (546/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL1548361 in Train_ER_alpha.smi (547/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL375334 in Train_ER_alpha.smi (548/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL222522 in Train_ER_alpha.smi (549/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL222580 in Train_ER_alpha.smi (550/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL1449885 in Train_ER_alpha.smi (551/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL1584646 in Train_ER_alpha.smi (552/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL1506716 in Train_ER_alpha.smi (553/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL1472407 in Train_ER_alpha.smi (554/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL1448466 in Train_ER_alpha.smi (555/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL1454402 in Train_ER_alpha.smi (556/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL1431650 in Train_ER_alpha.smi (557/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL1367590 in Train_ER_alpha.smi (558/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL1587260 in Train_ER_alpha.smi (559/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL1522044 in Train_ER_alpha.smi (560/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL1461574 in Train_ER_alpha.smi (561/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL1328182 in Train_ER_alpha.smi (562/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL250109 in Train_ER_alpha.smi (563/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL191716 in Train_ER_alpha.smi (564/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL194772 in Train_ER_alpha.smi (565/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL362425 in Train_ER_alpha.smi (566/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL363177 in Train_ER_alpha.smi (567/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL364329 in Train_ER_alpha.smi (568/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL361226 in Train_ER_alpha.smi (569/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL379543 in Train_ER_alpha.smi (570/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL361063 in Train_ER_alpha.smi (571/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL189822 in Train_ER_alpha.smi (572/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL192782 in Train_ER_alpha.smi (573/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL192901 in Train_ER_alpha.smi (574/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL362307 in Train_ER_alpha.smi (575/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL190487 in Train_ER_alpha.smi (576/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL1479 in Train_ER_alpha.smi (577/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL104416 in Train_ER_alpha.smi (578/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL195991 in Train_ER_alpha.smi (579/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL190038 in Train_ER_alpha.smi (580/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL189303 in Train_ER_alpha.smi (581/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL1627354 in Train_ER_alpha.smi (582/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL178665 in Train_ER_alpha.smi (583/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL435696 in Train_ER_alpha.smi (584/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL371564 in Train_ER_alpha.smi (585/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL1628170 in Train_ER_alpha.smi (586/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL362349 in Train_ER_alpha.smi (587/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL954 in Train_ER_alpha.smi (588/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL184974 in Train_ER_alpha.smi (589/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL1627353 in Train_ER_alpha.smi (590/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL188846 in Train_ER_alpha.smi (591/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL196131 in Train_ER_alpha.smi (592/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL372304 in Train_ER_alpha.smi (593/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL427180 in Train_ER_alpha.smi (594/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL194265 in Train_ER_alpha.smi (595/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL195788 in Train_ER_alpha.smi (596/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL183344 in Train_ER_alpha.smi (597/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL369545 in Train_ER_alpha.smi (598/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL361078 in Train_ER_alpha.smi (599/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL187393 in Train_ER_alpha.smi (600/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL194762 in Train_ER_alpha.smi (602/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL1200623 in Train_ER_alpha.smi (601/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL187392 in Train_ER_alpha.smi (604/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL188051 in Train_ER_alpha.smi (603/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL418971 in Train_ER_alpha.smi (605/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL184367 in Train_ER_alpha.smi (606/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL370282 in Train_ER_alpha.smi (607/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL180702 in Train_ER_alpha.smi (608/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL103650 in Train_ER_alpha.smi (609/1238). Average speed: 0.11 s/mol.\n"" + ] + }, + { + ""name"": ""stdout"", + ""output_type"": ""stream"", + ""text"": [ + ""Processing CHEMBL1411687 in Train_ER_alpha.smi (611/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL181936 in Train_ER_alpha.smi (610/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL1302802 in Train_ER_alpha.smi (612/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL1521414 in Train_ER_alpha.smi (613/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL1491602 in Train_ER_alpha.smi (614/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL1322601 in Train_ER_alpha.smi (616/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL1341814 in Train_ER_alpha.smi (615/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL1604803 in Train_ER_alpha.smi (617/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL1393131 in Train_ER_alpha.smi (618/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL1541552 in Train_ER_alpha.smi (619/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL1350028 in Train_ER_alpha.smi (620/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL183782 in Train_ER_alpha.smi (621/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL1423880 in Train_ER_alpha.smi (622/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL1493034 in Train_ER_alpha.smi (623/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL1347829 in Train_ER_alpha.smi (624/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL1310171 in Train_ER_alpha.smi (625/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL1411209 in Train_ER_alpha.smi (626/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL1507788 in Train_ER_alpha.smi (627/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL196583 in Train_ER_alpha.smi (628/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL1505283 in Train_ER_alpha.smi (629/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL1532659 in Train_ER_alpha.smi (630/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL187673 in Train_ER_alpha.smi (632/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL189039 in Train_ER_alpha.smi (631/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL197757 in Train_ER_alpha.smi (633/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL191715 in Train_ER_alpha.smi (634/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL122208 in Train_ER_alpha.smi (635/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL153395 in Train_ER_alpha.smi (636/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL195289 in Train_ER_alpha.smi (637/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL1381306 in Train_ER_alpha.smi (638/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL1421864 in Train_ER_alpha.smi (639/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL1560682 in Train_ER_alpha.smi (640/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL1475434 in Train_ER_alpha.smi (641/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL1434961 in Train_ER_alpha.smi (642/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL1552932 in Train_ER_alpha.smi (643/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL1449110 in Train_ER_alpha.smi (645/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL1593653 in Train_ER_alpha.smi (644/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL192390 in Train_ER_alpha.smi (646/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL340606 in Train_ER_alpha.smi (647/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL371305 in Train_ER_alpha.smi (648/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL195280 in Train_ER_alpha.smi (649/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL193482 in Train_ER_alpha.smi (650/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL1443090 in Train_ER_alpha.smi (651/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL1519669 in Train_ER_alpha.smi (652/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL1581690 in Train_ER_alpha.smi (654/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL1299357 in Train_ER_alpha.smi (653/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL1433062 in Train_ER_alpha.smi (655/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL125362 in Train_ER_alpha.smi (656/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL361871 in Train_ER_alpha.smi (657/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL482116 in Train_ER_alpha.smi (658/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL190682 in Train_ER_alpha.smi (659/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL363872 in Train_ER_alpha.smi (660/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL184421 in Train_ER_alpha.smi (661/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL205920 in Train_ER_alpha.smi (662/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL185383 in Train_ER_alpha.smi (663/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL226329 in Train_ER_alpha.smi (664/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL200799 in Train_ER_alpha.smi (665/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL371012 in Train_ER_alpha.smi (666/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL362356 in Train_ER_alpha.smi (667/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL238890 in Train_ER_alpha.smi (668/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL240157 in Train_ER_alpha.smi (669/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL511971 in Train_ER_alpha.smi (670/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL364419 in Train_ER_alpha.smi (671/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL391795 in Train_ER_alpha.smi (672/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL398455 in Train_ER_alpha.smi (673/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL430395 in Train_ER_alpha.smi (674/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL183044 in Train_ER_alpha.smi (675/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL189002 in Train_ER_alpha.smi (676/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL438256 in Train_ER_alpha.smi (677/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL188957 in Train_ER_alpha.smi (678/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL204697 in Train_ER_alpha.smi (679/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL203561 in Train_ER_alpha.smi (680/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL211739 in Train_ER_alpha.smi (681/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL213529 in Train_ER_alpha.smi (682/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL214200 in Train_ER_alpha.smi (683/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL187978 in Train_ER_alpha.smi (684/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL187219 in Train_ER_alpha.smi (685/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL394357 in Train_ER_alpha.smi (686/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL388473 in Train_ER_alpha.smi (687/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL242818 in Train_ER_alpha.smi (688/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL106773 in Train_ER_alpha.smi (689/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL192000 in Train_ER_alpha.smi (690/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL202781 in Train_ER_alpha.smi (691/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL198445 in Train_ER_alpha.smi (692/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL432454 in Train_ER_alpha.smi (693/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL393982 in Train_ER_alpha.smi (695/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL184491 in Train_ER_alpha.smi (694/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL240438 in Train_ER_alpha.smi (696/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL346455 in Train_ER_alpha.smi (698/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL188246 in Train_ER_alpha.smi (697/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL232022 in Train_ER_alpha.smi (699/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL206120 in Train_ER_alpha.smi (700/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL210074 in Train_ER_alpha.smi (701/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL209738 in Train_ER_alpha.smi (702/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL377254 in Train_ER_alpha.smi (703/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL455215 in Train_ER_alpha.smi (705/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL213829 in Train_ER_alpha.smi (704/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL522990 in Train_ER_alpha.smi (706/1238). Average speed: 0.11 s/mol.\n"" + ] + }, + { + ""name"": ""stdout"", + ""output_type"": ""stream"", + ""text"": [ + ""Processing CHEMBL267431 in Train_ER_alpha.smi (707/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL469510 in Train_ER_alpha.smi (708/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL368688 in Train_ER_alpha.smi (709/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL1076306 in Train_ER_alpha.smi (710/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL198680 in Train_ER_alpha.smi (711/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL125119 in Train_ER_alpha.smi (712/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL414859 in Train_ER_alpha.smi (713/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL378798 in Train_ER_alpha.smi (714/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL46903 in Train_ER_alpha.smi (715/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL195311 in Train_ER_alpha.smi (716/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL1450791 in Train_ER_alpha.smi (717/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL203370 in Train_ER_alpha.smi (718/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL378572 in Train_ER_alpha.smi (719/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL206137 in Train_ER_alpha.smi (720/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL1389780 in Train_ER_alpha.smi (721/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL1312693 in Train_ER_alpha.smi (722/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL1324290 in Train_ER_alpha.smi (723/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL1377973 in Train_ER_alpha.smi (724/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL1453264 in Train_ER_alpha.smi (725/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL1533166 in Train_ER_alpha.smi (726/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL1304227 in Train_ER_alpha.smi (727/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL1329686 in Train_ER_alpha.smi (728/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL1498999 in Train_ER_alpha.smi (729/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL1386537 in Train_ER_alpha.smi (730/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL1520373 in Train_ER_alpha.smi (731/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL1393976 in Train_ER_alpha.smi (733/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL1452952 in Train_ER_alpha.smi (732/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL1419810 in Train_ER_alpha.smi (734/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL1531320 in Train_ER_alpha.smi (735/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL1484547 in Train_ER_alpha.smi (737/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL1439181 in Train_ER_alpha.smi (736/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL1577757 in Train_ER_alpha.smi (738/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL1505467 in Train_ER_alpha.smi (739/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL1372307 in Train_ER_alpha.smi (740/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL1482875 in Train_ER_alpha.smi (742/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL1525366 in Train_ER_alpha.smi (741/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL1345810 in Train_ER_alpha.smi (743/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL1374573 in Train_ER_alpha.smi (744/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL1577661 in Train_ER_alpha.smi (745/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL1370120 in Train_ER_alpha.smi (746/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL1407054 in Train_ER_alpha.smi (747/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL1548616 in Train_ER_alpha.smi (748/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL1559731 in Train_ER_alpha.smi (749/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL1555033 in Train_ER_alpha.smi (750/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL1405182 in Train_ER_alpha.smi (751/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL1455849 in Train_ER_alpha.smi (752/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL1356953 in Train_ER_alpha.smi (753/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL1326803 in Train_ER_alpha.smi (754/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL1597805 in Train_ER_alpha.smi (755/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL1084746 in Train_ER_alpha.smi (756/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL1083326 in Train_ER_alpha.smi (757/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL426897 in Train_ER_alpha.smi (758/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL386502 in Train_ER_alpha.smi (759/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL223026 in Train_ER_alpha.smi (760/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL144897 in Train_ER_alpha.smi (761/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL433769 in Train_ER_alpha.smi (762/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL179852 in Train_ER_alpha.smi (763/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL183033 in Train_ER_alpha.smi (764/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL190025 in Train_ER_alpha.smi (765/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL198088 in Train_ER_alpha.smi (766/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL1576748 in Train_ER_alpha.smi (767/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL1509933 in Train_ER_alpha.smi (769/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL1388431 in Train_ER_alpha.smi (768/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL360817 in Train_ER_alpha.smi (770/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL369724 in Train_ER_alpha.smi (771/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL52 in Train_ER_alpha.smi (772/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL195217 in Train_ER_alpha.smi (773/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL1522446 in Train_ER_alpha.smi (774/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL1434556 in Train_ER_alpha.smi (775/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL1414407 in Train_ER_alpha.smi (776/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL1397845 in Train_ER_alpha.smi (777/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL282575 in Train_ER_alpha.smi (778/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL361056 in Train_ER_alpha.smi (779/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL1491959 in Train_ER_alpha.smi (780/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL1326483 in Train_ER_alpha.smi (781/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL188516 in Train_ER_alpha.smi (782/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL178334 in Train_ER_alpha.smi (783/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL9352 in Train_ER_alpha.smi (784/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL198204 in Train_ER_alpha.smi (785/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL187311 in Train_ER_alpha.smi (786/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL425194 in Train_ER_alpha.smi (787/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL1580991 in Train_ER_alpha.smi (788/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL1300662 in Train_ER_alpha.smi (789/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL1442450 in Train_ER_alpha.smi (790/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL1539865 in Train_ER_alpha.smi (791/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL1466928 in Train_ER_alpha.smi (792/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL182157 in Train_ER_alpha.smi (793/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL1467399 in Train_ER_alpha.smi (794/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL192260 in Train_ER_alpha.smi (795/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL1303477 in Train_ER_alpha.smi (796/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL1350069 in Train_ER_alpha.smi (797/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL187313 in Train_ER_alpha.smi (798/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL1391547 in Train_ER_alpha.smi (799/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL1526157 in Train_ER_alpha.smi (800/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL1572777 in Train_ER_alpha.smi (801/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL1320937 in Train_ER_alpha.smi (802/1238). Average speed: 0.10 s/mol.\n"" + ] + }, + { + ""name"": ""stdout"", + ""output_type"": ""stream"", + ""text"": [ + ""Processing CHEMBL1582403 in Train_ER_alpha.smi (803/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL189556 in Train_ER_alpha.smi (805/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL1447311 in Train_ER_alpha.smi (804/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL1431081 in Train_ER_alpha.smi (806/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL1421887 in Train_ER_alpha.smi (807/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL1231453 in Train_ER_alpha.smi (808/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL204376 in Train_ER_alpha.smi (809/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL460647 in Train_ER_alpha.smi (810/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL216334 in Train_ER_alpha.smi (811/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL377295 in Train_ER_alpha.smi (812/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL1325233 in Train_ER_alpha.smi (813/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL1430316 in Train_ER_alpha.smi (814/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL1574512 in Train_ER_alpha.smi (815/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL379314 in Train_ER_alpha.smi (816/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL385949 in Train_ER_alpha.smi (817/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL378883 in Train_ER_alpha.smi (818/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL215428 in Train_ER_alpha.smi (819/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL385028 in Train_ER_alpha.smi (820/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL212391 in Train_ER_alpha.smi (821/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL377457 in Train_ER_alpha.smi (822/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL214692 in Train_ER_alpha.smi (823/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL372783 in Train_ER_alpha.smi (824/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL398456 in Train_ER_alpha.smi (825/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL249904 in Train_ER_alpha.smi (826/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL250110 in Train_ER_alpha.smi (827/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL380452 in Train_ER_alpha.smi (828/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL380358 in Train_ER_alpha.smi (829/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL438443 in Train_ER_alpha.smi (830/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL380838 in Train_ER_alpha.smi (831/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL394614 in Train_ER_alpha.smi (833/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL188299 in Train_ER_alpha.smi (832/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL469511 in Train_ER_alpha.smi (834/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL1300008 in Train_ER_alpha.smi (835/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL1531512 in Train_ER_alpha.smi (836/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL1404523 in Train_ER_alpha.smi (837/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL1386091 in Train_ER_alpha.smi (838/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL211953 in Train_ER_alpha.smi (839/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL214034 in Train_ER_alpha.smi (840/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL211666 in Train_ER_alpha.smi (841/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL206500 in Train_ER_alpha.smi (842/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL206439 in Train_ER_alpha.smi (843/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL1307301 in Train_ER_alpha.smi (844/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL10560 in Train_ER_alpha.smi (845/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL231810 in Train_ER_alpha.smi (846/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL1470395 in Train_ER_alpha.smi (847/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL1417890 in Train_ER_alpha.smi (849/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL1320651 in Train_ER_alpha.smi (848/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL1429596 in Train_ER_alpha.smi (850/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL1404155 in Train_ER_alpha.smi (851/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL1442697 in Train_ER_alpha.smi (852/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL1603599 in Train_ER_alpha.smi (853/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL203283 in Train_ER_alpha.smi (854/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL377083 in Train_ER_alpha.smi (855/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL206426 in Train_ER_alpha.smi (856/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL202738 in Train_ER_alpha.smi (857/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL205577 in Train_ER_alpha.smi (858/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL208324 in Train_ER_alpha.smi (859/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL192621 in Train_ER_alpha.smi (861/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL1471823 in Train_ER_alpha.smi (860/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL192586 in Train_ER_alpha.smi (862/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL434577 in Train_ER_alpha.smi (863/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL465879 in Train_ER_alpha.smi (864/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL384708 in Train_ER_alpha.smi (865/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL221626 in Train_ER_alpha.smi (866/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL222452 in Train_ER_alpha.smi (867/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL372411 in Train_ER_alpha.smi (868/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL201365 in Train_ER_alpha.smi (869/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL199766 in Train_ER_alpha.smi (870/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL204317 in Train_ER_alpha.smi (871/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL383253 in Train_ER_alpha.smi (872/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL425603 in Train_ER_alpha.smi (873/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL212675 in Train_ER_alpha.smi (874/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL180995 in Train_ER_alpha.smi (875/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL203123 in Train_ER_alpha.smi (876/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL211599 in Train_ER_alpha.smi (877/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL213457 in Train_ER_alpha.smi (878/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL214291 in Train_ER_alpha.smi (879/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL240230 in Train_ER_alpha.smi (880/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL394213 in Train_ER_alpha.smi (881/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL193146 in Train_ER_alpha.smi (882/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL193108 in Train_ER_alpha.smi (883/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL182794 in Train_ER_alpha.smi (884/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL194873 in Train_ER_alpha.smi (885/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL187671 in Train_ER_alpha.smi (886/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL189287 in Train_ER_alpha.smi (887/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL1732228 in Train_ER_alpha.smi (888/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL187663 in Train_ER_alpha.smi (889/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL198089 in Train_ER_alpha.smi (890/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL189706 in Train_ER_alpha.smi (891/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL185012 in Train_ER_alpha.smi (893/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL188230 in Train_ER_alpha.smi (892/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL122237 in Train_ER_alpha.smi (894/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL361005 in Train_ER_alpha.smi (895/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL184679 in Train_ER_alpha.smi (896/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL1424468 in Train_ER_alpha.smi (897/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL187105 in Train_ER_alpha.smi (898/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL276030 in Train_ER_alpha.smi (899/1238). Average speed: 0.10 s/mol.\n"" + ] + }, + { + ""name"": ""stdout"", + ""output_type"": ""stream"", + ""text"": [ + ""Processing CHEMBL188528 in Train_ER_alpha.smi (900/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL195290 in Train_ER_alpha.smi (902/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL181368 in Train_ER_alpha.smi (901/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL363412 in Train_ER_alpha.smi (903/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL186874 in Train_ER_alpha.smi (904/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL364176 in Train_ER_alpha.smi (905/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL365903 in Train_ER_alpha.smi (906/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL195389 in Train_ER_alpha.smi (907/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL360189 in Train_ER_alpha.smi (908/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL365484 in Train_ER_alpha.smi (909/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL193207 in Train_ER_alpha.smi (910/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL189077 in Train_ER_alpha.smi (911/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL1627351 in Train_ER_alpha.smi (912/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL360338 in Train_ER_alpha.smi (913/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL440757 in Train_ER_alpha.smi (914/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL178857 in Train_ER_alpha.smi (915/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL188882 in Train_ER_alpha.smi (916/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL360385 in Train_ER_alpha.smi (917/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL182980 in Train_ER_alpha.smi (918/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL195039 in Train_ER_alpha.smi (919/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL180085 in Train_ER_alpha.smi (920/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL183162 in Train_ER_alpha.smi (921/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL427324 in Train_ER_alpha.smi (922/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL1596554 in Train_ER_alpha.smi (923/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL1301994 in Train_ER_alpha.smi (924/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL185837 in Train_ER_alpha.smi (925/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL425246 in Train_ER_alpha.smi (927/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL1466913 in Train_ER_alpha.smi (926/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL380032 in Train_ER_alpha.smi (928/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL211349 in Train_ER_alpha.smi (929/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL377988 in Train_ER_alpha.smi (930/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL1323006 in Train_ER_alpha.smi (931/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL234524 in Train_ER_alpha.smi (932/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL396947 in Train_ER_alpha.smi (933/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL212468 in Train_ER_alpha.smi (934/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL383518 in Train_ER_alpha.smi (935/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL204272 in Train_ER_alpha.smi (936/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL203340 in Train_ER_alpha.smi (937/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL380720 in Train_ER_alpha.smi (938/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL205614 in Train_ER_alpha.smi (939/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL204118 in Train_ER_alpha.smi (940/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL222453 in Train_ER_alpha.smi (941/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL577709 in Train_ER_alpha.smi (942/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL1566266 in Train_ER_alpha.smi (943/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL183889 in Train_ER_alpha.smi (944/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL205398 in Train_ER_alpha.smi (945/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL379252 in Train_ER_alpha.smi (946/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL203122 in Train_ER_alpha.smi (947/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL225514 in Train_ER_alpha.smi (949/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL206286 in Train_ER_alpha.smi (948/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL222454 in Train_ER_alpha.smi (950/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL215593 in Train_ER_alpha.smi (951/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL391909 in Train_ER_alpha.smi (952/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL241256 in Train_ER_alpha.smi (953/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL241484 in Train_ER_alpha.smi (954/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL396119 in Train_ER_alpha.smi (955/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL372030 in Train_ER_alpha.smi (956/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL202400 in Train_ER_alpha.smi (957/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL437065 in Train_ER_alpha.smi (958/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL377847 in Train_ER_alpha.smi (959/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL226268 in Train_ER_alpha.smi (960/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL226327 in Train_ER_alpha.smi (961/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL239723 in Train_ER_alpha.smi (962/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL190467 in Train_ER_alpha.smi (963/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL192849 in Train_ER_alpha.smi (964/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL205119 in Train_ER_alpha.smi (965/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL210143 in Train_ER_alpha.smi (966/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL208085 in Train_ER_alpha.smi (967/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL383112 in Train_ER_alpha.smi (968/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL209978 in Train_ER_alpha.smi (969/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL213070 in Train_ER_alpha.smi (970/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL341367 in Train_ER_alpha.smi (971/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL426154 in Train_ER_alpha.smi (972/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL1628029 in Train_ER_alpha.smi (974/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL216596 in Train_ER_alpha.smi (973/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL188517 in Train_ER_alpha.smi (975/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL197889 in Train_ER_alpha.smi (976/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL513397 in Train_ER_alpha.smi (977/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL121720 in Train_ER_alpha.smi (978/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL1377389 in Train_ER_alpha.smi (979/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL1516948 in Train_ER_alpha.smi (980/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL1519384 in Train_ER_alpha.smi (981/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL1330161 in Train_ER_alpha.smi (982/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL1309506 in Train_ER_alpha.smi (983/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL1162 in Train_ER_alpha.smi (984/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL1468124 in Train_ER_alpha.smi (986/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL1507012 in Train_ER_alpha.smi (985/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL1476347 in Train_ER_alpha.smi (987/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL1612880 in Train_ER_alpha.smi (988/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL1585431 in Train_ER_alpha.smi (990/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL1359354 in Train_ER_alpha.smi (989/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL1501342 in Train_ER_alpha.smi (991/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL1580543 in Train_ER_alpha.smi (992/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL245378 in Train_ER_alpha.smi (995/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL1448272 in Train_ER_alpha.smi (993/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL1406018 in Train_ER_alpha.smi (994/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL1532234 in Train_ER_alpha.smi (996/1238). Average speed: 0.10 s/mol.\n"" + ] + }, + { + ""name"": ""stdout"", + ""output_type"": ""stream"", + ""text"": [ + ""Processing CHEMBL1312859 in Train_ER_alpha.smi (997/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL249700 in Train_ER_alpha.smi (998/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL1584299 in Train_ER_alpha.smi (999/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL457035 in Train_ER_alpha.smi (1000/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL1983345 in Train_ER_alpha.smi (1001/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL1996233 in Train_ER_alpha.smi (1002/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL1976499 in Train_ER_alpha.smi (1003/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL1984703 in Train_ER_alpha.smi (1004/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL2103774 in Train_ER_alpha.smi (1005/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL1999723 in Train_ER_alpha.smi (1006/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL1972915 in Train_ER_alpha.smi (1007/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL2024377 in Train_ER_alpha.smi (1008/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL2113002 in Train_ER_alpha.smi (1010/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL2024376 in Train_ER_alpha.smi (1009/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL2112471 in Train_ER_alpha.smi (1011/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL2112469 in Train_ER_alpha.smi (1012/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL2112470 in Train_ER_alpha.smi (1013/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL2113027 in Train_ER_alpha.smi (1014/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL2113001 in Train_ER_alpha.smi (1015/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL2071180 in Train_ER_alpha.smi (1016/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL2403351 in Train_ER_alpha.smi (1017/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL2403361 in Train_ER_alpha.smi (1019/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL1208076 in Train_ER_alpha.smi (1018/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL2403346 in Train_ER_alpha.smi (1021/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL2403363 in Train_ER_alpha.smi (1020/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL2403359 in Train_ER_alpha.smi (1022/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL2403358 in Train_ER_alpha.smi (1023/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL2403360 in Train_ER_alpha.smi (1024/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL2401849 in Train_ER_alpha.smi (1025/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL2403362 in Train_ER_alpha.smi (1026/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL2401848 in Train_ER_alpha.smi (1027/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL2403348 in Train_ER_alpha.smi (1028/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL2407651 in Train_ER_alpha.smi (1029/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL2407647 in Train_ER_alpha.smi (1030/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL2403347 in Train_ER_alpha.smi (1031/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL2392777 in Train_ER_alpha.smi (1032/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL2441817 in Train_ER_alpha.smi (1033/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL2441818 in Train_ER_alpha.smi (1034/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL2441819 in Train_ER_alpha.smi (1035/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL3099431 in Train_ER_alpha.smi (1036/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL3099432 in Train_ER_alpha.smi (1037/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL3098274 in Train_ER_alpha.smi (1038/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL3098273 in Train_ER_alpha.smi (1039/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL3099429 in Train_ER_alpha.smi (1040/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL3145004 in Train_ER_alpha.smi (1041/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL3098275 in Train_ER_alpha.smi (1042/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL3208438 in Train_ER_alpha.smi (1043/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL3208556 in Train_ER_alpha.smi (1044/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL3189964 in Train_ER_alpha.smi (1045/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL3190827 in Train_ER_alpha.smi (1046/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL3199523 in Train_ER_alpha.smi (1047/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL3189836 in Train_ER_alpha.smi (1048/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL3213380 in Train_ER_alpha.smi (1049/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL3109601 in Train_ER_alpha.smi (1050/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL3109240 in Train_ER_alpha.smi (1051/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL3109156 in Train_ER_alpha.smi (1052/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL3109154 in Train_ER_alpha.smi (1053/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL3189442 in Train_ER_alpha.smi (1054/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL3189821 in Train_ER_alpha.smi (1055/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL3198515 in Train_ER_alpha.smi (1056/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL3193600 in Train_ER_alpha.smi (1057/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL3207314 in Train_ER_alpha.smi (1058/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL3214324 in Train_ER_alpha.smi (1059/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL3212870 in Train_ER_alpha.smi (1060/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL3208784 in Train_ER_alpha.smi (1061/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL3235685 in Train_ER_alpha.smi (1062/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL3235686 in Train_ER_alpha.smi (1063/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL3195623 in Train_ER_alpha.smi (1064/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL3196534 in Train_ER_alpha.smi (1065/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL3360841 in Train_ER_alpha.smi (1066/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL3290287 in Train_ER_alpha.smi (1067/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL3290288 in Train_ER_alpha.smi (1068/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL3288736 in Train_ER_alpha.smi (1069/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL3427502 in Train_ER_alpha.smi (1070/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL3360850 in Train_ER_alpha.smi (1071/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL3581693 in Train_ER_alpha.smi (1072/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL3586190 in Train_ER_alpha.smi (1073/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL3586194 in Train_ER_alpha.smi (1074/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL3586191 in Train_ER_alpha.smi (1075/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL3360845 in Train_ER_alpha.smi (1076/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL3586192 in Train_ER_alpha.smi (1077/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL3586193 in Train_ER_alpha.smi (1078/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL3586195 in Train_ER_alpha.smi (1079/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL3360843 in Train_ER_alpha.smi (1080/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL3360846 in Train_ER_alpha.smi (1081/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL3360848 in Train_ER_alpha.smi (1082/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL3589347 in Train_ER_alpha.smi (1083/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL3589365 in Train_ER_alpha.smi (1084/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL3589701 in Train_ER_alpha.smi (1085/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL3589349 in Train_ER_alpha.smi (1086/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL3589367 in Train_ER_alpha.smi (1087/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL3578274 in Train_ER_alpha.smi (1088/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL3578276 in Train_ER_alpha.smi (1089/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL3578271 in Train_ER_alpha.smi (1090/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL3589346 in Train_ER_alpha.smi (1091/1238). Average speed: 0.10 s/mol.\n"" + ] + }, + { + ""name"": ""stdout"", + ""output_type"": ""stream"", + ""text"": [ + ""Processing CHEMBL3589361 in Train_ER_alpha.smi (1092/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL3589363 in Train_ER_alpha.smi (1093/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL3589364 in Train_ER_alpha.smi (1094/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL3588865 in Train_ER_alpha.smi (1095/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL3589352 in Train_ER_alpha.smi (1096/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL3589356 in Train_ER_alpha.smi (1097/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL3597498 in Train_ER_alpha.smi (1098/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL3597499 in Train_ER_alpha.smi (1099/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL3597501 in Train_ER_alpha.smi (1100/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL3597505 in Train_ER_alpha.smi (1101/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL3597508 in Train_ER_alpha.smi (1102/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL3597509 in Train_ER_alpha.smi (1103/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL3589354 in Train_ER_alpha.smi (1104/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL3589368 in Train_ER_alpha.smi (1105/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL3597502 in Train_ER_alpha.smi (1106/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL3360847 in Train_ER_alpha.smi (1107/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL3360849 in Train_ER_alpha.smi (1108/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL3427411 in Train_ER_alpha.smi (1109/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL3427406 in Train_ER_alpha.smi (1110/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL3578285 in Train_ER_alpha.smi (1111/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL3578286 in Train_ER_alpha.smi (1112/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL3576885 in Train_ER_alpha.smi (1113/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL3578284 in Train_ER_alpha.smi (1114/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL3622365 in Train_ER_alpha.smi (1116/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL3622364 in Train_ER_alpha.smi (1115/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL3657137 in Train_ER_alpha.smi (1117/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL3657148 in Train_ER_alpha.smi (1118/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL3657159 in Train_ER_alpha.smi (1119/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL3657170 in Train_ER_alpha.smi (1120/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL3657181 in Train_ER_alpha.smi (1121/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL3667040 in Train_ER_alpha.smi (1122/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL3657142 in Train_ER_alpha.smi (1123/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL3657143 in Train_ER_alpha.smi (1124/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL3657144 in Train_ER_alpha.smi (1125/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL3657145 in Train_ER_alpha.smi (1126/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL3657146 in Train_ER_alpha.smi (1127/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL3657172 in Train_ER_alpha.smi (1128/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL3657173 in Train_ER_alpha.smi (1129/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL3657174 in Train_ER_alpha.smi (1130/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL3657175 in Train_ER_alpha.smi (1131/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL3657147 in Train_ER_alpha.smi (1132/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL3657154 in Train_ER_alpha.smi (1133/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL3657155 in Train_ER_alpha.smi (1134/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL3657149 in Train_ER_alpha.smi (1135/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL3657150 in Train_ER_alpha.smi (1136/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL3657156 in Train_ER_alpha.smi (1137/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL3657157 in Train_ER_alpha.smi (1138/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL3657158 in Train_ER_alpha.smi (1139/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL3657151 in Train_ER_alpha.smi (1140/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL3657152 in Train_ER_alpha.smi (1141/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL3657153 in Train_ER_alpha.smi (1142/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL3657135 in Train_ER_alpha.smi (1143/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL3657176 in Train_ER_alpha.smi (1144/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL3657177 in Train_ER_alpha.smi (1145/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL3657184 in Train_ER_alpha.smi (1146/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL3657185 in Train_ER_alpha.smi (1147/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL3657164 in Train_ER_alpha.smi (1148/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL3657165 in Train_ER_alpha.smi (1149/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL3648370 in Train_ER_alpha.smi (1150/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL3657178 in Train_ER_alpha.smi (1151/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL3657179 in Train_ER_alpha.smi (1152/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL3657161 in Train_ER_alpha.smi (1154/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL3657160 in Train_ER_alpha.smi (1153/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL3657162 in Train_ER_alpha.smi (1155/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL3657163 in Train_ER_alpha.smi (1156/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL3657182 in Train_ER_alpha.smi (1158/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL3657180 in Train_ER_alpha.smi (1157/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL3657183 in Train_ER_alpha.smi (1159/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL3657166 in Train_ER_alpha.smi (1160/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL3657167 in Train_ER_alpha.smi (1161/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL3657168 in Train_ER_alpha.smi (1162/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL3657136 in Train_ER_alpha.smi (1163/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL3657138 in Train_ER_alpha.smi (1164/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL3657139 in Train_ER_alpha.smi (1165/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL3657140 in Train_ER_alpha.smi (1166/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL3657141 in Train_ER_alpha.smi (1167/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL3657169 in Train_ER_alpha.smi (1168/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL3657171 in Train_ER_alpha.smi (1169/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL3775116 in Train_ER_alpha.smi (1170/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL3774660 in Train_ER_alpha.smi (1171/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL3775016 in Train_ER_alpha.smi (1172/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL3774647 in Train_ER_alpha.smi (1173/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL3775129 in Train_ER_alpha.smi (1174/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL3775006 in Train_ER_alpha.smi (1175/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL3775410 in Train_ER_alpha.smi (1176/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL3775434 in Train_ER_alpha.smi (1177/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL3775224 in Train_ER_alpha.smi (1178/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL3775978 in Train_ER_alpha.smi (1179/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL3775283 in Train_ER_alpha.smi (1180/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL3775375 in Train_ER_alpha.smi (1181/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL3774642 in Train_ER_alpha.smi (1182/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL3805921 in Train_ER_alpha.smi (1183/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL3804907 in Train_ER_alpha.smi (1185/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL3805170 in Train_ER_alpha.smi (1184/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL3805025 in Train_ER_alpha.smi (1186/1238). Average speed: 0.10 s/mol.\n"" + ] + }, + { + ""name"": ""stdout"", + ""output_type"": ""stream"", + ""text"": [ + ""Processing CHEMBL3805385 in Train_ER_alpha.smi (1187/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL3823498 in Train_ER_alpha.smi (1188/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL3815076 in Train_ER_alpha.smi (1189/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL3823426 in Train_ER_alpha.smi (1190/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL3827137 in Train_ER_alpha.smi (1192/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL3827870 in Train_ER_alpha.smi (1191/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL3827692 in Train_ER_alpha.smi (1193/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL3827784 in Train_ER_alpha.smi (1195/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL3827034 in Train_ER_alpha.smi (1194/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL3827200 in Train_ER_alpha.smi (1196/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL3828208 in Train_ER_alpha.smi (1197/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL3827633 in Train_ER_alpha.smi (1198/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL3827098 in Train_ER_alpha.smi (1199/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL3828416 in Train_ER_alpha.smi (1200/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL3827045 in Train_ER_alpha.smi (1201/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL3827152 in Train_ER_alpha.smi (1202/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL3828422 in Train_ER_alpha.smi (1203/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL3827193 in Train_ER_alpha.smi (1204/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL3827286 in Train_ER_alpha.smi (1205/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL3828117 in Train_ER_alpha.smi (1206/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL3823192 in Train_ER_alpha.smi (1207/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL101382 in Train_ER_alpha.smi (1208/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL180792 in Train_ER_alpha.smi (1209/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL183584 in Train_ER_alpha.smi (1210/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL184202 in Train_ER_alpha.smi (1211/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL191974 in Train_ER_alpha.smi (1212/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL192548 in Train_ER_alpha.smi (1213/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL193518 in Train_ER_alpha.smi (1214/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL209269 in Train_ER_alpha.smi (1215/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL2403356 in Train_ER_alpha.smi (1217/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL209851 in Train_ER_alpha.smi (1216/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL2403357 in Train_ER_alpha.smi (1218/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL246138 in Train_ER_alpha.smi (1219/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL267385 in Train_ER_alpha.smi (1220/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL281499 in Train_ER_alpha.smi (1221/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL304552 in Train_ER_alpha.smi (1222/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL30707 in Train_ER_alpha.smi (1223/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL328190 in Train_ER_alpha.smi (1224/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL3427402 in Train_ER_alpha.smi (1225/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL3623002 in Train_ER_alpha.smi (1226/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL3623003 in Train_ER_alpha.smi (1227/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL3623004 in Train_ER_alpha.smi (1228/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL362623 in Train_ER_alpha.smi (1229/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL367588 in Train_ER_alpha.smi (1230/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL370039 in Train_ER_alpha.smi (1231/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL3774510 in Train_ER_alpha.smi (1232/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL3774584 in Train_ER_alpha.smi (1233/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL3774737 in Train_ER_alpha.smi (1234/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL3774944 in Train_ER_alpha.smi (1235/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL3775766 in Train_ER_alpha.smi (1236/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL3775908 in Train_ER_alpha.smi (1237/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL691 in Train_ER_alpha.smi (1238/1238). Average speed: 0.11 s/mol.\n"", + ""Descriptor calculation completed in 2 mins 10.254 secs . Average speed: 0.11 s/mol.\n"", + ""Train_ER_alpha.smi\n"", + ""Train_ER_alpha.csv\n"", + ""SubstructureFingerprinter.xml\n"", + ""SubstructureFingerprinter_\n"", + ""Processing CHEMBL370037 in Train_ER_alpha.smi (1/1238). \n"", + ""Processing CHEMBL189073 in Train_ER_alpha.smi (2/1238). \n"", + ""Processing CHEMBL365290 in Train_ER_alpha.smi (3/1238). \n"", + ""Processing CHEMBL191648 in Train_ER_alpha.smi (4/1238). \n"", + ""Processing CHEMBL184431 in Train_ER_alpha.smi (5/1238). \n"", + ""Processing CHEMBL124482 in Train_ER_alpha.smi (7/1238). Average speed: 0.90 s/mol.\n"", + ""Processing CHEMBL364551 in Train_ER_alpha.smi (6/1238). Average speed: 1.75 s/mol.\n"", + ""Processing CHEMBL198410 in Train_ER_alpha.smi (8/1238). Average speed: 0.45 s/mol.\n"", + ""Processing CHEMBL1496978 in Train_ER_alpha.smi (9/1238). Average speed: 0.49 s/mol.\n"", + ""Processing CHEMBL1305485 in Train_ER_alpha.smi (10/1238). Average speed: 0.40 s/mol.\n"", + ""Processing CHEMBL1570659 in Train_ER_alpha.smi (11/1238). Average speed: 0.36 s/mol.\n"", + ""Processing CHEMBL1331084 in Train_ER_alpha.smi (12/1238). Average speed: 0.32 s/mol.\n"", + ""Processing CHEMBL184448 in Train_ER_alpha.smi (13/1238). Average speed: 0.28 s/mol.\n"", + ""Processing CHEMBL45068 in Train_ER_alpha.smi (14/1238). Average speed: 0.27 s/mol.\n"", + ""Processing CHEMBL434502 in Train_ER_alpha.smi (15/1238). Average speed: 0.25 s/mol.\n"", + ""Processing CHEMBL1628199 in Train_ER_alpha.smi (16/1238). Average speed: 0.23 s/mol.\n"", + ""Processing CHEMBL123025 in Train_ER_alpha.smi (17/1238). Average speed: 0.22 s/mol.\n"", + ""Processing CHEMBL153062 in Train_ER_alpha.smi (18/1238). Average speed: 0.20 s/mol.\n"", + ""Processing CHEMBL1468708 in Train_ER_alpha.smi (19/1238). Average speed: 0.20 s/mol.\n"", + ""Processing CHEMBL1442416 in Train_ER_alpha.smi (20/1238). Average speed: 0.18 s/mol.\n"", + ""Processing CHEMBL1388271 in Train_ER_alpha.smi (21/1238). Average speed: 0.17 s/mol.\n"", + ""Processing CHEMBL1331872 in Train_ER_alpha.smi (22/1238). Average speed: 0.18 s/mol.\n"", + ""Processing CHEMBL1401227 in Train_ER_alpha.smi (23/1238). Average speed: 0.18 s/mol.\n"", + ""Processing CHEMBL1460126 in Train_ER_alpha.smi (24/1238). Average speed: 0.17 s/mol.\n"", + ""Processing CHEMBL1499638 in Train_ER_alpha.smi (25/1238). Average speed: 0.17 s/mol.\n"", + ""Processing CHEMBL1608989 in Train_ER_alpha.smi (28/1238). Average speed: 0.15 s/mol.\n"", + ""Processing CHEMBL1484998 in Train_ER_alpha.smi (27/1238). Average speed: 0.15 s/mol.\n"", + ""Processing CHEMBL1413557 in Train_ER_alpha.smi (26/1238). Average speed: 0.16 s/mol.\n"", + ""Processing CHEMBL1469743 in Train_ER_alpha.smi (29/1238). Average speed: 0.15 s/mol.\n"", + ""Processing CHEMBL1538325 in Train_ER_alpha.smi (30/1238). Average speed: 0.14 s/mol.\n"", + ""Processing CHEMBL1605393 in Train_ER_alpha.smi (31/1238). Average speed: 0.14 s/mol.\n"", + ""Processing CHEMBL1384049 in Train_ER_alpha.smi (32/1238). Average speed: 0.13 s/mol.\n"", + ""Processing CHEMBL188049 in Train_ER_alpha.smi (33/1238). Average speed: 0.13 s/mol.\n"", + ""Processing CHEMBL1361641 in Train_ER_alpha.smi (34/1238). Average speed: 0.13 s/mol.\n"", + ""Processing CHEMBL1489063 in Train_ER_alpha.smi (35/1238). Average speed: 0.13 s/mol.\n"", + ""Processing CHEMBL1381495 in Train_ER_alpha.smi (36/1238). Average speed: 0.12 s/mol.\n"", + ""Processing CHEMBL1343011 in Train_ER_alpha.smi (37/1238). Average speed: 0.12 s/mol.\n"", + ""Processing CHEMBL1613068 in Train_ER_alpha.smi (38/1238). Average speed: 0.12 s/mol.\n"", + ""Processing CHEMBL1464837 in Train_ER_alpha.smi (39/1238). Average speed: 0.12 s/mol.\n"", + ""Processing CHEMBL1349347 in Train_ER_alpha.smi (40/1238). Average speed: 0.12 s/mol.\n"", + ""Processing CHEMBL1385373 in Train_ER_alpha.smi (41/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL1573735 in Train_ER_alpha.smi (42/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL1455258 in Train_ER_alpha.smi (43/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL1431555 in Train_ER_alpha.smi (44/1238). Average speed: 0.11 s/mol.\n"" + ] + }, + { + ""name"": ""stdout"", + ""output_type"": ""stream"", + ""text"": [ + ""Processing CHEMBL1565187 in Train_ER_alpha.smi (45/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL1597933 in Train_ER_alpha.smi (46/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL1466882 in Train_ER_alpha.smi (47/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL1313979 in Train_ER_alpha.smi (48/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL1329455 in Train_ER_alpha.smi (49/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL1322939 in Train_ER_alpha.smi (50/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL1480843 in Train_ER_alpha.smi (51/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL1310614 in Train_ER_alpha.smi (52/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL1535728 in Train_ER_alpha.smi (53/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL1341057 in Train_ER_alpha.smi (54/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL1420411 in Train_ER_alpha.smi (55/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL184372 in Train_ER_alpha.smi (56/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL442037 in Train_ER_alpha.smi (57/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL512579 in Train_ER_alpha.smi (58/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL124474 in Train_ER_alpha.smi (59/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL231811 in Train_ER_alpha.smi (60/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL385801 in Train_ER_alpha.smi (61/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL447111 in Train_ER_alpha.smi (62/1238). Average speed: 0.09 s/mol.\n"", + ""Processing CHEMBL427496 in Train_ER_alpha.smi (63/1238). Average speed: 0.09 s/mol.\n"", + ""Processing CHEMBL225404 in Train_ER_alpha.smi (64/1238). Average speed: 0.09 s/mol.\n"", + ""Processing CHEMBL226269 in Train_ER_alpha.smi (65/1238). Average speed: 0.09 s/mol.\n"", + ""Processing CHEMBL1081361 in Train_ER_alpha.smi (66/1238). Average speed: 0.09 s/mol.\n"", + ""Processing CHEMBL235496 in Train_ER_alpha.smi (67/1238). Average speed: 0.09 s/mol.\n"", + ""Processing CHEMBL235497 in Train_ER_alpha.smi (68/1238). Average speed: 0.09 s/mol.\n"", + ""Processing CHEMBL361762 in Train_ER_alpha.smi (69/1238). Average speed: 0.09 s/mol.\n"", + ""Processing CHEMBL184371 in Train_ER_alpha.smi (70/1238). Average speed: 0.09 s/mol.\n"", + ""Processing CHEMBL188458 in Train_ER_alpha.smi (71/1238). Average speed: 0.09 s/mol.\n"", + ""Processing CHEMBL361272 in Train_ER_alpha.smi (72/1238). Average speed: 0.09 s/mol.\n"", + ""Processing CHEMBL241257 in Train_ER_alpha.smi (73/1238). Average speed: 0.09 s/mol.\n"", + ""Processing CHEMBL396118 in Train_ER_alpha.smi (74/1238). Average speed: 0.09 s/mol.\n"", + ""Processing CHEMBL59438 in Train_ER_alpha.smi (75/1238). Average speed: 0.09 s/mol.\n"", + ""Processing CHEMBL365625 in Train_ER_alpha.smi (77/1238). Average speed: 0.09 s/mol.\n"", + ""Processing CHEMBL150279 in Train_ER_alpha.smi (76/1238). Average speed: 0.09 s/mol.\n"", + ""Processing CHEMBL365016 in Train_ER_alpha.smi (78/1238). Average speed: 0.09 s/mol.\n"", + ""Processing CHEMBL179929 in Train_ER_alpha.smi (79/1238). Average speed: 0.09 s/mol.\n"", + ""Processing CHEMBL511269 in Train_ER_alpha.smi (81/1238). Average speed: 0.09 s/mol.\n"", + ""Processing CHEMBL470993 in Train_ER_alpha.smi (80/1238). Average speed: 0.09 s/mol.\n"", + ""Processing CHEMBL469512 in Train_ER_alpha.smi (82/1238). Average speed: 0.08 s/mol.\n"", + ""Processing CHEMBL470785 in Train_ER_alpha.smi (83/1238). Average speed: 0.09 s/mol.\n"", + ""Processing CHEMBL577938 in Train_ER_alpha.smi (84/1238). Average speed: 0.09 s/mol.\n"", + ""Processing CHEMBL185114 in Train_ER_alpha.smi (85/1238). Average speed: 0.08 s/mol.\n"", + ""Processing CHEMBL377171 in Train_ER_alpha.smi (86/1238). Average speed: 0.08 s/mol.\n"", + ""Processing CHEMBL385680 in Train_ER_alpha.smi (87/1238). Average speed: 0.08 s/mol.\n"", + ""Processing CHEMBL204902 in Train_ER_alpha.smi (88/1238). Average speed: 0.08 s/mol.\n"", + ""Processing CHEMBL206727 in Train_ER_alpha.smi (89/1238). Average speed: 0.08 s/mol.\n"", + ""Processing CHEMBL380362 in Train_ER_alpha.smi (90/1238). Average speed: 0.08 s/mol.\n"", + ""Processing CHEMBL206547 in Train_ER_alpha.smi (91/1238). Average speed: 0.08 s/mol.\n"", + ""Processing CHEMBL471626 in Train_ER_alpha.smi (92/1238). Average speed: 0.08 s/mol.\n"", + ""Processing CHEMBL511492 in Train_ER_alpha.smi (93/1238). Average speed: 0.08 s/mol.\n"", + ""Processing CHEMBL196836 in Train_ER_alpha.smi (94/1238). Average speed: 0.08 s/mol.\n"", + ""Processing CHEMBL25228 in Train_ER_alpha.smi (95/1238). Average speed: 0.08 s/mol.\n"", + ""Processing CHEMBL194326 in Train_ER_alpha.smi (96/1238). Average speed: 0.08 s/mol.\n"", + ""Processing CHEMBL203330 in Train_ER_alpha.smi (97/1238). Average speed: 0.08 s/mol.\n"", + ""Processing CHEMBL206648 in Train_ER_alpha.smi (98/1238). Average speed: 0.08 s/mol.\n"", + ""Processing CHEMBL425231 in Train_ER_alpha.smi (99/1238). Average speed: 0.08 s/mol.\n"", + ""Processing CHEMBL206336 in Train_ER_alpha.smi (100/1238). Average speed: 0.08 s/mol.\n"", + ""Processing CHEMBL205137 in Train_ER_alpha.smi (101/1238). Average speed: 0.08 s/mol.\n"", + ""Processing CHEMBL425941 in Train_ER_alpha.smi (102/1238). Average speed: 0.08 s/mol.\n"", + ""Processing CHEMBL206499 in Train_ER_alpha.smi (103/1238). Average speed: 0.08 s/mol.\n"", + ""Processing CHEMBL429446 in Train_ER_alpha.smi (104/1238). Average speed: 0.08 s/mol.\n"", + ""Processing CHEMBL191300 in Train_ER_alpha.smi (105/1238). Average speed: 0.08 s/mol.\n"", + ""Processing CHEMBL447837 in Train_ER_alpha.smi (106/1238). Average speed: 0.08 s/mol.\n"", + ""Processing CHEMBL187314 in Train_ER_alpha.smi (107/1238). Average speed: 0.08 s/mol.\n"", + ""Processing CHEMBL371308 in Train_ER_alpha.smi (108/1238). Average speed: 0.08 s/mol.\n"", + ""Processing CHEMBL191710 in Train_ER_alpha.smi (109/1238). Average speed: 0.08 s/mol.\n"", + ""Processing CHEMBL426085 in Train_ER_alpha.smi (110/1238). Average speed: 0.08 s/mol.\n"", + ""Processing CHEMBL200719 in Train_ER_alpha.smi (111/1238). Average speed: 0.08 s/mol.\n"", + ""Processing CHEMBL212419 in Train_ER_alpha.smi (112/1238). Average speed: 0.08 s/mol.\n"", + ""Processing CHEMBL211493 in Train_ER_alpha.smi (113/1238). Average speed: 0.08 s/mol.\n"", + ""Processing CHEMBL377475 in Train_ER_alpha.smi (114/1238). Average speed: 0.08 s/mol.\n"", + ""Processing CHEMBL204024 in Train_ER_alpha.smi (115/1238). Average speed: 0.08 s/mol.\n"", + ""Processing CHEMBL183333 in Train_ER_alpha.smi (116/1238). Average speed: 0.08 s/mol.\n"", + ""Processing CHEMBL185231 in Train_ER_alpha.smi (117/1238). Average speed: 0.08 s/mol.\n"", + ""Processing CHEMBL378473 in Train_ER_alpha.smi (118/1238). Average speed: 0.08 s/mol.\n"", + ""Processing CHEMBL382734 in Train_ER_alpha.smi (119/1238). Average speed: 0.08 s/mol.\n"", + ""Processing CHEMBL195807 in Train_ER_alpha.smi (120/1238). Average speed: 0.08 s/mol.\n"", + ""Processing CHEMBL212185 in Train_ER_alpha.smi (121/1238). Average speed: 0.08 s/mol.\n"", + ""Processing CHEMBL198914 in Train_ER_alpha.smi (122/1238). Average speed: 0.08 s/mol.\n"", + ""Processing CHEMBL372808 in Train_ER_alpha.smi (123/1238). Average speed: 0.08 s/mol.\n"", + ""Processing CHEMBL197495 in Train_ER_alpha.smi (124/1238). Average speed: 0.08 s/mol.\n"", + ""Processing CHEMBL191901 in Train_ER_alpha.smi (125/1238). Average speed: 0.08 s/mol.\n"", + ""Processing CHEMBL241304 in Train_ER_alpha.smi (126/1238). Average speed: 0.08 s/mol.\n"", + ""Processing CHEMBL241485 in Train_ER_alpha.smi (127/1238). Average speed: 0.08 s/mol.\n"", + ""Processing CHEMBL437190 in Train_ER_alpha.smi (128/1238). Average speed: 0.08 s/mol.\n"", + ""Processing CHEMBL182402 in Train_ER_alpha.smi (129/1238). Average speed: 0.08 s/mol.\n"", + ""Processing CHEMBL188391 in Train_ER_alpha.smi (130/1238). Average speed: 0.08 s/mol.\n"", + ""Processing CHEMBL184598 in Train_ER_alpha.smi (131/1238). Average speed: 0.08 s/mol.\n"", + ""Processing CHEMBL360315 in Train_ER_alpha.smi (132/1238). Average speed: 0.08 s/mol.\n"", + ""Processing CHEMBL371777 in Train_ER_alpha.smi (133/1238). Average speed: 0.08 s/mol.\n"", + ""Processing CHEMBL126164 in Train_ER_alpha.smi (134/1238). Average speed: 0.08 s/mol.\n"", + ""Processing CHEMBL195786 in Train_ER_alpha.smi (135/1238). Average speed: 0.08 s/mol.\n"", + ""Processing CHEMBL219134 in Train_ER_alpha.smi (136/1238). Average speed: 0.08 s/mol.\n"", + ""Processing CHEMBL204119 in Train_ER_alpha.smi (137/1238). Average speed: 0.08 s/mol.\n"", + ""Processing CHEMBL204222 in Train_ER_alpha.smi (138/1238). Average speed: 0.08 s/mol.\n"", + ""Processing CHEMBL212392 in Train_ER_alpha.smi (139/1238). Average speed: 0.08 s/mol.\n"", + ""Processing CHEMBL441366 in Train_ER_alpha.smi (140/1238). Average speed: 0.08 s/mol.\n"", + ""Processing CHEMBL384709 in Train_ER_alpha.smi (141/1238). Average speed: 0.08 s/mol.\n"", + ""Processing CHEMBL189080 in Train_ER_alpha.smi (142/1238). Average speed: 0.08 s/mol.\n"", + ""Processing CHEMBL180707 in Train_ER_alpha.smi (143/1238). Average speed: 0.08 s/mol.\n"" + ] + }, + { + ""name"": ""stdout"", + ""output_type"": ""stream"", + ""text"": [ + ""Processing CHEMBL362272 in Train_ER_alpha.smi (144/1238). Average speed: 0.08 s/mol.\n"", + ""Processing CHEMBL438516 in Train_ER_alpha.smi (145/1238). Average speed: 0.08 s/mol.\n"", + ""Processing CHEMBL234733 in Train_ER_alpha.smi (146/1238). Average speed: 0.08 s/mol.\n"", + ""Processing CHEMBL206441 in Train_ER_alpha.smi (147/1238). Average speed: 0.08 s/mol.\n"", + ""Processing CHEMBL379188 in Train_ER_alpha.smi (148/1238). Average speed: 0.08 s/mol.\n"", + ""Processing CHEMBL425439 in Train_ER_alpha.smi (149/1238). Average speed: 0.08 s/mol.\n"", + ""Processing CHEMBL192102 in Train_ER_alpha.smi (150/1238). Average speed: 0.08 s/mol.\n"", + ""Processing CHEMBL395308 in Train_ER_alpha.smi (151/1238). Average speed: 0.08 s/mol.\n"", + ""Processing CHEMBL394788 in Train_ER_alpha.smi (152/1238). Average speed: 0.08 s/mol.\n"", + ""Processing CHEMBL245133 in Train_ER_alpha.smi (153/1238). Average speed: 0.08 s/mol.\n"", + ""Processing CHEMBL234527 in Train_ER_alpha.smi (154/1238). Average speed: 0.08 s/mol.\n"", + ""Processing CHEMBL195208 in Train_ER_alpha.smi (155/1238). Average speed: 0.08 s/mol.\n"", + ""Processing CHEMBL8145 in Train_ER_alpha.smi (156/1238). Average speed: 0.08 s/mol.\n"", + ""Processing CHEMBL195437 in Train_ER_alpha.smi (157/1238). Average speed: 0.08 s/mol.\n"", + ""Processing CHEMBL435090 in Train_ER_alpha.smi (158/1238). Average speed: 0.08 s/mol.\n"", + ""Processing CHEMBL1201151 in Train_ER_alpha.smi (159/1238). Average speed: 0.08 s/mol.\n"", + ""Processing CHEMBL366928 in Train_ER_alpha.smi (160/1238). Average speed: 0.07 s/mol.\n"", + ""Processing CHEMBL192068 in Train_ER_alpha.smi (161/1238). Average speed: 0.07 s/mol.\n"", + ""Processing CHEMBL360651 in Train_ER_alpha.smi (162/1238). Average speed: 0.07 s/mol.\n"", + ""Processing CHEMBL195878 in Train_ER_alpha.smi (163/1238). Average speed: 0.07 s/mol.\n"", + ""Processing CHEMBL182237 in Train_ER_alpha.smi (164/1238). Average speed: 0.07 s/mol.\n"", + ""Processing CHEMBL365293 in Train_ER_alpha.smi (165/1238). Average speed: 0.07 s/mol.\n"", + ""Processing CHEMBL183086 in Train_ER_alpha.smi (166/1238). Average speed: 0.07 s/mol.\n"", + ""Processing CHEMBL426641 in Train_ER_alpha.smi (167/1238). Average speed: 0.07 s/mol.\n"", + ""Processing CHEMBL374799 in Train_ER_alpha.smi (168/1238). Average speed: 0.07 s/mol.\n"", + ""Processing CHEMBL225349 in Train_ER_alpha.smi (169/1238). Average speed: 0.07 s/mol.\n"", + ""Processing CHEMBL222434 in Train_ER_alpha.smi (170/1238). Average speed: 0.07 s/mol.\n"", + ""Processing CHEMBL373743 in Train_ER_alpha.smi (171/1238). Average speed: 0.07 s/mol.\n"", + ""Processing CHEMBL451496 in Train_ER_alpha.smi (172/1238). Average speed: 0.07 s/mol.\n"", + ""Processing CHEMBL186643 in Train_ER_alpha.smi (173/1238). Average speed: 0.07 s/mol.\n"", + ""Processing CHEMBL362718 in Train_ER_alpha.smi (174/1238). Average speed: 0.07 s/mol.\n"", + ""Processing CHEMBL418327 in Train_ER_alpha.smi (175/1238). Average speed: 0.07 s/mol.\n"", + ""Processing CHEMBL187207 in Train_ER_alpha.smi (176/1238). Average speed: 0.07 s/mol.\n"", + ""Processing CHEMBL181248 in Train_ER_alpha.smi (177/1238). Average speed: 0.07 s/mol.\n"", + ""Processing CHEMBL183371 in Train_ER_alpha.smi (178/1238). Average speed: 0.07 s/mol.\n"", + ""Processing CHEMBL439806 in Train_ER_alpha.smi (179/1238). Average speed: 0.07 s/mol.\n"", + ""Processing CHEMBL121879 in Train_ER_alpha.smi (180/1238). Average speed: 0.07 s/mol.\n"", + ""Processing CHEMBL202996 in Train_ER_alpha.smi (181/1238). Average speed: 0.07 s/mol.\n"", + ""Processing CHEMBL363705 in Train_ER_alpha.smi (182/1238). Average speed: 0.07 s/mol.\n"", + ""Processing CHEMBL373095 in Train_ER_alpha.smi (183/1238). Average speed: 0.07 s/mol.\n"", + ""Processing CHEMBL185480 in Train_ER_alpha.smi (184/1238). Average speed: 0.07 s/mol.\n"", + ""Processing CHEMBL389683 in Train_ER_alpha.smi (185/1238). Average speed: 0.07 s/mol.\n"", + ""Processing CHEMBL449038 in Train_ER_alpha.smi (186/1238). Average speed: 0.07 s/mol.\n"", + ""Processing CHEMBL225022 in Train_ER_alpha.smi (187/1238). Average speed: 0.07 s/mol.\n"", + ""Processing CHEMBL389393 in Train_ER_alpha.smi (188/1238). Average speed: 0.07 s/mol.\n"", + ""Processing CHEMBL360802 in Train_ER_alpha.smi (189/1238). Average speed: 0.07 s/mol.\n"", + ""Processing CHEMBL332625 in Train_ER_alpha.smi (190/1238). Average speed: 0.07 s/mol.\n"", + ""Processing CHEMBL194936 in Train_ER_alpha.smi (191/1238). Average speed: 0.07 s/mol.\n"", + ""Processing CHEMBL192154 in Train_ER_alpha.smi (192/1238). Average speed: 0.07 s/mol.\n"", + ""Processing CHEMBL369991 in Train_ER_alpha.smi (193/1238). Average speed: 0.07 s/mol.\n"", + ""Processing CHEMBL377342 in Train_ER_alpha.smi (194/1238). Average speed: 0.07 s/mol.\n"", + ""Processing CHEMBL381298 in Train_ER_alpha.smi (195/1238). Average speed: 0.07 s/mol.\n"", + ""Processing CHEMBL204025 in Train_ER_alpha.smi (196/1238). Average speed: 0.07 s/mol.\n"", + ""Processing CHEMBL202615 in Train_ER_alpha.smi (197/1238). Average speed: 0.07 s/mol.\n"", + ""Processing CHEMBL201013 in Train_ER_alpha.smi (198/1238). Average speed: 0.07 s/mol.\n"", + ""Processing CHEMBL385279 in Train_ER_alpha.smi (199/1238). Average speed: 0.07 s/mol.\n"", + ""Processing CHEMBL219387 in Train_ER_alpha.smi (200/1238). Average speed: 0.07 s/mol.\n"", + ""Processing CHEMBL85826 in Train_ER_alpha.smi (201/1238). Average speed: 0.07 s/mol.\n"", + ""Processing CHEMBL195371 in Train_ER_alpha.smi (202/1238). Average speed: 0.07 s/mol.\n"", + ""Processing CHEMBL187915 in Train_ER_alpha.smi (203/1238). Average speed: 0.07 s/mol.\n"", + ""Processing CHEMBL364092 in Train_ER_alpha.smi (204/1238). Average speed: 0.07 s/mol.\n"", + ""Processing CHEMBL193726 in Train_ER_alpha.smi (205/1238). Average speed: 0.07 s/mol.\n"", + ""Processing CHEMBL192218 in Train_ER_alpha.smi (206/1238). Average speed: 0.07 s/mol.\n"", + ""Processing CHEMBL441499 in Train_ER_alpha.smi (207/1238). Average speed: 0.07 s/mol.\n"", + ""Processing CHEMBL183084 in Train_ER_alpha.smi (208/1238). Average speed: 0.07 s/mol.\n"", + ""Processing CHEMBL187000 in Train_ER_alpha.smi (209/1238). Average speed: 0.07 s/mol.\n"", + ""Processing CHEMBL369979 in Train_ER_alpha.smi (210/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL381299 in Train_ER_alpha.smi (211/1238). Average speed: 0.07 s/mol.\n"", + ""Processing CHEMBL206584 in Train_ER_alpha.smi (212/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL212923 in Train_ER_alpha.smi (213/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL385358 in Train_ER_alpha.smi (214/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL219577 in Train_ER_alpha.smi (216/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL383533 in Train_ER_alpha.smi (215/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL241303 in Train_ER_alpha.smi (217/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL240227 in Train_ER_alpha.smi (218/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL393278 in Train_ER_alpha.smi (219/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL362428 in Train_ER_alpha.smi (220/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL434525 in Train_ER_alpha.smi (222/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL496 in Train_ER_alpha.smi (221/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL365999 in Train_ER_alpha.smi (223/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL198411 in Train_ER_alpha.smi (224/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL186945 in Train_ER_alpha.smi (225/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL197667 in Train_ER_alpha.smi (226/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL382632 in Train_ER_alpha.smi (227/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL206440 in Train_ER_alpha.smi (228/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL380844 in Train_ER_alpha.smi (229/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL383753 in Train_ER_alpha.smi (230/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL206406 in Train_ER_alpha.smi (231/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL204056 in Train_ER_alpha.smi (232/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL381697 in Train_ER_alpha.smi (233/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL204922 in Train_ER_alpha.smi (234/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL206600 in Train_ER_alpha.smi (235/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL206227 in Train_ER_alpha.smi (236/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL211816 in Train_ER_alpha.smi (237/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL208929 in Train_ER_alpha.smi (238/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL209623 in Train_ER_alpha.smi (239/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL211887 in Train_ER_alpha.smi (240/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL204819 in Train_ER_alpha.smi (243/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL191714 in Train_ER_alpha.smi (241/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL188792 in Train_ER_alpha.smi (242/1238). Average speed: 0.06 s/mol.\n"" + ] + }, + { + ""name"": ""stdout"", + ""output_type"": ""stream"", + ""text"": [ + ""Processing CHEMBL206546 in Train_ER_alpha.smi (244/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL205934 in Train_ER_alpha.smi (245/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL381337 in Train_ER_alpha.smi (246/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL203442 in Train_ER_alpha.smi (247/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL206814 in Train_ER_alpha.smi (248/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL203351 in Train_ER_alpha.smi (249/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL382698 in Train_ER_alpha.smi (250/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL191238 in Train_ER_alpha.smi (251/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL364502 in Train_ER_alpha.smi (252/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL331996 in Train_ER_alpha.smi (253/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL363634 in Train_ER_alpha.smi (254/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL185890 in Train_ER_alpha.smi (255/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL362793 in Train_ER_alpha.smi (257/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL185083 in Train_ER_alpha.smi (256/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL187473 in Train_ER_alpha.smi (258/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL213950 in Train_ER_alpha.smi (260/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL373987 in Train_ER_alpha.smi (259/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL290106 in Train_ER_alpha.smi (261/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL239938 in Train_ER_alpha.smi (262/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL391619 in Train_ER_alpha.smi (263/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL389907 in Train_ER_alpha.smi (264/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL187115 in Train_ER_alpha.smi (265/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL363633 in Train_ER_alpha.smi (266/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL182850 in Train_ER_alpha.smi (267/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL361751 in Train_ER_alpha.smi (268/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL188189 in Train_ER_alpha.smi (269/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL196742 in Train_ER_alpha.smi (270/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL360410 in Train_ER_alpha.smi (272/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL316003 in Train_ER_alpha.smi (271/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL1570364 in Train_ER_alpha.smi (274/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL196530 in Train_ER_alpha.smi (273/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL1491936 in Train_ER_alpha.smi (275/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL1487772 in Train_ER_alpha.smi (276/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL51483 in Train_ER_alpha.smi (277/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL359633 in Train_ER_alpha.smi (278/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL463804 in Train_ER_alpha.smi (279/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL1403999 in Train_ER_alpha.smi (281/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL1414254 in Train_ER_alpha.smi (280/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL319244 in Train_ER_alpha.smi (283/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL1572403 in Train_ER_alpha.smi (284/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL1627356 in Train_ER_alpha.smi (282/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL1541779 in Train_ER_alpha.smi (285/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL1458486 in Train_ER_alpha.smi (286/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL1587920 in Train_ER_alpha.smi (287/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL1328083 in Train_ER_alpha.smi (290/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL1399039 in Train_ER_alpha.smi (288/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL1332402 in Train_ER_alpha.smi (289/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL150924 in Train_ER_alpha.smi (291/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL182751 in Train_ER_alpha.smi (293/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL184003 in Train_ER_alpha.smi (292/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL1465370 in Train_ER_alpha.smi (294/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL1383151 in Train_ER_alpha.smi (295/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL1407483 in Train_ER_alpha.smi (296/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL1511067 in Train_ER_alpha.smi (297/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL192876 in Train_ER_alpha.smi (298/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL363017 in Train_ER_alpha.smi (299/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL1428161 in Train_ER_alpha.smi (300/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL1390963 in Train_ER_alpha.smi (301/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL340111 in Train_ER_alpha.smi (302/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL185783 in Train_ER_alpha.smi (303/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL294889 in Train_ER_alpha.smi (304/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL184151 in Train_ER_alpha.smi (305/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL1504315 in Train_ER_alpha.smi (306/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL63768 in Train_ER_alpha.smi (307/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL1564209 in Train_ER_alpha.smi (308/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL1469865 in Train_ER_alpha.smi (309/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL380188 in Train_ER_alpha.smi (310/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL214693 in Train_ER_alpha.smi (311/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL196283 in Train_ER_alpha.smi (312/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL363498 in Train_ER_alpha.smi (314/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL195154 in Train_ER_alpha.smi (313/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL1392188 in Train_ER_alpha.smi (315/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL183368 in Train_ER_alpha.smi (317/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL1577029 in Train_ER_alpha.smi (316/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL215409 in Train_ER_alpha.smi (318/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL211837 in Train_ER_alpha.smi (319/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL379566 in Train_ER_alpha.smi (320/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL384268 in Train_ER_alpha.smi (321/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL203348 in Train_ER_alpha.smi (322/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL371072 in Train_ER_alpha.smi (323/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL180873 in Train_ER_alpha.smi (324/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL426626 in Train_ER_alpha.smi (325/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL191711 in Train_ER_alpha.smi (326/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL231812 in Train_ER_alpha.smi (328/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL225081 in Train_ER_alpha.smi (327/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL182887 in Train_ER_alpha.smi (329/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL1368345 in Train_ER_alpha.smi (330/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL188900 in Train_ER_alpha.smi (331/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL362966 in Train_ER_alpha.smi (332/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL379213 in Train_ER_alpha.smi (333/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL380359 in Train_ER_alpha.smi (334/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL207101 in Train_ER_alpha.smi (335/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL385693 in Train_ER_alpha.smi (336/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL193727 in Train_ER_alpha.smi (338/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL192765 in Train_ER_alpha.smi (337/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL183367 in Train_ER_alpha.smi (339/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL1395826 in Train_ER_alpha.smi (340/1238). Average speed: 0.05 s/mol.\n"" + ] + }, + { + ""name"": ""stdout"", + ""output_type"": ""stream"", + ""text"": [ + ""Processing CHEMBL364345 in Train_ER_alpha.smi (343/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL1434189 in Train_ER_alpha.smi (341/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL1386105 in Train_ER_alpha.smi (342/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL371126 in Train_ER_alpha.smi (344/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL1324414 in Train_ER_alpha.smi (345/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL1334930 in Train_ER_alpha.smi (346/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL363902 in Train_ER_alpha.smi (347/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL472468 in Train_ER_alpha.smi (348/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL194718 in Train_ER_alpha.smi (350/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL189024 in Train_ER_alpha.smi (349/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL209803 in Train_ER_alpha.smi (351/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL209402 in Train_ER_alpha.smi (352/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL204970 in Train_ER_alpha.smi (353/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL203472 in Train_ER_alpha.smi (354/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL1303886 in Train_ER_alpha.smi (355/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL246341 in Train_ER_alpha.smi (356/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL425092 in Train_ER_alpha.smi (357/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL380469 in Train_ER_alpha.smi (358/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL376986 in Train_ER_alpha.smi (359/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL208978 in Train_ER_alpha.smi (360/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL377644 in Train_ER_alpha.smi (361/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL249903 in Train_ER_alpha.smi (362/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL398634 in Train_ER_alpha.smi (363/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL232023 in Train_ER_alpha.smi (364/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL1340020 in Train_ER_alpha.smi (365/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL511812 in Train_ER_alpha.smi (366/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL191836 in Train_ER_alpha.smi (367/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL189356 in Train_ER_alpha.smi (368/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL1606765 in Train_ER_alpha.smi (369/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL1348377 in Train_ER_alpha.smi (370/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL1426947 in Train_ER_alpha.smi (371/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL1453259 in Train_ER_alpha.smi (372/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL347560 in Train_ER_alpha.smi (373/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL364110 in Train_ER_alpha.smi (374/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL189025 in Train_ER_alpha.smi (375/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL25580 in Train_ER_alpha.smi (376/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL1604500 in Train_ER_alpha.smi (377/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL1509453 in Train_ER_alpha.smi (378/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL1482201 in Train_ER_alpha.smi (379/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL1326210 in Train_ER_alpha.smi (380/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL221515 in Train_ER_alpha.smi (381/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL389394 in Train_ER_alpha.smi (382/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL374277 in Train_ER_alpha.smi (383/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL379889 in Train_ER_alpha.smi (384/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL203724 in Train_ER_alpha.smi (385/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL226379 in Train_ER_alpha.smi (386/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL1318094 in Train_ER_alpha.smi (387/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL191759 in Train_ER_alpha.smi (389/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL368449 in Train_ER_alpha.smi (388/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL365941 in Train_ER_alpha.smi (390/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL191808 in Train_ER_alpha.smi (391/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL360978 in Train_ER_alpha.smi (392/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL392029 in Train_ER_alpha.smi (393/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL200483 in Train_ER_alpha.smi (394/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL199599 in Train_ER_alpha.smi (395/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL200232 in Train_ER_alpha.smi (396/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL201517 in Train_ER_alpha.smi (397/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL202673 in Train_ER_alpha.smi (398/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL203072 in Train_ER_alpha.smi (399/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL241300 in Train_ER_alpha.smi (400/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL428732 in Train_ER_alpha.smi (401/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL393179 in Train_ER_alpha.smi (402/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL442249 in Train_ER_alpha.smi (403/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL238959 in Train_ER_alpha.smi (404/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL1410824 in Train_ER_alpha.smi (405/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL1359893 in Train_ER_alpha.smi (406/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL1427643 in Train_ER_alpha.smi (407/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL1464457 in Train_ER_alpha.smi (408/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL1403448 in Train_ER_alpha.smi (409/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL1575187 in Train_ER_alpha.smi (410/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL1548231 in Train_ER_alpha.smi (411/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL1531555 in Train_ER_alpha.smi (412/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL1521410 in Train_ER_alpha.smi (413/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL153405 in Train_ER_alpha.smi (415/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL1538223 in Train_ER_alpha.smi (414/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL327320 in Train_ER_alpha.smi (416/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL178559 in Train_ER_alpha.smi (417/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL180973 in Train_ER_alpha.smi (418/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL1528123 in Train_ER_alpha.smi (419/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL1536373 in Train_ER_alpha.smi (420/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL144431 in Train_ER_alpha.smi (422/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL1386676 in Train_ER_alpha.smi (421/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL1580036 in Train_ER_alpha.smi (423/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL189658 in Train_ER_alpha.smi (424/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL188619 in Train_ER_alpha.smi (425/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL1522300 in Train_ER_alpha.smi (427/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL1525824 in Train_ER_alpha.smi (426/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL193064 in Train_ER_alpha.smi (428/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL186513 in Train_ER_alpha.smi (429/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL1495194 in Train_ER_alpha.smi (430/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL1606490 in Train_ER_alpha.smi (431/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL1548186 in Train_ER_alpha.smi (432/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL2079587 in Train_ER_alpha.smi (433/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL180253 in Train_ER_alpha.smi (434/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL93705 in Train_ER_alpha.smi (435/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL190104 in Train_ER_alpha.smi (436/1238). Average speed: 0.05 s/mol.\n"" + ] + }, + { + ""name"": ""stdout"", + ""output_type"": ""stream"", + ""text"": [ + ""Processing CHEMBL1548834 in Train_ER_alpha.smi (437/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL188527 in Train_ER_alpha.smi (438/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL360498 in Train_ER_alpha.smi (439/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL1501269 in Train_ER_alpha.smi (440/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL1560367 in Train_ER_alpha.smi (441/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL1427879 in Train_ER_alpha.smi (442/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL1544241 in Train_ER_alpha.smi (443/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL577758 in Train_ER_alpha.smi (444/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL186597 in Train_ER_alpha.smi (445/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL145576 in Train_ER_alpha.smi (446/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL361258 in Train_ER_alpha.smi (447/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL363367 in Train_ER_alpha.smi (448/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL181369 in Train_ER_alpha.smi (449/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL57150 in Train_ER_alpha.smi (450/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL433944 in Train_ER_alpha.smi (451/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL371624 in Train_ER_alpha.smi (452/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL371788 in Train_ER_alpha.smi (454/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL188613 in Train_ER_alpha.smi (453/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL189200 in Train_ER_alpha.smi (455/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL186073 in Train_ER_alpha.smi (456/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL187087 in Train_ER_alpha.smi (457/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL200021 in Train_ER_alpha.smi (458/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL193010 in Train_ER_alpha.smi (459/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL363963 in Train_ER_alpha.smi (460/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL450940 in Train_ER_alpha.smi (461/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL365045 in Train_ER_alpha.smi (462/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL188410 in Train_ER_alpha.smi (463/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL201569 in Train_ER_alpha.smi (464/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL380876 in Train_ER_alpha.smi (465/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL377694 in Train_ER_alpha.smi (467/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL441537 in Train_ER_alpha.smi (466/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL192386 in Train_ER_alpha.smi (468/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL362020 in Train_ER_alpha.smi (469/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL183152 in Train_ER_alpha.smi (470/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL192587 in Train_ER_alpha.smi (471/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL370849 in Train_ER_alpha.smi (472/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL178679 in Train_ER_alpha.smi (473/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL185126 in Train_ER_alpha.smi (474/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL185918 in Train_ER_alpha.smi (475/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL264216 in Train_ER_alpha.smi (476/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL1337826 in Train_ER_alpha.smi (477/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL1480378 in Train_ER_alpha.smi (478/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL1481770 in Train_ER_alpha.smi (479/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL198679 in Train_ER_alpha.smi (480/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL121978 in Train_ER_alpha.smi (481/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL371009 in Train_ER_alpha.smi (482/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL334096 in Train_ER_alpha.smi (483/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL192539 in Train_ER_alpha.smi (484/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL186598 in Train_ER_alpha.smi (485/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL377224 in Train_ER_alpha.smi (487/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL180146 in Train_ER_alpha.smi (486/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL213051 in Train_ER_alpha.smi (488/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL377848 in Train_ER_alpha.smi (489/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL264349 in Train_ER_alpha.smi (490/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL378675 in Train_ER_alpha.smi (491/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL1402 in Train_ER_alpha.smi (492/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL369935 in Train_ER_alpha.smi (493/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL219126 in Train_ER_alpha.smi (495/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL216279 in Train_ER_alpha.smi (494/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL381566 in Train_ER_alpha.smi (496/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL209429 in Train_ER_alpha.smi (497/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL379821 in Train_ER_alpha.smi (499/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL379109 in Train_ER_alpha.smi (498/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL426854 in Train_ER_alpha.smi (500/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL202896 in Train_ER_alpha.smi (501/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL179518 in Train_ER_alpha.smi (502/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL187069 in Train_ER_alpha.smi (503/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL363425 in Train_ER_alpha.smi (504/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL597498 in Train_ER_alpha.smi (505/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL1116 in Train_ER_alpha.smi (506/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL206226 in Train_ER_alpha.smi (507/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL378990 in Train_ER_alpha.smi (508/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL211800 in Train_ER_alpha.smi (509/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL379567 in Train_ER_alpha.smi (510/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL379595 in Train_ER_alpha.smi (511/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL211870 in Train_ER_alpha.smi (512/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL187937 in Train_ER_alpha.smi (513/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL204768 in Train_ER_alpha.smi (514/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL380717 in Train_ER_alpha.smi (516/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL205350 in Train_ER_alpha.smi (515/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL206582 in Train_ER_alpha.smi (517/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL362355 in Train_ER_alpha.smi (518/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL221676 in Train_ER_alpha.smi (519/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL363955 in Train_ER_alpha.smi (520/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL206764 in Train_ER_alpha.smi (521/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL206131 in Train_ER_alpha.smi (522/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL381720 in Train_ER_alpha.smi (523/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL202894 in Train_ER_alpha.smi (524/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL206349 in Train_ER_alpha.smi (525/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL206773 in Train_ER_alpha.smi (526/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL379008 in Train_ER_alpha.smi (527/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL192016 in Train_ER_alpha.smi (528/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL191841 in Train_ER_alpha.smi (529/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL238960 in Train_ER_alpha.smi (530/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL238961 in Train_ER_alpha.smi (531/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL184360 in Train_ER_alpha.smi (532/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL180176 in Train_ER_alpha.smi (533/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL437695 in Train_ER_alpha.smi (534/1238). Average speed: 0.04 s/mol.\n"" + ] + }, + { + ""name"": ""stdout"", + ""output_type"": ""stream"", + ""text"": [ + ""Processing CHEMBL1276308 in Train_ER_alpha.smi (535/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1485005 in Train_ER_alpha.smi (536/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1521111 in Train_ER_alpha.smi (537/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1327977 in Train_ER_alpha.smi (538/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL388418 in Train_ER_alpha.smi (539/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL396948 in Train_ER_alpha.smi (540/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1433745 in Train_ER_alpha.smi (541/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1597831 in Train_ER_alpha.smi (543/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1475062 in Train_ER_alpha.smi (542/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL471627 in Train_ER_alpha.smi (544/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1362841 in Train_ER_alpha.smi (545/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1429150 in Train_ER_alpha.smi (546/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1548361 in Train_ER_alpha.smi (547/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL375334 in Train_ER_alpha.smi (548/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL222522 in Train_ER_alpha.smi (549/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL222580 in Train_ER_alpha.smi (550/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1449885 in Train_ER_alpha.smi (551/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1584646 in Train_ER_alpha.smi (552/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1506716 in Train_ER_alpha.smi (553/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1472407 in Train_ER_alpha.smi (554/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1454402 in Train_ER_alpha.smi (556/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1448466 in Train_ER_alpha.smi (555/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1431650 in Train_ER_alpha.smi (557/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1367590 in Train_ER_alpha.smi (558/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1587260 in Train_ER_alpha.smi (559/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1522044 in Train_ER_alpha.smi (560/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1461574 in Train_ER_alpha.smi (561/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1328182 in Train_ER_alpha.smi (562/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL250109 in Train_ER_alpha.smi (563/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL191716 in Train_ER_alpha.smi (564/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL194772 in Train_ER_alpha.smi (565/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL363177 in Train_ER_alpha.smi (567/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL362425 in Train_ER_alpha.smi (566/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL364329 in Train_ER_alpha.smi (568/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL361226 in Train_ER_alpha.smi (569/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL379543 in Train_ER_alpha.smi (570/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL361063 in Train_ER_alpha.smi (571/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL189822 in Train_ER_alpha.smi (572/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL192782 in Train_ER_alpha.smi (573/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL362307 in Train_ER_alpha.smi (575/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL192901 in Train_ER_alpha.smi (574/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL190487 in Train_ER_alpha.smi (576/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1479 in Train_ER_alpha.smi (577/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL104416 in Train_ER_alpha.smi (578/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL195991 in Train_ER_alpha.smi (579/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL190038 in Train_ER_alpha.smi (580/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL189303 in Train_ER_alpha.smi (581/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1627354 in Train_ER_alpha.smi (582/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL178665 in Train_ER_alpha.smi (583/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL435696 in Train_ER_alpha.smi (584/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL371564 in Train_ER_alpha.smi (585/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1628170 in Train_ER_alpha.smi (586/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL362349 in Train_ER_alpha.smi (587/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL954 in Train_ER_alpha.smi (588/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL184974 in Train_ER_alpha.smi (589/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1627353 in Train_ER_alpha.smi (590/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL196131 in Train_ER_alpha.smi (592/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL188846 in Train_ER_alpha.smi (591/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL372304 in Train_ER_alpha.smi (593/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL427180 in Train_ER_alpha.smi (594/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL194265 in Train_ER_alpha.smi (595/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL195788 in Train_ER_alpha.smi (596/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL369545 in Train_ER_alpha.smi (598/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL183344 in Train_ER_alpha.smi (597/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL361078 in Train_ER_alpha.smi (599/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL187393 in Train_ER_alpha.smi (600/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1200623 in Train_ER_alpha.smi (601/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL188051 in Train_ER_alpha.smi (603/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL194762 in Train_ER_alpha.smi (602/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL187392 in Train_ER_alpha.smi (604/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL418971 in Train_ER_alpha.smi (605/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL370282 in Train_ER_alpha.smi (607/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL184367 in Train_ER_alpha.smi (606/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL180702 in Train_ER_alpha.smi (608/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL103650 in Train_ER_alpha.smi (609/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL181936 in Train_ER_alpha.smi (610/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1411687 in Train_ER_alpha.smi (611/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1302802 in Train_ER_alpha.smi (612/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1521414 in Train_ER_alpha.smi (613/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1491602 in Train_ER_alpha.smi (614/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1341814 in Train_ER_alpha.smi (615/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1322601 in Train_ER_alpha.smi (616/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1604803 in Train_ER_alpha.smi (617/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1393131 in Train_ER_alpha.smi (618/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1541552 in Train_ER_alpha.smi (619/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL183782 in Train_ER_alpha.smi (621/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1350028 in Train_ER_alpha.smi (620/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1423880 in Train_ER_alpha.smi (622/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1493034 in Train_ER_alpha.smi (623/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1347829 in Train_ER_alpha.smi (624/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1310171 in Train_ER_alpha.smi (625/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1411209 in Train_ER_alpha.smi (626/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1507788 in Train_ER_alpha.smi (627/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL196583 in Train_ER_alpha.smi (628/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1505283 in Train_ER_alpha.smi (629/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1532659 in Train_ER_alpha.smi (630/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL189039 in Train_ER_alpha.smi (631/1238). Average speed: 0.04 s/mol.\n"" + ] + }, + { + ""name"": ""stdout"", + ""output_type"": ""stream"", + ""text"": [ + ""Processing CHEMBL187673 in Train_ER_alpha.smi (632/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL197757 in Train_ER_alpha.smi (633/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL191715 in Train_ER_alpha.smi (634/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL153395 in Train_ER_alpha.smi (636/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL122208 in Train_ER_alpha.smi (635/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL195289 in Train_ER_alpha.smi (637/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1381306 in Train_ER_alpha.smi (638/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1421864 in Train_ER_alpha.smi (639/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1560682 in Train_ER_alpha.smi (640/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1475434 in Train_ER_alpha.smi (641/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1434961 in Train_ER_alpha.smi (642/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1552932 in Train_ER_alpha.smi (643/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1593653 in Train_ER_alpha.smi (644/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1449110 in Train_ER_alpha.smi (645/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL192390 in Train_ER_alpha.smi (646/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL340606 in Train_ER_alpha.smi (647/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL371305 in Train_ER_alpha.smi (648/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL195280 in Train_ER_alpha.smi (649/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL193482 in Train_ER_alpha.smi (650/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1443090 in Train_ER_alpha.smi (651/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1519669 in Train_ER_alpha.smi (652/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1299357 in Train_ER_alpha.smi (653/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1581690 in Train_ER_alpha.smi (654/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1433062 in Train_ER_alpha.smi (655/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL125362 in Train_ER_alpha.smi (656/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL361871 in Train_ER_alpha.smi (657/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL482116 in Train_ER_alpha.smi (658/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL363872 in Train_ER_alpha.smi (660/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL190682 in Train_ER_alpha.smi (659/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL184421 in Train_ER_alpha.smi (661/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL205920 in Train_ER_alpha.smi (662/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL185383 in Train_ER_alpha.smi (663/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL226329 in Train_ER_alpha.smi (664/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL200799 in Train_ER_alpha.smi (665/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL371012 in Train_ER_alpha.smi (666/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL238890 in Train_ER_alpha.smi (668/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL362356 in Train_ER_alpha.smi (667/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL240157 in Train_ER_alpha.smi (669/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL511971 in Train_ER_alpha.smi (670/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL364419 in Train_ER_alpha.smi (671/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL391795 in Train_ER_alpha.smi (672/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL398455 in Train_ER_alpha.smi (673/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL430395 in Train_ER_alpha.smi (674/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL183044 in Train_ER_alpha.smi (675/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL189002 in Train_ER_alpha.smi (676/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL438256 in Train_ER_alpha.smi (677/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL204697 in Train_ER_alpha.smi (679/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL188957 in Train_ER_alpha.smi (678/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL203561 in Train_ER_alpha.smi (680/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL213529 in Train_ER_alpha.smi (682/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL211739 in Train_ER_alpha.smi (681/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL214200 in Train_ER_alpha.smi (683/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL187978 in Train_ER_alpha.smi (684/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL187219 in Train_ER_alpha.smi (685/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL394357 in Train_ER_alpha.smi (686/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL106773 in Train_ER_alpha.smi (689/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL242818 in Train_ER_alpha.smi (688/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL388473 in Train_ER_alpha.smi (687/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL192000 in Train_ER_alpha.smi (690/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL202781 in Train_ER_alpha.smi (691/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL198445 in Train_ER_alpha.smi (692/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL432454 in Train_ER_alpha.smi (693/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL184491 in Train_ER_alpha.smi (694/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL240438 in Train_ER_alpha.smi (696/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL393982 in Train_ER_alpha.smi (695/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL188246 in Train_ER_alpha.smi (697/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL346455 in Train_ER_alpha.smi (698/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL232022 in Train_ER_alpha.smi (699/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL206120 in Train_ER_alpha.smi (700/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL210074 in Train_ER_alpha.smi (701/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL209738 in Train_ER_alpha.smi (702/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL377254 in Train_ER_alpha.smi (703/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL213829 in Train_ER_alpha.smi (704/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL455215 in Train_ER_alpha.smi (705/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL522990 in Train_ER_alpha.smi (706/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL267431 in Train_ER_alpha.smi (707/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL469510 in Train_ER_alpha.smi (708/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL368688 in Train_ER_alpha.smi (709/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1076306 in Train_ER_alpha.smi (710/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL198680 in Train_ER_alpha.smi (711/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL125119 in Train_ER_alpha.smi (712/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL414859 in Train_ER_alpha.smi (713/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL378798 in Train_ER_alpha.smi (714/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL46903 in Train_ER_alpha.smi (715/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL195311 in Train_ER_alpha.smi (716/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1450791 in Train_ER_alpha.smi (717/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL203370 in Train_ER_alpha.smi (718/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL378572 in Train_ER_alpha.smi (719/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL206137 in Train_ER_alpha.smi (720/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1389780 in Train_ER_alpha.smi (721/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1324290 in Train_ER_alpha.smi (723/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1312693 in Train_ER_alpha.smi (722/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1377973 in Train_ER_alpha.smi (724/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1453264 in Train_ER_alpha.smi (725/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1533166 in Train_ER_alpha.smi (726/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1304227 in Train_ER_alpha.smi (727/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1329686 in Train_ER_alpha.smi (728/1238). Average speed: 0.04 s/mol.\n"" + ] + }, + { + ""name"": ""stdout"", + ""output_type"": ""stream"", + ""text"": [ + ""Processing CHEMBL1498999 in Train_ER_alpha.smi (729/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1386537 in Train_ER_alpha.smi (730/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1520373 in Train_ER_alpha.smi (731/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1452952 in Train_ER_alpha.smi (732/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1393976 in Train_ER_alpha.smi (733/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1419810 in Train_ER_alpha.smi (734/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1439181 in Train_ER_alpha.smi (736/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1531320 in Train_ER_alpha.smi (735/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1577757 in Train_ER_alpha.smi (738/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1484547 in Train_ER_alpha.smi (737/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1505467 in Train_ER_alpha.smi (739/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1372307 in Train_ER_alpha.smi (740/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1525366 in Train_ER_alpha.smi (741/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1482875 in Train_ER_alpha.smi (742/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1345810 in Train_ER_alpha.smi (743/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1374573 in Train_ER_alpha.smi (744/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1577661 in Train_ER_alpha.smi (745/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1370120 in Train_ER_alpha.smi (746/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1407054 in Train_ER_alpha.smi (747/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1548616 in Train_ER_alpha.smi (748/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1559731 in Train_ER_alpha.smi (749/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1555033 in Train_ER_alpha.smi (750/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1405182 in Train_ER_alpha.smi (751/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1455849 in Train_ER_alpha.smi (752/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1356953 in Train_ER_alpha.smi (753/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1597805 in Train_ER_alpha.smi (755/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1326803 in Train_ER_alpha.smi (754/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1084746 in Train_ER_alpha.smi (756/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1083326 in Train_ER_alpha.smi (757/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL426897 in Train_ER_alpha.smi (758/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL386502 in Train_ER_alpha.smi (759/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL223026 in Train_ER_alpha.smi (760/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL144897 in Train_ER_alpha.smi (761/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL433769 in Train_ER_alpha.smi (762/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL179852 in Train_ER_alpha.smi (763/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL183033 in Train_ER_alpha.smi (764/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL190025 in Train_ER_alpha.smi (765/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL198088 in Train_ER_alpha.smi (766/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1576748 in Train_ER_alpha.smi (767/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1388431 in Train_ER_alpha.smi (768/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1509933 in Train_ER_alpha.smi (769/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL360817 in Train_ER_alpha.smi (770/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL369724 in Train_ER_alpha.smi (771/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL52 in Train_ER_alpha.smi (772/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL195217 in Train_ER_alpha.smi (773/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1522446 in Train_ER_alpha.smi (774/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1434556 in Train_ER_alpha.smi (775/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1414407 in Train_ER_alpha.smi (776/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1397845 in Train_ER_alpha.smi (777/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL282575 in Train_ER_alpha.smi (778/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL361056 in Train_ER_alpha.smi (779/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1326483 in Train_ER_alpha.smi (781/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1491959 in Train_ER_alpha.smi (780/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL188516 in Train_ER_alpha.smi (782/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL178334 in Train_ER_alpha.smi (783/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL9352 in Train_ER_alpha.smi (784/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL198204 in Train_ER_alpha.smi (785/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL187311 in Train_ER_alpha.smi (786/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1580991 in Train_ER_alpha.smi (788/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL425194 in Train_ER_alpha.smi (787/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1300662 in Train_ER_alpha.smi (789/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1442450 in Train_ER_alpha.smi (790/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1539865 in Train_ER_alpha.smi (791/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1466928 in Train_ER_alpha.smi (792/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL182157 in Train_ER_alpha.smi (793/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1467399 in Train_ER_alpha.smi (794/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL192260 in Train_ER_alpha.smi (795/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1303477 in Train_ER_alpha.smi (796/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1350069 in Train_ER_alpha.smi (797/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL187313 in Train_ER_alpha.smi (798/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1391547 in Train_ER_alpha.smi (799/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1526157 in Train_ER_alpha.smi (800/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1572777 in Train_ER_alpha.smi (801/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1320937 in Train_ER_alpha.smi (802/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1582403 in Train_ER_alpha.smi (803/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1447311 in Train_ER_alpha.smi (804/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL189556 in Train_ER_alpha.smi (805/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1431081 in Train_ER_alpha.smi (806/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1421887 in Train_ER_alpha.smi (807/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1231453 in Train_ER_alpha.smi (808/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL204376 in Train_ER_alpha.smi (809/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL460647 in Train_ER_alpha.smi (810/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL216334 in Train_ER_alpha.smi (811/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL377295 in Train_ER_alpha.smi (812/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1325233 in Train_ER_alpha.smi (813/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1430316 in Train_ER_alpha.smi (814/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1574512 in Train_ER_alpha.smi (815/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL379314 in Train_ER_alpha.smi (816/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL385949 in Train_ER_alpha.smi (817/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL378883 in Train_ER_alpha.smi (818/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL215428 in Train_ER_alpha.smi (819/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL385028 in Train_ER_alpha.smi (820/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL212391 in Train_ER_alpha.smi (821/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL377457 in Train_ER_alpha.smi (822/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL214692 in Train_ER_alpha.smi (823/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL372783 in Train_ER_alpha.smi (824/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL398456 in Train_ER_alpha.smi (825/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL249904 in Train_ER_alpha.smi (826/1238). Average speed: 0.04 s/mol.\n"" + ] + }, + { + ""name"": ""stdout"", + ""output_type"": ""stream"", + ""text"": [ + ""Processing CHEMBL250110 in Train_ER_alpha.smi (827/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL380452 in Train_ER_alpha.smi (828/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL380358 in Train_ER_alpha.smi (829/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL380838 in Train_ER_alpha.smi (831/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL438443 in Train_ER_alpha.smi (830/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL188299 in Train_ER_alpha.smi (832/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL394614 in Train_ER_alpha.smi (833/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL469511 in Train_ER_alpha.smi (834/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1300008 in Train_ER_alpha.smi (835/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1531512 in Train_ER_alpha.smi (836/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1404523 in Train_ER_alpha.smi (837/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1386091 in Train_ER_alpha.smi (838/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL211953 in Train_ER_alpha.smi (839/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL214034 in Train_ER_alpha.smi (840/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL211666 in Train_ER_alpha.smi (841/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL206439 in Train_ER_alpha.smi (843/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL206500 in Train_ER_alpha.smi (842/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1307301 in Train_ER_alpha.smi (844/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL10560 in Train_ER_alpha.smi (845/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1470395 in Train_ER_alpha.smi (847/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL231810 in Train_ER_alpha.smi (846/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1320651 in Train_ER_alpha.smi (848/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1417890 in Train_ER_alpha.smi (849/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1429596 in Train_ER_alpha.smi (850/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1404155 in Train_ER_alpha.smi (851/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1442697 in Train_ER_alpha.smi (852/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1603599 in Train_ER_alpha.smi (853/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL203283 in Train_ER_alpha.smi (854/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL377083 in Train_ER_alpha.smi (855/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL206426 in Train_ER_alpha.smi (856/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL202738 in Train_ER_alpha.smi (857/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL205577 in Train_ER_alpha.smi (858/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL208324 in Train_ER_alpha.smi (859/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1471823 in Train_ER_alpha.smi (860/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL192621 in Train_ER_alpha.smi (861/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL192586 in Train_ER_alpha.smi (862/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL434577 in Train_ER_alpha.smi (863/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL465879 in Train_ER_alpha.smi (864/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL384708 in Train_ER_alpha.smi (865/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL221626 in Train_ER_alpha.smi (866/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL222452 in Train_ER_alpha.smi (867/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL372411 in Train_ER_alpha.smi (868/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL201365 in Train_ER_alpha.smi (869/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL199766 in Train_ER_alpha.smi (870/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL204317 in Train_ER_alpha.smi (871/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL425603 in Train_ER_alpha.smi (873/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL383253 in Train_ER_alpha.smi (872/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL212675 in Train_ER_alpha.smi (874/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL180995 in Train_ER_alpha.smi (875/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL203123 in Train_ER_alpha.smi (876/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL211599 in Train_ER_alpha.smi (877/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL213457 in Train_ER_alpha.smi (878/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL214291 in Train_ER_alpha.smi (879/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL240230 in Train_ER_alpha.smi (880/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL394213 in Train_ER_alpha.smi (881/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL193146 in Train_ER_alpha.smi (882/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL193108 in Train_ER_alpha.smi (883/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL182794 in Train_ER_alpha.smi (884/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL194873 in Train_ER_alpha.smi (885/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL187671 in Train_ER_alpha.smi (886/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL189287 in Train_ER_alpha.smi (887/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1732228 in Train_ER_alpha.smi (888/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL187663 in Train_ER_alpha.smi (889/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL198089 in Train_ER_alpha.smi (890/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL189706 in Train_ER_alpha.smi (891/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL185012 in Train_ER_alpha.smi (893/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL188230 in Train_ER_alpha.smi (892/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL122237 in Train_ER_alpha.smi (894/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL184679 in Train_ER_alpha.smi (896/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL361005 in Train_ER_alpha.smi (895/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1424468 in Train_ER_alpha.smi (897/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL187105 in Train_ER_alpha.smi (898/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL188528 in Train_ER_alpha.smi (900/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL276030 in Train_ER_alpha.smi (899/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL181368 in Train_ER_alpha.smi (901/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL195290 in Train_ER_alpha.smi (902/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL363412 in Train_ER_alpha.smi (903/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL186874 in Train_ER_alpha.smi (904/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL364176 in Train_ER_alpha.smi (905/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL365903 in Train_ER_alpha.smi (906/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL195389 in Train_ER_alpha.smi (907/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL365484 in Train_ER_alpha.smi (909/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL360189 in Train_ER_alpha.smi (908/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL189077 in Train_ER_alpha.smi (911/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL193207 in Train_ER_alpha.smi (910/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1627351 in Train_ER_alpha.smi (912/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL360338 in Train_ER_alpha.smi (913/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL440757 in Train_ER_alpha.smi (914/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL178857 in Train_ER_alpha.smi (915/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL188882 in Train_ER_alpha.smi (916/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL360385 in Train_ER_alpha.smi (917/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL182980 in Train_ER_alpha.smi (918/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL195039 in Train_ER_alpha.smi (919/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL180085 in Train_ER_alpha.smi (920/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL183162 in Train_ER_alpha.smi (921/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL427324 in Train_ER_alpha.smi (922/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1301994 in Train_ER_alpha.smi (924/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1596554 in Train_ER_alpha.smi (923/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL185837 in Train_ER_alpha.smi (925/1238). Average speed: 0.04 s/mol.\n"" + ] + }, + { + ""name"": ""stdout"", + ""output_type"": ""stream"", + ""text"": [ + ""Processing CHEMBL1466913 in Train_ER_alpha.smi (926/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL425246 in Train_ER_alpha.smi (927/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL380032 in Train_ER_alpha.smi (928/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL211349 in Train_ER_alpha.smi (929/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL377988 in Train_ER_alpha.smi (930/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1323006 in Train_ER_alpha.smi (931/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL234524 in Train_ER_alpha.smi (932/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL396947 in Train_ER_alpha.smi (933/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL212468 in Train_ER_alpha.smi (934/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL383518 in Train_ER_alpha.smi (935/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL204272 in Train_ER_alpha.smi (936/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL203340 in Train_ER_alpha.smi (937/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL380720 in Train_ER_alpha.smi (938/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL205614 in Train_ER_alpha.smi (939/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL204118 in Train_ER_alpha.smi (940/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL222453 in Train_ER_alpha.smi (941/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL577709 in Train_ER_alpha.smi (942/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL183889 in Train_ER_alpha.smi (944/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1566266 in Train_ER_alpha.smi (943/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL205398 in Train_ER_alpha.smi (945/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL379252 in Train_ER_alpha.smi (946/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL203122 in Train_ER_alpha.smi (947/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL225514 in Train_ER_alpha.smi (949/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL206286 in Train_ER_alpha.smi (948/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL222454 in Train_ER_alpha.smi (950/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL215593 in Train_ER_alpha.smi (951/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL391909 in Train_ER_alpha.smi (952/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL241256 in Train_ER_alpha.smi (953/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL241484 in Train_ER_alpha.smi (954/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL396119 in Train_ER_alpha.smi (955/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL372030 in Train_ER_alpha.smi (956/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL202400 in Train_ER_alpha.smi (957/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL437065 in Train_ER_alpha.smi (958/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL377847 in Train_ER_alpha.smi (959/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL226327 in Train_ER_alpha.smi (961/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL226268 in Train_ER_alpha.smi (960/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL239723 in Train_ER_alpha.smi (962/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL190467 in Train_ER_alpha.smi (963/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL192849 in Train_ER_alpha.smi (964/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL205119 in Train_ER_alpha.smi (965/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL208085 in Train_ER_alpha.smi (967/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL210143 in Train_ER_alpha.smi (966/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL209978 in Train_ER_alpha.smi (969/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL383112 in Train_ER_alpha.smi (968/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL213070 in Train_ER_alpha.smi (970/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL341367 in Train_ER_alpha.smi (971/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL426154 in Train_ER_alpha.smi (972/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL216596 in Train_ER_alpha.smi (973/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1628029 in Train_ER_alpha.smi (974/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL188517 in Train_ER_alpha.smi (975/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL197889 in Train_ER_alpha.smi (976/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL513397 in Train_ER_alpha.smi (977/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL121720 in Train_ER_alpha.smi (978/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1377389 in Train_ER_alpha.smi (979/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1516948 in Train_ER_alpha.smi (980/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1519384 in Train_ER_alpha.smi (981/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1330161 in Train_ER_alpha.smi (982/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1309506 in Train_ER_alpha.smi (983/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1162 in Train_ER_alpha.smi (984/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1507012 in Train_ER_alpha.smi (985/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1476347 in Train_ER_alpha.smi (987/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1468124 in Train_ER_alpha.smi (986/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1612880 in Train_ER_alpha.smi (988/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1359354 in Train_ER_alpha.smi (989/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1585431 in Train_ER_alpha.smi (990/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1501342 in Train_ER_alpha.smi (991/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1580543 in Train_ER_alpha.smi (992/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1448272 in Train_ER_alpha.smi (993/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1406018 in Train_ER_alpha.smi (994/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL245378 in Train_ER_alpha.smi (995/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1532234 in Train_ER_alpha.smi (996/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1312859 in Train_ER_alpha.smi (997/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL249700 in Train_ER_alpha.smi (998/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1584299 in Train_ER_alpha.smi (999/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL457035 in Train_ER_alpha.smi (1000/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1983345 in Train_ER_alpha.smi (1001/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1996233 in Train_ER_alpha.smi (1002/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1976499 in Train_ER_alpha.smi (1003/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1984703 in Train_ER_alpha.smi (1004/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL2103774 in Train_ER_alpha.smi (1005/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1999723 in Train_ER_alpha.smi (1006/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1972915 in Train_ER_alpha.smi (1007/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL2024377 in Train_ER_alpha.smi (1008/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL2024376 in Train_ER_alpha.smi (1009/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL2113002 in Train_ER_alpha.smi (1010/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL2112471 in Train_ER_alpha.smi (1011/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL2112469 in Train_ER_alpha.smi (1012/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL2113027 in Train_ER_alpha.smi (1014/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL2112470 in Train_ER_alpha.smi (1013/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL2113001 in Train_ER_alpha.smi (1015/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL2071180 in Train_ER_alpha.smi (1016/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL2403351 in Train_ER_alpha.smi (1017/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1208076 in Train_ER_alpha.smi (1018/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL2403361 in Train_ER_alpha.smi (1019/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL2403346 in Train_ER_alpha.smi (1021/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL2403363 in Train_ER_alpha.smi (1020/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL2403359 in Train_ER_alpha.smi (1022/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL2403358 in Train_ER_alpha.smi (1023/1238). Average speed: 0.04 s/mol.\n"" + ] + }, + { + ""name"": ""stdout"", + ""output_type"": ""stream"", + ""text"": [ + ""Processing CHEMBL2403360 in Train_ER_alpha.smi (1024/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL2401849 in Train_ER_alpha.smi (1025/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL2403362 in Train_ER_alpha.smi (1026/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL2401848 in Train_ER_alpha.smi (1027/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL2403348 in Train_ER_alpha.smi (1028/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL2407651 in Train_ER_alpha.smi (1029/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL2407647 in Train_ER_alpha.smi (1030/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL2403347 in Train_ER_alpha.smi (1031/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL2392777 in Train_ER_alpha.smi (1032/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL2441817 in Train_ER_alpha.smi (1033/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL2441818 in Train_ER_alpha.smi (1034/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL2441819 in Train_ER_alpha.smi (1035/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3099431 in Train_ER_alpha.smi (1036/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3099432 in Train_ER_alpha.smi (1037/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3098274 in Train_ER_alpha.smi (1038/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3099429 in Train_ER_alpha.smi (1040/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3098273 in Train_ER_alpha.smi (1039/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3145004 in Train_ER_alpha.smi (1041/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3098275 in Train_ER_alpha.smi (1042/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3208556 in Train_ER_alpha.smi (1044/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3208438 in Train_ER_alpha.smi (1043/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3189964 in Train_ER_alpha.smi (1045/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3199523 in Train_ER_alpha.smi (1047/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3190827 in Train_ER_alpha.smi (1046/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3189836 in Train_ER_alpha.smi (1048/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3213380 in Train_ER_alpha.smi (1049/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3109601 in Train_ER_alpha.smi (1050/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3109240 in Train_ER_alpha.smi (1051/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3109156 in Train_ER_alpha.smi (1052/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3109154 in Train_ER_alpha.smi (1053/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3189442 in Train_ER_alpha.smi (1054/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3189821 in Train_ER_alpha.smi (1055/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3198515 in Train_ER_alpha.smi (1056/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3193600 in Train_ER_alpha.smi (1057/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3207314 in Train_ER_alpha.smi (1058/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3214324 in Train_ER_alpha.smi (1059/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3212870 in Train_ER_alpha.smi (1060/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3208784 in Train_ER_alpha.smi (1061/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3235685 in Train_ER_alpha.smi (1062/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3235686 in Train_ER_alpha.smi (1063/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3195623 in Train_ER_alpha.smi (1064/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3196534 in Train_ER_alpha.smi (1065/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3360841 in Train_ER_alpha.smi (1066/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3290287 in Train_ER_alpha.smi (1067/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3290288 in Train_ER_alpha.smi (1068/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3288736 in Train_ER_alpha.smi (1069/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3427502 in Train_ER_alpha.smi (1070/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3360850 in Train_ER_alpha.smi (1071/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3586190 in Train_ER_alpha.smi (1073/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3581693 in Train_ER_alpha.smi (1072/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3586194 in Train_ER_alpha.smi (1074/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3586191 in Train_ER_alpha.smi (1075/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3360845 in Train_ER_alpha.smi (1076/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3586192 in Train_ER_alpha.smi (1077/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3586193 in Train_ER_alpha.smi (1078/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3586195 in Train_ER_alpha.smi (1079/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3360843 in Train_ER_alpha.smi (1080/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3360846 in Train_ER_alpha.smi (1081/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3360848 in Train_ER_alpha.smi (1082/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3589347 in Train_ER_alpha.smi (1083/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3589365 in Train_ER_alpha.smi (1084/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3589701 in Train_ER_alpha.smi (1085/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3589349 in Train_ER_alpha.smi (1086/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3589367 in Train_ER_alpha.smi (1087/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3578274 in Train_ER_alpha.smi (1088/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3578276 in Train_ER_alpha.smi (1089/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3578271 in Train_ER_alpha.smi (1090/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3589346 in Train_ER_alpha.smi (1091/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3589361 in Train_ER_alpha.smi (1092/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3589363 in Train_ER_alpha.smi (1093/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3589364 in Train_ER_alpha.smi (1094/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3588865 in Train_ER_alpha.smi (1095/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3589352 in Train_ER_alpha.smi (1096/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3589356 in Train_ER_alpha.smi (1097/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3597498 in Train_ER_alpha.smi (1098/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3597499 in Train_ER_alpha.smi (1099/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3597501 in Train_ER_alpha.smi (1100/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3597505 in Train_ER_alpha.smi (1101/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3597508 in Train_ER_alpha.smi (1102/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3597509 in Train_ER_alpha.smi (1103/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3589354 in Train_ER_alpha.smi (1104/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3589368 in Train_ER_alpha.smi (1105/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3597502 in Train_ER_alpha.smi (1106/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3360847 in Train_ER_alpha.smi (1107/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3360849 in Train_ER_alpha.smi (1108/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3427411 in Train_ER_alpha.smi (1109/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3427406 in Train_ER_alpha.smi (1110/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3578285 in Train_ER_alpha.smi (1111/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3578286 in Train_ER_alpha.smi (1112/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3576885 in Train_ER_alpha.smi (1113/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3578284 in Train_ER_alpha.smi (1114/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3622364 in Train_ER_alpha.smi (1115/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3622365 in Train_ER_alpha.smi (1116/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3657137 in Train_ER_alpha.smi (1117/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3657148 in Train_ER_alpha.smi (1118/1238). Average speed: 0.04 s/mol.\n"" + ] + }, + { + ""name"": ""stdout"", + ""output_type"": ""stream"", + ""text"": [ + ""Processing CHEMBL3657159 in Train_ER_alpha.smi (1119/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3657170 in Train_ER_alpha.smi (1120/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3657181 in Train_ER_alpha.smi (1121/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3667040 in Train_ER_alpha.smi (1122/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3657142 in Train_ER_alpha.smi (1123/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3657144 in Train_ER_alpha.smi (1125/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3657143 in Train_ER_alpha.smi (1124/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3657145 in Train_ER_alpha.smi (1126/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3657146 in Train_ER_alpha.smi (1127/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3657172 in Train_ER_alpha.smi (1128/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3657173 in Train_ER_alpha.smi (1129/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3657174 in Train_ER_alpha.smi (1130/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3657175 in Train_ER_alpha.smi (1131/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3657147 in Train_ER_alpha.smi (1132/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3657155 in Train_ER_alpha.smi (1134/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3657154 in Train_ER_alpha.smi (1133/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3657149 in Train_ER_alpha.smi (1135/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3657156 in Train_ER_alpha.smi (1137/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3657150 in Train_ER_alpha.smi (1136/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3657157 in Train_ER_alpha.smi (1138/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3657158 in Train_ER_alpha.smi (1139/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3657151 in Train_ER_alpha.smi (1140/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3657152 in Train_ER_alpha.smi (1141/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3657135 in Train_ER_alpha.smi (1143/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3657153 in Train_ER_alpha.smi (1142/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3657176 in Train_ER_alpha.smi (1144/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3657177 in Train_ER_alpha.smi (1145/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3657185 in Train_ER_alpha.smi (1147/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3657184 in Train_ER_alpha.smi (1146/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3657164 in Train_ER_alpha.smi (1148/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3657165 in Train_ER_alpha.smi (1149/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3648370 in Train_ER_alpha.smi (1150/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3657178 in Train_ER_alpha.smi (1151/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3657179 in Train_ER_alpha.smi (1152/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3657160 in Train_ER_alpha.smi (1153/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3657161 in Train_ER_alpha.smi (1154/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3657162 in Train_ER_alpha.smi (1155/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3657163 in Train_ER_alpha.smi (1156/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3657180 in Train_ER_alpha.smi (1157/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3657182 in Train_ER_alpha.smi (1158/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3657183 in Train_ER_alpha.smi (1159/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3657166 in Train_ER_alpha.smi (1160/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3657167 in Train_ER_alpha.smi (1161/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3657168 in Train_ER_alpha.smi (1162/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3657136 in Train_ER_alpha.smi (1163/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3657138 in Train_ER_alpha.smi (1164/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3657139 in Train_ER_alpha.smi (1165/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3657140 in Train_ER_alpha.smi (1166/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3657141 in Train_ER_alpha.smi (1167/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3657169 in Train_ER_alpha.smi (1168/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3657171 in Train_ER_alpha.smi (1169/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3775116 in Train_ER_alpha.smi (1170/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3774660 in Train_ER_alpha.smi (1171/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3775016 in Train_ER_alpha.smi (1172/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3775129 in Train_ER_alpha.smi (1174/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3774647 in Train_ER_alpha.smi (1173/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3775006 in Train_ER_alpha.smi (1175/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3775410 in Train_ER_alpha.smi (1176/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3775434 in Train_ER_alpha.smi (1177/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3775224 in Train_ER_alpha.smi (1178/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3775978 in Train_ER_alpha.smi (1179/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3775283 in Train_ER_alpha.smi (1180/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3775375 in Train_ER_alpha.smi (1181/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3774642 in Train_ER_alpha.smi (1182/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3805921 in Train_ER_alpha.smi (1183/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3805170 in Train_ER_alpha.smi (1184/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3804907 in Train_ER_alpha.smi (1185/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3805025 in Train_ER_alpha.smi (1186/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3805385 in Train_ER_alpha.smi (1187/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3823498 in Train_ER_alpha.smi (1188/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3815076 in Train_ER_alpha.smi (1189/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3823426 in Train_ER_alpha.smi (1190/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3827137 in Train_ER_alpha.smi (1192/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3827870 in Train_ER_alpha.smi (1191/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3827692 in Train_ER_alpha.smi (1193/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3827784 in Train_ER_alpha.smi (1195/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3827034 in Train_ER_alpha.smi (1194/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3828208 in Train_ER_alpha.smi (1197/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3827200 in Train_ER_alpha.smi (1196/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3827633 in Train_ER_alpha.smi (1198/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3827098 in Train_ER_alpha.smi (1199/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3828416 in Train_ER_alpha.smi (1200/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3827045 in Train_ER_alpha.smi (1201/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3827152 in Train_ER_alpha.smi (1202/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3828422 in Train_ER_alpha.smi (1203/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3827193 in Train_ER_alpha.smi (1204/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3827286 in Train_ER_alpha.smi (1205/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3828117 in Train_ER_alpha.smi (1206/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3823192 in Train_ER_alpha.smi (1207/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL180792 in Train_ER_alpha.smi (1209/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL101382 in Train_ER_alpha.smi (1208/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL183584 in Train_ER_alpha.smi (1210/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL184202 in Train_ER_alpha.smi (1211/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL191974 in Train_ER_alpha.smi (1212/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL192548 in Train_ER_alpha.smi (1213/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL193518 in Train_ER_alpha.smi (1214/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL209269 in Train_ER_alpha.smi (1215/1238). Average speed: 0.04 s/mol.\n"" + ] + }, + { + ""name"": ""stdout"", + ""output_type"": ""stream"", + ""text"": [ + ""Processing CHEMBL209851 in Train_ER_alpha.smi (1216/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL2403356 in Train_ER_alpha.smi (1217/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL2403357 in Train_ER_alpha.smi (1218/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL246138 in Train_ER_alpha.smi (1219/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL267385 in Train_ER_alpha.smi (1220/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL281499 in Train_ER_alpha.smi (1221/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL304552 in Train_ER_alpha.smi (1222/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL30707 in Train_ER_alpha.smi (1223/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL328190 in Train_ER_alpha.smi (1224/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3427402 in Train_ER_alpha.smi (1225/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3623002 in Train_ER_alpha.smi (1226/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3623003 in Train_ER_alpha.smi (1227/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3623004 in Train_ER_alpha.smi (1228/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL362623 in Train_ER_alpha.smi (1229/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL367588 in Train_ER_alpha.smi (1230/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3774510 in Train_ER_alpha.smi (1232/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL370039 in Train_ER_alpha.smi (1231/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3774737 in Train_ER_alpha.smi (1234/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3774584 in Train_ER_alpha.smi (1233/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3774944 in Train_ER_alpha.smi (1235/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3775766 in Train_ER_alpha.smi (1236/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL3775908 in Train_ER_alpha.smi (1237/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL691 in Train_ER_alpha.smi (1238/1238). Average speed: 0.04 s/mol.\n"", + ""Descriptor calculation completed in 43.560 secs . Average speed: 0.04 s/mol.\n"", + ""Train_ER_alpha.smi\n"", + ""Train_ER_alpha.csv\n"", + ""KlekotaRothFingerprinter.xml\n"", + ""KlekotaRothFingerprinter_\n"", + ""Processing CHEMBL370037 in Train_ER_alpha.smi (1/1238). \n"", + ""Processing CHEMBL189073 in Train_ER_alpha.smi (2/1238). \n"", + ""Processing CHEMBL365290 in Train_ER_alpha.smi (3/1238). \n"", + ""Processing CHEMBL191648 in Train_ER_alpha.smi (4/1238). \n"", + ""Processing CHEMBL184431 in Train_ER_alpha.smi (5/1238). Average speed: 7.58 s/mol.\n"", + ""Processing CHEMBL364551 in Train_ER_alpha.smi (6/1238). Average speed: 4.22 s/mol.\n"", + ""Processing CHEMBL124482 in Train_ER_alpha.smi (7/1238). Average speed: 3.09 s/mol.\n"", + ""Processing CHEMBL198410 in Train_ER_alpha.smi (8/1238). Average speed: 2.46 s/mol.\n"", + ""Processing CHEMBL1496978 in Train_ER_alpha.smi (9/1238). Average speed: 2.12 s/mol.\n"", + ""Processing CHEMBL1305485 in Train_ER_alpha.smi (10/1238). Average speed: 2.01 s/mol.\n"", + ""Processing CHEMBL1570659 in Train_ER_alpha.smi (11/1238). Average speed: 1.79 s/mol.\n"", + ""Processing CHEMBL1331084 in Train_ER_alpha.smi (12/1238). Average speed: 1.80 s/mol.\n"", + ""Processing CHEMBL184448 in Train_ER_alpha.smi (13/1238). Average speed: 1.62 s/mol.\n"", + ""Processing CHEMBL45068 in Train_ER_alpha.smi (14/1238). Average speed: 1.37 s/mol.\n"", + ""Processing CHEMBL434502 in Train_ER_alpha.smi (15/1238). Average speed: 1.26 s/mol.\n"", + ""Processing CHEMBL1628199 in Train_ER_alpha.smi (16/1238). Average speed: 1.25 s/mol.\n"", + ""Processing CHEMBL123025 in Train_ER_alpha.smi (17/1238). Average speed: 1.25 s/mol.\n"", + ""Processing CHEMBL153062 in Train_ER_alpha.smi (18/1238). Average speed: 1.09 s/mol.\n"", + ""Processing CHEMBL1468708 in Train_ER_alpha.smi (19/1238). Average speed: 1.08 s/mol.\n"", + ""Processing CHEMBL1442416 in Train_ER_alpha.smi (20/1238). Average speed: 1.05 s/mol.\n"", + ""Processing CHEMBL1388271 in Train_ER_alpha.smi (21/1238). Average speed: 1.00 s/mol.\n"", + ""Processing CHEMBL1331872 in Train_ER_alpha.smi (22/1238). Average speed: 1.02 s/mol.\n"", + ""Processing CHEMBL1401227 in Train_ER_alpha.smi (23/1238). Average speed: 0.95 s/mol.\n"", + ""Processing CHEMBL1460126 in Train_ER_alpha.smi (24/1238). Average speed: 0.92 s/mol.\n"", + ""Processing CHEMBL1499638 in Train_ER_alpha.smi (25/1238). Average speed: 0.93 s/mol.\n"", + ""Processing CHEMBL1413557 in Train_ER_alpha.smi (26/1238). Average speed: 0.88 s/mol.\n"", + ""Processing CHEMBL1484998 in Train_ER_alpha.smi (27/1238). Average speed: 0.84 s/mol.\n"", + ""Processing CHEMBL1608989 in Train_ER_alpha.smi (28/1238). Average speed: 0.86 s/mol.\n"", + ""Processing CHEMBL1469743 in Train_ER_alpha.smi (29/1238). Average speed: 0.83 s/mol.\n"", + ""Processing CHEMBL1538325 in Train_ER_alpha.smi (30/1238). Average speed: 0.80 s/mol.\n"", + ""Processing CHEMBL1605393 in Train_ER_alpha.smi (31/1238). Average speed: 0.79 s/mol.\n"", + ""Processing CHEMBL1384049 in Train_ER_alpha.smi (32/1238). Average speed: 0.81 s/mol.\n"", + ""Processing CHEMBL188049 in Train_ER_alpha.smi (33/1238). Average speed: 0.79 s/mol.\n"", + ""Processing CHEMBL1361641 in Train_ER_alpha.smi (34/1238). Average speed: 0.77 s/mol.\n"", + ""Processing CHEMBL1489063 in Train_ER_alpha.smi (35/1238). Average speed: 0.76 s/mol.\n"", + ""Processing CHEMBL1381495 in Train_ER_alpha.smi (36/1238). Average speed: 0.74 s/mol.\n"", + ""Processing CHEMBL1343011 in Train_ER_alpha.smi (37/1238). Average speed: 0.74 s/mol.\n"", + ""Processing CHEMBL1613068 in Train_ER_alpha.smi (38/1238). Average speed: 0.73 s/mol.\n"", + ""Processing CHEMBL1464837 in Train_ER_alpha.smi (39/1238). Average speed: 0.72 s/mol.\n"", + ""Processing CHEMBL1349347 in Train_ER_alpha.smi (40/1238). Average speed: 0.73 s/mol.\n"", + ""Processing CHEMBL1385373 in Train_ER_alpha.smi (41/1238). Average speed: 0.72 s/mol.\n"", + ""Processing CHEMBL1573735 in Train_ER_alpha.smi (42/1238). Average speed: 0.71 s/mol.\n"", + ""Processing CHEMBL1455258 in Train_ER_alpha.smi (43/1238). Average speed: 0.70 s/mol.\n"", + ""Processing CHEMBL1431555 in Train_ER_alpha.smi (44/1238). Average speed: 0.70 s/mol.\n"", + ""Processing CHEMBL1565187 in Train_ER_alpha.smi (45/1238). Average speed: 0.70 s/mol.\n"", + ""Processing CHEMBL1597933 in Train_ER_alpha.smi (46/1238). Average speed: 0.68 s/mol.\n"", + ""Processing CHEMBL1466882 in Train_ER_alpha.smi (47/1238). Average speed: 0.68 s/mol.\n"", + ""Processing CHEMBL1313979 in Train_ER_alpha.smi (48/1238). Average speed: 0.68 s/mol.\n"", + ""Processing CHEMBL1329455 in Train_ER_alpha.smi (49/1238). Average speed: 0.68 s/mol.\n"", + ""Processing CHEMBL1322939 in Train_ER_alpha.smi (50/1238). Average speed: 0.67 s/mol.\n"", + ""Processing CHEMBL1480843 in Train_ER_alpha.smi (51/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL1310614 in Train_ER_alpha.smi (52/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL1535728 in Train_ER_alpha.smi (53/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL1341057 in Train_ER_alpha.smi (54/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL1420411 in Train_ER_alpha.smi (55/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL184372 in Train_ER_alpha.smi (56/1238). Average speed: 0.64 s/mol.\n"", + ""Processing CHEMBL442037 in Train_ER_alpha.smi (57/1238). Average speed: 0.64 s/mol.\n"", + ""Processing CHEMBL512579 in Train_ER_alpha.smi (58/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL124474 in Train_ER_alpha.smi (59/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL231811 in Train_ER_alpha.smi (60/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL385801 in Train_ER_alpha.smi (61/1238). Average speed: 0.66 s/mol.\n"", + ""Processing CHEMBL447111 in Train_ER_alpha.smi (62/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL427496 in Train_ER_alpha.smi (63/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL225404 in Train_ER_alpha.smi (64/1238). Average speed: 0.64 s/mol.\n"", + ""Processing CHEMBL226269 in Train_ER_alpha.smi (65/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL1081361 in Train_ER_alpha.smi (66/1238). Average speed: 0.64 s/mol.\n"", + ""Processing CHEMBL235497 in Train_ER_alpha.smi (68/1238). Average speed: 0.64 s/mol.\n"", + ""Processing CHEMBL235496 in Train_ER_alpha.smi (67/1238). Average speed: 0.64 s/mol.\n"", + ""Processing CHEMBL361762 in Train_ER_alpha.smi (69/1238). Average speed: 0.66 s/mol.\n"", + ""Processing CHEMBL184371 in Train_ER_alpha.smi (70/1238). Average speed: 0.67 s/mol.\n"", + ""Processing CHEMBL188458 in Train_ER_alpha.smi (71/1238). Average speed: 0.66 s/mol.\n"", + ""Processing CHEMBL361272 in Train_ER_alpha.smi (72/1238). Average speed: 0.66 s/mol.\n"", + ""Processing CHEMBL241257 in Train_ER_alpha.smi (73/1238). Average speed: 0.66 s/mol.\n"" + ] + }, + { + ""name"": ""stdout"", + ""output_type"": ""stream"", + ""text"": [ + ""Processing CHEMBL396118 in Train_ER_alpha.smi (74/1238). Average speed: 0.66 s/mol.\n"", + ""Processing CHEMBL59438 in Train_ER_alpha.smi (75/1238). Average speed: 0.67 s/mol.\n"", + ""Processing CHEMBL150279 in Train_ER_alpha.smi (76/1238). Average speed: 0.68 s/mol.\n"", + ""Processing CHEMBL365625 in Train_ER_alpha.smi (77/1238). Average speed: 0.68 s/mol.\n"", + ""Processing CHEMBL365016 in Train_ER_alpha.smi (78/1238). Average speed: 0.68 s/mol.\n"", + ""Processing CHEMBL179929 in Train_ER_alpha.smi (79/1238). Average speed: 0.68 s/mol.\n"", + ""Processing CHEMBL470993 in Train_ER_alpha.smi (80/1238). Average speed: 0.68 s/mol.\n"", + ""Processing CHEMBL511269 in Train_ER_alpha.smi (81/1238). Average speed: 0.68 s/mol.\n"", + ""Processing CHEMBL469512 in Train_ER_alpha.smi (82/1238). Average speed: 0.70 s/mol.\n"", + ""Processing CHEMBL470785 in Train_ER_alpha.smi (83/1238). Average speed: 0.70 s/mol.\n"", + ""Processing CHEMBL577938 in Train_ER_alpha.smi (84/1238). Average speed: 0.70 s/mol.\n"", + ""Processing CHEMBL185114 in Train_ER_alpha.smi (85/1238). Average speed: 0.69 s/mol.\n"", + ""Processing CHEMBL377171 in Train_ER_alpha.smi (86/1238). Average speed: 0.69 s/mol.\n"", + ""Processing CHEMBL385680 in Train_ER_alpha.smi (87/1238). Average speed: 0.69 s/mol.\n"", + ""Processing CHEMBL204902 in Train_ER_alpha.smi (88/1238). Average speed: 0.69 s/mol.\n"", + ""Processing CHEMBL206727 in Train_ER_alpha.smi (89/1238). Average speed: 0.69 s/mol.\n"", + ""Processing CHEMBL380362 in Train_ER_alpha.smi (90/1238). Average speed: 0.69 s/mol.\n"", + ""Processing CHEMBL471626 in Train_ER_alpha.smi (92/1238). Average speed: 0.69 s/mol.\n"", + ""Processing CHEMBL206547 in Train_ER_alpha.smi (91/1238). Average speed: 0.69 s/mol.\n"", + ""Processing CHEMBL511492 in Train_ER_alpha.smi (93/1238). Average speed: 0.68 s/mol.\n"", + ""Processing CHEMBL196836 in Train_ER_alpha.smi (94/1238). Average speed: 0.67 s/mol.\n"", + ""Processing CHEMBL25228 in Train_ER_alpha.smi (95/1238). Average speed: 0.68 s/mol.\n"", + ""Processing CHEMBL194326 in Train_ER_alpha.smi (96/1238). Average speed: 0.69 s/mol.\n"", + ""Processing CHEMBL203330 in Train_ER_alpha.smi (97/1238). Average speed: 0.69 s/mol.\n"", + ""Processing CHEMBL206648 in Train_ER_alpha.smi (98/1238). Average speed: 0.69 s/mol.\n"", + ""Processing CHEMBL425231 in Train_ER_alpha.smi (99/1238). Average speed: 0.69 s/mol.\n"", + ""Processing CHEMBL206336 in Train_ER_alpha.smi (100/1238). Average speed: 0.68 s/mol.\n"", + ""Processing CHEMBL205137 in Train_ER_alpha.smi (101/1238). Average speed: 0.68 s/mol.\n"", + ""Processing CHEMBL425941 in Train_ER_alpha.smi (102/1238). Average speed: 0.68 s/mol.\n"", + ""Processing CHEMBL206499 in Train_ER_alpha.smi (103/1238). Average speed: 0.68 s/mol.\n"", + ""Processing CHEMBL429446 in Train_ER_alpha.smi (104/1238). Average speed: 0.68 s/mol.\n"", + ""Processing CHEMBL191300 in Train_ER_alpha.smi (105/1238). Average speed: 0.68 s/mol.\n"", + ""Processing CHEMBL447837 in Train_ER_alpha.smi (106/1238). Average speed: 0.67 s/mol.\n"", + ""Processing CHEMBL187314 in Train_ER_alpha.smi (107/1238). Average speed: 0.67 s/mol.\n"", + ""Processing CHEMBL371308 in Train_ER_alpha.smi (108/1238). Average speed: 0.68 s/mol.\n"", + ""Processing CHEMBL191710 in Train_ER_alpha.smi (109/1238). Average speed: 0.68 s/mol.\n"", + ""Processing CHEMBL426085 in Train_ER_alpha.smi (110/1238). Average speed: 0.68 s/mol.\n"", + ""Processing CHEMBL200719 in Train_ER_alpha.smi (111/1238). Average speed: 0.68 s/mol.\n"", + ""Processing CHEMBL212419 in Train_ER_alpha.smi (112/1238). Average speed: 0.68 s/mol.\n"", + ""Processing CHEMBL211493 in Train_ER_alpha.smi (113/1238). Average speed: 0.68 s/mol.\n"", + ""Processing CHEMBL377475 in Train_ER_alpha.smi (114/1238). Average speed: 0.68 s/mol.\n"", + ""Processing CHEMBL204024 in Train_ER_alpha.smi (115/1238). Average speed: 0.68 s/mol.\n"", + ""Processing CHEMBL183333 in Train_ER_alpha.smi (116/1238). Average speed: 0.67 s/mol.\n"", + ""Processing CHEMBL185231 in Train_ER_alpha.smi (117/1238). Average speed: 0.67 s/mol.\n"", + ""Processing CHEMBL378473 in Train_ER_alpha.smi (118/1238). Average speed: 0.67 s/mol.\n"", + ""Processing CHEMBL382734 in Train_ER_alpha.smi (119/1238). Average speed: 0.67 s/mol.\n"", + ""Processing CHEMBL195807 in Train_ER_alpha.smi (120/1238). Average speed: 0.67 s/mol.\n"", + ""Processing CHEMBL212185 in Train_ER_alpha.smi (121/1238). Average speed: 0.68 s/mol.\n"", + ""Processing CHEMBL198914 in Train_ER_alpha.smi (122/1238). Average speed: 0.67 s/mol.\n"", + ""Processing CHEMBL372808 in Train_ER_alpha.smi (123/1238). Average speed: 0.67 s/mol.\n"", + ""Processing CHEMBL197495 in Train_ER_alpha.smi (124/1238). Average speed: 0.67 s/mol.\n"", + ""Processing CHEMBL191901 in Train_ER_alpha.smi (125/1238). Average speed: 0.67 s/mol.\n"", + ""Processing CHEMBL241304 in Train_ER_alpha.smi (126/1238). Average speed: 0.68 s/mol.\n"", + ""Processing CHEMBL241485 in Train_ER_alpha.smi (127/1238). Average speed: 0.68 s/mol.\n"", + ""Processing CHEMBL437190 in Train_ER_alpha.smi (128/1238). Average speed: 0.67 s/mol.\n"", + ""Processing CHEMBL182402 in Train_ER_alpha.smi (129/1238). Average speed: 0.68 s/mol.\n"", + ""Processing CHEMBL188391 in Train_ER_alpha.smi (130/1238). Average speed: 0.69 s/mol.\n"", + ""Processing CHEMBL184598 in Train_ER_alpha.smi (131/1238). Average speed: 0.69 s/mol.\n"", + ""Processing CHEMBL360315 in Train_ER_alpha.smi (132/1238). Average speed: 0.69 s/mol.\n"", + ""Processing CHEMBL371777 in Train_ER_alpha.smi (133/1238). Average speed: 0.69 s/mol.\n"", + ""Processing CHEMBL126164 in Train_ER_alpha.smi (134/1238). Average speed: 0.69 s/mol.\n"", + ""Processing CHEMBL195786 in Train_ER_alpha.smi (135/1238). Average speed: 0.70 s/mol.\n"", + ""Processing CHEMBL219134 in Train_ER_alpha.smi (136/1238). Average speed: 0.70 s/mol.\n"", + ""Processing CHEMBL204119 in Train_ER_alpha.smi (137/1238). Average speed: 0.70 s/mol.\n"", + ""Processing CHEMBL204222 in Train_ER_alpha.smi (138/1238). Average speed: 0.70 s/mol.\n"", + ""Processing CHEMBL212392 in Train_ER_alpha.smi (139/1238). Average speed: 0.70 s/mol.\n"", + ""Processing CHEMBL441366 in Train_ER_alpha.smi (140/1238). Average speed: 0.70 s/mol.\n"", + ""Processing CHEMBL384709 in Train_ER_alpha.smi (141/1238). Average speed: 0.70 s/mol.\n"", + ""Processing CHEMBL189080 in Train_ER_alpha.smi (142/1238). Average speed: 0.69 s/mol.\n"", + ""Processing CHEMBL180707 in Train_ER_alpha.smi (143/1238). Average speed: 0.69 s/mol.\n"", + ""Processing CHEMBL362272 in Train_ER_alpha.smi (144/1238). Average speed: 0.69 s/mol.\n"", + ""Processing CHEMBL438516 in Train_ER_alpha.smi (145/1238). Average speed: 0.68 s/mol.\n"", + ""Processing CHEMBL234733 in Train_ER_alpha.smi (146/1238). Average speed: 0.69 s/mol.\n"", + ""Processing CHEMBL206441 in Train_ER_alpha.smi (147/1238). Average speed: 0.69 s/mol.\n"", + ""Processing CHEMBL379188 in Train_ER_alpha.smi (148/1238). Average speed: 0.69 s/mol.\n"", + ""Processing CHEMBL425439 in Train_ER_alpha.smi (149/1238). Average speed: 0.68 s/mol.\n"", + ""Processing CHEMBL192102 in Train_ER_alpha.smi (150/1238). Average speed: 0.69 s/mol.\n"", + ""Processing CHEMBL395308 in Train_ER_alpha.smi (151/1238). Average speed: 0.68 s/mol.\n"", + ""Processing CHEMBL394788 in Train_ER_alpha.smi (152/1238). Average speed: 0.68 s/mol.\n"", + ""Processing CHEMBL245133 in Train_ER_alpha.smi (153/1238). Average speed: 0.69 s/mol.\n"", + ""Processing CHEMBL234527 in Train_ER_alpha.smi (154/1238). Average speed: 0.68 s/mol.\n"", + ""Processing CHEMBL195208 in Train_ER_alpha.smi (155/1238). Average speed: 0.69 s/mol.\n"", + ""Processing CHEMBL8145 in Train_ER_alpha.smi (156/1238). Average speed: 0.68 s/mol.\n"", + ""Processing CHEMBL195437 in Train_ER_alpha.smi (157/1238). Average speed: 0.68 s/mol.\n"", + ""Processing CHEMBL435090 in Train_ER_alpha.smi (158/1238). Average speed: 0.68 s/mol.\n"", + ""Processing CHEMBL1201151 in Train_ER_alpha.smi (159/1238). Average speed: 0.68 s/mol.\n"", + ""Processing CHEMBL366928 in Train_ER_alpha.smi (160/1238). Average speed: 0.68 s/mol.\n"", + ""Processing CHEMBL192068 in Train_ER_alpha.smi (161/1238). Average speed: 0.68 s/mol.\n"", + ""Processing CHEMBL360651 in Train_ER_alpha.smi (162/1238). Average speed: 0.67 s/mol.\n"", + ""Processing CHEMBL195878 in Train_ER_alpha.smi (163/1238). Average speed: 0.67 s/mol.\n"", + ""Processing CHEMBL182237 in Train_ER_alpha.smi (164/1238). Average speed: 0.67 s/mol.\n"", + ""Processing CHEMBL365293 in Train_ER_alpha.smi (165/1238). Average speed: 0.67 s/mol.\n"", + ""Processing CHEMBL183086 in Train_ER_alpha.smi (166/1238). Average speed: 0.67 s/mol.\n"", + ""Processing CHEMBL374799 in Train_ER_alpha.smi (168/1238). Average speed: 0.67 s/mol.\n"", + ""Processing CHEMBL426641 in Train_ER_alpha.smi (167/1238). Average speed: 0.67 s/mol.\n"", + ""Processing CHEMBL225349 in Train_ER_alpha.smi (169/1238). Average speed: 0.68 s/mol.\n"", + ""Processing CHEMBL222434 in Train_ER_alpha.smi (170/1238). Average speed: 0.67 s/mol.\n"" + ] + }, + { + ""name"": ""stdout"", + ""output_type"": ""stream"", + ""text"": [ + ""Processing CHEMBL373743 in Train_ER_alpha.smi (171/1238). Average speed: 0.68 s/mol.\n"", + ""Processing CHEMBL451496 in Train_ER_alpha.smi (172/1238). Average speed: 0.67 s/mol.\n"", + ""Processing CHEMBL186643 in Train_ER_alpha.smi (173/1238). Average speed: 0.67 s/mol.\n"", + ""Processing CHEMBL362718 in Train_ER_alpha.smi (174/1238). Average speed: 0.67 s/mol.\n"", + ""Processing CHEMBL418327 in Train_ER_alpha.smi (175/1238). Average speed: 0.67 s/mol.\n"", + ""Processing CHEMBL187207 in Train_ER_alpha.smi (176/1238). Average speed: 0.67 s/mol.\n"", + ""Processing CHEMBL181248 in Train_ER_alpha.smi (177/1238). Average speed: 0.67 s/mol.\n"", + ""Processing CHEMBL183371 in Train_ER_alpha.smi (178/1238). Average speed: 0.67 s/mol.\n"", + ""Processing CHEMBL439806 in Train_ER_alpha.smi (179/1238). Average speed: 0.67 s/mol.\n"", + ""Processing CHEMBL121879 in Train_ER_alpha.smi (180/1238). Average speed: 0.67 s/mol.\n"", + ""Processing CHEMBL202996 in Train_ER_alpha.smi (181/1238). Average speed: 0.67 s/mol.\n"", + ""Processing CHEMBL363705 in Train_ER_alpha.smi (182/1238). Average speed: 0.67 s/mol.\n"", + ""Processing CHEMBL373095 in Train_ER_alpha.smi (183/1238). Average speed: 0.67 s/mol.\n"", + ""Processing CHEMBL185480 in Train_ER_alpha.smi (184/1238). Average speed: 0.67 s/mol.\n"", + ""Processing CHEMBL389683 in Train_ER_alpha.smi (185/1238). Average speed: 0.67 s/mol.\n"", + ""Processing CHEMBL449038 in Train_ER_alpha.smi (186/1238). Average speed: 0.67 s/mol.\n"", + ""Processing CHEMBL225022 in Train_ER_alpha.smi (187/1238). Average speed: 0.67 s/mol.\n"", + ""Processing CHEMBL389393 in Train_ER_alpha.smi (188/1238). Average speed: 0.67 s/mol.\n"", + ""Processing CHEMBL360802 in Train_ER_alpha.smi (189/1238). Average speed: 0.67 s/mol.\n"", + ""Processing CHEMBL332625 in Train_ER_alpha.smi (190/1238). Average speed: 0.67 s/mol.\n"", + ""Processing CHEMBL194936 in Train_ER_alpha.smi (191/1238). Average speed: 0.67 s/mol.\n"", + ""Processing CHEMBL192154 in Train_ER_alpha.smi (192/1238). Average speed: 0.67 s/mol.\n"", + ""Processing CHEMBL369991 in Train_ER_alpha.smi (193/1238). Average speed: 0.67 s/mol.\n"", + ""Processing CHEMBL377342 in Train_ER_alpha.smi (194/1238). Average speed: 0.67 s/mol.\n"", + ""Processing CHEMBL381298 in Train_ER_alpha.smi (195/1238). Average speed: 0.67 s/mol.\n"", + ""Processing CHEMBL204025 in Train_ER_alpha.smi (196/1238). Average speed: 0.67 s/mol.\n"", + ""Processing CHEMBL202615 in Train_ER_alpha.smi (197/1238). Average speed: 0.67 s/mol.\n"", + ""Processing CHEMBL201013 in Train_ER_alpha.smi (198/1238). Average speed: 0.67 s/mol.\n"", + ""Processing CHEMBL385279 in Train_ER_alpha.smi (199/1238). Average speed: 0.66 s/mol.\n"", + ""Processing CHEMBL219387 in Train_ER_alpha.smi (200/1238). Average speed: 0.66 s/mol.\n"", + ""Processing CHEMBL85826 in Train_ER_alpha.smi (201/1238). Average speed: 0.66 s/mol.\n"", + ""Processing CHEMBL195371 in Train_ER_alpha.smi (202/1238). Average speed: 0.66 s/mol.\n"", + ""Processing CHEMBL187915 in Train_ER_alpha.smi (203/1238). Average speed: 0.67 s/mol.\n"", + ""Processing CHEMBL364092 in Train_ER_alpha.smi (204/1238). Average speed: 0.66 s/mol.\n"", + ""Processing CHEMBL193726 in Train_ER_alpha.smi (205/1238). Average speed: 0.66 s/mol.\n"", + ""Processing CHEMBL192218 in Train_ER_alpha.smi (206/1238). Average speed: 0.66 s/mol.\n"", + ""Processing CHEMBL441499 in Train_ER_alpha.smi (207/1238). Average speed: 0.66 s/mol.\n"", + ""Processing CHEMBL183084 in Train_ER_alpha.smi (208/1238). Average speed: 0.66 s/mol.\n"", + ""Processing CHEMBL187000 in Train_ER_alpha.smi (209/1238). Average speed: 0.66 s/mol.\n"", + ""Processing CHEMBL369979 in Train_ER_alpha.smi (210/1238). Average speed: 0.66 s/mol.\n"", + ""Processing CHEMBL381299 in Train_ER_alpha.smi (211/1238). Average speed: 0.66 s/mol.\n"", + ""Processing CHEMBL206584 in Train_ER_alpha.smi (212/1238). Average speed: 0.67 s/mol.\n"", + ""Processing CHEMBL212923 in Train_ER_alpha.smi (213/1238). Average speed: 0.66 s/mol.\n"", + ""Processing CHEMBL385358 in Train_ER_alpha.smi (214/1238). Average speed: 0.66 s/mol.\n"", + ""Processing CHEMBL383533 in Train_ER_alpha.smi (215/1238). Average speed: 0.66 s/mol.\n"", + ""Processing CHEMBL219577 in Train_ER_alpha.smi (216/1238). Average speed: 0.67 s/mol.\n"", + ""Processing CHEMBL241303 in Train_ER_alpha.smi (217/1238). Average speed: 0.67 s/mol.\n"", + ""Processing CHEMBL240227 in Train_ER_alpha.smi (218/1238). Average speed: 0.67 s/mol.\n"", + ""Processing CHEMBL393278 in Train_ER_alpha.smi (219/1238). Average speed: 0.67 s/mol.\n"", + ""Processing CHEMBL362428 in Train_ER_alpha.smi (220/1238). Average speed: 0.67 s/mol.\n"", + ""Processing CHEMBL496 in Train_ER_alpha.smi (221/1238). Average speed: 0.67 s/mol.\n"", + ""Processing CHEMBL434525 in Train_ER_alpha.smi (222/1238). Average speed: 0.68 s/mol.\n"", + ""Processing CHEMBL365999 in Train_ER_alpha.smi (223/1238). Average speed: 0.68 s/mol.\n"", + ""Processing CHEMBL198411 in Train_ER_alpha.smi (224/1238). Average speed: 0.68 s/mol.\n"", + ""Processing CHEMBL186945 in Train_ER_alpha.smi (225/1238). Average speed: 0.69 s/mol.\n"", + ""Processing CHEMBL197667 in Train_ER_alpha.smi (226/1238). Average speed: 0.69 s/mol.\n"", + ""Processing CHEMBL382632 in Train_ER_alpha.smi (227/1238). Average speed: 0.68 s/mol.\n"", + ""Processing CHEMBL206440 in Train_ER_alpha.smi (228/1238). Average speed: 0.68 s/mol.\n"", + ""Processing CHEMBL383753 in Train_ER_alpha.smi (230/1238). Average speed: 0.68 s/mol.\n"", + ""Processing CHEMBL380844 in Train_ER_alpha.smi (229/1238). Average speed: 0.68 s/mol.\n"", + ""Processing CHEMBL206406 in Train_ER_alpha.smi (231/1238). Average speed: 0.68 s/mol.\n"", + ""Processing CHEMBL204056 in Train_ER_alpha.smi (232/1238). Average speed: 0.68 s/mol.\n"", + ""Processing CHEMBL381697 in Train_ER_alpha.smi (233/1238). Average speed: 0.68 s/mol.\n"", + ""Processing CHEMBL204922 in Train_ER_alpha.smi (234/1238). Average speed: 0.68 s/mol.\n"", + ""Processing CHEMBL206600 in Train_ER_alpha.smi (235/1238). Average speed: 0.68 s/mol.\n"", + ""Processing CHEMBL206227 in Train_ER_alpha.smi (236/1238). Average speed: 0.67 s/mol.\n"", + ""Processing CHEMBL211816 in Train_ER_alpha.smi (237/1238). Average speed: 0.67 s/mol.\n"", + ""Processing CHEMBL208929 in Train_ER_alpha.smi (238/1238). Average speed: 0.67 s/mol.\n"", + ""Processing CHEMBL209623 in Train_ER_alpha.smi (239/1238). Average speed: 0.67 s/mol.\n"", + ""Processing CHEMBL211887 in Train_ER_alpha.smi (240/1238). Average speed: 0.67 s/mol.\n"", + ""Processing CHEMBL191714 in Train_ER_alpha.smi (241/1238). Average speed: 0.67 s/mol.\n"", + ""Processing CHEMBL188792 in Train_ER_alpha.smi (242/1238). Average speed: 0.66 s/mol.\n"", + ""Processing CHEMBL204819 in Train_ER_alpha.smi (243/1238). Average speed: 0.66 s/mol.\n"", + ""Processing CHEMBL206546 in Train_ER_alpha.smi (244/1238). Average speed: 0.66 s/mol.\n"", + ""Processing CHEMBL205934 in Train_ER_alpha.smi (245/1238). Average speed: 0.66 s/mol.\n"", + ""Processing CHEMBL381337 in Train_ER_alpha.smi (246/1238). Average speed: 0.66 s/mol.\n"", + ""Processing CHEMBL203442 in Train_ER_alpha.smi (247/1238). Average speed: 0.66 s/mol.\n"", + ""Processing CHEMBL206814 in Train_ER_alpha.smi (248/1238). Average speed: 0.66 s/mol.\n"", + ""Processing CHEMBL203351 in Train_ER_alpha.smi (249/1238). Average speed: 0.66 s/mol.\n"", + ""Processing CHEMBL382698 in Train_ER_alpha.smi (250/1238). Average speed: 0.66 s/mol.\n"", + ""Processing CHEMBL191238 in Train_ER_alpha.smi (251/1238). Average speed: 0.66 s/mol.\n"", + ""Processing CHEMBL364502 in Train_ER_alpha.smi (252/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL331996 in Train_ER_alpha.smi (253/1238). Average speed: 0.66 s/mol.\n"", + ""Processing CHEMBL363634 in Train_ER_alpha.smi (254/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL185890 in Train_ER_alpha.smi (255/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL185083 in Train_ER_alpha.smi (256/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL362793 in Train_ER_alpha.smi (257/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL187473 in Train_ER_alpha.smi (258/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL373987 in Train_ER_alpha.smi (259/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL213950 in Train_ER_alpha.smi (260/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL290106 in Train_ER_alpha.smi (261/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL239938 in Train_ER_alpha.smi (262/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL391619 in Train_ER_alpha.smi (263/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL389907 in Train_ER_alpha.smi (264/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL187115 in Train_ER_alpha.smi (265/1238). Average speed: 0.64 s/mol.\n"", + ""Processing CHEMBL363633 in Train_ER_alpha.smi (266/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL182850 in Train_ER_alpha.smi (267/1238). Average speed: 0.65 s/mol.\n"" + ] + }, + { + ""name"": ""stdout"", + ""output_type"": ""stream"", + ""text"": [ + ""Processing CHEMBL361751 in Train_ER_alpha.smi (268/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL188189 in Train_ER_alpha.smi (269/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL196742 in Train_ER_alpha.smi (270/1238). Average speed: 0.64 s/mol.\n"", + ""Processing CHEMBL316003 in Train_ER_alpha.smi (271/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL360410 in Train_ER_alpha.smi (272/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL196530 in Train_ER_alpha.smi (273/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL1570364 in Train_ER_alpha.smi (274/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL1487772 in Train_ER_alpha.smi (276/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL1491936 in Train_ER_alpha.smi (275/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL51483 in Train_ER_alpha.smi (277/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL359633 in Train_ER_alpha.smi (278/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL463804 in Train_ER_alpha.smi (279/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL1414254 in Train_ER_alpha.smi (280/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL1403999 in Train_ER_alpha.smi (281/1238). Average speed: 0.64 s/mol.\n"", + ""Processing CHEMBL1627356 in Train_ER_alpha.smi (282/1238). Average speed: 0.64 s/mol.\n"", + ""Processing CHEMBL319244 in Train_ER_alpha.smi (283/1238). Average speed: 0.64 s/mol.\n"", + ""Processing CHEMBL1572403 in Train_ER_alpha.smi (284/1238). Average speed: 0.64 s/mol.\n"", + ""Processing CHEMBL1541779 in Train_ER_alpha.smi (285/1238). Average speed: 0.64 s/mol.\n"", + ""Processing CHEMBL1458486 in Train_ER_alpha.smi (286/1238). Average speed: 0.64 s/mol.\n"", + ""Processing CHEMBL1399039 in Train_ER_alpha.smi (288/1238). Average speed: 0.64 s/mol.\n"", + ""Processing CHEMBL1587920 in Train_ER_alpha.smi (287/1238). Average speed: 0.64 s/mol.\n"", + ""Processing CHEMBL1332402 in Train_ER_alpha.smi (289/1238). Average speed: 0.64 s/mol.\n"", + ""Processing CHEMBL1328083 in Train_ER_alpha.smi (290/1238). Average speed: 0.64 s/mol.\n"", + ""Processing CHEMBL150924 in Train_ER_alpha.smi (291/1238). Average speed: 0.64 s/mol.\n"", + ""Processing CHEMBL184003 in Train_ER_alpha.smi (292/1238). Average speed: 0.64 s/mol.\n"", + ""Processing CHEMBL182751 in Train_ER_alpha.smi (293/1238). Average speed: 0.64 s/mol.\n"", + ""Processing CHEMBL1465370 in Train_ER_alpha.smi (294/1238). Average speed: 0.64 s/mol.\n"", + ""Processing CHEMBL1383151 in Train_ER_alpha.smi (295/1238). Average speed: 0.64 s/mol.\n"", + ""Processing CHEMBL1407483 in Train_ER_alpha.smi (296/1238). Average speed: 0.64 s/mol.\n"", + ""Processing CHEMBL1511067 in Train_ER_alpha.smi (297/1238). Average speed: 0.63 s/mol.\n"", + ""Processing CHEMBL192876 in Train_ER_alpha.smi (298/1238). Average speed: 0.63 s/mol.\n"", + ""Processing CHEMBL363017 in Train_ER_alpha.smi (299/1238). Average speed: 0.63 s/mol.\n"", + ""Processing CHEMBL1428161 in Train_ER_alpha.smi (300/1238). Average speed: 0.63 s/mol.\n"", + ""Processing CHEMBL1390963 in Train_ER_alpha.smi (301/1238). Average speed: 0.63 s/mol.\n"", + ""Processing CHEMBL340111 in Train_ER_alpha.smi (302/1238). Average speed: 0.63 s/mol.\n"", + ""Processing CHEMBL185783 in Train_ER_alpha.smi (303/1238). Average speed: 0.63 s/mol.\n"", + ""Processing CHEMBL294889 in Train_ER_alpha.smi (304/1238). Average speed: 0.63 s/mol.\n"", + ""Processing CHEMBL184151 in Train_ER_alpha.smi (305/1238). Average speed: 0.63 s/mol.\n"", + ""Processing CHEMBL1504315 in Train_ER_alpha.smi (306/1238). Average speed: 0.63 s/mol.\n"", + ""Processing CHEMBL63768 in Train_ER_alpha.smi (307/1238). Average speed: 0.63 s/mol.\n"", + ""Processing CHEMBL1564209 in Train_ER_alpha.smi (308/1238). Average speed: 0.63 s/mol.\n"", + ""Processing CHEMBL1469865 in Train_ER_alpha.smi (309/1238). Average speed: 0.63 s/mol.\n"", + ""Processing CHEMBL380188 in Train_ER_alpha.smi (310/1238). Average speed: 0.63 s/mol.\n"", + ""Processing CHEMBL214693 in Train_ER_alpha.smi (311/1238). Average speed: 0.63 s/mol.\n"", + ""Processing CHEMBL196283 in Train_ER_alpha.smi (312/1238). Average speed: 0.63 s/mol.\n"", + ""Processing CHEMBL195154 in Train_ER_alpha.smi (313/1238). Average speed: 0.63 s/mol.\n"", + ""Processing CHEMBL1392188 in Train_ER_alpha.smi (315/1238). Average speed: 0.63 s/mol.\n"", + ""Processing CHEMBL363498 in Train_ER_alpha.smi (314/1238). Average speed: 0.63 s/mol.\n"", + ""Processing CHEMBL1577029 in Train_ER_alpha.smi (316/1238). Average speed: 0.63 s/mol.\n"", + ""Processing CHEMBL183368 in Train_ER_alpha.smi (317/1238). Average speed: 0.63 s/mol.\n"", + ""Processing CHEMBL215409 in Train_ER_alpha.smi (318/1238). Average speed: 0.63 s/mol.\n"", + ""Processing CHEMBL211837 in Train_ER_alpha.smi (319/1238). Average speed: 0.63 s/mol.\n"", + ""Processing CHEMBL379566 in Train_ER_alpha.smi (320/1238). Average speed: 0.63 s/mol.\n"", + ""Processing CHEMBL384268 in Train_ER_alpha.smi (321/1238). Average speed: 0.63 s/mol.\n"", + ""Processing CHEMBL203348 in Train_ER_alpha.smi (322/1238). Average speed: 0.63 s/mol.\n"", + ""Processing CHEMBL371072 in Train_ER_alpha.smi (323/1238). Average speed: 0.63 s/mol.\n"", + ""Processing CHEMBL180873 in Train_ER_alpha.smi (324/1238). Average speed: 0.63 s/mol.\n"", + ""Processing CHEMBL426626 in Train_ER_alpha.smi (325/1238). Average speed: 0.62 s/mol.\n"", + ""Processing CHEMBL191711 in Train_ER_alpha.smi (326/1238). Average speed: 0.62 s/mol.\n"", + ""Processing CHEMBL225081 in Train_ER_alpha.smi (327/1238). Average speed: 0.62 s/mol.\n"", + ""Processing CHEMBL231812 in Train_ER_alpha.smi (328/1238). Average speed: 0.63 s/mol.\n"", + ""Processing CHEMBL182887 in Train_ER_alpha.smi (329/1238). Average speed: 0.63 s/mol.\n"", + ""Processing CHEMBL1368345 in Train_ER_alpha.smi (330/1238). Average speed: 0.63 s/mol.\n"", + ""Processing CHEMBL188900 in Train_ER_alpha.smi (331/1238). Average speed: 0.63 s/mol.\n"", + ""Processing CHEMBL362966 in Train_ER_alpha.smi (332/1238). Average speed: 0.63 s/mol.\n"", + ""Processing CHEMBL379213 in Train_ER_alpha.smi (333/1238). Average speed: 0.62 s/mol.\n"", + ""Processing CHEMBL380359 in Train_ER_alpha.smi (334/1238). Average speed: 0.63 s/mol.\n"", + ""Processing CHEMBL207101 in Train_ER_alpha.smi (335/1238). Average speed: 0.63 s/mol.\n"", + ""Processing CHEMBL385693 in Train_ER_alpha.smi (336/1238). Average speed: 0.62 s/mol.\n"", + ""Processing CHEMBL192765 in Train_ER_alpha.smi (337/1238). Average speed: 0.62 s/mol.\n"", + ""Processing CHEMBL193727 in Train_ER_alpha.smi (338/1238). Average speed: 0.62 s/mol.\n"", + ""Processing CHEMBL183367 in Train_ER_alpha.smi (339/1238). Average speed: 0.62 s/mol.\n"", + ""Processing CHEMBL1395826 in Train_ER_alpha.smi (340/1238). Average speed: 0.62 s/mol.\n"", + ""Processing CHEMBL1434189 in Train_ER_alpha.smi (341/1238). Average speed: 0.62 s/mol.\n"", + ""Processing CHEMBL1386105 in Train_ER_alpha.smi (342/1238). Average speed: 0.62 s/mol.\n"", + ""Processing CHEMBL364345 in Train_ER_alpha.smi (343/1238). Average speed: 0.62 s/mol.\n"", + ""Processing CHEMBL371126 in Train_ER_alpha.smi (344/1238). Average speed: 0.62 s/mol.\n"", + ""Processing CHEMBL1324414 in Train_ER_alpha.smi (345/1238). Average speed: 0.62 s/mol.\n"", + ""Processing CHEMBL1334930 in Train_ER_alpha.smi (346/1238). Average speed: 0.62 s/mol.\n"", + ""Processing CHEMBL363902 in Train_ER_alpha.smi (347/1238). Average speed: 0.62 s/mol.\n"", + ""Processing CHEMBL472468 in Train_ER_alpha.smi (348/1238). Average speed: 0.62 s/mol.\n"", + ""Processing CHEMBL189024 in Train_ER_alpha.smi (349/1238). Average speed: 0.62 s/mol.\n"", + ""Processing CHEMBL194718 in Train_ER_alpha.smi (350/1238). Average speed: 0.62 s/mol.\n"", + ""Processing CHEMBL209803 in Train_ER_alpha.smi (351/1238). Average speed: 0.62 s/mol.\n"", + ""Processing CHEMBL209402 in Train_ER_alpha.smi (352/1238). Average speed: 0.62 s/mol.\n"", + ""Processing CHEMBL204970 in Train_ER_alpha.smi (353/1238). Average speed: 0.62 s/mol.\n"", + ""Processing CHEMBL203472 in Train_ER_alpha.smi (354/1238). Average speed: 0.62 s/mol.\n"", + ""Processing CHEMBL1303886 in Train_ER_alpha.smi (355/1238). Average speed: 0.62 s/mol.\n"", + ""Processing CHEMBL246341 in Train_ER_alpha.smi (356/1238). Average speed: 0.62 s/mol.\n"", + ""Processing CHEMBL425092 in Train_ER_alpha.smi (357/1238). Average speed: 0.62 s/mol.\n"", + ""Processing CHEMBL380469 in Train_ER_alpha.smi (358/1238). Average speed: 0.62 s/mol.\n"", + ""Processing CHEMBL376986 in Train_ER_alpha.smi (359/1238). Average speed: 0.62 s/mol.\n"", + ""Processing CHEMBL208978 in Train_ER_alpha.smi (360/1238). Average speed: 0.62 s/mol.\n"", + ""Processing CHEMBL249903 in Train_ER_alpha.smi (362/1238). Average speed: 0.62 s/mol.\n"", + ""Processing CHEMBL377644 in Train_ER_alpha.smi (361/1238). Average speed: 0.62 s/mol.\n"", + ""Processing CHEMBL398634 in Train_ER_alpha.smi (363/1238). Average speed: 0.61 s/mol.\n"", + ""Processing CHEMBL232023 in Train_ER_alpha.smi (364/1238). Average speed: 0.61 s/mol.\n"" + ] + }, + { + ""name"": ""stdout"", + ""output_type"": ""stream"", + ""text"": [ + ""Processing CHEMBL1340020 in Train_ER_alpha.smi (365/1238). Average speed: 0.61 s/mol.\n"", + ""Processing CHEMBL511812 in Train_ER_alpha.smi (366/1238). Average speed: 0.61 s/mol.\n"", + ""Processing CHEMBL191836 in Train_ER_alpha.smi (367/1238). Average speed: 0.61 s/mol.\n"", + ""Processing CHEMBL189356 in Train_ER_alpha.smi (368/1238). Average speed: 0.61 s/mol.\n"", + ""Processing CHEMBL1606765 in Train_ER_alpha.smi (369/1238). Average speed: 0.61 s/mol.\n"", + ""Processing CHEMBL1348377 in Train_ER_alpha.smi (370/1238). Average speed: 0.61 s/mol.\n"", + ""Processing CHEMBL1426947 in Train_ER_alpha.smi (371/1238). Average speed: 0.61 s/mol.\n"", + ""Processing CHEMBL1453259 in Train_ER_alpha.smi (372/1238). Average speed: 0.61 s/mol.\n"", + ""Processing CHEMBL347560 in Train_ER_alpha.smi (373/1238). Average speed: 0.61 s/mol.\n"", + ""Processing CHEMBL364110 in Train_ER_alpha.smi (374/1238). Average speed: 0.61 s/mol.\n"", + ""Processing CHEMBL189025 in Train_ER_alpha.smi (375/1238). Average speed: 0.61 s/mol.\n"", + ""Processing CHEMBL25580 in Train_ER_alpha.smi (376/1238). Average speed: 0.61 s/mol.\n"", + ""Processing CHEMBL1604500 in Train_ER_alpha.smi (377/1238). Average speed: 0.61 s/mol.\n"", + ""Processing CHEMBL1509453 in Train_ER_alpha.smi (378/1238). Average speed: 0.61 s/mol.\n"", + ""Processing CHEMBL1482201 in Train_ER_alpha.smi (379/1238). Average speed: 0.61 s/mol.\n"", + ""Processing CHEMBL1326210 in Train_ER_alpha.smi (380/1238). Average speed: 0.61 s/mol.\n"", + ""Processing CHEMBL221515 in Train_ER_alpha.smi (381/1238). Average speed: 0.61 s/mol.\n"", + ""Processing CHEMBL389394 in Train_ER_alpha.smi (382/1238). Average speed: 0.61 s/mol.\n"", + ""Processing CHEMBL374277 in Train_ER_alpha.smi (383/1238). Average speed: 0.61 s/mol.\n"", + ""Processing CHEMBL379889 in Train_ER_alpha.smi (384/1238). Average speed: 0.61 s/mol.\n"", + ""Processing CHEMBL203724 in Train_ER_alpha.smi (385/1238). Average speed: 0.61 s/mol.\n"", + ""Processing CHEMBL226379 in Train_ER_alpha.smi (386/1238). Average speed: 0.61 s/mol.\n"", + ""Processing CHEMBL1318094 in Train_ER_alpha.smi (387/1238). Average speed: 0.61 s/mol.\n"", + ""Processing CHEMBL368449 in Train_ER_alpha.smi (388/1238). Average speed: 0.61 s/mol.\n"", + ""Processing CHEMBL191759 in Train_ER_alpha.smi (389/1238). Average speed: 0.61 s/mol.\n"", + ""Processing CHEMBL365941 in Train_ER_alpha.smi (390/1238). Average speed: 0.61 s/mol.\n"", + ""Processing CHEMBL191808 in Train_ER_alpha.smi (391/1238). Average speed: 0.61 s/mol.\n"", + ""Processing CHEMBL360978 in Train_ER_alpha.smi (392/1238). Average speed: 0.61 s/mol.\n"", + ""Processing CHEMBL392029 in Train_ER_alpha.smi (393/1238). Average speed: 0.61 s/mol.\n"", + ""Processing CHEMBL200483 in Train_ER_alpha.smi (394/1238). Average speed: 0.61 s/mol.\n"", + ""Processing CHEMBL199599 in Train_ER_alpha.smi (395/1238). Average speed: 0.61 s/mol.\n"", + ""Processing CHEMBL200232 in Train_ER_alpha.smi (396/1238). Average speed: 0.61 s/mol.\n"", + ""Processing CHEMBL201517 in Train_ER_alpha.smi (397/1238). Average speed: 0.61 s/mol.\n"", + ""Processing CHEMBL202673 in Train_ER_alpha.smi (398/1238). Average speed: 0.61 s/mol.\n"", + ""Processing CHEMBL203072 in Train_ER_alpha.smi (399/1238). Average speed: 0.61 s/mol.\n"", + ""Processing CHEMBL241300 in Train_ER_alpha.smi (400/1238). Average speed: 0.61 s/mol.\n"", + ""Processing CHEMBL428732 in Train_ER_alpha.smi (401/1238). Average speed: 0.61 s/mol.\n"", + ""Processing CHEMBL393179 in Train_ER_alpha.smi (402/1238). Average speed: 0.61 s/mol.\n"", + ""Processing CHEMBL442249 in Train_ER_alpha.smi (403/1238). Average speed: 0.61 s/mol.\n"", + ""Processing CHEMBL238959 in Train_ER_alpha.smi (404/1238). Average speed: 0.62 s/mol.\n"", + ""Processing CHEMBL1410824 in Train_ER_alpha.smi (405/1238). Average speed: 0.62 s/mol.\n"", + ""Processing CHEMBL1359893 in Train_ER_alpha.smi (406/1238). Average speed: 0.62 s/mol.\n"", + ""Processing CHEMBL1427643 in Train_ER_alpha.smi (407/1238). Average speed: 0.62 s/mol.\n"", + ""Processing CHEMBL1464457 in Train_ER_alpha.smi (408/1238). Average speed: 0.62 s/mol.\n"", + ""Processing CHEMBL1403448 in Train_ER_alpha.smi (409/1238). Average speed: 0.62 s/mol.\n"", + ""Processing CHEMBL1575187 in Train_ER_alpha.smi (410/1238). Average speed: 0.62 s/mol.\n"", + ""Processing CHEMBL1548231 in Train_ER_alpha.smi (411/1238). Average speed: 0.62 s/mol.\n"", + ""Processing CHEMBL1531555 in Train_ER_alpha.smi (412/1238). Average speed: 0.62 s/mol.\n"", + ""Processing CHEMBL1521410 in Train_ER_alpha.smi (413/1238). Average speed: 0.62 s/mol.\n"", + ""Processing CHEMBL1538223 in Train_ER_alpha.smi (414/1238). Average speed: 0.62 s/mol.\n"", + ""Processing CHEMBL153405 in Train_ER_alpha.smi (415/1238). Average speed: 0.62 s/mol.\n"", + ""Processing CHEMBL327320 in Train_ER_alpha.smi (416/1238). Average speed: 0.62 s/mol.\n"", + ""Processing CHEMBL178559 in Train_ER_alpha.smi (417/1238). Average speed: 0.62 s/mol.\n"", + ""Processing CHEMBL180973 in Train_ER_alpha.smi (418/1238). Average speed: 0.62 s/mol.\n"", + ""Processing CHEMBL1528123 in Train_ER_alpha.smi (419/1238). Average speed: 0.62 s/mol.\n"", + ""Processing CHEMBL1536373 in Train_ER_alpha.smi (420/1238). Average speed: 0.62 s/mol.\n"", + ""Processing CHEMBL1386676 in Train_ER_alpha.smi (421/1238). Average speed: 0.62 s/mol.\n"", + ""Processing CHEMBL144431 in Train_ER_alpha.smi (422/1238). Average speed: 0.62 s/mol.\n"", + ""Processing CHEMBL1580036 in Train_ER_alpha.smi (423/1238). Average speed: 0.62 s/mol.\n"", + ""Processing CHEMBL189658 in Train_ER_alpha.smi (424/1238). Average speed: 0.62 s/mol.\n"", + ""Processing CHEMBL188619 in Train_ER_alpha.smi (425/1238). Average speed: 0.62 s/mol.\n"", + ""Processing CHEMBL1525824 in Train_ER_alpha.smi (426/1238). Average speed: 0.62 s/mol.\n"", + ""Processing CHEMBL1522300 in Train_ER_alpha.smi (427/1238). Average speed: 0.62 s/mol.\n"", + ""Processing CHEMBL193064 in Train_ER_alpha.smi (428/1238). Average speed: 0.62 s/mol.\n"", + ""Processing CHEMBL186513 in Train_ER_alpha.smi (429/1238). Average speed: 0.62 s/mol.\n"", + ""Processing CHEMBL1495194 in Train_ER_alpha.smi (430/1238). Average speed: 0.62 s/mol.\n"", + ""Processing CHEMBL1606490 in Train_ER_alpha.smi (431/1238). Average speed: 0.62 s/mol.\n"", + ""Processing CHEMBL1548186 in Train_ER_alpha.smi (432/1238). Average speed: 0.62 s/mol.\n"", + ""Processing CHEMBL2079587 in Train_ER_alpha.smi (433/1238). Average speed: 0.62 s/mol.\n"", + ""Processing CHEMBL180253 in Train_ER_alpha.smi (434/1238). Average speed: 0.62 s/mol.\n"", + ""Processing CHEMBL93705 in Train_ER_alpha.smi (435/1238). Average speed: 0.62 s/mol.\n"", + ""Processing CHEMBL190104 in Train_ER_alpha.smi (436/1238). Average speed: 0.62 s/mol.\n"", + ""Processing CHEMBL1548834 in Train_ER_alpha.smi (437/1238). Average speed: 0.62 s/mol.\n"", + ""Processing CHEMBL188527 in Train_ER_alpha.smi (438/1238). Average speed: 0.62 s/mol.\n"", + ""Processing CHEMBL360498 in Train_ER_alpha.smi (439/1238). Average speed: 0.62 s/mol.\n"", + ""Processing CHEMBL1501269 in Train_ER_alpha.smi (440/1238). Average speed: 0.62 s/mol.\n"", + ""Processing CHEMBL1560367 in Train_ER_alpha.smi (441/1238). Average speed: 0.62 s/mol.\n"", + ""Processing CHEMBL1427879 in Train_ER_alpha.smi (442/1238). Average speed: 0.62 s/mol.\n"", + ""Processing CHEMBL1544241 in Train_ER_alpha.smi (443/1238). Average speed: 0.62 s/mol.\n"", + ""Processing CHEMBL577758 in Train_ER_alpha.smi (444/1238). Average speed: 0.62 s/mol.\n"", + ""Processing CHEMBL186597 in Train_ER_alpha.smi (445/1238). Average speed: 0.62 s/mol.\n"", + ""Processing CHEMBL145576 in Train_ER_alpha.smi (446/1238). Average speed: 0.62 s/mol.\n"", + ""Processing CHEMBL361258 in Train_ER_alpha.smi (447/1238). Average speed: 0.62 s/mol.\n"", + ""Processing CHEMBL363367 in Train_ER_alpha.smi (448/1238). Average speed: 0.62 s/mol.\n"", + ""Processing CHEMBL181369 in Train_ER_alpha.smi (449/1238). Average speed: 0.62 s/mol.\n"", + ""Processing CHEMBL57150 in Train_ER_alpha.smi (450/1238). Average speed: 0.62 s/mol.\n"", + ""Processing CHEMBL433944 in Train_ER_alpha.smi (451/1238). Average speed: 0.62 s/mol.\n"", + ""Processing CHEMBL371624 in Train_ER_alpha.smi (452/1238). Average speed: 0.62 s/mol.\n"", + ""Processing CHEMBL188613 in Train_ER_alpha.smi (453/1238). Average speed: 0.62 s/mol.\n"", + ""Processing CHEMBL371788 in Train_ER_alpha.smi (454/1238). Average speed: 0.62 s/mol.\n"", + ""Processing CHEMBL189200 in Train_ER_alpha.smi (455/1238). Average speed: 0.62 s/mol.\n"", + ""Processing CHEMBL186073 in Train_ER_alpha.smi (456/1238). Average speed: 0.62 s/mol.\n"", + ""Processing CHEMBL187087 in Train_ER_alpha.smi (457/1238). Average speed: 0.62 s/mol.\n"", + ""Processing CHEMBL200021 in Train_ER_alpha.smi (458/1238). Average speed: 0.62 s/mol.\n"", + ""Processing CHEMBL193010 in Train_ER_alpha.smi (459/1238). Average speed: 0.62 s/mol.\n"", + ""Processing CHEMBL363963 in Train_ER_alpha.smi (460/1238). Average speed: 0.62 s/mol.\n"", + ""Processing CHEMBL450940 in Train_ER_alpha.smi (461/1238). Average speed: 0.62 s/mol.\n"" + ] + }, + { + ""name"": ""stdout"", + ""output_type"": ""stream"", + ""text"": [ + ""Processing CHEMBL188410 in Train_ER_alpha.smi (463/1238). Average speed: 0.62 s/mol.\n"", + ""Processing CHEMBL365045 in Train_ER_alpha.smi (462/1238). Average speed: 0.62 s/mol.\n"", + ""Processing CHEMBL201569 in Train_ER_alpha.smi (464/1238). Average speed: 0.62 s/mol.\n"", + ""Processing CHEMBL380876 in Train_ER_alpha.smi (465/1238). Average speed: 0.62 s/mol.\n"", + ""Processing CHEMBL441537 in Train_ER_alpha.smi (466/1238). Average speed: 0.61 s/mol.\n"", + ""Processing CHEMBL377694 in Train_ER_alpha.smi (467/1238). Average speed: 0.62 s/mol.\n"", + ""Processing CHEMBL192386 in Train_ER_alpha.smi (468/1238). Average speed: 0.61 s/mol.\n"", + ""Processing CHEMBL362020 in Train_ER_alpha.smi (469/1238). Average speed: 0.61 s/mol.\n"", + ""Processing CHEMBL183152 in Train_ER_alpha.smi (470/1238). Average speed: 0.61 s/mol.\n"", + ""Processing CHEMBL192587 in Train_ER_alpha.smi (471/1238). Average speed: 0.61 s/mol.\n"", + ""Processing CHEMBL370849 in Train_ER_alpha.smi (472/1238). Average speed: 0.61 s/mol.\n"", + ""Processing CHEMBL178679 in Train_ER_alpha.smi (473/1238). Average speed: 0.61 s/mol.\n"", + ""Processing CHEMBL185126 in Train_ER_alpha.smi (474/1238). Average speed: 0.61 s/mol.\n"", + ""Processing CHEMBL185918 in Train_ER_alpha.smi (475/1238). Average speed: 0.61 s/mol.\n"", + ""Processing CHEMBL264216 in Train_ER_alpha.smi (476/1238). Average speed: 0.62 s/mol.\n"", + ""Processing CHEMBL1337826 in Train_ER_alpha.smi (477/1238). Average speed: 0.62 s/mol.\n"", + ""Processing CHEMBL1480378 in Train_ER_alpha.smi (478/1238). Average speed: 0.62 s/mol.\n"", + ""Processing CHEMBL1481770 in Train_ER_alpha.smi (479/1238). Average speed: 0.62 s/mol.\n"", + ""Processing CHEMBL198679 in Train_ER_alpha.smi (480/1238). Average speed: 0.62 s/mol.\n"", + ""Processing CHEMBL121978 in Train_ER_alpha.smi (481/1238). Average speed: 0.62 s/mol.\n"", + ""Processing CHEMBL371009 in Train_ER_alpha.smi (482/1238). Average speed: 0.62 s/mol.\n"", + ""Processing CHEMBL334096 in Train_ER_alpha.smi (483/1238). Average speed: 0.62 s/mol.\n"", + ""Processing CHEMBL192539 in Train_ER_alpha.smi (484/1238). Average speed: 0.62 s/mol.\n"", + ""Processing CHEMBL186598 in Train_ER_alpha.smi (485/1238). Average speed: 0.62 s/mol.\n"", + ""Processing CHEMBL180146 in Train_ER_alpha.smi (486/1238). Average speed: 0.62 s/mol.\n"", + ""Processing CHEMBL377224 in Train_ER_alpha.smi (487/1238). Average speed: 0.62 s/mol.\n"", + ""Processing CHEMBL213051 in Train_ER_alpha.smi (488/1238). Average speed: 0.62 s/mol.\n"", + ""Processing CHEMBL377848 in Train_ER_alpha.smi (489/1238). Average speed: 0.62 s/mol.\n"", + ""Processing CHEMBL264349 in Train_ER_alpha.smi (490/1238). Average speed: 0.62 s/mol.\n"", + ""Processing CHEMBL378675 in Train_ER_alpha.smi (491/1238). Average speed: 0.62 s/mol.\n"", + ""Processing CHEMBL1402 in Train_ER_alpha.smi (492/1238). Average speed: 0.62 s/mol.\n"", + ""Processing CHEMBL369935 in Train_ER_alpha.smi (493/1238). Average speed: 0.62 s/mol.\n"", + ""Processing CHEMBL216279 in Train_ER_alpha.smi (494/1238). Average speed: 0.62 s/mol.\n"", + ""Processing CHEMBL219126 in Train_ER_alpha.smi (495/1238). Average speed: 0.62 s/mol.\n"", + ""Processing CHEMBL381566 in Train_ER_alpha.smi (496/1238). Average speed: 0.62 s/mol.\n"", + ""Processing CHEMBL209429 in Train_ER_alpha.smi (497/1238). Average speed: 0.62 s/mol.\n"", + ""Processing CHEMBL379109 in Train_ER_alpha.smi (498/1238). Average speed: 0.62 s/mol.\n"", + ""Processing CHEMBL379821 in Train_ER_alpha.smi (499/1238). Average speed: 0.62 s/mol.\n"", + ""Processing CHEMBL426854 in Train_ER_alpha.smi (500/1238). Average speed: 0.62 s/mol.\n"", + ""Processing CHEMBL202896 in Train_ER_alpha.smi (501/1238). Average speed: 0.62 s/mol.\n"", + ""Processing CHEMBL179518 in Train_ER_alpha.smi (502/1238). Average speed: 0.62 s/mol.\n"", + ""Processing CHEMBL187069 in Train_ER_alpha.smi (503/1238). Average speed: 0.62 s/mol.\n"", + ""Processing CHEMBL363425 in Train_ER_alpha.smi (504/1238). Average speed: 0.62 s/mol.\n"", + ""Processing CHEMBL597498 in Train_ER_alpha.smi (505/1238). Average speed: 0.62 s/mol.\n"", + ""Processing CHEMBL1116 in Train_ER_alpha.smi (506/1238). Average speed: 0.62 s/mol.\n"", + ""Processing CHEMBL206226 in Train_ER_alpha.smi (507/1238). Average speed: 0.62 s/mol.\n"", + ""Processing CHEMBL378990 in Train_ER_alpha.smi (508/1238). Average speed: 0.62 s/mol.\n"", + ""Processing CHEMBL211800 in Train_ER_alpha.smi (509/1238). Average speed: 0.62 s/mol.\n"", + ""Processing CHEMBL379567 in Train_ER_alpha.smi (510/1238). Average speed: 0.62 s/mol.\n"", + ""Processing CHEMBL379595 in Train_ER_alpha.smi (511/1238). Average speed: 0.62 s/mol.\n"", + ""Processing CHEMBL211870 in Train_ER_alpha.smi (512/1238). Average speed: 0.62 s/mol.\n"", + ""Processing CHEMBL187937 in Train_ER_alpha.smi (513/1238). Average speed: 0.62 s/mol.\n"", + ""Processing CHEMBL204768 in Train_ER_alpha.smi (514/1238). Average speed: 0.62 s/mol.\n"", + ""Processing CHEMBL205350 in Train_ER_alpha.smi (515/1238). Average speed: 0.62 s/mol.\n"", + ""Processing CHEMBL380717 in Train_ER_alpha.smi (516/1238). Average speed: 0.61 s/mol.\n"", + ""Processing CHEMBL206582 in Train_ER_alpha.smi (517/1238). Average speed: 0.62 s/mol.\n"", + ""Processing CHEMBL362355 in Train_ER_alpha.smi (518/1238). Average speed: 0.62 s/mol.\n"", + ""Processing CHEMBL221676 in Train_ER_alpha.smi (519/1238). Average speed: 0.61 s/mol.\n"", + ""Processing CHEMBL363955 in Train_ER_alpha.smi (520/1238). Average speed: 0.61 s/mol.\n"", + ""Processing CHEMBL206764 in Train_ER_alpha.smi (521/1238). Average speed: 0.61 s/mol.\n"", + ""Processing CHEMBL206131 in Train_ER_alpha.smi (522/1238). Average speed: 0.61 s/mol.\n"", + ""Processing CHEMBL381720 in Train_ER_alpha.smi (523/1238). Average speed: 0.62 s/mol.\n"", + ""Processing CHEMBL202894 in Train_ER_alpha.smi (524/1238). Average speed: 0.61 s/mol.\n"", + ""Processing CHEMBL206349 in Train_ER_alpha.smi (525/1238). Average speed: 0.61 s/mol.\n"", + ""Processing CHEMBL206773 in Train_ER_alpha.smi (526/1238). Average speed: 0.61 s/mol.\n"", + ""Processing CHEMBL379008 in Train_ER_alpha.smi (527/1238). Average speed: 0.61 s/mol.\n"", + ""Processing CHEMBL192016 in Train_ER_alpha.smi (528/1238). Average speed: 0.61 s/mol.\n"", + ""Processing CHEMBL191841 in Train_ER_alpha.smi (529/1238). Average speed: 0.61 s/mol.\n"", + ""Processing CHEMBL238960 in Train_ER_alpha.smi (530/1238). Average speed: 0.61 s/mol.\n"", + ""Processing CHEMBL238961 in Train_ER_alpha.smi (531/1238). Average speed: 0.61 s/mol.\n"", + ""Processing CHEMBL184360 in Train_ER_alpha.smi (532/1238). Average speed: 0.61 s/mol.\n"", + ""Processing CHEMBL180176 in Train_ER_alpha.smi (533/1238). Average speed: 0.61 s/mol.\n"", + ""Processing CHEMBL437695 in Train_ER_alpha.smi (534/1238). Average speed: 0.62 s/mol.\n"", + ""Processing CHEMBL1276308 in Train_ER_alpha.smi (535/1238). Average speed: 0.62 s/mol.\n"", + ""Processing CHEMBL1485005 in Train_ER_alpha.smi (536/1238). Average speed: 0.62 s/mol.\n"", + ""Processing CHEMBL1327977 in Train_ER_alpha.smi (538/1238). Average speed: 0.62 s/mol.\n"", + ""Processing CHEMBL1521111 in Train_ER_alpha.smi (537/1238). Average speed: 0.62 s/mol.\n"", + ""Processing CHEMBL388418 in Train_ER_alpha.smi (539/1238). Average speed: 0.62 s/mol.\n"", + ""Processing CHEMBL396948 in Train_ER_alpha.smi (540/1238). Average speed: 0.62 s/mol.\n"", + ""Processing CHEMBL1433745 in Train_ER_alpha.smi (541/1238). Average speed: 0.62 s/mol.\n"", + ""Processing CHEMBL1475062 in Train_ER_alpha.smi (542/1238). Average speed: 0.62 s/mol.\n"", + ""Processing CHEMBL1597831 in Train_ER_alpha.smi (543/1238). Average speed: 0.62 s/mol.\n"", + ""Processing CHEMBL471627 in Train_ER_alpha.smi (544/1238). Average speed: 0.62 s/mol.\n"", + ""Processing CHEMBL1362841 in Train_ER_alpha.smi (545/1238). Average speed: 0.62 s/mol.\n"", + ""Processing CHEMBL1429150 in Train_ER_alpha.smi (546/1238). Average speed: 0.62 s/mol.\n"", + ""Processing CHEMBL1548361 in Train_ER_alpha.smi (547/1238). Average speed: 0.62 s/mol.\n"", + ""Processing CHEMBL222522 in Train_ER_alpha.smi (549/1238). Average speed: 0.62 s/mol.\n"", + ""Processing CHEMBL375334 in Train_ER_alpha.smi (548/1238). Average speed: 0.62 s/mol.\n"", + ""Processing CHEMBL222580 in Train_ER_alpha.smi (550/1238). Average speed: 0.62 s/mol.\n"", + ""Processing CHEMBL1449885 in Train_ER_alpha.smi (551/1238). Average speed: 0.62 s/mol.\n"", + ""Processing CHEMBL1584646 in Train_ER_alpha.smi (552/1238). Average speed: 0.63 s/mol.\n"", + ""Processing CHEMBL1506716 in Train_ER_alpha.smi (553/1238). Average speed: 0.63 s/mol.\n"", + ""Processing CHEMBL1472407 in Train_ER_alpha.smi (554/1238). Average speed: 0.63 s/mol.\n"", + ""Processing CHEMBL1448466 in Train_ER_alpha.smi (555/1238). Average speed: 0.62 s/mol.\n"", + ""Processing CHEMBL1454402 in Train_ER_alpha.smi (556/1238). Average speed: 0.63 s/mol.\n"", + ""Processing CHEMBL1431650 in Train_ER_alpha.smi (557/1238). Average speed: 0.62 s/mol.\n"", + ""Processing CHEMBL1367590 in Train_ER_alpha.smi (558/1238). Average speed: 0.63 s/mol.\n"", + ""Processing CHEMBL1587260 in Train_ER_alpha.smi (559/1238). Average speed: 0.62 s/mol.\n"" + ] + }, + { + ""name"": ""stdout"", + ""output_type"": ""stream"", + ""text"": [ + ""Processing CHEMBL1522044 in Train_ER_alpha.smi (560/1238). Average speed: 0.62 s/mol.\n"", + ""Processing CHEMBL1461574 in Train_ER_alpha.smi (561/1238). Average speed: 0.62 s/mol.\n"", + ""Processing CHEMBL1328182 in Train_ER_alpha.smi (562/1238). Average speed: 0.62 s/mol.\n"", + ""Processing CHEMBL250109 in Train_ER_alpha.smi (563/1238). Average speed: 0.62 s/mol.\n"", + ""Processing CHEMBL191716 in Train_ER_alpha.smi (564/1238). Average speed: 0.62 s/mol.\n"", + ""Processing CHEMBL194772 in Train_ER_alpha.smi (565/1238). Average speed: 0.62 s/mol.\n"", + ""Processing CHEMBL362425 in Train_ER_alpha.smi (566/1238). Average speed: 0.62 s/mol.\n"", + ""Processing CHEMBL363177 in Train_ER_alpha.smi (567/1238). Average speed: 0.63 s/mol.\n"", + ""Processing CHEMBL364329 in Train_ER_alpha.smi (568/1238). Average speed: 0.63 s/mol.\n"", + ""Processing CHEMBL361226 in Train_ER_alpha.smi (569/1238). Average speed: 0.63 s/mol.\n"", + ""Processing CHEMBL379543 in Train_ER_alpha.smi (570/1238). Average speed: 0.63 s/mol.\n"", + ""Processing CHEMBL361063 in Train_ER_alpha.smi (571/1238). Average speed: 0.63 s/mol.\n"", + ""Processing CHEMBL189822 in Train_ER_alpha.smi (572/1238). Average speed: 0.63 s/mol.\n"", + ""Processing CHEMBL192782 in Train_ER_alpha.smi (573/1238). Average speed: 0.63 s/mol.\n"", + ""Processing CHEMBL192901 in Train_ER_alpha.smi (574/1238). Average speed: 0.63 s/mol.\n"", + ""Processing CHEMBL362307 in Train_ER_alpha.smi (575/1238). Average speed: 0.63 s/mol.\n"", + ""Processing CHEMBL190487 in Train_ER_alpha.smi (576/1238). Average speed: 0.63 s/mol.\n"", + ""Processing CHEMBL1479 in Train_ER_alpha.smi (577/1238). Average speed: 0.63 s/mol.\n"", + ""Processing CHEMBL104416 in Train_ER_alpha.smi (578/1238). Average speed: 0.63 s/mol.\n"", + ""Processing CHEMBL195991 in Train_ER_alpha.smi (579/1238). Average speed: 0.63 s/mol.\n"", + ""Processing CHEMBL190038 in Train_ER_alpha.smi (580/1238). Average speed: 0.63 s/mol.\n"", + ""Processing CHEMBL189303 in Train_ER_alpha.smi (581/1238). Average speed: 0.63 s/mol.\n"", + ""Processing CHEMBL1627354 in Train_ER_alpha.smi (582/1238). Average speed: 0.63 s/mol.\n"", + ""Processing CHEMBL178665 in Train_ER_alpha.smi (583/1238). Average speed: 0.63 s/mol.\n"", + ""Processing CHEMBL435696 in Train_ER_alpha.smi (584/1238). Average speed: 0.63 s/mol.\n"", + ""Processing CHEMBL371564 in Train_ER_alpha.smi (585/1238). Average speed: 0.64 s/mol.\n"", + ""Processing CHEMBL1628170 in Train_ER_alpha.smi (586/1238). Average speed: 0.64 s/mol.\n"", + ""Processing CHEMBL362349 in Train_ER_alpha.smi (587/1238). Average speed: 0.64 s/mol.\n"", + ""Processing CHEMBL954 in Train_ER_alpha.smi (588/1238). Average speed: 0.64 s/mol.\n"", + ""Processing CHEMBL184974 in Train_ER_alpha.smi (589/1238). Average speed: 0.64 s/mol.\n"", + ""Processing CHEMBL1627353 in Train_ER_alpha.smi (590/1238). Average speed: 0.64 s/mol.\n"", + ""Processing CHEMBL188846 in Train_ER_alpha.smi (591/1238). Average speed: 0.64 s/mol.\n"", + ""Processing CHEMBL196131 in Train_ER_alpha.smi (592/1238). Average speed: 0.64 s/mol.\n"", + ""Processing CHEMBL372304 in Train_ER_alpha.smi (593/1238). Average speed: 0.64 s/mol.\n"", + ""Processing CHEMBL427180 in Train_ER_alpha.smi (594/1238). Average speed: 0.64 s/mol.\n"", + ""Processing CHEMBL194265 in Train_ER_alpha.smi (595/1238). Average speed: 0.64 s/mol.\n"", + ""Processing CHEMBL195788 in Train_ER_alpha.smi (596/1238). Average speed: 0.64 s/mol.\n"", + ""Processing CHEMBL183344 in Train_ER_alpha.smi (597/1238). Average speed: 0.64 s/mol.\n"", + ""Processing CHEMBL369545 in Train_ER_alpha.smi (598/1238). Average speed: 0.64 s/mol.\n"", + ""Processing CHEMBL361078 in Train_ER_alpha.smi (599/1238). Average speed: 0.64 s/mol.\n"", + ""Processing CHEMBL187393 in Train_ER_alpha.smi (600/1238). Average speed: 0.64 s/mol.\n"", + ""Processing CHEMBL1200623 in Train_ER_alpha.smi (601/1238). Average speed: 0.64 s/mol.\n"", + ""Processing CHEMBL194762 in Train_ER_alpha.smi (602/1238). Average speed: 0.64 s/mol.\n"", + ""Processing CHEMBL188051 in Train_ER_alpha.smi (603/1238). Average speed: 0.64 s/mol.\n"", + ""Processing CHEMBL187392 in Train_ER_alpha.smi (604/1238). Average speed: 0.64 s/mol.\n"", + ""Processing CHEMBL418971 in Train_ER_alpha.smi (605/1238). Average speed: 0.64 s/mol.\n"", + ""Processing CHEMBL184367 in Train_ER_alpha.smi (606/1238). Average speed: 0.64 s/mol.\n"", + ""Processing CHEMBL370282 in Train_ER_alpha.smi (607/1238). Average speed: 0.64 s/mol.\n"", + ""Processing CHEMBL180702 in Train_ER_alpha.smi (608/1238). Average speed: 0.64 s/mol.\n"", + ""Processing CHEMBL103650 in Train_ER_alpha.smi (609/1238). Average speed: 0.64 s/mol.\n"", + ""Processing CHEMBL181936 in Train_ER_alpha.smi (610/1238). Average speed: 0.64 s/mol.\n"", + ""Processing CHEMBL1411687 in Train_ER_alpha.smi (611/1238). Average speed: 0.64 s/mol.\n"", + ""Processing CHEMBL1302802 in Train_ER_alpha.smi (612/1238). Average speed: 0.64 s/mol.\n"", + ""Processing CHEMBL1521414 in Train_ER_alpha.smi (613/1238). Average speed: 0.64 s/mol.\n"", + ""Processing CHEMBL1491602 in Train_ER_alpha.smi (614/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL1341814 in Train_ER_alpha.smi (615/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL1322601 in Train_ER_alpha.smi (616/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL1604803 in Train_ER_alpha.smi (617/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL1393131 in Train_ER_alpha.smi (618/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL1541552 in Train_ER_alpha.smi (619/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL1350028 in Train_ER_alpha.smi (620/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL183782 in Train_ER_alpha.smi (621/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL1423880 in Train_ER_alpha.smi (622/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL1493034 in Train_ER_alpha.smi (623/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL1347829 in Train_ER_alpha.smi (624/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL1310171 in Train_ER_alpha.smi (625/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL1411209 in Train_ER_alpha.smi (626/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL1507788 in Train_ER_alpha.smi (627/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL196583 in Train_ER_alpha.smi (628/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL1505283 in Train_ER_alpha.smi (629/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL1532659 in Train_ER_alpha.smi (630/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL189039 in Train_ER_alpha.smi (631/1238). Average speed: 0.64 s/mol.\n"", + ""Processing CHEMBL187673 in Train_ER_alpha.smi (632/1238). Average speed: 0.64 s/mol.\n"", + ""Processing CHEMBL197757 in Train_ER_alpha.smi (633/1238). Average speed: 0.64 s/mol.\n"", + ""Processing CHEMBL191715 in Train_ER_alpha.smi (634/1238). Average speed: 0.64 s/mol.\n"", + ""Processing CHEMBL122208 in Train_ER_alpha.smi (635/1238). Average speed: 0.64 s/mol.\n"", + ""Processing CHEMBL153395 in Train_ER_alpha.smi (636/1238). Average speed: 0.64 s/mol.\n"", + ""Processing CHEMBL195289 in Train_ER_alpha.smi (637/1238). Average speed: 0.64 s/mol.\n"", + ""Processing CHEMBL1381306 in Train_ER_alpha.smi (638/1238). Average speed: 0.64 s/mol.\n"", + ""Processing CHEMBL1421864 in Train_ER_alpha.smi (639/1238). Average speed: 0.64 s/mol.\n"", + ""Processing CHEMBL1560682 in Train_ER_alpha.smi (640/1238). Average speed: 0.64 s/mol.\n"", + ""Processing CHEMBL1475434 in Train_ER_alpha.smi (641/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL1434961 in Train_ER_alpha.smi (642/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL1552932 in Train_ER_alpha.smi (643/1238). Average speed: 0.64 s/mol.\n"", + ""Processing CHEMBL1593653 in Train_ER_alpha.smi (644/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL1449110 in Train_ER_alpha.smi (645/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL192390 in Train_ER_alpha.smi (646/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL371305 in Train_ER_alpha.smi (648/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL340606 in Train_ER_alpha.smi (647/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL195280 in Train_ER_alpha.smi (649/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL193482 in Train_ER_alpha.smi (650/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL1443090 in Train_ER_alpha.smi (651/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL1519669 in Train_ER_alpha.smi (652/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL1299357 in Train_ER_alpha.smi (653/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL1581690 in Train_ER_alpha.smi (654/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL1433062 in Train_ER_alpha.smi (655/1238). Average speed: 0.65 s/mol.\n"" + ] + }, + { + ""name"": ""stdout"", + ""output_type"": ""stream"", + ""text"": [ + ""Processing CHEMBL125362 in Train_ER_alpha.smi (656/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL361871 in Train_ER_alpha.smi (657/1238). Average speed: 0.64 s/mol.\n"", + ""Processing CHEMBL482116 in Train_ER_alpha.smi (658/1238). Average speed: 0.64 s/mol.\n"", + ""Processing CHEMBL190682 in Train_ER_alpha.smi (659/1238). Average speed: 0.64 s/mol.\n"", + ""Processing CHEMBL363872 in Train_ER_alpha.smi (660/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL184421 in Train_ER_alpha.smi (661/1238). Average speed: 0.64 s/mol.\n"", + ""Processing CHEMBL205920 in Train_ER_alpha.smi (662/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL185383 in Train_ER_alpha.smi (663/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL226329 in Train_ER_alpha.smi (664/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL200799 in Train_ER_alpha.smi (665/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL371012 in Train_ER_alpha.smi (666/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL362356 in Train_ER_alpha.smi (667/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL238890 in Train_ER_alpha.smi (668/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL240157 in Train_ER_alpha.smi (669/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL511971 in Train_ER_alpha.smi (670/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL364419 in Train_ER_alpha.smi (671/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL391795 in Train_ER_alpha.smi (672/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL398455 in Train_ER_alpha.smi (673/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL430395 in Train_ER_alpha.smi (674/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL183044 in Train_ER_alpha.smi (675/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL189002 in Train_ER_alpha.smi (676/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL438256 in Train_ER_alpha.smi (677/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL188957 in Train_ER_alpha.smi (678/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL204697 in Train_ER_alpha.smi (679/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL203561 in Train_ER_alpha.smi (680/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL211739 in Train_ER_alpha.smi (681/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL213529 in Train_ER_alpha.smi (682/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL214200 in Train_ER_alpha.smi (683/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL187978 in Train_ER_alpha.smi (684/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL394357 in Train_ER_alpha.smi (686/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL187219 in Train_ER_alpha.smi (685/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL388473 in Train_ER_alpha.smi (687/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL242818 in Train_ER_alpha.smi (688/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL106773 in Train_ER_alpha.smi (689/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL192000 in Train_ER_alpha.smi (690/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL202781 in Train_ER_alpha.smi (691/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL198445 in Train_ER_alpha.smi (692/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL432454 in Train_ER_alpha.smi (693/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL184491 in Train_ER_alpha.smi (694/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL393982 in Train_ER_alpha.smi (695/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL240438 in Train_ER_alpha.smi (696/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL188246 in Train_ER_alpha.smi (697/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL346455 in Train_ER_alpha.smi (698/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL232022 in Train_ER_alpha.smi (699/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL206120 in Train_ER_alpha.smi (700/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL210074 in Train_ER_alpha.smi (701/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL209738 in Train_ER_alpha.smi (702/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL213829 in Train_ER_alpha.smi (704/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL377254 in Train_ER_alpha.smi (703/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL522990 in Train_ER_alpha.smi (706/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL455215 in Train_ER_alpha.smi (705/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL267431 in Train_ER_alpha.smi (707/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL469510 in Train_ER_alpha.smi (708/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL368688 in Train_ER_alpha.smi (709/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL1076306 in Train_ER_alpha.smi (710/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL198680 in Train_ER_alpha.smi (711/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL125119 in Train_ER_alpha.smi (712/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL414859 in Train_ER_alpha.smi (713/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL378798 in Train_ER_alpha.smi (714/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL46903 in Train_ER_alpha.smi (715/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL195311 in Train_ER_alpha.smi (716/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL1450791 in Train_ER_alpha.smi (717/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL203370 in Train_ER_alpha.smi (718/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL378572 in Train_ER_alpha.smi (719/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL206137 in Train_ER_alpha.smi (720/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL1389780 in Train_ER_alpha.smi (721/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL1312693 in Train_ER_alpha.smi (722/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL1324290 in Train_ER_alpha.smi (723/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL1377973 in Train_ER_alpha.smi (724/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL1453264 in Train_ER_alpha.smi (725/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL1533166 in Train_ER_alpha.smi (726/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL1304227 in Train_ER_alpha.smi (727/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL1329686 in Train_ER_alpha.smi (728/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL1498999 in Train_ER_alpha.smi (729/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL1386537 in Train_ER_alpha.smi (730/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL1520373 in Train_ER_alpha.smi (731/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL1452952 in Train_ER_alpha.smi (732/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL1393976 in Train_ER_alpha.smi (733/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL1419810 in Train_ER_alpha.smi (734/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL1531320 in Train_ER_alpha.smi (735/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL1439181 in Train_ER_alpha.smi (736/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL1484547 in Train_ER_alpha.smi (737/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL1577757 in Train_ER_alpha.smi (738/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL1505467 in Train_ER_alpha.smi (739/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL1372307 in Train_ER_alpha.smi (740/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL1525366 in Train_ER_alpha.smi (741/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL1482875 in Train_ER_alpha.smi (742/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL1345810 in Train_ER_alpha.smi (743/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL1374573 in Train_ER_alpha.smi (744/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL1577661 in Train_ER_alpha.smi (745/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL1370120 in Train_ER_alpha.smi (746/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL1407054 in Train_ER_alpha.smi (747/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL1548616 in Train_ER_alpha.smi (748/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL1559731 in Train_ER_alpha.smi (749/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL1555033 in Train_ER_alpha.smi (750/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL1405182 in Train_ER_alpha.smi (751/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL1455849 in Train_ER_alpha.smi (752/1238). Average speed: 0.65 s/mol.\n"" + ] + }, + { + ""name"": ""stdout"", + ""output_type"": ""stream"", + ""text"": [ + ""Processing CHEMBL1356953 in Train_ER_alpha.smi (753/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL1326803 in Train_ER_alpha.smi (754/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL1597805 in Train_ER_alpha.smi (755/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL1084746 in Train_ER_alpha.smi (756/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL1083326 in Train_ER_alpha.smi (757/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL426897 in Train_ER_alpha.smi (758/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL386502 in Train_ER_alpha.smi (759/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL223026 in Train_ER_alpha.smi (760/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL144897 in Train_ER_alpha.smi (761/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL433769 in Train_ER_alpha.smi (762/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL179852 in Train_ER_alpha.smi (763/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL183033 in Train_ER_alpha.smi (764/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL190025 in Train_ER_alpha.smi (765/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL198088 in Train_ER_alpha.smi (766/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL1576748 in Train_ER_alpha.smi (767/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL1388431 in Train_ER_alpha.smi (768/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL1509933 in Train_ER_alpha.smi (769/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL360817 in Train_ER_alpha.smi (770/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL369724 in Train_ER_alpha.smi (771/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL52 in Train_ER_alpha.smi (772/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL195217 in Train_ER_alpha.smi (773/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL1522446 in Train_ER_alpha.smi (774/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL1434556 in Train_ER_alpha.smi (775/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL1414407 in Train_ER_alpha.smi (776/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL1397845 in Train_ER_alpha.smi (777/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL282575 in Train_ER_alpha.smi (778/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL361056 in Train_ER_alpha.smi (779/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL1491959 in Train_ER_alpha.smi (780/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL1326483 in Train_ER_alpha.smi (781/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL188516 in Train_ER_alpha.smi (782/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL178334 in Train_ER_alpha.smi (783/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL9352 in Train_ER_alpha.smi (784/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL198204 in Train_ER_alpha.smi (785/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL187311 in Train_ER_alpha.smi (786/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL425194 in Train_ER_alpha.smi (787/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL1580991 in Train_ER_alpha.smi (788/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL1300662 in Train_ER_alpha.smi (789/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL1442450 in Train_ER_alpha.smi (790/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL1539865 in Train_ER_alpha.smi (791/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL1466928 in Train_ER_alpha.smi (792/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL182157 in Train_ER_alpha.smi (793/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL1467399 in Train_ER_alpha.smi (794/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL192260 in Train_ER_alpha.smi (795/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL1303477 in Train_ER_alpha.smi (796/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL1350069 in Train_ER_alpha.smi (797/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL187313 in Train_ER_alpha.smi (798/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL1391547 in Train_ER_alpha.smi (799/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL1526157 in Train_ER_alpha.smi (800/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL1572777 in Train_ER_alpha.smi (801/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL1320937 in Train_ER_alpha.smi (802/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL1582403 in Train_ER_alpha.smi (803/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL1447311 in Train_ER_alpha.smi (804/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL189556 in Train_ER_alpha.smi (805/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL1431081 in Train_ER_alpha.smi (806/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL1421887 in Train_ER_alpha.smi (807/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL204376 in Train_ER_alpha.smi (809/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL1231453 in Train_ER_alpha.smi (808/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL460647 in Train_ER_alpha.smi (810/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL377295 in Train_ER_alpha.smi (812/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL216334 in Train_ER_alpha.smi (811/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL1325233 in Train_ER_alpha.smi (813/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL1430316 in Train_ER_alpha.smi (814/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL1574512 in Train_ER_alpha.smi (815/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL379314 in Train_ER_alpha.smi (816/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL385949 in Train_ER_alpha.smi (817/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL378883 in Train_ER_alpha.smi (818/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL215428 in Train_ER_alpha.smi (819/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL385028 in Train_ER_alpha.smi (820/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL212391 in Train_ER_alpha.smi (821/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL377457 in Train_ER_alpha.smi (822/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL372783 in Train_ER_alpha.smi (824/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL214692 in Train_ER_alpha.smi (823/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL398456 in Train_ER_alpha.smi (825/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL249904 in Train_ER_alpha.smi (826/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL250110 in Train_ER_alpha.smi (827/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL380452 in Train_ER_alpha.smi (828/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL380358 in Train_ER_alpha.smi (829/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL438443 in Train_ER_alpha.smi (830/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL380838 in Train_ER_alpha.smi (831/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL188299 in Train_ER_alpha.smi (832/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL394614 in Train_ER_alpha.smi (833/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL469511 in Train_ER_alpha.smi (834/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL1300008 in Train_ER_alpha.smi (835/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL1531512 in Train_ER_alpha.smi (836/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL1404523 in Train_ER_alpha.smi (837/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL1386091 in Train_ER_alpha.smi (838/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL211953 in Train_ER_alpha.smi (839/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL214034 in Train_ER_alpha.smi (840/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL211666 in Train_ER_alpha.smi (841/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL206500 in Train_ER_alpha.smi (842/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL206439 in Train_ER_alpha.smi (843/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL1307301 in Train_ER_alpha.smi (844/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL10560 in Train_ER_alpha.smi (845/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL231810 in Train_ER_alpha.smi (846/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL1470395 in Train_ER_alpha.smi (847/1238). Average speed: 0.64 s/mol.\n"", + ""Processing CHEMBL1320651 in Train_ER_alpha.smi (848/1238). Average speed: 0.64 s/mol.\n"" + ] + }, + { + ""name"": ""stdout"", + ""output_type"": ""stream"", + ""text"": [ + ""Processing CHEMBL1417890 in Train_ER_alpha.smi (849/1238). Average speed: 0.64 s/mol.\n"", + ""Processing CHEMBL1429596 in Train_ER_alpha.smi (850/1238). Average speed: 0.64 s/mol.\n"", + ""Processing CHEMBL1404155 in Train_ER_alpha.smi (851/1238). Average speed: 0.64 s/mol.\n"", + ""Processing CHEMBL1442697 in Train_ER_alpha.smi (852/1238). Average speed: 0.64 s/mol.\n"", + ""Processing CHEMBL1603599 in Train_ER_alpha.smi (853/1238). Average speed: 0.64 s/mol.\n"", + ""Processing CHEMBL203283 in Train_ER_alpha.smi (854/1238). Average speed: 0.64 s/mol.\n"", + ""Processing CHEMBL206426 in Train_ER_alpha.smi (856/1238). Average speed: 0.64 s/mol.\n"", + ""Processing CHEMBL377083 in Train_ER_alpha.smi (855/1238). Average speed: 0.64 s/mol.\n"", + ""Processing CHEMBL202738 in Train_ER_alpha.smi (857/1238). Average speed: 0.64 s/mol.\n"", + ""Processing CHEMBL205577 in Train_ER_alpha.smi (858/1238). Average speed: 0.64 s/mol.\n"", + ""Processing CHEMBL208324 in Train_ER_alpha.smi (859/1238). Average speed: 0.64 s/mol.\n"", + ""Processing CHEMBL1471823 in Train_ER_alpha.smi (860/1238). Average speed: 0.64 s/mol.\n"", + ""Processing CHEMBL192621 in Train_ER_alpha.smi (861/1238). Average speed: 0.64 s/mol.\n"", + ""Processing CHEMBL434577 in Train_ER_alpha.smi (863/1238). Average speed: 0.64 s/mol.\n"", + ""Processing CHEMBL192586 in Train_ER_alpha.smi (862/1238). Average speed: 0.64 s/mol.\n"", + ""Processing CHEMBL465879 in Train_ER_alpha.smi (864/1238). Average speed: 0.64 s/mol.\n"", + ""Processing CHEMBL384708 in Train_ER_alpha.smi (865/1238). Average speed: 0.64 s/mol.\n"", + ""Processing CHEMBL221626 in Train_ER_alpha.smi (866/1238). Average speed: 0.64 s/mol.\n"", + ""Processing CHEMBL222452 in Train_ER_alpha.smi (867/1238). Average speed: 0.64 s/mol.\n"", + ""Processing CHEMBL372411 in Train_ER_alpha.smi (868/1238). Average speed: 0.64 s/mol.\n"", + ""Processing CHEMBL201365 in Train_ER_alpha.smi (869/1238). Average speed: 0.64 s/mol.\n"", + ""Processing CHEMBL199766 in Train_ER_alpha.smi (870/1238). Average speed: 0.64 s/mol.\n"", + ""Processing CHEMBL204317 in Train_ER_alpha.smi (871/1238). Average speed: 0.64 s/mol.\n"", + ""Processing CHEMBL383253 in Train_ER_alpha.smi (872/1238). Average speed: 0.64 s/mol.\n"", + ""Processing CHEMBL425603 in Train_ER_alpha.smi (873/1238). Average speed: 0.64 s/mol.\n"", + ""Processing CHEMBL212675 in Train_ER_alpha.smi (874/1238). Average speed: 0.64 s/mol.\n"", + ""Processing CHEMBL180995 in Train_ER_alpha.smi (875/1238). Average speed: 0.64 s/mol.\n"", + ""Processing CHEMBL203123 in Train_ER_alpha.smi (876/1238). Average speed: 0.64 s/mol.\n"", + ""Processing CHEMBL211599 in Train_ER_alpha.smi (877/1238). Average speed: 0.64 s/mol.\n"", + ""Processing CHEMBL213457 in Train_ER_alpha.smi (878/1238). Average speed: 0.64 s/mol.\n"", + ""Processing CHEMBL214291 in Train_ER_alpha.smi (879/1238). Average speed: 0.64 s/mol.\n"", + ""Processing CHEMBL240230 in Train_ER_alpha.smi (880/1238). Average speed: 0.64 s/mol.\n"", + ""Processing CHEMBL394213 in Train_ER_alpha.smi (881/1238). Average speed: 0.64 s/mol.\n"", + ""Processing CHEMBL193146 in Train_ER_alpha.smi (882/1238). Average speed: 0.64 s/mol.\n"", + ""Processing CHEMBL193108 in Train_ER_alpha.smi (883/1238). Average speed: 0.64 s/mol.\n"", + ""Processing CHEMBL182794 in Train_ER_alpha.smi (884/1238). Average speed: 0.64 s/mol.\n"", + ""Processing CHEMBL194873 in Train_ER_alpha.smi (885/1238). Average speed: 0.64 s/mol.\n"", + ""Processing CHEMBL187671 in Train_ER_alpha.smi (886/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL189287 in Train_ER_alpha.smi (887/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL1732228 in Train_ER_alpha.smi (888/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL187663 in Train_ER_alpha.smi (889/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL198089 in Train_ER_alpha.smi (890/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL189706 in Train_ER_alpha.smi (891/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL188230 in Train_ER_alpha.smi (892/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL122237 in Train_ER_alpha.smi (894/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL185012 in Train_ER_alpha.smi (893/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL184679 in Train_ER_alpha.smi (896/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL361005 in Train_ER_alpha.smi (895/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL1424468 in Train_ER_alpha.smi (897/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL187105 in Train_ER_alpha.smi (898/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL188528 in Train_ER_alpha.smi (900/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL276030 in Train_ER_alpha.smi (899/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL181368 in Train_ER_alpha.smi (901/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL363412 in Train_ER_alpha.smi (903/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL195290 in Train_ER_alpha.smi (902/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL186874 in Train_ER_alpha.smi (904/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL364176 in Train_ER_alpha.smi (905/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL365903 in Train_ER_alpha.smi (906/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL195389 in Train_ER_alpha.smi (907/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL360189 in Train_ER_alpha.smi (908/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL365484 in Train_ER_alpha.smi (909/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL193207 in Train_ER_alpha.smi (910/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL189077 in Train_ER_alpha.smi (911/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL1627351 in Train_ER_alpha.smi (912/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL360338 in Train_ER_alpha.smi (913/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL178857 in Train_ER_alpha.smi (915/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL440757 in Train_ER_alpha.smi (914/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL188882 in Train_ER_alpha.smi (916/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL360385 in Train_ER_alpha.smi (917/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL182980 in Train_ER_alpha.smi (918/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL195039 in Train_ER_alpha.smi (919/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL180085 in Train_ER_alpha.smi (920/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL183162 in Train_ER_alpha.smi (921/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL427324 in Train_ER_alpha.smi (922/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL1596554 in Train_ER_alpha.smi (923/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL1301994 in Train_ER_alpha.smi (924/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL185837 in Train_ER_alpha.smi (925/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL1466913 in Train_ER_alpha.smi (926/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL425246 in Train_ER_alpha.smi (927/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL380032 in Train_ER_alpha.smi (928/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL211349 in Train_ER_alpha.smi (929/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL377988 in Train_ER_alpha.smi (930/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL1323006 in Train_ER_alpha.smi (931/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL234524 in Train_ER_alpha.smi (932/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL396947 in Train_ER_alpha.smi (933/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL212468 in Train_ER_alpha.smi (934/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL383518 in Train_ER_alpha.smi (935/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL204272 in Train_ER_alpha.smi (936/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL203340 in Train_ER_alpha.smi (937/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL380720 in Train_ER_alpha.smi (938/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL205614 in Train_ER_alpha.smi (939/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL204118 in Train_ER_alpha.smi (940/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL222453 in Train_ER_alpha.smi (941/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL577709 in Train_ER_alpha.smi (942/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL1566266 in Train_ER_alpha.smi (943/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL183889 in Train_ER_alpha.smi (944/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL205398 in Train_ER_alpha.smi (945/1238). Average speed: 0.65 s/mol.\n"" + ] + }, + { + ""name"": ""stdout"", + ""output_type"": ""stream"", + ""text"": [ + ""Processing CHEMBL379252 in Train_ER_alpha.smi (946/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL203122 in Train_ER_alpha.smi (947/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL206286 in Train_ER_alpha.smi (948/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL225514 in Train_ER_alpha.smi (949/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL222454 in Train_ER_alpha.smi (950/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL391909 in Train_ER_alpha.smi (952/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL215593 in Train_ER_alpha.smi (951/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL241256 in Train_ER_alpha.smi (953/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL241484 in Train_ER_alpha.smi (954/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL396119 in Train_ER_alpha.smi (955/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL372030 in Train_ER_alpha.smi (956/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL202400 in Train_ER_alpha.smi (957/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL437065 in Train_ER_alpha.smi (958/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL377847 in Train_ER_alpha.smi (959/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL226268 in Train_ER_alpha.smi (960/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL226327 in Train_ER_alpha.smi (961/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL239723 in Train_ER_alpha.smi (962/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL190467 in Train_ER_alpha.smi (963/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL192849 in Train_ER_alpha.smi (964/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL205119 in Train_ER_alpha.smi (965/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL210143 in Train_ER_alpha.smi (966/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL208085 in Train_ER_alpha.smi (967/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL383112 in Train_ER_alpha.smi (968/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL209978 in Train_ER_alpha.smi (969/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL213070 in Train_ER_alpha.smi (970/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL341367 in Train_ER_alpha.smi (971/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL426154 in Train_ER_alpha.smi (972/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL216596 in Train_ER_alpha.smi (973/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL1628029 in Train_ER_alpha.smi (974/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL188517 in Train_ER_alpha.smi (975/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL197889 in Train_ER_alpha.smi (976/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL513397 in Train_ER_alpha.smi (977/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL121720 in Train_ER_alpha.smi (978/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL1377389 in Train_ER_alpha.smi (979/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL1516948 in Train_ER_alpha.smi (980/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL1519384 in Train_ER_alpha.smi (981/1238). Average speed: 0.66 s/mol.\n"", + ""Processing CHEMBL1330161 in Train_ER_alpha.smi (982/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL1309506 in Train_ER_alpha.smi (983/1238). Average speed: 0.66 s/mol.\n"", + ""Processing CHEMBL1162 in Train_ER_alpha.smi (984/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL1507012 in Train_ER_alpha.smi (985/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL1468124 in Train_ER_alpha.smi (986/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL1476347 in Train_ER_alpha.smi (987/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL1612880 in Train_ER_alpha.smi (988/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL1359354 in Train_ER_alpha.smi (989/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL1585431 in Train_ER_alpha.smi (990/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL1501342 in Train_ER_alpha.smi (991/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL1580543 in Train_ER_alpha.smi (992/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL1448272 in Train_ER_alpha.smi (993/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL1406018 in Train_ER_alpha.smi (994/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL245378 in Train_ER_alpha.smi (995/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL1532234 in Train_ER_alpha.smi (996/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL1312859 in Train_ER_alpha.smi (997/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL249700 in Train_ER_alpha.smi (998/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL1584299 in Train_ER_alpha.smi (999/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL457035 in Train_ER_alpha.smi (1000/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL1983345 in Train_ER_alpha.smi (1001/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL1996233 in Train_ER_alpha.smi (1002/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL1976499 in Train_ER_alpha.smi (1003/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL1984703 in Train_ER_alpha.smi (1004/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL2103774 in Train_ER_alpha.smi (1005/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL1999723 in Train_ER_alpha.smi (1006/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL1972915 in Train_ER_alpha.smi (1007/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL2024377 in Train_ER_alpha.smi (1008/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL2024376 in Train_ER_alpha.smi (1009/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL2113002 in Train_ER_alpha.smi (1010/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL2112471 in Train_ER_alpha.smi (1011/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL2112469 in Train_ER_alpha.smi (1012/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL2112470 in Train_ER_alpha.smi (1013/1238). Average speed: 0.65 s/mol.\n"", + ""Processing CHEMBL2113027 in Train_ER_alpha.smi (1014/1238). Average speed: 0.66 s/mol.\n"", + ""Processing CHEMBL2113001 in Train_ER_alpha.smi (1015/1238). Average speed: 0.66 s/mol.\n"", + ""Processing CHEMBL2071180 in Train_ER_alpha.smi (1016/1238). Average speed: 0.66 s/mol.\n"", + ""Processing CHEMBL2403351 in Train_ER_alpha.smi (1017/1238). Average speed: 0.66 s/mol.\n"", + ""Processing CHEMBL1208076 in Train_ER_alpha.smi (1018/1238). Average speed: 0.66 s/mol.\n"", + ""Processing CHEMBL2403361 in Train_ER_alpha.smi (1019/1238). Average speed: 0.66 s/mol.\n"", + ""Processing CHEMBL2403363 in Train_ER_alpha.smi (1020/1238). Average speed: 0.66 s/mol.\n"", + ""Processing CHEMBL2403346 in Train_ER_alpha.smi (1021/1238). Average speed: 0.66 s/mol.\n"", + ""Processing CHEMBL2403359 in Train_ER_alpha.smi (1022/1238). Average speed: 0.66 s/mol.\n"", + ""Processing CHEMBL2403358 in Train_ER_alpha.smi (1023/1238). Average speed: 0.66 s/mol.\n"", + ""Processing CHEMBL2403360 in Train_ER_alpha.smi (1024/1238). Average speed: 0.66 s/mol.\n"", + ""Processing CHEMBL2401849 in Train_ER_alpha.smi (1025/1238). Average speed: 0.66 s/mol.\n"", + ""Processing CHEMBL2403362 in Train_ER_alpha.smi (1026/1238). Average speed: 0.66 s/mol.\n"", + ""Processing CHEMBL2401848 in Train_ER_alpha.smi (1027/1238). Average speed: 0.66 s/mol.\n"", + ""Processing CHEMBL2403348 in Train_ER_alpha.smi (1028/1238). Average speed: 0.66 s/mol.\n"", + ""Processing CHEMBL2407651 in Train_ER_alpha.smi (1029/1238). Average speed: 0.66 s/mol.\n"", + ""Processing CHEMBL2407647 in Train_ER_alpha.smi (1030/1238). Average speed: 0.66 s/mol.\n"", + ""Processing CHEMBL2403347 in Train_ER_alpha.smi (1031/1238). Average speed: 0.66 s/mol.\n"", + ""Processing CHEMBL2392777 in Train_ER_alpha.smi (1032/1238). Average speed: 0.66 s/mol.\n"", + ""Processing CHEMBL2441817 in Train_ER_alpha.smi (1033/1238). Average speed: 0.66 s/mol.\n"", + ""Processing CHEMBL2441818 in Train_ER_alpha.smi (1034/1238). Average speed: 0.66 s/mol.\n"", + ""Processing CHEMBL2441819 in Train_ER_alpha.smi (1035/1238). Average speed: 0.66 s/mol.\n"", + ""Processing CHEMBL3099431 in Train_ER_alpha.smi (1036/1238). Average speed: 0.66 s/mol.\n"", + ""Processing CHEMBL3099432 in Train_ER_alpha.smi (1037/1238). Average speed: 0.66 s/mol.\n"", + ""Processing CHEMBL3098274 in Train_ER_alpha.smi (1038/1238). Average speed: 0.66 s/mol.\n"", + ""Processing CHEMBL3098273 in Train_ER_alpha.smi (1039/1238). Average speed: 0.66 s/mol.\n"", + ""Processing CHEMBL3099429 in Train_ER_alpha.smi (1040/1238). Average speed: 0.66 s/mol.\n"", + ""Processing CHEMBL3145004 in Train_ER_alpha.smi (1041/1238). Average speed: 0.66 s/mol.\n"" + ] + }, + { + ""name"": ""stdout"", + ""output_type"": ""stream"", + ""text"": [ + ""Processing CHEMBL3098275 in Train_ER_alpha.smi (1042/1238). Average speed: 0.66 s/mol.\n"", + ""Processing CHEMBL3208438 in Train_ER_alpha.smi (1043/1238). Average speed: 0.66 s/mol.\n"", + ""Processing CHEMBL3208556 in Train_ER_alpha.smi (1044/1238). Average speed: 0.66 s/mol.\n"", + ""Processing CHEMBL3189964 in Train_ER_alpha.smi (1045/1238). Average speed: 0.66 s/mol.\n"", + ""Processing CHEMBL3190827 in Train_ER_alpha.smi (1046/1238). Average speed: 0.66 s/mol.\n"", + ""Processing CHEMBL3199523 in Train_ER_alpha.smi (1047/1238). Average speed: 0.66 s/mol.\n"", + ""Processing CHEMBL3189836 in Train_ER_alpha.smi (1048/1238). Average speed: 0.66 s/mol.\n"", + ""Processing CHEMBL3109601 in Train_ER_alpha.smi (1050/1238). Average speed: 0.66 s/mol.\n"", + ""Processing CHEMBL3213380 in Train_ER_alpha.smi (1049/1238). Average speed: 0.66 s/mol.\n"", + ""Processing CHEMBL3109240 in Train_ER_alpha.smi (1051/1238). Average speed: 0.66 s/mol.\n"", + ""Processing CHEMBL3109156 in Train_ER_alpha.smi (1052/1238). Average speed: 0.66 s/mol.\n"", + ""Processing CHEMBL3109154 in Train_ER_alpha.smi (1053/1238). Average speed: 0.66 s/mol.\n"", + ""Processing CHEMBL3189442 in Train_ER_alpha.smi (1054/1238). Average speed: 0.66 s/mol.\n"", + ""Processing CHEMBL3189821 in Train_ER_alpha.smi (1055/1238). Average speed: 0.66 s/mol.\n"", + ""Processing CHEMBL3198515 in Train_ER_alpha.smi (1056/1238). Average speed: 0.66 s/mol.\n"", + ""Processing CHEMBL3193600 in Train_ER_alpha.smi (1057/1238). Average speed: 0.66 s/mol.\n"", + ""Processing CHEMBL3207314 in Train_ER_alpha.smi (1058/1238). Average speed: 0.66 s/mol.\n"", + ""Processing CHEMBL3214324 in Train_ER_alpha.smi (1059/1238). Average speed: 0.66 s/mol.\n"", + ""Processing CHEMBL3212870 in Train_ER_alpha.smi (1060/1238). Average speed: 0.66 s/mol.\n"", + ""Processing CHEMBL3235685 in Train_ER_alpha.smi (1062/1238). Average speed: 0.66 s/mol.\n"", + ""Processing CHEMBL3208784 in Train_ER_alpha.smi (1061/1238). Average speed: 0.66 s/mol.\n"", + ""Processing CHEMBL3235686 in Train_ER_alpha.smi (1063/1238). Average speed: 0.66 s/mol.\n"", + ""Processing CHEMBL3195623 in Train_ER_alpha.smi (1064/1238). Average speed: 0.66 s/mol.\n"", + ""Processing CHEMBL3196534 in Train_ER_alpha.smi (1065/1238). Average speed: 0.66 s/mol.\n"", + ""Processing CHEMBL3360841 in Train_ER_alpha.smi (1066/1238). Average speed: 0.66 s/mol.\n"", + ""Processing CHEMBL3290287 in Train_ER_alpha.smi (1067/1238). Average speed: 0.66 s/mol.\n"", + ""Processing CHEMBL3290288 in Train_ER_alpha.smi (1068/1238). Average speed: 0.66 s/mol.\n"", + ""Processing CHEMBL3288736 in Train_ER_alpha.smi (1069/1238). Average speed: 0.66 s/mol.\n"", + ""Processing CHEMBL3427502 in Train_ER_alpha.smi (1070/1238). Average speed: 0.66 s/mol.\n"", + ""Processing CHEMBL3360850 in Train_ER_alpha.smi (1071/1238). Average speed: 0.66 s/mol.\n"", + ""Processing CHEMBL3581693 in Train_ER_alpha.smi (1072/1238). Average speed: 0.66 s/mol.\n"", + ""Processing CHEMBL3586190 in Train_ER_alpha.smi (1073/1238). Average speed: 0.66 s/mol.\n"", + ""Processing CHEMBL3586194 in Train_ER_alpha.smi (1074/1238). Average speed: 0.66 s/mol.\n"", + ""Processing CHEMBL3586191 in Train_ER_alpha.smi (1075/1238). Average speed: 0.66 s/mol.\n"", + ""Processing CHEMBL3360845 in Train_ER_alpha.smi (1076/1238). Average speed: 0.66 s/mol.\n"", + ""Processing CHEMBL3586192 in Train_ER_alpha.smi (1077/1238). Average speed: 0.66 s/mol.\n"", + ""Processing CHEMBL3586193 in Train_ER_alpha.smi (1078/1238). Average speed: 0.66 s/mol.\n"", + ""Processing CHEMBL3586195 in Train_ER_alpha.smi (1079/1238). Average speed: 0.67 s/mol.\n"", + ""Processing CHEMBL3360843 in Train_ER_alpha.smi (1080/1238). Average speed: 0.67 s/mol.\n"", + ""Processing CHEMBL3360846 in Train_ER_alpha.smi (1081/1238). Average speed: 0.67 s/mol.\n"", + ""Processing CHEMBL3360848 in Train_ER_alpha.smi (1082/1238). Average speed: 0.67 s/mol.\n"", + ""Processing CHEMBL3589347 in Train_ER_alpha.smi (1083/1238). Average speed: 0.67 s/mol.\n"", + ""Processing CHEMBL3589365 in Train_ER_alpha.smi (1084/1238). Average speed: 0.67 s/mol.\n"", + ""Processing CHEMBL3589701 in Train_ER_alpha.smi (1085/1238). Average speed: 0.67 s/mol.\n"", + ""Processing CHEMBL3589349 in Train_ER_alpha.smi (1086/1238). Average speed: 0.67 s/mol.\n"", + ""Processing CHEMBL3589367 in Train_ER_alpha.smi (1087/1238). Average speed: 0.67 s/mol.\n"", + ""Processing CHEMBL3578274 in Train_ER_alpha.smi (1088/1238). Average speed: 0.67 s/mol.\n"", + ""Processing CHEMBL3578276 in Train_ER_alpha.smi (1089/1238). Average speed: 0.67 s/mol.\n"", + ""Processing CHEMBL3578271 in Train_ER_alpha.smi (1090/1238). Average speed: 0.67 s/mol.\n"", + ""Processing CHEMBL3589346 in Train_ER_alpha.smi (1091/1238). Average speed: 0.67 s/mol.\n"", + ""Processing CHEMBL3589361 in Train_ER_alpha.smi (1092/1238). Average speed: 0.67 s/mol.\n"", + ""Processing CHEMBL3589363 in Train_ER_alpha.smi (1093/1238). Average speed: 0.67 s/mol.\n"", + ""Processing CHEMBL3589364 in Train_ER_alpha.smi (1094/1238). Average speed: 0.67 s/mol.\n"", + ""Processing CHEMBL3588865 in Train_ER_alpha.smi (1095/1238). Average speed: 0.67 s/mol.\n"", + ""Processing CHEMBL3589352 in Train_ER_alpha.smi (1096/1238). Average speed: 0.67 s/mol.\n"", + ""Processing CHEMBL3589356 in Train_ER_alpha.smi (1097/1238). Average speed: 0.67 s/mol.\n"", + ""Processing CHEMBL3597498 in Train_ER_alpha.smi (1098/1238). Average speed: 0.67 s/mol.\n"", + ""Processing CHEMBL3597499 in Train_ER_alpha.smi (1099/1238). Average speed: 0.67 s/mol.\n"", + ""Processing CHEMBL3597501 in Train_ER_alpha.smi (1100/1238). Average speed: 0.68 s/mol.\n"", + ""Processing CHEMBL3597505 in Train_ER_alpha.smi (1101/1238). Average speed: 0.68 s/mol.\n"", + ""Processing CHEMBL3597508 in Train_ER_alpha.smi (1102/1238). Average speed: 0.68 s/mol.\n"", + ""Processing CHEMBL3597509 in Train_ER_alpha.smi (1103/1238). Average speed: 0.68 s/mol.\n"", + ""Processing CHEMBL3589354 in Train_ER_alpha.smi (1104/1238). Average speed: 0.68 s/mol.\n"", + ""Processing CHEMBL3589368 in Train_ER_alpha.smi (1105/1238). Average speed: 0.68 s/mol.\n"", + ""Processing CHEMBL3597502 in Train_ER_alpha.smi (1106/1238). Average speed: 0.68 s/mol.\n"", + ""Processing CHEMBL3360847 in Train_ER_alpha.smi (1107/1238). Average speed: 0.68 s/mol.\n"", + ""Processing CHEMBL3360849 in Train_ER_alpha.smi (1108/1238). Average speed: 0.69 s/mol.\n"", + ""Processing CHEMBL3427411 in Train_ER_alpha.smi (1109/1238). Average speed: 0.69 s/mol.\n"", + ""Processing CHEMBL3427406 in Train_ER_alpha.smi (1110/1238). Average speed: 0.69 s/mol.\n"", + ""Processing CHEMBL3578285 in Train_ER_alpha.smi (1111/1238). Average speed: 0.69 s/mol.\n"", + ""Processing CHEMBL3578286 in Train_ER_alpha.smi (1112/1238). Average speed: 0.69 s/mol.\n"", + ""Processing CHEMBL3576885 in Train_ER_alpha.smi (1113/1238). Average speed: 0.69 s/mol.\n"", + ""Processing CHEMBL3578284 in Train_ER_alpha.smi (1114/1238). Average speed: 0.69 s/mol.\n"", + ""Processing CHEMBL3622364 in Train_ER_alpha.smi (1115/1238). Average speed: 0.69 s/mol.\n"", + ""Processing CHEMBL3622365 in Train_ER_alpha.smi (1116/1238). Average speed: 0.70 s/mol.\n"", + ""Processing CHEMBL3657148 in Train_ER_alpha.smi (1118/1238). Average speed: 0.70 s/mol.\n"", + ""Processing CHEMBL3657137 in Train_ER_alpha.smi (1117/1238). Average speed: 0.70 s/mol.\n"", + ""Processing CHEMBL3657159 in Train_ER_alpha.smi (1119/1238). Average speed: 0.70 s/mol.\n"", + ""Processing CHEMBL3657170 in Train_ER_alpha.smi (1120/1238). Average speed: 0.70 s/mol.\n"", + ""Processing CHEMBL3657181 in Train_ER_alpha.smi (1121/1238). Average speed: 0.70 s/mol.\n"", + ""Processing CHEMBL3667040 in Train_ER_alpha.smi (1122/1238). Average speed: 0.70 s/mol.\n"", + ""Processing CHEMBL3657142 in Train_ER_alpha.smi (1123/1238). Average speed: 0.70 s/mol.\n"", + ""Processing CHEMBL3657143 in Train_ER_alpha.smi (1124/1238). Average speed: 0.70 s/mol.\n"", + ""Processing CHEMBL3657144 in Train_ER_alpha.smi (1125/1238). Average speed: 0.70 s/mol.\n"", + ""Processing CHEMBL3657145 in Train_ER_alpha.smi (1126/1238). Average speed: 0.70 s/mol.\n"", + ""Processing CHEMBL3657146 in Train_ER_alpha.smi (1127/1238). Average speed: 0.70 s/mol.\n"", + ""Processing CHEMBL3657172 in Train_ER_alpha.smi (1128/1238). Average speed: 0.70 s/mol.\n"", + ""Processing CHEMBL3657173 in Train_ER_alpha.smi (1129/1238). Average speed: 0.70 s/mol.\n"", + ""Processing CHEMBL3657174 in Train_ER_alpha.smi (1130/1238). Average speed: 0.71 s/mol.\n"", + ""Processing CHEMBL3657147 in Train_ER_alpha.smi (1132/1238). Average speed: 0.71 s/mol.\n"", + ""Processing CHEMBL3657175 in Train_ER_alpha.smi (1131/1238). Average speed: 0.71 s/mol.\n"", + ""Processing CHEMBL3657154 in Train_ER_alpha.smi (1133/1238). Average speed: 0.71 s/mol.\n"", + ""Processing CHEMBL3657155 in Train_ER_alpha.smi (1134/1238). Average speed: 0.71 s/mol.\n"", + ""Processing CHEMBL3657150 in Train_ER_alpha.smi (1136/1238). Average speed: 0.71 s/mol.\n"", + ""Processing CHEMBL3657149 in Train_ER_alpha.smi (1135/1238). Average speed: 0.71 s/mol.\n"" + ] + }, + { + ""name"": ""stdout"", + ""output_type"": ""stream"", + ""text"": [ + ""Processing CHEMBL3657156 in Train_ER_alpha.smi (1137/1238). Average speed: 0.71 s/mol.\n"", + ""Processing CHEMBL3657157 in Train_ER_alpha.smi (1138/1238). Average speed: 0.71 s/mol.\n"", + ""Processing CHEMBL3657158 in Train_ER_alpha.smi (1139/1238). Average speed: 0.71 s/mol.\n"", + ""Processing CHEMBL3657151 in Train_ER_alpha.smi (1140/1238). Average speed: 0.71 s/mol.\n"", + ""Processing CHEMBL3657152 in Train_ER_alpha.smi (1141/1238). Average speed: 0.72 s/mol.\n"", + ""Processing CHEMBL3657153 in Train_ER_alpha.smi (1142/1238). Average speed: 0.72 s/mol.\n"", + ""Processing CHEMBL3657135 in Train_ER_alpha.smi (1143/1238). Average speed: 0.72 s/mol.\n"", + ""Processing CHEMBL3657176 in Train_ER_alpha.smi (1144/1238). Average speed: 0.72 s/mol.\n"", + ""Processing CHEMBL3657177 in Train_ER_alpha.smi (1145/1238). Average speed: 0.72 s/mol.\n"", + ""Processing CHEMBL3657184 in Train_ER_alpha.smi (1146/1238). Average speed: 0.72 s/mol.\n"", + ""Processing CHEMBL3657185 in Train_ER_alpha.smi (1147/1238). Average speed: 0.72 s/mol.\n"", + ""Processing CHEMBL3657164 in Train_ER_alpha.smi (1148/1238). Average speed: 0.72 s/mol.\n"", + ""Processing CHEMBL3657165 in Train_ER_alpha.smi (1149/1238). Average speed: 0.72 s/mol.\n"", + ""Processing CHEMBL3648370 in Train_ER_alpha.smi (1150/1238). Average speed: 0.72 s/mol.\n"", + ""Processing CHEMBL3657178 in Train_ER_alpha.smi (1151/1238). Average speed: 0.72 s/mol.\n"", + ""Processing CHEMBL3657179 in Train_ER_alpha.smi (1152/1238). Average speed: 0.72 s/mol.\n"", + ""Processing CHEMBL3657160 in Train_ER_alpha.smi (1153/1238). Average speed: 0.72 s/mol.\n"", + ""Processing CHEMBL3657161 in Train_ER_alpha.smi (1154/1238). Average speed: 0.72 s/mol.\n"", + ""Processing CHEMBL3657162 in Train_ER_alpha.smi (1155/1238). Average speed: 0.72 s/mol.\n"", + ""Processing CHEMBL3657163 in Train_ER_alpha.smi (1156/1238). Average speed: 0.72 s/mol.\n"", + ""Processing CHEMBL3657180 in Train_ER_alpha.smi (1157/1238). Average speed: 0.73 s/mol.\n"", + ""Processing CHEMBL3657182 in Train_ER_alpha.smi (1158/1238). Average speed: 0.73 s/mol.\n"", + ""Processing CHEMBL3657183 in Train_ER_alpha.smi (1159/1238). Average speed: 0.73 s/mol.\n"", + ""Processing CHEMBL3657166 in Train_ER_alpha.smi (1160/1238). Average speed: 0.73 s/mol.\n"", + ""Processing CHEMBL3657167 in Train_ER_alpha.smi (1161/1238). Average speed: 0.73 s/mol.\n"", + ""Processing CHEMBL3657168 in Train_ER_alpha.smi (1162/1238). Average speed: 0.73 s/mol.\n"", + ""Processing CHEMBL3657136 in Train_ER_alpha.smi (1163/1238). Average speed: 0.73 s/mol.\n"", + ""Processing CHEMBL3657138 in Train_ER_alpha.smi (1164/1238). Average speed: 0.73 s/mol.\n"", + ""Processing CHEMBL3657139 in Train_ER_alpha.smi (1165/1238). Average speed: 0.73 s/mol.\n"", + ""Processing CHEMBL3657140 in Train_ER_alpha.smi (1166/1238). Average speed: 0.73 s/mol.\n"", + ""Processing CHEMBL3657141 in Train_ER_alpha.smi (1167/1238). Average speed: 0.73 s/mol.\n"", + ""Processing CHEMBL3657169 in Train_ER_alpha.smi (1168/1238). Average speed: 0.73 s/mol.\n"", + ""Processing CHEMBL3657171 in Train_ER_alpha.smi (1169/1238). Average speed: 0.73 s/mol.\n"", + ""Processing CHEMBL3775116 in Train_ER_alpha.smi (1170/1238). Average speed: 0.73 s/mol.\n"", + ""Processing CHEMBL3774660 in Train_ER_alpha.smi (1171/1238). Average speed: 0.73 s/mol.\n"", + ""Processing CHEMBL3775016 in Train_ER_alpha.smi (1172/1238). Average speed: 0.73 s/mol.\n"", + ""Processing CHEMBL3774647 in Train_ER_alpha.smi (1173/1238). Average speed: 0.73 s/mol.\n"", + ""Processing CHEMBL3775129 in Train_ER_alpha.smi (1174/1238). Average speed: 0.73 s/mol.\n"", + ""Processing CHEMBL3775006 in Train_ER_alpha.smi (1175/1238). Average speed: 0.73 s/mol.\n"", + ""Processing CHEMBL3775410 in Train_ER_alpha.smi (1176/1238). Average speed: 0.74 s/mol.\n"", + ""Processing CHEMBL3775434 in Train_ER_alpha.smi (1177/1238). Average speed: 0.73 s/mol.\n"", + ""Processing CHEMBL3775224 in Train_ER_alpha.smi (1178/1238). Average speed: 0.73 s/mol.\n"", + ""Processing CHEMBL3775978 in Train_ER_alpha.smi (1179/1238). Average speed: 0.74 s/mol.\n"", + ""Processing CHEMBL3775283 in Train_ER_alpha.smi (1180/1238). Average speed: 0.74 s/mol.\n"", + ""Processing CHEMBL3775375 in Train_ER_alpha.smi (1181/1238). Average speed: 0.73 s/mol.\n"", + ""Processing CHEMBL3774642 in Train_ER_alpha.smi (1182/1238). Average speed: 0.74 s/mol.\n"", + ""Processing CHEMBL3805921 in Train_ER_alpha.smi (1183/1238). Average speed: 0.74 s/mol.\n"", + ""Processing CHEMBL3805170 in Train_ER_alpha.smi (1184/1238). Average speed: 0.74 s/mol.\n"", + ""Processing CHEMBL3804907 in Train_ER_alpha.smi (1185/1238). Average speed: 0.74 s/mol.\n"", + ""Processing CHEMBL3805025 in Train_ER_alpha.smi (1186/1238). Average speed: 0.74 s/mol.\n"", + ""Processing CHEMBL3805385 in Train_ER_alpha.smi (1187/1238). Average speed: 0.74 s/mol.\n"", + ""Processing CHEMBL3823498 in Train_ER_alpha.smi (1188/1238). Average speed: 0.74 s/mol.\n"", + ""Processing CHEMBL3815076 in Train_ER_alpha.smi (1189/1238). Average speed: 0.74 s/mol.\n"", + ""Processing CHEMBL3823426 in Train_ER_alpha.smi (1190/1238). Average speed: 0.74 s/mol.\n"", + ""Processing CHEMBL3827870 in Train_ER_alpha.smi (1191/1238). Average speed: 0.74 s/mol.\n"", + ""Processing CHEMBL3827137 in Train_ER_alpha.smi (1192/1238). Average speed: 0.74 s/mol.\n"", + ""Processing CHEMBL3827692 in Train_ER_alpha.smi (1193/1238). Average speed: 0.74 s/mol.\n"", + ""Processing CHEMBL3827034 in Train_ER_alpha.smi (1194/1238). Average speed: 0.74 s/mol.\n"", + ""Processing CHEMBL3827784 in Train_ER_alpha.smi (1195/1238). Average speed: 0.74 s/mol.\n"", + ""Processing CHEMBL3827200 in Train_ER_alpha.smi (1196/1238). Average speed: 0.74 s/mol.\n"", + ""Processing CHEMBL3828208 in Train_ER_alpha.smi (1197/1238). Average speed: 0.74 s/mol.\n"", + ""Processing CHEMBL3827633 in Train_ER_alpha.smi (1198/1238). Average speed: 0.74 s/mol.\n"", + ""Processing CHEMBL3827098 in Train_ER_alpha.smi (1199/1238). Average speed: 0.74 s/mol.\n"", + ""Processing CHEMBL3828416 in Train_ER_alpha.smi (1200/1238). Average speed: 0.74 s/mol.\n"", + ""Processing CHEMBL3827045 in Train_ER_alpha.smi (1201/1238). Average speed: 0.74 s/mol.\n"", + ""Processing CHEMBL3827152 in Train_ER_alpha.smi (1202/1238). Average speed: 0.74 s/mol.\n"", + ""Processing CHEMBL3828422 in Train_ER_alpha.smi (1203/1238). Average speed: 0.74 s/mol.\n"", + ""Processing CHEMBL3827193 in Train_ER_alpha.smi (1204/1238). Average speed: 0.74 s/mol.\n"", + ""Processing CHEMBL3827286 in Train_ER_alpha.smi (1205/1238). Average speed: 0.74 s/mol.\n"", + ""Processing CHEMBL3828117 in Train_ER_alpha.smi (1206/1238). Average speed: 0.74 s/mol.\n"", + ""Processing CHEMBL3823192 in Train_ER_alpha.smi (1207/1238). Average speed: 0.74 s/mol.\n"", + ""Processing CHEMBL101382 in Train_ER_alpha.smi (1208/1238). Average speed: 0.74 s/mol.\n"", + ""Processing CHEMBL180792 in Train_ER_alpha.smi (1209/1238). Average speed: 0.74 s/mol.\n"", + ""Processing CHEMBL183584 in Train_ER_alpha.smi (1210/1238). Average speed: 0.74 s/mol.\n"", + ""Processing CHEMBL184202 in Train_ER_alpha.smi (1211/1238). Average speed: 0.74 s/mol.\n"", + ""Processing CHEMBL191974 in Train_ER_alpha.smi (1212/1238). Average speed: 0.74 s/mol.\n"", + ""Processing CHEMBL192548 in Train_ER_alpha.smi (1213/1238). Average speed: 0.74 s/mol.\n"", + ""Processing CHEMBL193518 in Train_ER_alpha.smi (1214/1238). Average speed: 0.74 s/mol.\n"", + ""Processing CHEMBL209269 in Train_ER_alpha.smi (1215/1238). Average speed: 0.74 s/mol.\n"", + ""Processing CHEMBL209851 in Train_ER_alpha.smi (1216/1238). Average speed: 0.74 s/mol.\n"", + ""Processing CHEMBL2403356 in Train_ER_alpha.smi (1217/1238). Average speed: 0.74 s/mol.\n"", + ""Processing CHEMBL2403357 in Train_ER_alpha.smi (1218/1238). Average speed: 0.74 s/mol.\n"", + ""Processing CHEMBL246138 in Train_ER_alpha.smi (1219/1238). Average speed: 0.74 s/mol.\n"", + ""Processing CHEMBL267385 in Train_ER_alpha.smi (1220/1238). Average speed: 0.74 s/mol.\n"", + ""Processing CHEMBL281499 in Train_ER_alpha.smi (1221/1238). Average speed: 0.74 s/mol.\n"", + ""Processing CHEMBL304552 in Train_ER_alpha.smi (1222/1238). Average speed: 0.74 s/mol.\n"", + ""Processing CHEMBL30707 in Train_ER_alpha.smi (1223/1238). Average speed: 0.74 s/mol.\n"", + ""Processing CHEMBL328190 in Train_ER_alpha.smi (1224/1238). Average speed: 0.74 s/mol.\n"", + ""Processing CHEMBL3427402 in Train_ER_alpha.smi (1225/1238). Average speed: 0.74 s/mol.\n"", + ""Processing CHEMBL3623002 in Train_ER_alpha.smi (1226/1238). Average speed: 0.74 s/mol.\n"", + ""Processing CHEMBL3623003 in Train_ER_alpha.smi (1227/1238). Average speed: 0.74 s/mol.\n"", + ""Processing CHEMBL3623004 in Train_ER_alpha.smi (1228/1238). Average speed: 0.74 s/mol.\n"", + ""Processing CHEMBL362623 in Train_ER_alpha.smi (1229/1238). Average speed: 0.74 s/mol.\n"", + ""Processing CHEMBL367588 in Train_ER_alpha.smi (1230/1238). Average speed: 0.74 s/mol.\n"", + ""Processing CHEMBL370039 in Train_ER_alpha.smi (1231/1238). Average speed: 0.74 s/mol.\n"" + ] + }, + { + ""name"": ""stdout"", + ""output_type"": ""stream"", + ""text"": [ + ""Processing CHEMBL3774510 in Train_ER_alpha.smi (1232/1238). Average speed: 0.74 s/mol.\n"", + ""Processing CHEMBL3774584 in Train_ER_alpha.smi (1233/1238). Average speed: 0.74 s/mol.\n"", + ""Processing CHEMBL3774737 in Train_ER_alpha.smi (1234/1238). Average speed: 0.74 s/mol.\n"", + ""Processing CHEMBL3774944 in Train_ER_alpha.smi (1235/1238). Average speed: 0.74 s/mol.\n"", + ""Processing CHEMBL3775766 in Train_ER_alpha.smi (1236/1238). Average speed: 0.74 s/mol.\n"", + ""Processing CHEMBL3775908 in Train_ER_alpha.smi (1237/1238). Average speed: 0.74 s/mol.\n"", + ""Processing CHEMBL691 in Train_ER_alpha.smi (1238/1238). Average speed: 0.74 s/mol.\n"", + ""Descriptor calculation completed in 15 mins 12.291 secs . Average speed: 0.74 s/mol.\n"", + ""Train_ER_alpha.smi\n"", + ""Train_ER_alpha.csv\n"", + ""AtomPairs2DFingerprinter.xml\n"", + ""AtomPairs2DFingerprinter_\n"", + ""Processing CHEMBL370037 in Train_ER_alpha.smi (1/1238). \n"", + ""Processing CHEMBL189073 in Train_ER_alpha.smi (2/1238). \n"", + ""Processing CHEMBL365290 in Train_ER_alpha.smi (3/1238). \n"", + ""Processing CHEMBL184431 in Train_ER_alpha.smi (5/1238). \n"", + ""Processing CHEMBL191648 in Train_ER_alpha.smi (4/1238). \n"", + ""Processing CHEMBL364551 in Train_ER_alpha.smi (6/1238). \n"", + ""Processing CHEMBL124482 in Train_ER_alpha.smi (7/1238). Average speed: 1.74 s/mol.\n"", + ""Processing CHEMBL1628199 in Train_ER_alpha.smi (16/1238). Average speed: 0.37 s/mol.\n"", + ""Processing CHEMBL153062 in Train_ER_alpha.smi (18/1238). Average speed: 0.32 s/mol.\n"", + ""Processing CHEMBL1442416 in Train_ER_alpha.smi (20/1238). Average speed: 0.32 s/mol.\n"", + ""Processing CHEMBL1388271 in Train_ER_alpha.smi (21/1238). Average speed: 0.32 s/mol.\n"", + ""Processing CHEMBL1413557 in Train_ER_alpha.smi (26/1238). Average speed: 0.18 s/mol.\n"", + ""Processing CHEMBL1608989 in Train_ER_alpha.smi (28/1238). Average speed: 0.17 s/mol.\n"", + ""Processing CHEMBL1538325 in Train_ER_alpha.smi (30/1238). Average speed: 0.17 s/mol.\n"", + ""Processing CHEMBL1343011 in Train_ER_alpha.smi (37/1238). Average speed: 0.13 s/mol.\n"", + ""Processing CHEMBL1613068 in Train_ER_alpha.smi (38/1238). Average speed: 0.13 s/mol.\n"", + ""Processing CHEMBL1464837 in Train_ER_alpha.smi (39/1238). Average speed: 0.13 s/mol.\n"", + ""Processing CHEMBL1466882 in Train_ER_alpha.smi (47/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL1329455 in Train_ER_alpha.smi (49/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL1420411 in Train_ER_alpha.smi (55/1238). Average speed: 0.09 s/mol.\n"", + ""Processing CHEMBL124474 in Train_ER_alpha.smi (59/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL1081361 in Train_ER_alpha.smi (66/1238). Average speed: 0.09 s/mol.\n"", + ""Processing CHEMBL235496 in Train_ER_alpha.smi (67/1238). Average speed: 0.09 s/mol.\n"", + ""Processing CHEMBL184371 in Train_ER_alpha.smi (70/1238). Average speed: 0.08 s/mol.\n"", + ""Processing CHEMBL365625 in Train_ER_alpha.smi (77/1238). Average speed: 0.07 s/mol.\n"", + ""Processing CHEMBL179929 in Train_ER_alpha.smi (79/1238). Average speed: 0.07 s/mol.\n"", + ""Processing CHEMBL470785 in Train_ER_alpha.smi (83/1238). Average speed: 0.07 s/mol.\n"", + ""Processing CHEMBL185114 in Train_ER_alpha.smi (85/1238). Average speed: 0.07 s/mol.\n"", + ""Processing CHEMBL511492 in Train_ER_alpha.smi (93/1238). Average speed: 0.07 s/mol.\n"", + ""Processing CHEMBL205137 in Train_ER_alpha.smi (101/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL429446 in Train_ER_alpha.smi (104/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL212419 in Train_ER_alpha.smi (112/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL211493 in Train_ER_alpha.smi (113/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL377475 in Train_ER_alpha.smi (114/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL204024 in Train_ER_alpha.smi (115/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL195807 in Train_ER_alpha.smi (120/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL372808 in Train_ER_alpha.smi (123/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL241485 in Train_ER_alpha.smi (127/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL360315 in Train_ER_alpha.smi (132/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL371777 in Train_ER_alpha.smi (133/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL204222 in Train_ER_alpha.smi (138/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL441366 in Train_ER_alpha.smi (140/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL189080 in Train_ER_alpha.smi (142/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL180707 in Train_ER_alpha.smi (143/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL8145 in Train_ER_alpha.smi (156/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL192068 in Train_ER_alpha.smi (161/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL360651 in Train_ER_alpha.smi (162/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL195878 in Train_ER_alpha.smi (163/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL365293 in Train_ER_alpha.smi (165/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL426641 in Train_ER_alpha.smi (167/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL374799 in Train_ER_alpha.smi (168/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL222434 in Train_ER_alpha.smi (170/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL187207 in Train_ER_alpha.smi (176/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL181248 in Train_ER_alpha.smi (177/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL121879 in Train_ER_alpha.smi (180/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL389393 in Train_ER_alpha.smi (188/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL194936 in Train_ER_alpha.smi (191/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL202615 in Train_ER_alpha.smi (197/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL219387 in Train_ER_alpha.smi (200/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL187915 in Train_ER_alpha.smi (203/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL441499 in Train_ER_alpha.smi (207/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL240227 in Train_ER_alpha.smi (218/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL198410 in Train_ER_alpha.smi (8/1238). Average speed: 1.74 s/mol.\n"", + ""Processing CHEMBL1305485 in Train_ER_alpha.smi (10/1238). Average speed: 0.89 s/mol.\n"", + ""Processing CHEMBL1570659 in Train_ER_alpha.smi (11/1238). Average speed: 0.90 s/mol.\n"", + ""Processing CHEMBL184448 in Train_ER_alpha.smi (13/1238). Average speed: 0.46 s/mol.\n"", + ""Processing CHEMBL45068 in Train_ER_alpha.smi (14/1238). Average speed: 0.37 s/mol.\n"", + ""Processing CHEMBL123025 in Train_ER_alpha.smi (17/1238). Average speed: 0.37 s/mol.\n"", + ""Processing CHEMBL1468708 in Train_ER_alpha.smi (19/1238). Average speed: 0.32 s/mol.\n"", + ""Processing CHEMBL1460126 in Train_ER_alpha.smi (24/1238). Average speed: 0.25 s/mol.\n"", + ""Processing CHEMBL1499638 in Train_ER_alpha.smi (25/1238). Average speed: 0.20 s/mol.\n"", + ""Processing CHEMBL1605393 in Train_ER_alpha.smi (31/1238). Average speed: 0.17 s/mol.\n"", + ""Processing CHEMBL188049 in Train_ER_alpha.smi (33/1238). Average speed: 0.14 s/mol.\n"", + ""Processing CHEMBL1489063 in Train_ER_alpha.smi (35/1238). Average speed: 0.13 s/mol.\n"", + ""Processing CHEMBL1385373 in Train_ER_alpha.smi (41/1238). Average speed: 0.12 s/mol.\n"", + ""Processing CHEMBL1431555 in Train_ER_alpha.smi (44/1238). Average speed: 0.11 s/mol.\n"", + ""Processing CHEMBL1597933 in Train_ER_alpha.smi (46/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL1313979 in Train_ER_alpha.smi (48/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL1322939 in Train_ER_alpha.smi (50/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL1310614 in Train_ER_alpha.smi (52/1238). Average speed: 0.09 s/mol.\n"", + ""Processing CHEMBL1341057 in Train_ER_alpha.smi (54/1238). Average speed: 0.09 s/mol.\n"", + ""Processing CHEMBL184372 in Train_ER_alpha.smi (56/1238). Average speed: 0.09 s/mol.\n"", + ""Processing CHEMBL235497 in Train_ER_alpha.smi (68/1238). Average speed: 0.08 s/mol.\n"", + ""Processing CHEMBL241257 in Train_ER_alpha.smi (73/1238). Average speed: 0.08 s/mol.\n"", + ""Processing CHEMBL365016 in Train_ER_alpha.smi (78/1238). Average speed: 0.07 s/mol.\n"", + ""Processing CHEMBL469512 in Train_ER_alpha.smi (82/1238). Average speed: 0.07 s/mol.\n"", + ""Processing CHEMBL206727 in Train_ER_alpha.smi (89/1238). Average speed: 0.07 s/mol.\n"", + ""Processing CHEMBL206547 in Train_ER_alpha.smi (91/1238). Average speed: 0.07 s/mol.\n"", + ""Processing CHEMBL196836 in Train_ER_alpha.smi (94/1238). Average speed: 0.07 s/mol.\n"", + ""Processing CHEMBL425231 in Train_ER_alpha.smi (99/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL191300 in Train_ER_alpha.smi (105/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL187314 in Train_ER_alpha.smi (107/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL191710 in Train_ER_alpha.smi (109/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL426085 in Train_ER_alpha.smi (110/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL382734 in Train_ER_alpha.smi (119/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL191901 in Train_ER_alpha.smi (125/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL241304 in Train_ER_alpha.smi (126/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL184598 in Train_ER_alpha.smi (131/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL204119 in Train_ER_alpha.smi (137/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL212392 in Train_ER_alpha.smi (139/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL384709 in Train_ER_alpha.smi (141/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL438516 in Train_ER_alpha.smi (145/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL195437 in Train_ER_alpha.smi (157/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL435090 in Train_ER_alpha.smi (158/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL1201151 in Train_ER_alpha.smi (159/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL366928 in Train_ER_alpha.smi (160/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL451496 in Train_ER_alpha.smi (172/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL362718 in Train_ER_alpha.smi (174/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL363705 in Train_ER_alpha.smi (182/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL185480 in Train_ER_alpha.smi (184/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL360802 in Train_ER_alpha.smi (189/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL369991 in Train_ER_alpha.smi (193/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL204025 in Train_ER_alpha.smi (196/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL364092 in Train_ER_alpha.smi (204/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL193726 in Train_ER_alpha.smi (205/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL183084 in Train_ER_alpha.smi (208/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL219577 in Train_ER_alpha.smi (216/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL241303 in Train_ER_alpha.smi (217/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL1331084 in Train_ER_alpha.smi (12/1238). Average speed: 0.45 s/mol.\n"", + ""Processing CHEMBL1484998 in Train_ER_alpha.smi (27/1238). Average speed: 0.17 s/mol.\n"", + ""Processing CHEMBL1469743 in Train_ER_alpha.smi (29/1238). Average speed: 0.17 s/mol.\n"", + ""Processing CHEMBL1361641 in Train_ER_alpha.smi (34/1238). Average speed: 0.14 s/mol.\n"", + ""Processing CHEMBL1381495 in Train_ER_alpha.smi (36/1238). Average speed: 0.13 s/mol.\n"", + ""Processing CHEMBL1455258 in Train_ER_alpha.smi (43/1238). Average speed: 0.12 s/mol.\n"", + ""Processing CHEMBL1565187 in Train_ER_alpha.smi (45/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL512579 in Train_ER_alpha.smi (58/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL231811 in Train_ER_alpha.smi (60/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL385801 in Train_ER_alpha.smi (61/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL427496 in Train_ER_alpha.smi (63/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL361762 in Train_ER_alpha.smi (69/1238). Average speed: 0.08 s/mol.\n"", + ""Processing CHEMBL188458 in Train_ER_alpha.smi (71/1238). Average speed: 0.08 s/mol.\n"", + ""Processing CHEMBL396118 in Train_ER_alpha.smi (74/1238). Average speed: 0.08 s/mol.\n"", + ""Processing CHEMBL470993 in Train_ER_alpha.smi (80/1238). Average speed: 0.07 s/mol.\n"", + ""Processing CHEMBL377171 in Train_ER_alpha.smi (86/1238). Average speed: 0.07 s/mol.\n"", + ""Processing CHEMBL204902 in Train_ER_alpha.smi (88/1238). Average speed: 0.07 s/mol.\n"", + ""Processing CHEMBL380362 in Train_ER_alpha.smi (90/1238). Average speed: 0.07 s/mol.\n"", + ""Processing CHEMBL194326 in Train_ER_alpha.smi (96/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL203330 in Train_ER_alpha.smi (97/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL206648 in Train_ER_alpha.smi (98/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL206336 in Train_ER_alpha.smi (100/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL206499 in Train_ER_alpha.smi (103/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL371308 in Train_ER_alpha.smi (108/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL185231 in Train_ER_alpha.smi (117/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL197495 in Train_ER_alpha.smi (124/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL437190 in Train_ER_alpha.smi (128/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL126164 in Train_ER_alpha.smi (134/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL362272 in Train_ER_alpha.smi (144/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL379188 in Train_ER_alpha.smi (148/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL395308 in Train_ER_alpha.smi (151/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL394788 in Train_ER_alpha.smi (152/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL245133 in Train_ER_alpha.smi (153/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL234527 in Train_ER_alpha.smi (154/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL195208 in Train_ER_alpha.smi (155/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL183086 in Train_ER_alpha.smi (166/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL186643 in Train_ER_alpha.smi (173/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL418327 in Train_ER_alpha.smi (175/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL183371 in Train_ER_alpha.smi (178/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL389683 in Train_ER_alpha.smi (185/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL225022 in Train_ER_alpha.smi (187/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL192154 in Train_ER_alpha.smi (192/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL381298 in Train_ER_alpha.smi (195/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL201013 in Train_ER_alpha.smi (198/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL385279 in Train_ER_alpha.smi (199/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL85826 in Train_ER_alpha.smi (201/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL187000 in Train_ER_alpha.smi (209/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL381299 in Train_ER_alpha.smi (211/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL212923 in Train_ER_alpha.smi (213/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL393278 in Train_ER_alpha.smi (219/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL362428 in Train_ER_alpha.smi (220/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL1496978 in Train_ER_alpha.smi (9/1238). Average speed: 0.88 s/mol.\n"", + ""Processing CHEMBL434502 in Train_ER_alpha.smi (15/1238). Average speed: 0.37 s/mol.\n"", + ""Processing CHEMBL1331872 in Train_ER_alpha.smi (22/1238). Average speed: 0.32 s/mol.\n"", + ""Processing CHEMBL1401227 in Train_ER_alpha.smi (23/1238). Average speed: 0.33 s/mol.\n"", + ""Processing CHEMBL1384049 in Train_ER_alpha.smi (32/1238). Average speed: 0.15 s/mol.\n"", + ""Processing CHEMBL1349347 in Train_ER_alpha.smi (40/1238). Average speed: 0.13 s/mol.\n"", + ""Processing CHEMBL1573735 in Train_ER_alpha.smi (42/1238). Average speed: 0.12 s/mol.\n"", + ""Processing CHEMBL1480843 in Train_ER_alpha.smi (51/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL1535728 in Train_ER_alpha.smi (53/1238). Average speed: 0.09 s/mol.\n"", + ""Processing CHEMBL442037 in Train_ER_alpha.smi (57/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL447111 in Train_ER_alpha.smi (62/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL225404 in Train_ER_alpha.smi (64/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL226269 in Train_ER_alpha.smi (65/1238). Average speed: 0.10 s/mol.\n"", + ""Processing CHEMBL361272 in Train_ER_alpha.smi (72/1238). Average speed: 0.08 s/mol.\n"", + ""Processing CHEMBL59438 in Train_ER_alpha.smi (75/1238). Average speed: 0.08 s/mol.\n"", + ""Processing CHEMBL150279 in Train_ER_alpha.smi (76/1238). Average speed: 0.08 s/mol.\n"", + ""Processing CHEMBL511269 in Train_ER_alpha.smi (81/1238). Average speed: 0.07 s/mol.\n"", + ""Processing CHEMBL577938 in Train_ER_alpha.smi (84/1238). Average speed: 0.07 s/mol.\n"", + ""Processing CHEMBL385680 in Train_ER_alpha.smi (87/1238). Average speed: 0.07 s/mol.\n"", + ""Processing CHEMBL471626 in Train_ER_alpha.smi (92/1238). Average speed: 0.07 s/mol.\n"", + ""Processing CHEMBL25228 in Train_ER_alpha.smi (95/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL425941 in Train_ER_alpha.smi (102/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL447837 in Train_ER_alpha.smi (106/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL200719 in Train_ER_alpha.smi (111/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL183333 in Train_ER_alpha.smi (116/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL378473 in Train_ER_alpha.smi (118/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL212185 in Train_ER_alpha.smi (121/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL198914 in Train_ER_alpha.smi (122/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL182402 in Train_ER_alpha.smi (129/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL188391 in Train_ER_alpha.smi (130/1238). Average speed: 0.06 s/mol.\n"", + ""Processing CHEMBL195786 in Train_ER_alpha.smi (135/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL219134 in Train_ER_alpha.smi (136/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL234733 in Train_ER_alpha.smi (146/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL206441 in Train_ER_alpha.smi (147/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL425439 in Train_ER_alpha.smi (149/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL192102 in Train_ER_alpha.smi (150/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL182237 in Train_ER_alpha.smi (164/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL225349 in Train_ER_alpha.smi (169/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL373743 in Train_ER_alpha.smi (171/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL439806 in Train_ER_alpha.smi (179/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL202996 in Train_ER_alpha.smi (181/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL373095 in Train_ER_alpha.smi (183/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL449038 in Train_ER_alpha.smi (186/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL332625 in Train_ER_alpha.smi (190/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL377342 in Train_ER_alpha.smi (194/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL195371 in Train_ER_alpha.smi (202/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL192218 in Train_ER_alpha.smi (206/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL369979 in Train_ER_alpha.smi (210/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL206584 in Train_ER_alpha.smi (212/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL385358 in Train_ER_alpha.smi (214/1238). Average speed: 0.05 s/mol.\n"", + ""Processing CHEMBL383533 in Train_ER_alpha.smi (215/1238). Average speed: 0.05 s/mol.\n"" + ] + }, + { + ""name"": ""stdout"", + ""output_type"": ""stream"", + ""text"": [ + ""Processing CHEMBL206600 in Train_ER_alpha.smi (235/1238). Average speed: 0.05 s/mol.\r\n"", + ""Processing CHEMBL206227 in Train_ER_alpha.smi (236/1238). Average speed: 0.05 s/mol.\r\n"", + ""Processing CHEMBL211816 in Train_ER_alpha.smi (237/1238). Average speed: 0.05 s/mol.\r\n"", + ""Processing CHEMBL209623 in Train_ER_alpha.smi (239/1238). Average speed: 0.05 s/mol.\r\n"", + ""Processing CHEMBL206546 in Train_ER_alpha.smi (244/1238). Average speed: 0.05 s/mol.\r\n"", + ""Processing CHEMBL203442 in Train_ER_alpha.smi (247/1238). Average speed: 0.05 s/mol.\r\n"", + ""Processing CHEMBL185083 in Train_ER_alpha.smi (256/1238). Average speed: 0.05 s/mol.\r\n"", + ""Processing CHEMBL182850 in Train_ER_alpha.smi (267/1238). Average speed: 0.05 s/mol.\r\n"", + ""Processing CHEMBL196742 in Train_ER_alpha.smi (270/1238). Average speed: 0.05 s/mol.\r\n"", + ""Processing CHEMBL316003 in Train_ER_alpha.smi (271/1238). Average speed: 0.05 s/mol.\r\n"", + ""Processing CHEMBL1403999 in Train_ER_alpha.smi (281/1238). Average speed: 0.05 s/mol.\r\n"", + ""Processing CHEMBL1332402 in Train_ER_alpha.smi (289/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL182751 in Train_ER_alpha.smi (293/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL1511067 in Train_ER_alpha.smi (297/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL1428161 in Train_ER_alpha.smi (300/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL340111 in Train_ER_alpha.smi (302/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL294889 in Train_ER_alpha.smi (304/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL380188 in Train_ER_alpha.smi (310/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL196283 in Train_ER_alpha.smi (312/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL379566 in Train_ER_alpha.smi (320/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL371072 in Train_ER_alpha.smi (323/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL1368345 in Train_ER_alpha.smi (330/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL362966 in Train_ER_alpha.smi (332/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL379213 in Train_ER_alpha.smi (333/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL193727 in Train_ER_alpha.smi (338/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL1395826 in Train_ER_alpha.smi (340/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL472468 in Train_ER_alpha.smi (348/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL246341 in Train_ER_alpha.smi (356/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL425092 in Train_ER_alpha.smi (357/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL1340020 in Train_ER_alpha.smi (365/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL364110 in Train_ER_alpha.smi (374/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL1604500 in Train_ER_alpha.smi (377/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL1482201 in Train_ER_alpha.smi (379/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL221515 in Train_ER_alpha.smi (381/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL203724 in Train_ER_alpha.smi (385/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL1318094 in Train_ER_alpha.smi (387/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL191808 in Train_ER_alpha.smi (391/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL200483 in Train_ER_alpha.smi (394/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL197667 in Train_ER_alpha.smi (226/1238). Average speed: 0.05 s/mol.\r\n"", + ""Processing CHEMBL206406 in Train_ER_alpha.smi (231/1238). Average speed: 0.05 s/mol.\r\n"", + ""Processing CHEMBL381697 in Train_ER_alpha.smi (233/1238). Average speed: 0.05 s/mol.\r\n"", + ""Processing CHEMBL204922 in Train_ER_alpha.smi (234/1238). Average speed: 0.05 s/mol.\r\n"", + ""Processing CHEMBL208929 in Train_ER_alpha.smi (238/1238). Average speed: 0.05 s/mol.\r\n"", + ""Processing CHEMBL191714 in Train_ER_alpha.smi (241/1238). Average speed: 0.05 s/mol.\r\n"", + ""Processing CHEMBL204819 in Train_ER_alpha.smi (243/1238). Average speed: 0.05 s/mol.\r\n"", + ""Processing CHEMBL381337 in Train_ER_alpha.smi (246/1238). Average speed: 0.05 s/mol.\r\n"", + ""Processing CHEMBL382698 in Train_ER_alpha.smi (250/1238). Average speed: 0.05 s/mol.\r\n"", + ""Processing CHEMBL364502 in Train_ER_alpha.smi (252/1238). Average speed: 0.05 s/mol.\r\n"", + ""Processing CHEMBL363634 in Train_ER_alpha.smi (254/1238). Average speed: 0.05 s/mol.\r\n"", + ""Processing CHEMBL185890 in Train_ER_alpha.smi (255/1238). Average speed: 0.05 s/mol.\r\n"", + ""Processing CHEMBL362793 in Train_ER_alpha.smi (257/1238). Average speed: 0.05 s/mol.\r\n"", + ""Processing CHEMBL389907 in Train_ER_alpha.smi (264/1238). Average speed: 0.05 s/mol.\r\n"", + ""Processing CHEMBL1487772 in Train_ER_alpha.smi (276/1238). Average speed: 0.05 s/mol.\r\n"", + ""Processing CHEMBL51483 in Train_ER_alpha.smi (277/1238). Average speed: 0.05 s/mol.\r\n"", + ""Processing CHEMBL319244 in Train_ER_alpha.smi (283/1238). Average speed: 0.05 s/mol.\r\n"", + ""Processing CHEMBL1541779 in Train_ER_alpha.smi (285/1238). Average speed: 0.05 s/mol.\r\n"", + ""Processing CHEMBL1328083 in Train_ER_alpha.smi (290/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL1383151 in Train_ER_alpha.smi (295/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL63768 in Train_ER_alpha.smi (307/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL1469865 in Train_ER_alpha.smi (309/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL363498 in Train_ER_alpha.smi (314/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL183368 in Train_ER_alpha.smi (317/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL215409 in Train_ER_alpha.smi (318/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL384268 in Train_ER_alpha.smi (321/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL225081 in Train_ER_alpha.smi (327/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL231812 in Train_ER_alpha.smi (328/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL182887 in Train_ER_alpha.smi (329/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL183367 in Train_ER_alpha.smi (339/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL1434189 in Train_ER_alpha.smi (341/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL364345 in Train_ER_alpha.smi (343/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL371126 in Train_ER_alpha.smi (344/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL1324414 in Train_ER_alpha.smi (345/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL1334930 in Train_ER_alpha.smi (346/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL376986 in Train_ER_alpha.smi (359/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL377644 in Train_ER_alpha.smi (361/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL511812 in Train_ER_alpha.smi (366/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL189356 in Train_ER_alpha.smi (368/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL25580 in Train_ER_alpha.smi (376/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL389394 in Train_ER_alpha.smi (382/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL379889 in Train_ER_alpha.smi (384/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL226379 in Train_ER_alpha.smi (386/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL392029 in Train_ER_alpha.smi (393/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL199599 in Train_ER_alpha.smi (395/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL201517 in Train_ER_alpha.smi (397/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL496 in Train_ER_alpha.smi (221/1238). Average speed: 0.05 s/mol.\r\n"", + ""Processing CHEMBL434525 in Train_ER_alpha.smi (222/1238). Average speed: 0.05 s/mol.\r\n"", + ""Processing CHEMBL211887 in Train_ER_alpha.smi (240/1238). Average speed: 0.05 s/mol.\r\n"", + ""Processing CHEMBL203351 in Train_ER_alpha.smi (249/1238). Average speed: 0.05 s/mol.\r\n"", + ""Processing CHEMBL191238 in Train_ER_alpha.smi (251/1238). Average speed: 0.05 s/mol.\r\n"", + ""Processing CHEMBL187473 in Train_ER_alpha.smi (258/1238). Average speed: 0.05 s/mol.\r\n"", + ""Processing CHEMBL373987 in Train_ER_alpha.smi (259/1238). Average speed: 0.05 s/mol.\r\n"", + ""Processing CHEMBL290106 in Train_ER_alpha.smi (261/1238). Average speed: 0.05 s/mol.\r\n"", + ""Processing CHEMBL391619 in Train_ER_alpha.smi (263/1238). Average speed: 0.05 s/mol.\r\n"", + ""Processing CHEMBL187115 in Train_ER_alpha.smi (265/1238). Average speed: 0.05 s/mol.\r\n"", + ""Processing CHEMBL363633 in Train_ER_alpha.smi (266/1238). Average speed: 0.05 s/mol.\r\n"", + ""Processing CHEMBL188189 in Train_ER_alpha.smi (269/1238). Average speed: 0.05 s/mol.\r\n"", + ""Processing CHEMBL360410 in Train_ER_alpha.smi (272/1238). Average speed: 0.05 s/mol.\r\n"", + ""Processing CHEMBL196530 in Train_ER_alpha.smi (273/1238). Average speed: 0.05 s/mol.\r\n"", + ""Processing CHEMBL1491936 in Train_ER_alpha.smi (275/1238). Average speed: 0.05 s/mol.\r\n"", + ""Processing CHEMBL463804 in Train_ER_alpha.smi (279/1238). Average speed: 0.05 s/mol.\r\n"", + ""Processing CHEMBL1414254 in Train_ER_alpha.smi (280/1238). Average speed: 0.05 s/mol.\r\n"", + ""Processing CHEMBL1572403 in Train_ER_alpha.smi (284/1238). Average speed: 0.05 s/mol.\r\n"", + ""Processing CHEMBL1458486 in Train_ER_alpha.smi (286/1238). Average speed: 0.05 s/mol.\r\n"", + ""Processing CHEMBL1587920 in Train_ER_alpha.smi (287/1238). Average speed: 0.05 s/mol.\r\n"", + ""Processing CHEMBL150924 in Train_ER_alpha.smi (291/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL1465370 in Train_ER_alpha.smi (294/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL1504315 in Train_ER_alpha.smi (306/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL1564209 in Train_ER_alpha.smi (308/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL214693 in Train_ER_alpha.smi (311/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL195154 in Train_ER_alpha.smi (313/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL1392188 in Train_ER_alpha.smi (315/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL426626 in Train_ER_alpha.smi (325/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL191711 in Train_ER_alpha.smi (326/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL188900 in Train_ER_alpha.smi (331/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL207101 in Train_ER_alpha.smi (335/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL385693 in Train_ER_alpha.smi (336/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL192765 in Train_ER_alpha.smi (337/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL363902 in Train_ER_alpha.smi (347/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL209803 in Train_ER_alpha.smi (351/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL204970 in Train_ER_alpha.smi (353/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL1303886 in Train_ER_alpha.smi (355/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL232023 in Train_ER_alpha.smi (364/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL191836 in Train_ER_alpha.smi (367/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL1606765 in Train_ER_alpha.smi (369/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL1348377 in Train_ER_alpha.smi (370/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL1426947 in Train_ER_alpha.smi (371/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL347560 in Train_ER_alpha.smi (373/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL374277 in Train_ER_alpha.smi (383/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL191759 in Train_ER_alpha.smi (389/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL365941 in Train_ER_alpha.smi (390/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL360978 in Train_ER_alpha.smi (392/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL365999 in Train_ER_alpha.smi (223/1238). Average speed: 0.05 s/mol.\r\n"", + ""Processing CHEMBL198411 in Train_ER_alpha.smi (224/1238). Average speed: 0.05 s/mol.\r\n"", + ""Processing CHEMBL186945 in Train_ER_alpha.smi (225/1238). Average speed: 0.05 s/mol.\r\n"", + ""Processing CHEMBL382632 in Train_ER_alpha.smi (227/1238). Average speed: 0.05 s/mol.\r\n"", + ""Processing CHEMBL206440 in Train_ER_alpha.smi (228/1238). Average speed: 0.05 s/mol.\r\n"", + ""Processing CHEMBL380844 in Train_ER_alpha.smi (229/1238). Average speed: 0.05 s/mol.\r\n"", + ""Processing CHEMBL383753 in Train_ER_alpha.smi (230/1238). Average speed: 0.05 s/mol.\r\n"", + ""Processing CHEMBL204056 in Train_ER_alpha.smi (232/1238). Average speed: 0.05 s/mol.\r\n"", + ""Processing CHEMBL188792 in Train_ER_alpha.smi (242/1238). Average speed: 0.05 s/mol.\r\n"", + ""Processing CHEMBL205934 in Train_ER_alpha.smi (245/1238). Average speed: 0.05 s/mol.\r\n"", + ""Processing CHEMBL206814 in Train_ER_alpha.smi (248/1238). Average speed: 0.05 s/mol.\r\n"", + ""Processing CHEMBL331996 in Train_ER_alpha.smi (253/1238). Average speed: 0.05 s/mol.\r\n"", + ""Processing CHEMBL213950 in Train_ER_alpha.smi (260/1238). Average speed: 0.05 s/mol.\r\n"", + ""Processing CHEMBL239938 in Train_ER_alpha.smi (262/1238). Average speed: 0.05 s/mol.\r\n"", + ""Processing CHEMBL361751 in Train_ER_alpha.smi (268/1238). Average speed: 0.05 s/mol.\r\n"", + ""Processing CHEMBL1570364 in Train_ER_alpha.smi (274/1238). Average speed: 0.05 s/mol.\r\n"", + ""Processing CHEMBL359633 in Train_ER_alpha.smi (278/1238). Average speed: 0.05 s/mol.\r\n"", + ""Processing CHEMBL1627356 in Train_ER_alpha.smi (282/1238). Average speed: 0.05 s/mol.\r\n"", + ""Processing CHEMBL1399039 in Train_ER_alpha.smi (288/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL184003 in Train_ER_alpha.smi (292/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL1407483 in Train_ER_alpha.smi (296/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL192876 in Train_ER_alpha.smi (298/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL363017 in Train_ER_alpha.smi (299/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL1390963 in Train_ER_alpha.smi (301/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL185783 in Train_ER_alpha.smi (303/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL184151 in Train_ER_alpha.smi (305/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL1577029 in Train_ER_alpha.smi (316/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL211837 in Train_ER_alpha.smi (319/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL203348 in Train_ER_alpha.smi (322/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL180873 in Train_ER_alpha.smi (324/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL380359 in Train_ER_alpha.smi (334/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL1386105 in Train_ER_alpha.smi (342/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL189024 in Train_ER_alpha.smi (349/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL194718 in Train_ER_alpha.smi (350/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL209402 in Train_ER_alpha.smi (352/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL203472 in Train_ER_alpha.smi (354/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL380469 in Train_ER_alpha.smi (358/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL208978 in Train_ER_alpha.smi (360/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL249903 in Train_ER_alpha.smi (362/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL398634 in Train_ER_alpha.smi (363/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL1453259 in Train_ER_alpha.smi (372/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL189025 in Train_ER_alpha.smi (375/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL1509453 in Train_ER_alpha.smi (378/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL1326210 in Train_ER_alpha.smi (380/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL368449 in Train_ER_alpha.smi (388/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL200232 in Train_ER_alpha.smi (396/1238). Average speed: 0.04 s/mol.\r\n"" + ] + }, + { + ""name"": ""stdout"", + ""output_type"": ""stream"", + ""text"": [ + ""Processing CHEMBL202673 in Train_ER_alpha.smi (398/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL393179 in Train_ER_alpha.smi (402/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL1359893 in Train_ER_alpha.smi (406/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL1403448 in Train_ER_alpha.smi (409/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL1521410 in Train_ER_alpha.smi (413/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL180973 in Train_ER_alpha.smi (418/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL1525824 in Train_ER_alpha.smi (426/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL2079587 in Train_ER_alpha.smi (433/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL93705 in Train_ER_alpha.smi (435/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL1427879 in Train_ER_alpha.smi (442/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL186597 in Train_ER_alpha.smi (445/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL363367 in Train_ER_alpha.smi (448/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL181369 in Train_ER_alpha.smi (449/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL193010 in Train_ER_alpha.smi (459/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL365045 in Train_ER_alpha.smi (462/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL377694 in Train_ER_alpha.smi (467/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL370849 in Train_ER_alpha.smi (472/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL1337826 in Train_ER_alpha.smi (477/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL198679 in Train_ER_alpha.smi (480/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL371009 in Train_ER_alpha.smi (482/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL334096 in Train_ER_alpha.smi (483/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL213051 in Train_ER_alpha.smi (488/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL1402 in Train_ER_alpha.smi (492/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL209429 in Train_ER_alpha.smi (497/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL179518 in Train_ER_alpha.smi (502/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL379595 in Train_ER_alpha.smi (511/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL187937 in Train_ER_alpha.smi (513/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL205350 in Train_ER_alpha.smi (515/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL363955 in Train_ER_alpha.smi (520/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL206131 in Train_ER_alpha.smi (522/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL202894 in Train_ER_alpha.smi (524/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL206773 in Train_ER_alpha.smi (526/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL238961 in Train_ER_alpha.smi (531/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL1433745 in Train_ER_alpha.smi (541/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL471627 in Train_ER_alpha.smi (544/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL375334 in Train_ER_alpha.smi (548/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL1449885 in Train_ER_alpha.smi (551/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL1522044 in Train_ER_alpha.smi (560/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL1328182 in Train_ER_alpha.smi (562/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL361063 in Train_ER_alpha.smi (571/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL362307 in Train_ER_alpha.smi (575/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL104416 in Train_ER_alpha.smi (578/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL190038 in Train_ER_alpha.smi (580/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL189303 in Train_ER_alpha.smi (581/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL1628170 in Train_ER_alpha.smi (586/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL954 in Train_ER_alpha.smi (588/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL196131 in Train_ER_alpha.smi (592/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL241300 in Train_ER_alpha.smi (400/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL238959 in Train_ER_alpha.smi (404/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL1575187 in Train_ER_alpha.smi (410/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL1538223 in Train_ER_alpha.smi (414/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL178559 in Train_ER_alpha.smi (417/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL144431 in Train_ER_alpha.smi (422/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL188619 in Train_ER_alpha.smi (425/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL193064 in Train_ER_alpha.smi (428/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL1495194 in Train_ER_alpha.smi (430/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL1548186 in Train_ER_alpha.smi (432/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL180253 in Train_ER_alpha.smi (434/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL1544241 in Train_ER_alpha.smi (443/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL577758 in Train_ER_alpha.smi (444/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL361258 in Train_ER_alpha.smi (447/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL371624 in Train_ER_alpha.smi (452/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL188613 in Train_ER_alpha.smi (453/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL200021 in Train_ER_alpha.smi (458/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL201569 in Train_ER_alpha.smi (464/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL192386 in Train_ER_alpha.smi (468/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL183152 in Train_ER_alpha.smi (470/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL185126 in Train_ER_alpha.smi (474/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL185918 in Train_ER_alpha.smi (475/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL264216 in Train_ER_alpha.smi (476/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL121978 in Train_ER_alpha.smi (481/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL180146 in Train_ER_alpha.smi (486/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL378675 in Train_ER_alpha.smi (491/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL381566 in Train_ER_alpha.smi (496/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL379821 in Train_ER_alpha.smi (499/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL202896 in Train_ER_alpha.smi (501/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL363425 in Train_ER_alpha.smi (504/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL206226 in Train_ER_alpha.smi (507/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL378990 in Train_ER_alpha.smi (508/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL379567 in Train_ER_alpha.smi (510/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL204768 in Train_ER_alpha.smi (514/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL206582 in Train_ER_alpha.smi (517/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL221676 in Train_ER_alpha.smi (519/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL238960 in Train_ER_alpha.smi (530/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL437695 in Train_ER_alpha.smi (534/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL1485005 in Train_ER_alpha.smi (536/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL1327977 in Train_ER_alpha.smi (538/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL1597831 in Train_ER_alpha.smi (543/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL222580 in Train_ER_alpha.smi (550/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL1472407 in Train_ER_alpha.smi (554/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL1454402 in Train_ER_alpha.smi (556/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL1431650 in Train_ER_alpha.smi (557/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL1367590 in Train_ER_alpha.smi (558/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL194772 in Train_ER_alpha.smi (565/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL364329 in Train_ER_alpha.smi (568/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL379543 in Train_ER_alpha.smi (570/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL192901 in Train_ER_alpha.smi (574/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL190487 in Train_ER_alpha.smi (576/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL1627354 in Train_ER_alpha.smi (582/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL371564 in Train_ER_alpha.smi (585/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL362349 in Train_ER_alpha.smi (587/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL184974 in Train_ER_alpha.smi (589/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL183344 in Train_ER_alpha.smi (597/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL442249 in Train_ER_alpha.smi (403/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL1427643 in Train_ER_alpha.smi (407/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL1548231 in Train_ER_alpha.smi (411/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL327320 in Train_ER_alpha.smi (416/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL1528123 in Train_ER_alpha.smi (419/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL1386676 in Train_ER_alpha.smi (421/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL1580036 in Train_ER_alpha.smi (423/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL189658 in Train_ER_alpha.smi (424/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL1522300 in Train_ER_alpha.smi (427/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL190104 in Train_ER_alpha.smi (436/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL360498 in Train_ER_alpha.smi (439/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL57150 in Train_ER_alpha.smi (450/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL433944 in Train_ER_alpha.smi (451/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL371788 in Train_ER_alpha.smi (454/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL186073 in Train_ER_alpha.smi (456/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL450940 in Train_ER_alpha.smi (461/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL188410 in Train_ER_alpha.smi (463/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL441537 in Train_ER_alpha.smi (466/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL362020 in Train_ER_alpha.smi (469/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL1481770 in Train_ER_alpha.smi (479/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL377224 in Train_ER_alpha.smi (487/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL377848 in Train_ER_alpha.smi (489/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL369935 in Train_ER_alpha.smi (493/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL426854 in Train_ER_alpha.smi (500/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL187069 in Train_ER_alpha.smi (503/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL597498 in Train_ER_alpha.smi (505/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL211870 in Train_ER_alpha.smi (512/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL206764 in Train_ER_alpha.smi (521/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL192016 in Train_ER_alpha.smi (528/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL191841 in Train_ER_alpha.smi (529/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL180176 in Train_ER_alpha.smi (533/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL1521111 in Train_ER_alpha.smi (537/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL388418 in Train_ER_alpha.smi (539/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL1475062 in Train_ER_alpha.smi (542/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL1548361 in Train_ER_alpha.smi (547/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL1584646 in Train_ER_alpha.smi (552/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL1461574 in Train_ER_alpha.smi (561/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL191716 in Train_ER_alpha.smi (564/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL362425 in Train_ER_alpha.smi (566/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL195991 in Train_ER_alpha.smi (579/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL435696 in Train_ER_alpha.smi (584/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL188846 in Train_ER_alpha.smi (591/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL427180 in Train_ER_alpha.smi (594/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL194265 in Train_ER_alpha.smi (595/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL361078 in Train_ER_alpha.smi (599/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL203072 in Train_ER_alpha.smi (399/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL428732 in Train_ER_alpha.smi (401/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL1410824 in Train_ER_alpha.smi (405/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL1464457 in Train_ER_alpha.smi (408/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL1531555 in Train_ER_alpha.smi (412/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL153405 in Train_ER_alpha.smi (415/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL1536373 in Train_ER_alpha.smi (420/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL186513 in Train_ER_alpha.smi (429/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL1606490 in Train_ER_alpha.smi (431/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL1548834 in Train_ER_alpha.smi (437/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL188527 in Train_ER_alpha.smi (438/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL1501269 in Train_ER_alpha.smi (440/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL1560367 in Train_ER_alpha.smi (441/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL145576 in Train_ER_alpha.smi (446/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL189200 in Train_ER_alpha.smi (455/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL187087 in Train_ER_alpha.smi (457/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL363963 in Train_ER_alpha.smi (460/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL380876 in Train_ER_alpha.smi (465/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL192587 in Train_ER_alpha.smi (471/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL178679 in Train_ER_alpha.smi (473/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL1480378 in Train_ER_alpha.smi (478/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL192539 in Train_ER_alpha.smi (484/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL186598 in Train_ER_alpha.smi (485/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL264349 in Train_ER_alpha.smi (490/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL216279 in Train_ER_alpha.smi (494/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL219126 in Train_ER_alpha.smi (495/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL379109 in Train_ER_alpha.smi (498/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL1116 in Train_ER_alpha.smi (506/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL211800 in Train_ER_alpha.smi (509/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL380717 in Train_ER_alpha.smi (516/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL362355 in Train_ER_alpha.smi (518/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL381720 in Train_ER_alpha.smi (523/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL206349 in Train_ER_alpha.smi (525/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL379008 in Train_ER_alpha.smi (527/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL184360 in Train_ER_alpha.smi (532/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL1276308 in Train_ER_alpha.smi (535/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL396948 in Train_ER_alpha.smi (540/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL1362841 in Train_ER_alpha.smi (545/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL1429150 in Train_ER_alpha.smi (546/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL222522 in Train_ER_alpha.smi (549/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL1506716 in Train_ER_alpha.smi (553/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL1448466 in Train_ER_alpha.smi (555/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL1587260 in Train_ER_alpha.smi (559/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL250109 in Train_ER_alpha.smi (563/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL363177 in Train_ER_alpha.smi (567/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL361226 in Train_ER_alpha.smi (569/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL189822 in Train_ER_alpha.smi (572/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL192782 in Train_ER_alpha.smi (573/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL1479 in Train_ER_alpha.smi (577/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL178665 in Train_ER_alpha.smi (583/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL1627353 in Train_ER_alpha.smi (590/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL372304 in Train_ER_alpha.smi (593/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL195788 in Train_ER_alpha.smi (596/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL369545 in Train_ER_alpha.smi (598/1238). Average speed: 0.04 s/mol.\r\n"" + ] + }, + { + ""name"": ""stdout"", + ""output_type"": ""stream"", + ""text"": [ + ""Processing CHEMBL1200623 in Train_ER_alpha.smi (601/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL194762 in Train_ER_alpha.smi (602/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL187392 in Train_ER_alpha.smi (604/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL103650 in Train_ER_alpha.smi (609/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL181936 in Train_ER_alpha.smi (610/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1302802 in Train_ER_alpha.smi (612/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1322601 in Train_ER_alpha.smi (616/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1350028 in Train_ER_alpha.smi (620/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1423880 in Train_ER_alpha.smi (622/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1505283 in Train_ER_alpha.smi (629/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL187673 in Train_ER_alpha.smi (632/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL195289 in Train_ER_alpha.smi (637/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1421864 in Train_ER_alpha.smi (639/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1475434 in Train_ER_alpha.smi (641/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL192390 in Train_ER_alpha.smi (646/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL340606 in Train_ER_alpha.smi (647/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1581690 in Train_ER_alpha.smi (654/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL200799 in Train_ER_alpha.smi (665/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL238890 in Train_ER_alpha.smi (668/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL189002 in Train_ER_alpha.smi (676/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL188957 in Train_ER_alpha.smi (678/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL204697 in Train_ER_alpha.smi (679/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL394357 in Train_ER_alpha.smi (686/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL388473 in Train_ER_alpha.smi (687/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL198445 in Train_ER_alpha.smi (692/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL240438 in Train_ER_alpha.smi (696/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL377254 in Train_ER_alpha.smi (703/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL455215 in Train_ER_alpha.smi (705/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL368688 in Train_ER_alpha.smi (709/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL378572 in Train_ER_alpha.smi (719/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1377973 in Train_ER_alpha.smi (724/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1533166 in Train_ER_alpha.smi (726/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1329686 in Train_ER_alpha.smi (728/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1520373 in Train_ER_alpha.smi (731/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1419810 in Train_ER_alpha.smi (734/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1484547 in Train_ER_alpha.smi (737/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1372307 in Train_ER_alpha.smi (740/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1407054 in Train_ER_alpha.smi (747/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1405182 in Train_ER_alpha.smi (751/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1455849 in Train_ER_alpha.smi (752/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1326803 in Train_ER_alpha.smi (754/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1083326 in Train_ER_alpha.smi (757/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL223026 in Train_ER_alpha.smi (760/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL433769 in Train_ER_alpha.smi (762/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL188051 in Train_ER_alpha.smi (603/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL418971 in Train_ER_alpha.smi (605/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL370282 in Train_ER_alpha.smi (607/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1491602 in Train_ER_alpha.smi (614/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1604803 in Train_ER_alpha.smi (617/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1310171 in Train_ER_alpha.smi (625/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL196583 in Train_ER_alpha.smi (628/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1532659 in Train_ER_alpha.smi (630/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL191715 in Train_ER_alpha.smi (634/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL153395 in Train_ER_alpha.smi (636/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1593653 in Train_ER_alpha.smi (644/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL195280 in Train_ER_alpha.smi (649/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1443090 in Train_ER_alpha.smi (651/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL482116 in Train_ER_alpha.smi (658/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL190682 in Train_ER_alpha.smi (659/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL184421 in Train_ER_alpha.smi (661/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL371012 in Train_ER_alpha.smi (666/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL240157 in Train_ER_alpha.smi (669/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL391795 in Train_ER_alpha.smi (672/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL430395 in Train_ER_alpha.smi (674/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL211739 in Train_ER_alpha.smi (681/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL213529 in Train_ER_alpha.smi (682/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL187978 in Train_ER_alpha.smi (684/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL192000 in Train_ER_alpha.smi (690/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL393982 in Train_ER_alpha.smi (695/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL346455 in Train_ER_alpha.smi (698/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL213829 in Train_ER_alpha.smi (704/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL414859 in Train_ER_alpha.smi (713/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL378798 in Train_ER_alpha.smi (714/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL46903 in Train_ER_alpha.smi (715/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL206137 in Train_ER_alpha.smi (720/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1453264 in Train_ER_alpha.smi (725/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1304227 in Train_ER_alpha.smi (727/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1393976 in Train_ER_alpha.smi (733/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1531320 in Train_ER_alpha.smi (735/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1439181 in Train_ER_alpha.smi (736/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1577757 in Train_ER_alpha.smi (738/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1482875 in Train_ER_alpha.smi (742/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1374573 in Train_ER_alpha.smi (744/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1548616 in Train_ER_alpha.smi (748/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1559731 in Train_ER_alpha.smi (749/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1555033 in Train_ER_alpha.smi (750/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL426897 in Train_ER_alpha.smi (758/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL144897 in Train_ER_alpha.smi (761/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL198088 in Train_ER_alpha.smi (766/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1576748 in Train_ER_alpha.smi (767/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1509933 in Train_ER_alpha.smi (769/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL187393 in Train_ER_alpha.smi (600/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL184367 in Train_ER_alpha.smi (606/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1411687 in Train_ER_alpha.smi (611/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1521414 in Train_ER_alpha.smi (613/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1393131 in Train_ER_alpha.smi (618/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL183782 in Train_ER_alpha.smi (621/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1347829 in Train_ER_alpha.smi (624/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1411209 in Train_ER_alpha.smi (626/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL122208 in Train_ER_alpha.smi (635/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1560682 in Train_ER_alpha.smi (640/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1552932 in Train_ER_alpha.smi (643/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1519669 in Train_ER_alpha.smi (652/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1299357 in Train_ER_alpha.smi (653/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL125362 in Train_ER_alpha.smi (656/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL205920 in Train_ER_alpha.smi (662/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL226329 in Train_ER_alpha.smi (664/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL362356 in Train_ER_alpha.smi (667/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL511971 in Train_ER_alpha.smi (670/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL183044 in Train_ER_alpha.smi (675/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL187219 in Train_ER_alpha.smi (685/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL242818 in Train_ER_alpha.smi (688/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL202781 in Train_ER_alpha.smi (691/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL432454 in Train_ER_alpha.smi (693/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL188246 in Train_ER_alpha.smi (697/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL206120 in Train_ER_alpha.smi (700/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL267431 in Train_ER_alpha.smi (707/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1076306 in Train_ER_alpha.smi (710/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL198680 in Train_ER_alpha.smi (711/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL125119 in Train_ER_alpha.smi (712/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL195311 in Train_ER_alpha.smi (716/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1389780 in Train_ER_alpha.smi (721/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1312693 in Train_ER_alpha.smi (722/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1498999 in Train_ER_alpha.smi (729/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1386537 in Train_ER_alpha.smi (730/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1505467 in Train_ER_alpha.smi (739/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1577661 in Train_ER_alpha.smi (745/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1370120 in Train_ER_alpha.smi (746/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1597805 in Train_ER_alpha.smi (755/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1084746 in Train_ER_alpha.smi (756/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL386502 in Train_ER_alpha.smi (759/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL183033 in Train_ER_alpha.smi (764/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL190025 in Train_ER_alpha.smi (765/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1388431 in Train_ER_alpha.smi (768/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL180702 in Train_ER_alpha.smi (608/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1341814 in Train_ER_alpha.smi (615/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1541552 in Train_ER_alpha.smi (619/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1493034 in Train_ER_alpha.smi (623/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1507788 in Train_ER_alpha.smi (627/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL189039 in Train_ER_alpha.smi (631/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL197757 in Train_ER_alpha.smi (633/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1381306 in Train_ER_alpha.smi (638/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1434961 in Train_ER_alpha.smi (642/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1449110 in Train_ER_alpha.smi (645/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL371305 in Train_ER_alpha.smi (648/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL193482 in Train_ER_alpha.smi (650/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1433062 in Train_ER_alpha.smi (655/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL361871 in Train_ER_alpha.smi (657/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL363872 in Train_ER_alpha.smi (660/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL185383 in Train_ER_alpha.smi (663/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL364419 in Train_ER_alpha.smi (671/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL398455 in Train_ER_alpha.smi (673/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL438256 in Train_ER_alpha.smi (677/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL203561 in Train_ER_alpha.smi (680/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL214200 in Train_ER_alpha.smi (683/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL106773 in Train_ER_alpha.smi (689/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL184491 in Train_ER_alpha.smi (694/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL232022 in Train_ER_alpha.smi (699/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL210074 in Train_ER_alpha.smi (701/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL209738 in Train_ER_alpha.smi (702/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL522990 in Train_ER_alpha.smi (706/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL469510 in Train_ER_alpha.smi (708/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1450791 in Train_ER_alpha.smi (717/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL203370 in Train_ER_alpha.smi (718/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1324290 in Train_ER_alpha.smi (723/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1452952 in Train_ER_alpha.smi (732/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1525366 in Train_ER_alpha.smi (741/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1345810 in Train_ER_alpha.smi (743/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL1356953 in Train_ER_alpha.smi (753/1238). Average speed: 0.04 s/mol.\n"", + ""Processing CHEMBL179852 in Train_ER_alpha.smi (763/1238). Average speed: 0.04 s/mol.\n"" + ] + }, + { + ""name"": ""stdout"", + ""output_type"": ""stream"", + ""text"": [ + ""Processing CHEMBL195217 in Train_ER_alpha.smi (773/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL1434556 in Train_ER_alpha.smi (775/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL282575 in Train_ER_alpha.smi (778/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL188516 in Train_ER_alpha.smi (782/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL198204 in Train_ER_alpha.smi (785/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL425194 in Train_ER_alpha.smi (787/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL1300662 in Train_ER_alpha.smi (789/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL1539865 in Train_ER_alpha.smi (791/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL1303477 in Train_ER_alpha.smi (796/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL1320937 in Train_ER_alpha.smi (802/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL189556 in Train_ER_alpha.smi (805/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL204376 in Train_ER_alpha.smi (809/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL1325233 in Train_ER_alpha.smi (813/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL385949 in Train_ER_alpha.smi (817/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL377457 in Train_ER_alpha.smi (822/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL398456 in Train_ER_alpha.smi (825/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL249904 in Train_ER_alpha.smi (826/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL380838 in Train_ER_alpha.smi (831/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL394614 in Train_ER_alpha.smi (833/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL1404523 in Train_ER_alpha.smi (837/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL1386091 in Train_ER_alpha.smi (838/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL206500 in Train_ER_alpha.smi (842/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL10560 in Train_ER_alpha.smi (845/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL1320651 in Train_ER_alpha.smi (848/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL1404155 in Train_ER_alpha.smi (851/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL206426 in Train_ER_alpha.smi (856/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL434577 in Train_ER_alpha.smi (863/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL201365 in Train_ER_alpha.smi (869/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL180995 in Train_ER_alpha.smi (875/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL193146 in Train_ER_alpha.smi (882/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL193108 in Train_ER_alpha.smi (883/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL187671 in Train_ER_alpha.smi (886/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL187663 in Train_ER_alpha.smi (889/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL189706 in Train_ER_alpha.smi (891/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL185012 in Train_ER_alpha.smi (893/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL1424468 in Train_ER_alpha.smi (897/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL187105 in Train_ER_alpha.smi (898/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL276030 in Train_ER_alpha.smi (899/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL363412 in Train_ER_alpha.smi (903/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL1627351 in Train_ER_alpha.smi (912/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL182980 in Train_ER_alpha.smi (918/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL1466913 in Train_ER_alpha.smi (926/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL425246 in Train_ER_alpha.smi (927/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL211349 in Train_ER_alpha.smi (929/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL396947 in Train_ER_alpha.smi (933/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL204272 in Train_ER_alpha.smi (936/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL205614 in Train_ER_alpha.smi (939/1238). 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Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL425603 in Train_ER_alpha.smi (873/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL203123 in Train_ER_alpha.smi (876/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL214291 in Train_ER_alpha.smi (879/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL182794 in Train_ER_alpha.smi (884/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL184679 in Train_ER_alpha.smi (896/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL181368 in Train_ER_alpha.smi (901/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL360189 in Train_ER_alpha.smi (908/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL193207 in Train_ER_alpha.smi (910/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL360338 in Train_ER_alpha.smi (913/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL188882 in Train_ER_alpha.smi (916/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL360385 in Train_ER_alpha.smi (917/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL180085 in Train_ER_alpha.smi (920/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL1596554 in Train_ER_alpha.smi (923/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL212468 in Train_ER_alpha.smi (934/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL380720 in Train_ER_alpha.smi (938/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL204118 in Train_ER_alpha.smi (940/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL183889 in Train_ER_alpha.smi (944/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL205398 in Train_ER_alpha.smi (945/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL225514 in Train_ER_alpha.smi (949/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL241484 in Train_ER_alpha.smi (954/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL377847 in Train_ER_alpha.smi (959/1238). Average speed: 0.04 s/mol.\r\n"", + ""Processing CHEMBL226327 in Train_ER_alpha.smi (961/1238). Average speed: 0.04 s/mol.\r\n"" + ] + }, + { + ""name"": ""stdout"", + ""output_type"": ""stream"", + ""text"": [ + ""Descriptor calculation completed in 22.27 secs . Average speed: 0.02 s/mol.\r\n"" + ] + } + ], + ""source"": [ + ""import os\n"", + ""import glob\n"", + ""\n"", + ""path = r'smiles/'\n"", + ""for f in glob.glob(path + '*.smi'):\n"", + "" names = [os.path.basename(f)]\n"", + "" for Fp in (list_Fp):\n"", + "" Fingerprint(names, Fp)\n"", + "" "" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": null, + ""metadata"": { + ""collapsed"": true + }, + ""outputs"": [], + ""source"": [] + } + ], + ""metadata"": { + ""kernelspec"": { + ""display_name"": ""Python 2"", + ""language"": ""python"", + ""name"": ""python2"" + }, + ""language_info"": { + ""codemirror_mode"": { + ""name"": ""ipython"", + ""version"": 2 + }, + ""file_extension"": "".py"", + ""mimetype"": ""text/x-python"", + ""name"": ""python"", + ""nbconvert_exporter"": ""python"", + ""pygments_lexer"": ""ipython2"", + ""version"": ""2.7.13"" + } + }, + ""nbformat"": 4, + ""nbformat_minor"": 2 +} +","Unknown" +"Inhibition","chaninlab/estrogen-receptor-alpha-qsar","02_ER_alpha_RO5.ipynb",".ipynb","490820","2437","{ + ""cells"": [ + { + ""cell_type"": ""code"", + ""execution_count"": 1, + ""metadata"": {}, + ""outputs"": [ + { + ""data"": { + ""text/plain"": [ + ""1238"" + ] + }, + ""execution_count"": 1, + ""metadata"": {}, + ""output_type"": ""execute_result"" + } + ], + ""source"": [ + ""import numpy as np\n"", + ""import pandas as pd\n"", + ""\n"", + ""path = r'model/'\n"", + ""\n"", + ""df = pd.read_csv(path +\""Train_ER_alpha.csv\"" , header = 0)\n"", + ""#df = pd.read_csv(path +\""Train_ER_alpha.smi\"" ,sep= \""\\t\"")\n"", + ""#df = df.ix[:,0] # select first column\n"", + ""ID = df.chemblId\n"", + ""STATUS = df.STATUS\n"", + ""smiles = df.SMILES_desalt\n"", + ""smiles.to_csv('smiles/Train_smi_only.smi', header=False ,index=False)\n"", + ""len(smiles)"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 2, + ""metadata"": { + ""collapsed"": true + }, + ""outputs"": [], + ""source"": [ + ""from rdkit.Chem import Descriptors\n"", + ""from rdkit.Chem import MolFromSmiles\n"", + ""\n"", + ""mols = []\n"", + ""\n"", + ""for i in df.SMILES_desalt:\n"", + "" mol = MolFromSmiles(i)\n"", + "" mols.append(mol)\n"", + ""MW = [Descriptors.MolWt(n) for n in mols]\n"", + ""LogP = [Descriptors.MolLogP(o) for o in mols]\n"", + ""nHAcc = [Descriptors.NumHAcceptors(p) for p in mols]\n"", + ""nHDon = [Descriptors.NumHDonors(q) for q in mols]\n"", + ""\n"", + ""data = pd.DataFrame(\n"", + "" {'chemblId': ID,\n"", + "" 'STATUS' : STATUS,\n"", + "" 'MW': MW, \n"", + "" 'LogP': LogP,\n"", + "" 'nHAcc': nHAcc,\n"", + "" 'nHDon': nHDon\n"", + "" })\n"", + ""data = data[['chemblId','STATUS','MW','LogP','nHAcc','nHDon']]\n"", + ""\n"", + ""data.to_csv('SubFiles/ER_alpha_train_RO5.csv', sep=',' ,index=False)"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 3, + ""metadata"": {}, + ""outputs"": [ + { + ""data"": { + ""text/plain"": [ + ""1238"" + ] + }, + ""execution_count"": 3, + ""metadata"": {}, + ""output_type"": ""execute_result"" + } + ], + ""source"": [ + ""len(data)"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 4, + ""metadata"": {}, + ""outputs"": [ + { + ""data"": { + ""image/png"": 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AmrQATExN1hyBJWvlOx7xCktQj9lGaq9PCwxHl/Kq25d8o54cvLRytFH6oJUk9YF4hSeoZ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\nSb6a5LghbR6T5ANJtiS5vd5F7azB3fYGYjs0yflJbgE+sos+uFuSk+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+ ""text/plain"": [ + """" + ] + }, + ""metadata"": {}, + ""output_type"": ""display_data"" + } + ], + ""source"": [ + ""%matplotlib inline\n"", + ""import matplotlib as mpl\n"", + ""import matplotlib.mlab as mlab\n"", + ""import matplotlib.pyplot as plt\n"", + ""\n"", + ""figure, ((plt1,plt2), (plt3,plt4)) = plt.subplots(2, 2)\n"", + ""figure.set_size_inches(15,10)\n"", + ""\n"", + ""# Histogram for MW\n"", + ""hist, bins = np.histogram(MW, 50)\n"", + ""width = 1 * (bins[1] - bins[0])\n"", + ""center = (bins[:-1] + bins[1:]) / 2\n"", + ""plt1.bar(center, hist, align='center', width=width, color='blue',edgecolor='black',\\\n"", + "" error_kw=dict(ecolor='gray', lw=2, capsize=5, capthick=2,linestyle='-', linewidth=0.5))\n"", + ""plt1.set_xlabel('Molecular weight (Da)', fontsize=16, fontweight='bold')\n"", + ""plt1.set_ylabel('Frequency', fontsize=16, fontweight='bold')\n"", + ""plt1.tick_params(axis='both', which='major', labelsize=14)\n"", + ""#plt1.set_xlim(200,900)\n"", + ""plt1.set_ylim(0, 1200)\n"", + ""#plt1.grid(True)\n"", + ""plt1.axvline(500, color='gray',linestyle='--', dashes=(5, 10), linewidth=0.5)\n"", + ""\n"", + ""# Histogram for LogP\n"", + ""hist, bins = np.histogram(LogP, 50)\n"", + ""width = 1 * (bins[1] - bins[0])\n"", + ""center = (bins[:-1] + bins[1:]) / 2\n"", + ""plt2.bar(center, hist, align='center', width=width, color='red',edgecolor='black',\\\n"", + "" error_kw=dict(ecolor='gray', lw=2, capsize=5, capthick=2,linestyle='-', linewidth=0.5))\n"", + ""plt2.set_xlabel('LogP', fontsize=16, fontweight='bold')\n"", + ""plt2.set_ylabel('Frequency', fontsize=16, fontweight='bold')\n"", + ""plt2.tick_params(axis='both', which='major', labelsize=14)\n"", + ""plt2.set_xlim(-15,10)\n"", + ""plt2.set_ylim(0,1200)\n"", + ""#plt2.grid(True)\n"", + ""plt2.axvline(5, color='gray',linestyle='--', dashes=(5, 10), linewidth=0.5)\n"", + "" \n"", + ""# Histogram for nHAcc\n"", + ""hist, bins = np.histogram(nHAcc, 20)\n"", + ""width = 1 * (bins[1] - bins[0])\n"", + ""center = (bins[:-1] + bins[1:]) / 2\n"", + ""plt3.bar(center, hist, align='center', width=width, color='green',edgecolor='black',\\\n"", + "" error_kw=dict(ecolor='gray', lw=2, capsize=5, capthick=2, linestyle='-', linewidth=0.5))\n"", + ""plt3.set_xlabel('Number of Hydrogen Bond Acceptors', fontsize=16, fontweight='bold')\n"", + ""plt3.set_ylabel('Frequency', fontsize=16, fontweight='bold')\n"", + ""plt3.tick_params(axis='both', which='major', labelsize=14)\n"", + ""plt3.set_xlim(-2,30)\n"", + ""plt3.set_ylim(0,1200)\n"", + ""#plt3.grid(True)\n"", + ""plt3.axvline(10, color='gray',linestyle='--', dashes=(5, 10), linewidth=0.5)\n"", + ""\n"", + ""# Histogram for nHDon\n"", + ""hist, bins = np.histogram(nHDon, 20)\n"", + ""width = 1 * (bins[1] - bins[0])\n"", + ""center = (bins[:-1] + bins[1:]) / 2\n"", + ""plt4.bar(center, hist, align='center', width=width, color='orange',edgecolor='black',\\\n"", + "" error_kw=dict(ecolor='gray', lw=2, capsize=5, capthick=2, linestyle='-', linewidth=0.5))\n"", + ""plt4.set_xlabel('Number of Hydrogen Bond Donors', fontsize=16, fontweight='bold')\n"", + ""plt4.set_ylabel('Frequency', fontsize=16, fontweight='bold')\n"", + ""plt4.tick_params(axis='both', which='major', labelsize=14)\n"", + ""plt4.set_xlim(-3,50)\n"", + ""plt4.set_ylim(0,1200)\n"", + ""#plt4.grid(True)\n"", + ""plt4.axvline(5, color='gray',linestyle='--', dashes=(5, 10), linewidth=0.5)\n"", + ""\n"", + ""plt.tight_layout(pad=2.0, w_pad=2.0, h_pad=2.0)\n"", + ""plt.savefig('Result/ER_Alpha-the histogram plots of the descriptors.pdf', dpi=300)"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 5, + ""metadata"": {}, + ""outputs"": [ + { + ""data"": { + ""text/plain"": [ + ""chemblId CHEMBL954\n"", + ""STATUS intermediate\n"", + ""MW 2392.96\n"", + ""LogP 11.9912\n"", + ""nHAcc 32\n"", + ""nHDon 48\n"", + ""dtype: object"" + ] + }, + ""execution_count"": 5, + ""metadata"": {}, + ""output_type"": ""execute_result"" + } + ], + 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acdAcidicPkaacdBasicPkaacdLogdacdLogpalogpchemblIdknownDrugmolecularFormulamolecularWeightnumRo5Violations...organismparent_cmpd_chemblidreferencetarget_chemblidtarget_confidencetarget_nameunitsvalueSTATUSSMILES_desalt
07.69NaN0.330.503.31CHEMBL370037NoC16H12O3252.260.0...Homo sapiensCHEMBL370037Bioorg. Med. Chem. Lett., (2005) 15:12:3137CHEMBL2069Estrogen receptor alphanM6.0activeCC1=C(c2ccc(O)cc2)C(=O)c2ccc(O)cc21
18.15NaN4.064.123.86CHEMBL189073NoC17H11NO3277.270.0...Homo sapiensCHEMBL189073J. Med. Chem., (2004) 47:21:5021CHEMBL2069Estrogen receptor alphanM1727.0intermediateOc1ccc2c(-c3cccc4ccc(O)cc34)noc2c1
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"" + ], + ""text/plain"": [ + "" acdAcidicPka acdBasicPka acdLogd acdLogp alogp chemblId knownDrug \\\n"", + ""0 7.69 NaN 0.33 0.50 3.31 CHEMBL370037 No \n"", + ""1 8.15 NaN 4.06 4.12 3.86 CHEMBL189073 No \n"", + ""\n"", + "" molecularFormula molecularWeight numRo5Violations \\\n"", + ""0 C16H12O3 252.26 0.0 \n"", + ""1 C17H11NO3 277.27 0.0 \n"", + ""\n"", + "" ... organism parent_cmpd_chemblid \\\n"", + ""0 ... Homo sapiens CHEMBL370037 \n"", + ""1 ... Homo sapiens CHEMBL189073 \n"", + ""\n"", + "" reference target_chemblid \\\n"", + ""0 Bioorg. Med. Chem. Lett., (2005) 15:12:3137 CHEMBL206 \n"", + ""1 J. Med. Chem., (2004) 47:21:5021 CHEMBL206 \n"", + ""\n"", + "" target_confidence target_name units value STATUS \\\n"", + ""0 9 Estrogen receptor alpha nM 6.0 active \n"", + ""1 9 Estrogen receptor alpha nM 1727.0 intermediate \n"", + ""\n"", + "" SMILES_desalt \n"", + ""0 CC1=C(c2ccc(O)cc2)C(=O)c2ccc(O)cc21 \n"", + ""1 Oc1ccc2c(-c3cccc4ccc(O)cc34)noc2c1 \n"", + ""\n"", + ""[2 rows x 34 columns]"" + ] + }, + ""execution_count"": 7, + ""metadata"": {}, + ""output_type"": ""execute_result"" + } + ], + ""source"": [ + ""df.head(2)"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 9, + ""metadata"": {}, + ""outputs"": [ + { + ""data"": { + ""text/html"": [ + ""
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STATUSMWLogPnHAccnHDonacdAcidicPkaacdBasicPkaacdLogdacdLogpalogp...organismparent_cmpd_chemblidreferencetarget_chemblidtarget_confidencetarget_nameunitsvalueSTATUSSMILES_desalt
chemblId
CHEMBL370037active252.2693.2248327.69NaN0.330.503.31...Homo sapiensCHEMBL370037Bioorg. Med. Chem. Lett., (2005) 15:12:3137CHEMBL2069Estrogen receptor alphanM6.0activeCC1=C(c2ccc(O)cc2)C(=O)c2ccc(O)cc21
CHEMBL189073intermediate277.2794.0592428.15NaN4.064.123.86...Homo sapiensCHEMBL189073J. Med. Chem., (2004) 47:21:5021CHEMBL2069Estrogen receptor alphanM1727.0intermediateOc1ccc2c(-c3cccc4ccc(O)cc34)noc2c1
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2 rows × 38 columns

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"" + ], + ""text/plain"": [ + "" STATUS MW LogP nHAcc nHDon acdAcidicPka \\\n"", + ""chemblId \n"", + ""CHEMBL370037 active 252.269 3.2248 3 2 7.69 \n"", + ""CHEMBL189073 intermediate 277.279 4.0592 4 2 8.15 \n"", + ""\n"", + "" acdBasicPka acdLogd acdLogp alogp \\\n"", + ""chemblId \n"", + ""CHEMBL370037 NaN 0.33 0.50 3.31 \n"", + ""CHEMBL189073 NaN 4.06 4.12 3.86 \n"", + ""\n"", + "" ... organism \\\n"", + ""chemblId ... \n"", + ""CHEMBL370037 ... Homo sapiens \n"", + ""CHEMBL189073 ... Homo sapiens \n"", + ""\n"", + "" parent_cmpd_chemblid \\\n"", + ""chemblId \n"", + ""CHEMBL370037 CHEMBL370037 \n"", + ""CHEMBL189073 CHEMBL189073 \n"", + ""\n"", + "" reference target_chemblid \\\n"", + ""chemblId \n"", + ""CHEMBL370037 Bioorg. Med. Chem. Lett., (2005) 15:12:3137 CHEMBL206 \n"", + ""CHEMBL189073 J. Med. Chem., (2004) 47:21:5021 CHEMBL206 \n"", + ""\n"", + "" target_confidence target_name units value \\\n"", + ""chemblId \n"", + ""CHEMBL370037 9 Estrogen receptor alpha nM 6.0 \n"", + ""CHEMBL189073 9 Estrogen receptor alpha nM 1727.0 \n"", + ""\n"", + "" STATUS SMILES_desalt \n"", + ""chemblId \n"", + ""CHEMBL370037 active CC1=C(c2ccc(O)cc2)C(=O)c2ccc(O)cc21 \n"", + ""CHEMBL189073 intermediate Oc1ccc2c(-c3cccc4ccc(O)cc34)noc2c1 \n"", + ""\n"", + ""[2 rows x 38 columns]"" + ] + }, + ""execution_count"": 9, + ""metadata"": {}, + ""output_type"": ""execute_result"" + } + ], + ""source"": [ + ""RO5 = pd.concat([data.set_index('chemblId'),df.set_index('chemblId')], axis=1, join='inner')\n"", + ""RO5.head(2)"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 10, + ""metadata"": {}, + ""outputs"": [ + { + ""data"": { + ""text/plain"": [ + ""(1238, 1238)"" + ] + }, + ""execution_count"": 10, + ""metadata"": {}, + ""output_type"": ""execute_result"" + } + ], + ""source"": [ + ""len(RO5),len(df)"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 11, + ""metadata"": {}, + ""outputs"": [ + { + ""data"": { + ""text/plain"": [ + """" + ] + }, + ""execution_count"": 11, + ""metadata"": {}, + ""output_type"": ""execute_result"" + }, + { + ""data"": { + ""image/png"": 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wzwJlCvAO62UkLx0l+L8nLk3yz1vrCJK9M8sVl3SsAAAAAADpmMYXj8VrroSQ9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\niUW/JCdsBBFEZ/BJMn75xxMJ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+ ""text/plain"": [ + """" + ] + }, + ""metadata"": {}, + ""output_type"": ""display_data"" + } + ], + ""source"": [ + ""import seaborn as sns\n"", + ""\n"", + ""# visualize the relationship between the features and the response using scatterplots\n"", + ""sns.pairplot(RO5, x_vars=['MW','LogP','nHAcc','nHDon'], y_vars='value', size=7, aspect=0.7)"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 18, + ""metadata"": {}, + ""outputs"": [ + { + ""data"": { + ""text/plain"": [ + ""1231"" + ] + }, + ""execution_count"": 18, + ""metadata"": {}, + ""output_type"": ""execute_result"" + } + ], + ""source"": [ + ""# Remove Outlier\n"", + ""\n"", + ""RO5 = RO5[RO5.MW.between(200, 1000, inclusive=True) &\n"", + "" RO5.LogP.between(-5, 12, inclusive=True) &\n"", + "" RO5.nHAcc.between(0, 20, inclusive=True) &\n"", + "" RO5.nHDon.between(0, 20, inclusive=True)] # The inclusive (True: <=, False: <)\n"", + ""\n"", + ""len(RO5)"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 20, + ""metadata"": { + ""collapsed"": true + }, + ""outputs"": [], + ""source"": [ + ""# Convert IC50 to pIC 50\n"", + ""\n"", + ""from math import log10\n"", + ""\n"", + ""def pIC50(df0):\n"", + "" pIC50 = []\n"", + ""\n"", + "" for i in df0.value:\n"", + "" molar = i*(10**-9) # Converts nM to M\n"", + "" pIC50.append(-log10(molar))\n"", + "" #Y = pIC50\n"", + ""\n"", + "" df0['pIC50'] = pIC50\n"", + "" df0 = df0.drop('value', 1)\n"", + "" \n"", + "" return df0"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 21, + ""metadata"": {}, + ""outputs"": [ + { + ""name"": ""stderr"", + ""output_type"": ""stream"", + ""text"": [ + ""/Users/zeromtmu/anaconda2/envs/ER_alpha/lib/python2.7/site-packages/ipykernel_launcher.py:13: SettingWithCopyWarning: \n"", + ""A value is trying to be set on a copy of a slice from a DataFrame.\n"", + ""Try using .loc[row_indexer,col_indexer] = value instead\n"", + ""\n"", + ""See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n"", + "" del sys.path[0]\n"" + ] + }, + { + ""data"": { + ""text/html"": [ + ""
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STATUSMWLogPnHAccnHDonacdAcidicPkaacdBasicPkaacdLogdacdLogpalogp...parent_cmpd_chemblidreferencetarget_chemblidtarget_confidencetarget_nameunitsvalueSTATUSSMILES_desaltpIC50
chemblId
CHEMBL370037active252.2693.2248327.69NaN0.330.503.31...CHEMBL370037Bioorg. Med. Chem. Lett., (2005) 15:12:3137CHEMBL2069Estrogen receptor alphanM6.0activeCC1=C(c2ccc(O)cc2)C(=O)c2ccc(O)cc218.221849
CHEMBL189073intermediate277.2794.0592428.15NaN4.064.123.86...CHEMBL189073J. Med. Chem., (2004) 47:21:5021CHEMBL2069Estrogen receptor alphanM1727.0intermediateOc1ccc2c(-c3cccc4ccc(O)cc34)noc2c15.762708
\n"", + ""

2 rows × 39 columns

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"" + ], + ""text/plain"": [ + "" STATUS MW LogP nHAcc nHDon acdAcidicPka \\\n"", + ""chemblId \n"", + ""CHEMBL370037 active 252.269 3.2248 3 2 7.69 \n"", + ""CHEMBL189073 intermediate 277.279 4.0592 4 2 8.15 \n"", + ""\n"", + "" acdBasicPka acdLogd acdLogp alogp ... \\\n"", + ""chemblId ... \n"", + ""CHEMBL370037 NaN 0.33 0.50 3.31 ... \n"", + ""CHEMBL189073 NaN 4.06 4.12 3.86 ... \n"", + ""\n"", + "" parent_cmpd_chemblid \\\n"", + ""chemblId \n"", + ""CHEMBL370037 CHEMBL370037 \n"", + ""CHEMBL189073 CHEMBL189073 \n"", + ""\n"", + "" reference target_chemblid \\\n"", + ""chemblId \n"", + ""CHEMBL370037 Bioorg. Med. Chem. Lett., (2005) 15:12:3137 CHEMBL206 \n"", + ""CHEMBL189073 J. Med. Chem., (2004) 47:21:5021 CHEMBL206 \n"", + ""\n"", + "" target_confidence target_name units value \\\n"", + ""chemblId \n"", + ""CHEMBL370037 9 Estrogen receptor alpha nM 6.0 \n"", + ""CHEMBL189073 9 Estrogen receptor alpha nM 1727.0 \n"", + ""\n"", + "" STATUS SMILES_desalt pIC50 \n"", + ""chemblId \n"", + ""CHEMBL370037 active CC1=C(c2ccc(O)cc2)C(=O)c2ccc(O)cc21 8.221849 \n"", + ""CHEMBL189073 intermediate Oc1ccc2c(-c3cccc4ccc(O)cc34)noc2c1 5.762708 \n"", + ""\n"", + ""[2 rows x 39 columns]"" + ] + }, + ""execution_count"": 21, + ""metadata"": {}, + ""output_type"": ""execute_result"" + } + ], + ""source"": [ + ""pIC50(RO5)\n"", + ""RO5.head(2)"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 22, + ""metadata"": {}, + ""outputs"": [ + { + ""data"": { + ""text/plain"": [ + """" + ] + }, + ""execution_count"": 22, + ""metadata"": {}, + ""output_type"": ""execute_result"" + }, + { + ""data"": { + ""image/png"": 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tTNLIgd9V23bI5HXj07uvREgkWlU3gqutkEa4WTLapdNXkcs+rZDtGz9m2qguR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Akhq8p57cgFa6qcr+rhmg1yuB0Cz83VcDqfcgwllTMYggLqst8RHG2Yxa48H/uf/bsPTb7ajpX66\n8uyCXmulvJkysjJdn9V7l083VqdPcggIskGmTBs7vj09Z//4paPw0lRGvqIoF0p8bmWcqbt5LN44\nfRE7G2vAxo0z3NRcmm4SZzRulJV4lT7taKzB/hNfamRsznezpm3+4YtuTCxnUF7ihd8zsMWvhmdj\nfLJaox9ZsP3NhDY7TvM3eczYcro7NwGBHtG4qFQbyNVl+098iWjcngV+ITKUKBdSNsk2LamCnZKF\n+c58LkTQuhAg82Tnw+njqNO/NVEC9h3Tcv6+Y1/CKaol+ebWXMLp7w5BdiCmaGmgF8OnXMCPX0pv\naiMv4stKvKhcfxALZoxNcSRvqZ9ubHjWWIug33jXJxzjsXyXNVMvUZSS4v0+GrG4gDCnc/ntdb81\ncyIHkFMReSNR+m0NIbQe6Uwx1nn6b6pw1+Z3ceaJ+YamFf0RuBclCdPW9bnEA4Cbcpm2kW17Rsds\nWlKFzYfacek6l9W9y5cBg9ONAgY5itJcwsmwwgG5/vYGYsSoNzO4GubgcgGtRzqVcUTtuNv6cSe2\nv92Bb08qwwv31+DhV1KNh57+myrc/vQ7ylgDF/qMMHvHC5bjwYsSVuw+rpz/+WWzEBdENKkc1Dcu\nrsLmt9o1pqADvd5ccqgVbsv3WNIfEEOenINwqwr5fr/YOI+v2QR++tpJ5Zt75t5qDGc8YLy5z3jK\n9/cKJM3Jwr3XKRvRDGc8CHrdtpmUxeM8vo7xKU7sw/1ueG24r7F40vhN394wvxt+G9orFMg8OSsU\nLbc6eRxlOR7dbBxr957SzI3KGK+tMi65giCK+OpaTDNn3bi4CjeO8IOmij+nkOeT+uR6ritjvLbx\nea6Q5C9OM3ff3hBCeYnPMe8/AQCL3EoCulnA6uRQFCVE4rzi5ltfPQ4r76rAlFEliPYOKl2ROFap\nPrJNS6pQ6nMbug+KogS4YLlt9Qd8uPm7eHzfacNFNuOhTQe5XA+A+vO5XUA4ziMcE5RJb9BPg0uI\nWLP3lGkQoD9BA9NguC6AbnTNbEKw1J7mb1Wu8sU8eXDyJGeQo2gnxk7FQIKN/cXAN5KMeVweTypG\nBxGO8fjNiS9RN/0bGHODH51dLP7zzGXc/Zff0ARNNi+pxsY3/xsHTl6Am3KhfcN8LPvFEU2wdsww\nH1bPm4aJ5QwiHA/GSyOaEFOCvp1dLLYePpMSaB3I4tfs+bTUT0fd1vc0z8oKb1nltmw5sBDvEUFO\nQbi1F4UIVgmCiB4uNdhZ6nODpnO/OA7HEli+K3Vza2djDYI2OZSHOR5nL13HlFGlCPrdCMd4nLvS\ng6ljhtnGET2xBDou96S0WTG61BYn9p5YAh+cvYI5U0ZiWMCD69EEPjp3FbdNHVX0zu/ZgsyTLYNw\nqw1gOR7RhICeGK9wZqnfjYCHdkRAtyeWwK8++Bx1N49FxeggOi6HceiTi/jhbTc5gisKwee5BOGv\nQQFLD6z438Yiglx+o54cyuU36g+bolwo8bqxvWEmmvacwBunL+JKT3LxW8Z4EE2IGBn04bllsxD0\nuXHuSgRPv6nNdtK0mxBwtYez1HayJLRNOc5M/5Dx0hptXn2bstu40e/6A/35BFEEF5Hw+L7TmqDD\nmGH+tOUw/dG0YrxJTUr9LpX8N6IoIcYLiHC87piZKCvxWGpPc32qIHExE36unzEBQbFCLrvTBy/s\nLLtj4wKa5lakTGT1nG34t2l4/MDJC5rAbMu//xc2/PaPaN9wD0aW+nD/nEmIJQTsbKxVArByMBfo\nk0v46LMuXGMTeHzfaYwq9aH57kpt5m9DCCNVO/lBfzKYOrLUhy33hVImh7JZ584HarOePGbSIR8z\nzAdIyWwP/WaoURDKKrdly4GFeI8ICOyAWj4EgCIfYufmRJQXsWL3ceMNERsCupTLhWfurU7JCLYr\nOxdIcsT4ESV4+JVjeeMIxktjYlkJuiNxlPjc6I7EMbGsxDatV8ZLY/q44ZpKDju1kAsJMk8mKCT8\nXhp7j53HX4duhMsFDGc8+Le2r3D/nEmF7polMF46parMSVwR8FAYXRrQ8PnmJdUIeIo7O1cG4a+h\nA/J0s4B+Mdc0twIP3noTGC+NMMdnXNwG3FSKzIFsQCMv0I1IjvHS2Hr4DDYurtKQot6QTT5WvTCW\ntXszBYKtIJc7PWrnTgCKc+eOxpq0GSJWg+pqJJ+FzzDQIAgiutg4WE7QZDLLi5sdjTU5u38EBASF\nwUCCjZlgxosBN4Wlt0xMKdWy4rBulcfVOt3RhKhwkly+LIoSSnxuXOnh4KZcGrkEoC9Q/Num2xWj\nTiDJf6v2tGmkGSjKlXFy2N/Joxmvd1wOJw1G6yqxfNfRFLkisyCUXVkJdr5HBAT5RCEMn/Ldps9D\noUSg8dyyWUomqav353ahEBzBJUREE4ImQWLTkioEEjQYX+6vVe38DkBxft/RWINSvzMCHQQETkAs\nLuDuv/yGZvPkmXurEYsLjsjQdTpXpItVOKH/BEMH5G3MAuqJWvuGe7B09kQ8/MoxU7dreXErZ8JG\neTHFUOWx109h5V0VAMzF9tm4gEvXOWx+S2vqVeJLNfXSi/g/+06qi3l/sgVy7e6dzjky3cS3vwL3\n+mchlwhH4gJW7WkzzWQu8bkdLahPQECQhBEHDBTpeDHKi1jV2qbh+1WtbYjymR15rPD4piVVeO7d\njrScpB6zzjyRHDc2v9WuZOvKhmdmRp0BLz1grrcCI16Xr6/57mmKsZGpoagqIJTrsUoPO94jAoJ8\noxCGT/luk0uIiMQFrNh9HNPWHcSK3ccRiQvgbHZZzzdHiJKJ+ZtNlO1k53cCAidBkCT89LWTmm/7\np6+dhOAAuUzA+Vzh9P4TDB2QgG6WkCdq0YSIVXu0i/VMbtfpykrTLcrlxe6VHg4/2P4+lv3iCEp8\nbvjdZseGlIXxlR4OQZ8bOxtrks7qjcbO6pmQa3dvs4l9hEvvbK4PUOid4jVO6Rnc0dmEoJC1nAGn\n7w8bF9K21x9k00cCAoLiBRs358WBZKPpA5xXejiU+tzY2Vir8HhZiRdb7gspnATAkFeUrFkJmmzd\nOZPLMYLxYHtDSAnsqqGWZtBzfbYclun4FF5vrEEZk7y+ieV9m23peFp5JjkeqwgIBiMK4f6e7zZF\nk2CIaHMwJN9zPMZnMtb47LmvhdgMICAYinB6QDHC8WiaW4FDq+/AuScX4NDqO9A0twIRji901yyB\n5Uy4jiNcR1BccAYjFCGyWazL5Z+QJMOy0mhcUEzKjAKF2ZRwKfICvfqJ4RiPlz78XHFA394ws1/l\nbbkulTPSIty2NIRffaDtq1Hw1KysN1uTD8ZL40qvNvGz73QYSFrMVO5zrjRoiGsuAcHggChK5gvp\nXp7ur1wLRblQxniwo7EGJT530qTMQ8PlcinmZGxcACQoRmGZeMVsHAGAEq+QojOulmZQc322HGb1\neJlnRVFCNCEqxx9u/q5yH415Wis9VIhScgICp6EQ0gBmvGZXm4xJMMTOUuVCzPEiHG841kQ43hbj\nIT9NYdvSUIqckN8GHWQCgqEM1uTbZjneNmPHXCLgoY2lxxxS5Uq5gE1LqrB2b9+cc9OSKpDlOkGx\ngYy+/YTVHWp5cvfi+58hGhcM5A+S5lyZyrIoygXG0zvp9tJgE4Lprj9FuRD0u8HGBTz8yjFsOXx2\nwJlKud6R12dk7WisQevHnQPqa7aZWWxcQImXxqYlVbjSw2HL79rx1KIZSn/smICT7DECgsEBNiGg\ns8s4s1UOjvQ3G00UJXSzCTy0Kynp89CuY+hm4+iJJQylBKzyilEpMEW5wPjcis64kTSDmutlA8nd\nP5mN3zbdjlGlvvQ8my0v647f8rszyrj5xumL2H/iSzy/rMZUeohkjxEQWEPepQF6M1e7wnFIEtAV\njtuawSoHOtWQA512oRBzPNrlMpTkoW0yf4vyAlo/7lQk4Frqp6P1405EecKxBAS5BGXybdtp7JhL\nRBOCsfSYg9a8fg+FpxbNUOacfocYohEMLZAM3X5AFCVAAn69fDY6u1hsPXwGl65zhot1eXLXUj8d\nTa1tGFXqQ0v9dFSMDuJ8N5tRM1bdZra7/rnMVLLD3Vud+Vric2P72x0D6mu218t4aMAFrN//ifJM\nOi6H0fxqG7bcF7JlwCTZYwQEgwOZzCoHkgFn7EDfhi33Vmu4as+RP+FHt0/OCa+oM2T1Rmoy14ui\nhAjHa8x3Ni6uwpbftZvzbLa8rDv+wMkLoFzAzsZaBLw0Oi6H8Y//9oliJHrmifnav7dhrCIgIBg4\nYryASFxr3vXMvdXwuCnFyDGXkAOd+uwquwKdQGHmeH4vjc2/adeMDZsPtWPLfSFb2pPn61sOn1V+\n5qZc+PvvTbWlPQKCoYp8f9u5htMlI3weChFO+zNJtNdYk4CgP3DGF1VEMA6shhRNW/1iXZ7cyWYu\nvCgpWU9Gi1EzGC/wTyila2xcQMBNIcqLSvAAEjSlGvXV49B89zQASa3FbErd7C7P05cnW+2r2s1c\n1upRTzLVJc5GzucRjsel6xzqtr6n/M2cyeWWStWycVJXH3u4+bvY8rszKdlvmcqw7XJuJyAgyB56\ns0qjTbps5VrU3/gfvuhGffU4rK2rxI0jAmA5AZQLGFXqQ8flMA59chGLayaAQp/OlxV5h0w8ko7r\nwxyPpl7teKDPsfipRTNMeVY/DqXrm3xf9WPB6nnJsWDZL46kPY/cdnnQix2NNWC8NKIJUXONg41H\nB9v1EAxeiGKfpi0ARdN2Z2ONLe0VIhjCxgXsfeR/YMqoUgT9boRjPM5d6bE0x+svBjKP7W97+ZR4\nKCQEQVT8NmSJEJpISzgKTh4jIxyPySNLND+bPLLEMd+a07kimhBx9E/dmDNlJFwuYDjjwUfnruL2\naaMR9BEeICgekLcxA/TmBsYmOG0QJRgOEPLi1IqZS7q2zXb9S7xuTfnti+9/pvxfEEXFIG1haBwe\nvacSj+87rfz+aoSDIFh3+7WzPE9dnmzUVyOXcr2b+UO7jmHpLRPRPG9qSomzmfN5wE1j29KQppxl\n29IQXEDae5ONk7r+2Mf3ncaj91RiYWic5TJsu53bCQgIjGFmcJONWaXVduRv/OylMJrmVuCxe76F\nR//1VPKb33UU3Wwc1yJxHPrkIhbOHI/Xj53H5R4O6/efNpDz6eMV+RoEUcTVCJeRR8y43mwcmljO\nIOCmDHkKkAw5NuA2nn6YjQVWrlE/HnRHEinB3MHEo4PteggGN/KtactyghLonPKzN1C39T1cus7Z\namjjpyncOJzBw68k5XIefuUYbhzO2KovazaPDfRzLCq29goFQRDRFYlrpI+6IvGs1k0EhYXTx8iA\nO6lB23LgU1SuP4iWA59i6S0THfOtOZ0r/DSFmkllWLH7OKatO4gVu4+jZlIZ0QsnKDq4JJvdXnOB\n2tpa6ejRo3lv1ygb99fLZ2PauoNYMGMsVt5VgYrRQVz4OoryEi/8Brt/8jn2HPkTGm6ZiEhcwIQy\nBue7WYxgPCj1eywZyRxdPw8rdh/X7HLNmVyO55bNQujnv1P+31I/XdmlnzO5HL98sBaiBEAClu86\nmvL3L9xfkxftNCvoM48z7uvOB2o1GQ5hjsfyl1OPU2ctqzPLzI7lRQkuAMMCHlyPJrD/xFc49Okl\n7GisMd1BNDufvo9pj22sBVywtFucTXsEjkfePsZCcWu+MNDMjExSN7nM/FB/4y1/9ZdYNGs8Hn7l\nWMo3/9SiGeB4ES0HPlUyz6b87A2lqmFiOaMxPJNN1Dq7WHhoF9bsPdVvHjHjoS33VqM0kORK/e/f\nXXMnDrR9hbqbxypZcoc+uYgf3T7ZtE2zscDoGuX7bYUjBxuPDrbryQMItxYQPbGkLrjRPMyObC1R\nlNATS+Aam7A0784F8n2Ncpu/+uDzFI794W032dJmmOPx4vufZcXpTkQhnqWDUZTc6vQx0unvYL65\nKddw+v0nGBSwxK3Fz2YFhJHMQWcXi6a5FVg4c7xGN3HTkips/k27oqUrL/jl8nZYsssAACAASURB\nVNWf3DE5RXtwW0MIQUmC0bPStx30uVO0GjcurtIMSLK0g/r/fg8NyuWCKEmmOjZswr5SsGygaDia\n9FWvQWaatazKLLNyrCQBlesPglft2LopV1qNn2x00kyP9dGWdXqJ9i4BQXbIhdu4mdSNvBjIVlLB\nqI9yQBgSMGaYD/XV4zD3W2NMtccmlDHKv+UJMpDUmn3j9EWceWK+In1gJA80Zpgv5ZxWeSSZPRtC\n0542zTi08c3/VsqY9X2eUMZkrbdoNhbor1HTNwscOdh4dLBdD8HgBuOlDeexdr6vcUHUzLu3N9ir\nPVkIzch8a9oyXnpIaOg6Xf+TwPljpNPfQafrbTv9/hMMHZCc8TQwGgi2Hj6DB2+9CY+9fkoju7B2\n7ymsuLOiz9E2zqMnloAoSYjEk2W6svag4vS4pw2RuGBY+qFv+9yVCPaf+FLjKrv/xJc4dyWiHCNL\nO6j/rziTJwQcbv4uzj25AIdW34H66nHK8YyXTiknLiSsupSbHsf13fueWCKpgWVybITjcb7b2Kk+\n0ns/jMqts3FSz4XrOnFuJyDIDrlwG7dzMZBSCrjrKNbUVeKxeyrx2OuncOHrqOE3f76bVSR8IhyP\nKaNKNJwuc4Lx9bdh9bxpqK8eh0Or78C5JxfgcPN3EbN4T6jejS7Z8belfjo2v5XcyGTjgiFPmfGr\nFe7KNc8ONh4dbNdDMLjBxgXDeaxd7yubELDnSKemvT1HOrMaA7KFrBmphszVg6XNocI7hXiWBLmF\n099Vp7+DTu+/7E+hRjLO4Iz3h2DogAR008BoILh0nUPQb7xjI2fHyppgat0lM+0wOUMW0OocRjge\n7RvmKwv1Z9/pwKKa8Sk6Ooc+uajRpVH/38iZXP7bR++pxPbe4zu72KLSFlJrKBppJaY/LgRelFI0\nr/w0ZXish3JhBONJ0Wbc1pDU+DHTXrLax2yuJxf3hICAIIlcBGPtXAwYBVzX7j2FGwJejBnmg99N\n4Zl7qzXf/KYlVSjx0jj0yUVsawjhw44r+NY/vqlw+vPLZimcYHb9E8oCePSeSmUseXzfaWXcsbKp\n53fTGMF4cL6bRcXoIJrvnqa0a8RTIxiPouWeLXdlw3sBN5VRq3ew8ehgux6CwQ3GS2PhTO08duHM\n8bZlywU8lGF7ARsdygMeE81IG79JL+UybNNrk6yEnzbm2sGmK8mYPEvCr86B08dIn8m37SsCmUQr\ncLqGLuVCSnxg05IqOOT2Ewwh5F1D1+VyVQJ4VfWjyQD+SZKkrWZ/U0wautsaQhAECc0qp15Aq18r\n6xzeufld5fcf/V9z0fzaSYwq9Snau0lHdBrlQR8kUUIkniy97YrEsbpVW9K6/8SXaLhlInhRwpgb\n/GA5AX43hSjf574acNOICaKhM7mZ7qEgSXj6zXYcOHlB+XkxaAtZ1abUH0cB+LHBtf7ygaSWMOOj\nwXICInEeT73xR1y6zuH5ZbPgoSnl97KTbZQX02ovZaOfmQutTSc7tRJkhaLUInMacqGdlgvZBqNz\nyt/xtHWpUi9nnpiPzi4Wj+87jVGlPqxb8BcYNcwHlhNAuQCfm0IkLuClDz/XlLHJutyMl06rPzsy\n6DPUKH9u2SyU+j2IcDxKvOZO3sl7wmlkF7Y3hFBe4jPVFQaQkbvM+M0q71nVdRxsPDrYrsdmEG4t\nIMIxHu+fvYw5U0YqngUfnbuK26eORtCf+zlnofRsPzh7JeUab5s6yrY2wzEeZy9fx5RRpQj63QjH\neJy70oOpo4fZdl+drIuZDQRBBJvoW2cxHvOxcYijaLnVyWNkIfgkl+iJJRDhEijxeZRvSP6/E/ov\nCCJ6OB5fq3TYhzMelPrchAcI8gVLZJX3t1GSpHZJkkKSJIUA1ABgAfwm3/2wAln/ducDtTjzxHzs\nfKAW5SVebHzzv7FxceqOzXPvdiiZn1sPn1HOU189Dm7KheeWzUrJjJIAcAkBPRyPh185hnNXIljd\nqpVmeOz1U2j8ziT8r9/+EXP+77fxdzuPgKKAa9GEJhP1WrTX0duiM/mYG/zYfKgvmCv/vOi0hSQg\nxguGshB6N3a/wbWOGeZDJJ5cSFyPJsD4aAQ8NBaGxuGjz7rwyO7jEAEE/cnzlPo9oGkKjJfGmGE+\npTT50Oo7MGaYT7k/Zk7wRsjmWDvPQUAwVJCLzAyzMWAgwVw56//spbBh9m84xmNiOaPwGMeL+Lud\nRxD6+Vv4cW+VgKxhqIasyy2ff/3+00pmwcLQODx6TyUe33caAZPxoNTvQeX6pCt7F5t08jaUm0kI\nKfJBTXvalEoTI57KxF2CIOJqhLPkRC1JxjI4AQ+lCTA8+04Htr/dkTKeDTYeHWzXQzB44aGAmm/q\nHMO/WQa7EmYLoX/opVy4feoozc9unzrKtmxZAAh4KUweGYTYm6AjShImjwwi4LXnxsq6mHVb38OU\nn72Buq3vYfvbHYNSV5KmqaSJnmptQOAsOHmMLPG5cbmH0/zscg/nmG/NR7kQ9Hkg9M7TBFFC0Odx\nTIZxlBex6/dfgONFAMn5+K7ff4Fo7/8JCIoFhWaE7wE4J0nSnwrcD1PoTW/CHI9L1zlsfqtdcRjv\njnBgvG5suS8EluMhAXjm3hBW3FmBZ9/pwMq7KtDU2oYX7q/BWpXDuKyj+/z9NfiaTeCjz7pQMTpo\nutiWA69/+KIbooi0Zj3qHUlZw0adpSBr2Fy6rh0o5HLiQmboGmXFbVpShc2HUk3n9DC61tXzpuHo\nF93KQkLJtl4aQstf/SU2/PaPhkHsWELAmrpKrN2rNb+LJQQw3kJ/OgQEBOmgDsYOJDNjoMZnaqhl\nFp59pyPFIOj/XTYLEY5H0O/G4ebvwkO7sEY/ZrS24blls0w5XT0uiBLw1KIZmqxcWX9X/7cdl8NK\ngLb1SCeWzp6IVZos3CTv5lpXWOytTlnVGySWr7Npzwn88sFaRDhBGQua5lZg6S0TsapV268yxoMu\nNo6WA59qKlsqRpUUfDwjICBIIi5KWNXalsJnOxpr4Mvwt/1BurmvXdlhEpJSPWqO2rY0hGE2ZMrK\nSPAiOF5MadPHi6BtCOoW4r4SEAxFxBMC5t88NmXtGk8I8DtgHSrCmA/dNvJhLjFUDCAJnI9CbzUu\nBbCnwH3ICnLW15UeDs+924HL12MoD/pwpYfDKx99ga97s2Zlva4136/ElFEl+MMX3abau6V+t+Jc\nLi+21TAyO2N85otqvdnOrz74PEXDRs5UK0ZtITNtSY3pnImphZHm1cRyBnOmjFQWEoopXWsbFs68\n0VQTUxShBODV/RDJxhwBgSNQbJkZ6mDogZMXlI3BM0/Mxy8fqAUviGh+7SSmrUtWcHjdFMYM04Y6\n5A0+fZXIxsVVCHhSxwWOFzXjhRxI1v/ts+/0ZfzW3TxWCbDqDeVyrSssl7MajWfqjUtelJL9ajXu\nl76/j73+/7N37vFVlPe6f+ay7itBEi6FIioGqAWSBUnl0HrFC6LnZFvYaNJCvLRYPVrkIJbtpd3Z\n3SCbEilJtx+qtFtFLVS2lp1upVGK1nrZWC7hVhuIoIjQgAmYrNusNZfzx2QmM2tmVlaSNeuSvN/P\np59KkrVm1mWe953f+3uf5wDu/NYlWR/PCASCTKY7ZhmKMvU/ZCj7xoG4pmitnWvGbcymyPQxibcs\ngZAZsqEn6STfzz/fQ/UIQ4esLZFQFOUEUAngEYvf3wPgHgAYP358Bs8sOUrXl9w5xOt8BH+5sBz3\nvrgHIwtceG3JlapPLseL+MbFReiMxE1XtYNRHpHuAqVZ11Z9VQBbPjwBlqbUjiQleVH7XEtmlyDE\n8aBA6bq0lJWlZ2rK5RA2Tadakdeh/lzxh8qlooeCIXQuoRtM25HsdTL49R0VcHd3J4c5HoUeh+lz\nFnoceHpRObxOBsGE129ZNHeRSSshf8lVbR3syJ2octilYgnQuP8UznZxeKamvFu3E7tUm7F63jRs\na+6xxVEW+J56q1XdJdIVjWPbvs/x7enj1Oc/fT6M6eOL4HezurFH2emxet40jC/2IsTxePbd4zrr\nHaudIl4nA0hAQ/V0g69wf2/mvU5GtZ9IHBs9Tlp9ja1nguriaOJ5WRWKFBsdAiET5Ju2ZtpbMtOd\nnW4ng7rfteg0pK6pBetuD6T9WArZsHnIeKGcoVHscxruHQajHUE++68OJvJNW9NFNvQkneT7+Xsd\nDH65cAbOaTx0h3sdZPGKkHNkc/SdC2CvJEltZr+UJOkZSZIqJEmqGDlypNmf2Ioomvv0AXJRV5Rg\n8BH0u1mMLnRh+Y2T0XToNFrPBHFhkReCKGFjTTk++PgL01Xt91rPgqaAQ7U3ovyiC7Bt30n8clG5\n7NlYI3s23n3lBJ2Ho9ep765ddv1EVF0+Hvds2mPqkaj4W2k71URRQkdY78OreCdmE6sVMaVLOXF1\nLLEjefGmPQjF5FCgArcDXier3kgkPmeI4/Hce8dNfRvJyhxhMJJtbc1Xko0JqTy2PRQz7N5Ydv1E\n1FcF8Oy7xy29bccXew1jRtOh03j94GnUNh5GR4gDBeCGr38F974oP3/TodP4+thh6r83vf+Jbuw5\n28XB62Lw7388ih9vO4R55eN0xwhGzfUyHBPS7iscjgloOnTa0DW8YeEMtIdiuoT69lAMS2aXGM5L\nKVibnS+BkCnySVsN86YkvtXpwtWdJfH28mvw8RM34+3l12DDwhm2+SmGOQFtnZzO67Wtk0OYs08X\nks01B9Mxh4K3bDauEYI5+aSt6SQb13Y6yffzlyQJnCDikVcPqtlHnCBCkogGEHILKltfSoqitgBo\nkiTp2d7+NtNpwakkm4uSZEgob1p6FVwsjcbmz3Hr9HGGLluKAoZ7nYh0bzHtjMhdVbW//ytmTSjG\n6nnT4HUyYGjgX37/Ec52cfj1nRUQJZiuDif65Cppvk1Lr0Jt4+Fe092tUuB/dUcFJEnuUM3GirSV\nh+4fP2rD1ZNGYXyxF2GOh9cpF6ZTSbPn+Z6kWiWRt+ry8djzaQcmjCzAnPXvGB5nR8K98vrIij8h\nCTmbFpwNcuF6MdeCAHwuFm629/Ox0qhnasrx7LvHsW7HUWvdrinH5+ejKBnlRzDK4/2Pz2LCyAL5\n3xyPbftO4upJo/DIqwfVx5o917LrJ6LmmxejwO3AZx1h+N0M/nv/acy6dITa5VvgZvFZRwRvt5zB\n7K+N1o1h6dA+s/c1GhfkMc7FoCvKw9f9OdMUsNgkof7pReX4wQt7dF65O//WlpHzJeQ9RFu7SWXe\nlG7CHI/zkTgeenm/ep0+eVsZLvA44LXhmNEYjygvGhLK3Sxtm/9kpl8jIL/OTo7XeZ7XVwdQ6GJt\ne53ZGJczfcxsXCN5DNFWGwhzPLo4Hks1HrTrqwIocLG26Uk64WI8vozyBg/dYW4WrjzwAO6KxtXa\nioIydyd+4YQMkZK2ZuVqoijKB+AGAD/IxvF7Q+vhChgDx4Ce7k3tRd506DQeuG4i5kwdgxWvGINs\naiun4Bur/oiPn7jZUAz+yycduLDIi4W/2oUNC2egcf8p3BoYa7B1aKgOoNjn0iWHA/ptDWa2DWZb\nYs2sDUYXuhCOJR7TeGNs58TKEGbE8WBoCjdPG2P6XvQW0iOKEs5F4rpiTH11AH/9/Ev8cHMzWlbO\nNX1cukKVtNhVJCYQBiO5cr2YjwmyHYLPxfZ6PlYapaSFA+a6vWZ+KUIxAbWNh1FbOcW04FtbOQUX\nFnl1z29mmdCwsxX3z56ISx99HYBc4L398vG6G4WG6gCGex1oOtyG5s/Oq7YMYU7W+nQXc7uicXRx\nvC54sr4qIFvnJLFRUDT5aFsQdW+0oHH/Kew9cR61lVMwcbSfLJQRCL2Q7nDDVBAl4KGX9+t09KGX\n92NjTYVtxwvHBDzy6kFdcdVpYzepy0HDwVFYPW+aWkR20BRcDvuOyTI0nAytO6aTocHa9DqzMS5n\n45jZuEYIBC1OhoaL1V/bLpa2VcPSSVwE9nzagQ0LZ6DQ40BnJI4PPv4CV04cZUsQZrrJd8sIwtAh\nK99ISZJCAIqzcexUSGUQVwLFtJOL6pnjEeJ4S/9BxQM2Wcq44usKAEuvn2TqqbixpgJ+TUKk4s1o\n5ZFodXNrVpQ2P6a+mJ2JiZW2WA2KQlsnp+tA074XEiRLXzafkzUtxjy4ubvAbhI4p01FT2fCPZDa\nYkEuk1jI97A0IrxIuo0JtpAr14vVmKAswvV2PmZaq2iUlW53RuLwu1i0dUZRt6AMXxnmthxXEseU\nZGOMwpypY7B0i/n4ol3EggTdeAOkZ0EvHBNwLhw36PqDW+RCeXH3OSe+hjAnAJS8nVpb4Fb8iLOl\npbnQSU4gpIqVJmnnP+km07kEmS4gA/L7ukSjq4C2o8ueIkwkLuC+F/eaH9OGwk82xuVwXMDmXZ/q\n/JA37/oUd185wb5jZuEaIaSffB6bI3ySa5vN/aKu18Xgh5ubdQ1sLE3hyKq5SR6VO4Q4Hr+oDmDW\npSN0BWm7fN8JhP6SVA0oihpGUdS/URT1N4qiOiiKaqco6qPun12QqZPMNKl4p5r7CLrgS+LXqtxM\nNx06jfUJXrpKyrjiBzhrQjHGF3t7nfwqxdVn3z2u8yA828XB52IhKSJKweD76HUwqK/Wn4flMTXF\nbO1kLjFp3A68TsbQgaacl6d7gDZLMg7HBLSHYklD1hQ/SuVxAwn3SfW15OuKf6Kf2H/8+RjxFyPY\nSq5cL8l8vVM5H2UBUKtRitZof671tg389E2UPLYd31rzFl7Z8xnCMetx5am3WnUa2HTotMGvfe0C\neYxRsAw+czGyz7rGb11LOnwFRVGC12Wt6xcWeQ0+8fJ7FoAgilj8/G48vu2gQfft1u9kr4doISGf\nSKZJdhG2mBuHbfJTzEaw7VAIRcvGuOxx0Lh1+jidp/qt08fBY2PnczauEUJ6yfexOd87RPPdQ9fD\nMii/qAj3vbgXkx7bjvte3Ivyi4rgYYkGEHKL3hThZQA7AVwjSdLfAYCiqK8AuKP7dzfae3rZwaz7\n1mwQV7o3RU3RVFn9q68O6Pys1i4oRV1TC2ZNKMat08dh+8HTWH97ACMLXTjRHsa6N1twtotDfVUA\nfieLZ2rKAQA7ll2NdW8eUbu3lA4lpWNKW1xtPRtSV6/DMTl1tiMct+ykpWkKxV4nnl5UrnrLtn0Z\n7XVFOtOTuXBMwBddnGXHWckoPx56udmQZPzkbQEs/NUuPFNTbvGaeDVw7oHrJmZk5TafV/wTuzLm\nTB2DBw0dfvnTbUzIfXLlejEbExTv1h3LrgYgL5hZ6UeifUs0LkAUAYqm4HbQ+PUdFXA7GYQ5AR4H\njeqZF+GDYx2a3R8XmZ6DMq6c7eJQ4GKxsaYcXheLE+1hbD90Wu32/fxcBC6GxtkuDixN4RsXFyFo\nkTjf23ubju6scFzWdOWYiefwWUcYIwpcBssbmgK+91yPp6Eo9b4TJRPkSic5gZAqdlhK9XpMisLP\nbw/g//22Z27889sDoCl7jhmy0Dg7u6vCVrrK8fDbdMxMv86svK8xwWBlt+KVA7Z2PmfjGiGkl3wf\nm7NxraUTB00Z6iH11QE48uQaivCC4T73wS3NedMhTRg6JA1FoyiqRZKkyX39XbrJhgF6qls0Eu0H\nlswuQdXl47HlwxOYM3UMSkb5EeJ4sDQFd7fn31NvtaoF2n+p/DrmzRgHn0vu7PWwjMHvVblpb+vk\nsHZBKYp8Tni7zcTNwtmU7QzhmKAa+leWjcX915bg0pE+NZRNeV0A1NcajQkI9eKhm+mgACuvxboF\nZVjzh7/h/mtLLL0lb2n4M1pW3oSOkHVhO5Pkiieoci592YaU+F37+ImbMflx8++eXTdoQwQSLtFN\nzl0vMQFeF4MT7WH86cgZXHfZaJ0mpXJupq+pKgBelDB6mFst6ppZmYiihCgvF4O9LjkM0+tkEImJ\nqsetcp4eJ4PWM0F4nQx+9J8HMLLAhfuvLUHJKD/OdEZR4GZxPhLX+9dWBzCi26Pd8NoVreAEPL7t\nILY1n1J/39frXpQkLPttMx67+TJwgqg7hydvK4PPycgJ6onnkWS8y6bm5Op5EQwQbc0iYY5HOM4j\nGBVUP0i/m4HXYU/ADy+I6AjFDIE8RT6nbf6ygiCiPRwzFDCKvU4wdh1TFPH5uajBf/2rw91g6PQf\nM9PHA7Knsfm8XT/D5KS25vvYHIvxOG8SKnaBm4UzD0LFeEFEjBfBixL8bhbBqFwPcbL2eXynk3z/\n/hD9GhSkJRTtU4qifgTgeUmS2gCAoqjRAO4E8NmATi/HSdU7NVnX4rodRwH0FDujcQEXeB144tvT\nsL4qgM/PRfC7vSdx6nwUtY2H0VA9HRQow2riw1sPYGNNBb4Iyl1Ybk2rv9LBpr1Z/6wjjGj3BfyX\nTzpQWTYWy2+cjG37TuLW6eNMk8C9DgbtQbnIMLrQlTQMJ9UO5nRB0xQK3A44WBobayrgdTFo+zKK\n1dv/phbG1y4o1RUF1swvRd0bLfjGxUWIxMWcWWXPlRX//hTKErslrXw686HbmJAf5Mr1opyL3y3v\nyBhR4ML8GRdi8abdfe78SBwzRha4DEXN+uoAin1OtWAbjvcsvoU4Hpt39SwYBjkejObtoGkKoICF\nv9qlLuYpYWu3NPxZ1cc/Hz2LKyeOUjU1ZNFhbKYVaxeUQpSg3znSh+s+HBPQ1snhX1/7CCtumqye\nQ5jjQdMU3Kz5Z5wrHdv5cl4EQi5BU4CbZcB6aVAUcIHXAZamYJecR+KCaSDPFRNH2uItCwCcIIKm\noAsxoin5516bjhmOCdi276Rul9q2fSdx1xWX2NK9GomLpseT/Wzte42Z1thcWlAm9I98H5s5UcLn\n58N4elG5WhD9+GwXPKMK4Mz2yaVAjBcR5IwFaT/YvCjo5nOHNNGvoUVvHbrDAfwTgH8AMKr7x20A\nGgGskSSpw+qx6SSXOx1S7VpsWXkTglHeNNH79YOnUfv7v2LWhGK8tHhm0o7bxBtuq+7VhuoAfC4W\n33tut5qObpWSvvEOOSAi1a5bbadamBNA07C8AU83iV1y63ccQVsnh18unAGGptTtxsrPFfECYLlK\n1Z8VrHxY9Up2jv3psrbqRtcO1GSwSAs52elA0NPflfvExzUtvcpyh0Ft42GsmV+KbftOonrmRfA5\nGfzqz8cMC3P1VQH43WzPzg1RwhchTu0SWzK7BHd86xIUuFgcPdOzS2TWhGKsnjcNXheDYq8TFEUh\nHJc7hMMxQd05cs+mPYbzWz1vGq5f96d+XfeKlmze9aluJ4vPyRi62BK7gwVRxL0v7tVpTpHXkTSc\n0W69JhPnvIFoaxaJxnh0cryhe7XQxcJtQ7eZIIoIRnlIgFrQpSCHPNrVSRqM8rqFPqB7bpUQZpxO\nwhwPERJEEWrRh6YBGpQtnc/Z0LtsHDPTuxGzRZrGx5zU1nwfm/O9QzQYjeNMVxQjC9zqfPJsVxSj\nCty2WdCkk2iMR6dJh3Sh254xK50MFf0aAgy8Q1eSpHMAVnT/j9BNYkHzvX+ajVWvfYTG/acsuxa7\nojzOWyR611ZOASD70Ia5ntVExSZBudllujuvuqJxeZttXITXwYChaTy89UBCp5icVt5QPR1FPqca\nApbM+zYVX1yrwdFtg0F44iTDw9ImnsBy4VopKCvdc+tuD+gsJawG9GS/sxrs82GC0Ns59scH2axb\n0sPSOdE9SSBkGqvOD7kwaQwTs3qclS6XjPKrPoG1lVPk7t+aCsyZOsbgJfjglmZdcruZP7rPyWBS\nwmLj6EIXRvhd6lgmiCKef/8TXcG4ZeVc0/MbX+y1XGi0QqvpPieDqpnjdcWdRB0117EAfn1nBdyO\nZONCz/NkQq9zqZOcQMhV4qKEBzcn+BFulv0I3XYcjxfB8aLhZtzFi2Cc9hR0sxHE5mRodIRNrCW8\n9vTwZcV/OQvHzJVQVjvJh/uZgZDvY7O2JqCQmKWTy7hZBn6XA/ds2qPTJjtqBnbgdDDYvvsz3S6P\n/2r+HItmXZztU+uVoaBfhB76NKOhKOoKiqKWURQ1KMPQrBBFCUGOhyhJCMd4tIc4LN7UnZi5aTfi\ngoif/J/LcGtgrGW6eIHbYZnoXTLKD0AWaZoCGqqnY9n1E7H8xslqous9m/agIxTDst82455Ne/D5\nuSj+48/H0B6KJZ1AFvucajq6UmzWomw9sUpxD8cE3c+024V5UVK3GYfj+r8bKGbJpKGY2bGbIUpQ\nB2fFKkOb0J7snPvzejL1HgyE3s4x1c87kcT3l2Fow/utoL1ughyfN6myBEIqmCVg1y0ok68hSu7U\nMvvOe1haN0Z81hE2vRZbzwQB9IwRiqZbLsy5GN04FYmL8LtZhGM8SkbJ3una41SWjcXyOZN1Y1kX\nx+PbM8apBWNelJKOG2bXvRWJmv5lJK4Wd6x01FzHujW/+9gRXkyudRnSa7Oxh0Ag9JDpxPa4KKkW\naMq1/+CWZsRtnItkI9VdG9yjfZ0R3r45aTb0LtPH7O88OZ/Ih/uZgZLPYzNNA0/eVqabZz55Wxls\n2mCQdrKhTekkzAn4w6E2BH76JiY88joCP30TfzjUhjCX++c/FPSL0ENSSaAo6kPNfy8G8O8ACgD8\nM0VR/2TzueUEiTehZzo5bN51ArWVU9Cyci5qK6fg1T0nEYwKWHnrNNx95QR1NfDIqrnYWFOBuqYW\ntJ4JJr1xVwq/cUFEkdeBu664RHdTrXjp3ndNidq1NWfqGCzZvM9yAhmMyhNIn5NFQ/V0NB06jTXz\nS3UDg+J9a1acMPPFzdSKj9kkw+pmoLdje50MRhe60LT0Knz8xM1oWnoVRhe64HUy/Xo9+bDq1ds5\npvp59xezgnx7KEaKuoRBg9L58UxNOVpWzsXP/rEUTobC0i3NaoE08TuvdKhu+bBnDHEwlGERcM38\nUjz1VisAWctPnY9gx7KrAcgFgyWzS3Tnouj9F10cJAmICxL+491jmPSY/xwmsAAAIABJREFUshgY\nN1zzy26YpO7s0I4xXx3u0WnHU2+1Wo4bfUGr6TdPG4NRhS7UVk5RNbmybKxBR1PR2t7+xuOgez0O\ngUCwn0wXOzNdQAYAhqLwi+8E8Pbya/DxEzfj7eXX4BffCYCxcXt0Nl7nUCBx8XXWhGLUVwXgGUTp\n9vlwPzOUcTE0LvA48PSichxZNRdPLyrHBR4HXHngPwvkvzbRtJzRk9iklw8F9aGgX4QeeruitAYn\n9wC4QZKksxRF1QH4HwD/ZtuZ5QiJATbjhnsM/oVr5pdi7AVuUJQcFkApq38SAApo6+Tw1Fut+PEt\nlxnCu5TE3drKKfjZH1pwtovDxjsqLEVQ6ebVdm15HAzWVwWwVLPdas38Ujz33vHukAIWxT4n7r5y\nAjwOGs/UyNtwFf9bIPVtKZkymDebZPQ3hCsaF7B8zmTd+752QSkiMQGiJPXpOZWu01w32e/tc+rr\nNqS+emwlXjepBkYRCPkETVPwuVhMemw7XltyJX70n4nWNz3feWWRo8jnRMPOVjU0EwBaV81VA2aC\nUR7Pv38crx88jVkTirHu9jJ4HAzu0/jG1lcFAAANO1tVH8qYIOCRVw/qxoDWsyE07j+lnof2mgfM\nbXYSt/g17j+FkpG+nnGjn1sWtZq+4qbJaA/GUNt4WHe+JSN9Oh1NZbxJ9jdeB4P2UO/HIRAI9uOk\n5cWrRGsAp00dc9kItHE5aJwLSzotrltQhuFe+26is/E6BUFEON7js+51GP3P850IL6qLr0r425YP\nT8j3VYPkteZ7aNhgJy6Yh4rRFJsX11s+h4oBgNvBoK6pRacBdU0tWHd7INun1itDQb8IPfT2idIU\nRQ2nKKoYACNJ0lkAkCQpBMC+/UM5RGJhMcjxhs7ZFa8cQCgmmHYkelgaDdXTcbaLw6rXP4KDobGx\nRuneLcf2Q6fx8dkQSkb5cf+1JWrnqFWrvLINV2uh8PHZEIq7i8JK13DdGy1o2Nmq3rgrW04oUIjG\nRXx34y4EfvoGvvdcTxdZKttS7O7sVDB7/U2HTqO+OpBw7ABoCkm39YsiTDvRBFHCs+8e71P3WTgu\n4Ln3jI+prw6k/T0YCKl8TqluQ+pPty1Z9ScMdpTFHQDYsezqXj3KlUUOMwuDk+ciqG08jEsffR0/\n/q9DmDN1DFpWzsUzNeUodDtw34t7DVvWar55sboLxO9k8cPfNKvdr7WVU/DV4R789B+mmHakSpKE\nYNSiWy7GG1b15UC2gW1Z1Gr6MI/TsA1vxSsHcOe3LtFpVCo6luxvwjHBYOtgdhwCgWA/cRG63Qm1\nlVOw5cMTiIv2HM9BG3c/1FcF4LBxy3U4JmD51v06zVm+db+t21wz/ToFQUR7OIZ7Nu1Rd4G0h2MQ\nBJs+yCzhdTJo2NmKOevfwaWPvo4569/R3VcNBjJ1T0foH9mwjUkn2dDgdBLieLR1cjoNaOvkbLXQ\nSRdDQb8IPfS2/DYMwB7ICWsSRVFjJEk6TVGUHxlMtMwmiauXBW6H6U17gZvFvRv3mHZnmXZCUhRo\nmsJ1l402dI5GY0K3l24ASzSBMWsXlKKuqUXdkrtt30msmV+KnX9rw1cK3aZJ6YmrrAPtnMyUwbwy\nydAa9VfPvAhFXkfPsbtDfL733O6kZv5WHsM+F4t1O46i9WxIXcEKx5IHGikCqX1M65lgzgUIpPNz\n6s93hqz6EwYzZkEiv1xUnvQ7ryxyKBYG2l0efjeDugVlWL51P14/eBpnuzg18NHFmutXoccBSZTg\nd7MQJQl/+aRD9sW9cbJhB0nJSB+icQEhTr6WayunqBY82r9dXxXAqtc+SktHbiJaTbfSZL+b1SU3\np6Jjyf4m1eMQCAT78boYw+4ElqbwwHUTbTmeCLljVhtoQ1Hyz+0iG1uMMx3cE44LluF2BYOo82so\nzGPzPTRssJPvlgX5HCoGAB4HY7qrxJMHCx5DQb8IPST9RCVJutjiVwKAb6f9bHIQD0vrksLPdEYt\nEyeturOUriYAuotI2zkKQO0c/dk/luJ3e0+i5psXY/W8abiwyIsznVEUehxYd3tA3t7kZHDXFZfg\n2XePY87UMXj+/eOGm3OzrtF0dE4qnZ2JryedJJtkqFsFKODe7s41wLrQaCVqSrdz4/5TaNx/CixN\n4ciqueqNvpnNQLg7WEh5DCAXzuVj5tZENl2fU798hk0K8mTVn5CPWOlA4iLH8+8dR311AA9qFuG0\n33lFhxTd0C4iMRSF4V658FDgduCzjjCcDA23g8HRNnOrmRPtYfi67XSU577/2hJ1B4lyXiteOYCn\nF5VDFKGec8koP26xWJhStPCB6ybqxq6BotX0sJVtjUlycyo6ZvU3+Z4QrdBXyxsCIRfJ9PZbt4PB\no68exH3XlKDA7UBbJ4cNb7faul3WWtt4+G3aYhziePzhUBv+ufGv6s9mTSjGvBnjbHlf873IlCpD\nZR6biXs6Qv/Id8uCTGtTuonEBez5tENXkP7g4y9wxcSROb94NVT0iyBDSZJ12z5FUd8AMEKSpO0J\nP78ZQJskSXtsPj8AQEVFhbR79+5MHEqHWRfWhoUzEBdEXedsQ/V0+FwMvvfcbkOH7DM15ZYdn6Ik\nYdJj28Frtk6wNIWWlXPReiZo2nGrLVaKooSuaBwMTcPjZHDqfAQ0BXxlmAetZ4IoGeUDk+DcHY7x\n6IrKXaheF4PPz0Xwu70nVa/dxNdvx01kup7X7P27NTAWK2+dBq+r57kBoD3E6T6z+qoAtnx4Qtcp\non1/tZ/96EIXll4/CeOLvYjGBIRivOHzz7UO3XQS5Hgsft743e6tq5sUIfpNxt6kbGlrvmA2BjRU\nT0eRz4HJj//BRLtvQiQumn7nTbt6F84AQ9PwuhicaA9j3ZtHdAtFz9SU49l3jxt82+urA/A7Wbid\njOpf2BGOocjnwuTHjWPKkVVzAUDVy6alV5mOL9qC8qhCF7xO8+tbd213e7G7Half5/J7wSXoaABF\nXicivPn71x/CMR4doZjBt77Y75Q/pzzQJKvv4GAec2yEaGsW4QURHaGYodupyOcEa8PNcVc0jns2\n7TGdl9tVTAjHeASjRs9Lv5u11NOBwvMiOiIx3WJifXUARR4nWBsCcIJRHos3mcwJayryaqEsFcg8\nNmWIttpANMajk+MN13ahi4XbJj1JJ5nWpnQjiCI+Pxc17Hr76nC3ob6SixD9GhSk9IH1VtDdCeAu\nSZI+Tfj5RQCelSRp9oBOMUWyJd5Whaxf31kBUYLuAgFguOlSbBGqZ15kevNl9fxKx5TVjbm2gzTx\npnjN/FLUvdETrqbvCJYLwF0cr7/BNRFXu24i0/m8ie9fZdlY/OgmffiZ8tyA3CHndckFCJYGujjr\nwqzy3CMLXIYtzNoizFAQSFJQyDhkYpwjWGn0MzXlpoWCvixyyBYIvKl+K12yLStvQkcojs27PsWc\nqWNQMsqPEMdDgoR7X9hrKIaG44L5edVUABTU12JmzaAscilBaw3VART7XIZr3EwPFDugtk4uZW1I\nnGh6WBod4XhadUYZ886F47iwyIsgx+P5945rXmPu61h/F9QIphBtzSJd0bi6q0zZFdB06DTuuuIS\nWwqsPC+iI2xSQPbaV0zgeRHBGI/z3ZrzWUcYF3gd8DtZ244Z5niE4zyCUUE9pt/NwOtg4bVBI8Ic\nj45wzGAXV+R12nI8Ql5AtNUGsqEn6STM8YjEBXRFefX8C9wsPA4mL7QiG4uCBEICaSno/kWSpG9Y\n/O6AJEml/Ty5PpG1Dl2LDlptUVX396KEUIyHx8EgFBNQ4GYR5uSOzgIXCxGpFYHr3mjB/deWmHZQ\naYvJIY43FZrV86apW3G13WGhGA+fi7XsBNOKk103kel83sTCwo5lV+ORVw+m/NzJVq6Uz/61JVem\n1Ck92FfAhsJrzCHIxDhHSDYGtAcHtsiRbEFvzvp3sOz6ibjrikvUFHGGotSO3GffPW7YXfDrOysA\nCYYdBGsXlKLI54SbZXR6uWR2Ce781iXwu1nL5zTTzt7Ou796bteYo2gXJJh3lfVjfMgkfZ2HEJJC\ntDWLZPq73BWN492jZzHr0hHG7bI23YxnowCQ6Y7ZxIWyzzrCGO51oMDtIPPCoQvRVhvI94Jivnfz\ni5KEZb9txn3XlKiLkIptD5l/ETJESl+03q6m4Ul+5039XPKTvhpK0zQFr5PB37/ksHzrft0NtShJ\n6IrKxd4vujh18qP1iQ1GeTz33nG8fvA0Skb6DEbcv1w4A0HN1ouWlXNNfazGF3sBCQlbffWdvHUL\nygDI/rFm3lfp8No1I53Pm+izqzxXqs+dzDdK+ex7S64fKt2rxGOLMBRJNgYMNEjESgtLRvmx7PqJ\nqLp8PO7ZtEc3jvxxTxuunjQKD1w3EXdfcQm+jMSx5g8toCmo3b6jC11YPW8axhd78fm5COqaWtTJ\np1VAp8/FomFnq+FczLQz2Xkne1x/34+BjjmKdinBcak8fy7pOgm2IAwWMu1p7XUymDL2Atz3Ys9u\nhjXzS21N+c6Gv6xV+KPXZc/rpGkKBW4HGIYGRQEjClxkkZ9AsIF896vOtDalm2hcwPI5k43h9XHB\nNgsdAqE/9Navv4OiqFUU1bMMQcn8FMBOe08tO4iihCDHQ5Qk0BTQUB3ArAnFYGkKsyYU92ooHY4J\nWL51Pz441g5elNSgM0EClr28H5Mf345HXj2ILo5HlBfUm02aouDrDjo7smou7rriEhR5ndhYU4Ej\nq+Zi9bxpiAkiwpyAF78/E68tuRJ//zKCb1xcpDv+ktklCHE8QMkdT6IoIRwTsKQ7kVY5p+Vb9+Ph\nOZN1jxElCcEoj3CMVyfeWpSJ90BQbk4Nzxvr3/Nq3790PrdiJv5ZRzjpc2rDkZT3dsnmfXJHGIFA\nyGsUHTAbA7Ta43fJPuna8SPI8RAEUfdvUdOZlqhXlWVjsWPZ1aAo4K4rLsGDW3o0e2SBC7wgqcnA\ny37bjMWb9kAC8ONbLsOKm76mavy25lO4pu5tfHfjLgBAWyen6hVNU3KoW6y78zQuQBQlROMCdiy7\nGh8/cTOall6FyrKxBu1UXhsA7Fh2NSrLxqq/04ZM9ldz0z02DOT5c0nXk30HCYR8gqWBjTXlaP7J\nDTi2+mY0/+QGbKwph107h8MxAdv2nURt5RS0rJyL2sop2LbvZNo0xQwlxEiLEmJkF2GLY4ZtPKbZ\n+EcgENJLNvQkneT7+YuipIbXa2s62rk8gZAL9Ga54APwKwCXA2ju/nEZgN0Avi9JUtD2M0Tmtlf0\nFlqTSheW5ZaylXMx4dHX1Z/JWw7K1dRbq44gJWxtZIELP/7flxm6bJ0MhSXdXbxLZpeg6vLxuq7e\nZAE+R1bNxb//8ajhMWsXlOKPH7Xhhq9/BQ+9rO80LnCxfdpWlQmfRO2x0tlVJYoSorzR51L7nGQ7\nLMEGyNa1HCLVrfeJ+mOlx6p2JAQvarsAWlbOVT3UzfxutV7pSjeulQ51ReLwu1hEeBEeB432kD6g\n4pcLZyCWEPSZqPW9+eYq/z1hhE+1cehr17LdXbF9ef5c0/VcsX8YBBBtzSJcjMeXJoFhw9wsXDZ0\nOwmiaNgxV7egDF8Z5rIt0CYbITrhbo9N7Xz9ydvKcIHXQbrICJmCaKsNRGM8Ok00s9CdR6FoGfYx\nTye5NhckDEkG7qGr/hFFTQAwpfufhyVJOjaAE+szmRLvVDz8eruxsvK72bBwBgI/fVP9mSIIsbgA\np8PaD3djTQUCP33D0st13W1l8LtZ1WfRymvH6rklSKa/q62cAr+LQVyQcGGRF61ngnjqrVbTsDUr\nLBPivY60JpknHnMg6eu9PmfC85DAGoINkIlxHpCoC4kerU1Lr0rZfzvxse+tuFbV3q5oHPe9uNdU\no29p+DNaVs5FJCaY+pStnjcNI/xOROIilmzeh9rKKYZzenv5Nebe4xqPM0udq6kAANA04GKNxeK+\nFmTtLlym+vxE1wctRFuzSKb9ILPhP5np4DeA+DzaCVlMSxmirTYQjPL4j3ePGfTk7ism5IUHbTb0\nMJ3kuwcwYVAwcA9diqJmaP75eff/X6D8XJKkvf07t9ykNw+/VDp8vE4Ga+aXGlbn/U4WTUuv0gna\n2S4OHgcDB2vtk+N1MUm9XEcPc8t+ud0+iFZeO2sXlBo8YORip/ljFD9EpUtMge32CU4F7bZVAOq2\nVe1NcbpvjlW/RFFCpPv4A+32SuYfq2yHTTzOUNoOSya8hKGG2Vjw0uKZOi3tzX8bMPd3rSwbC7eD\nQVyQt6QVehyWGv2Ni4vwWUcYowpcaKgO6Lps18wvxbo3Zf/cxZt2Yc6U0Rh7gRsvLZ6Jzkgc2/Z9\njtrf/xUXFnl79TizHBtdjFowUPzdk+l9b9jt1Z3q85vpen11AJ486CghEHKVTPtBZsN/0sMyqJo5\nXrewJWuHfXPCcExAWyeHOevfUX82a0LxoPPZzvRcM5e81AlDE6+LQcPOVl1gLUtTeOC6iVk8q9RR\n8hny9fxpCub1E3L5E3KM3kb6JzX/rW3lpbr/PTvtZ5RFegsfsSxQalZqIjFR9exSireHT52H21mE\n2sbDqiBsWDgDkbiAH7wgh97sWHa1ZViE1svV+HseoOQiq+JVY/Y3BS4Wq+dNUxNpC1ws3Cxj+Zq1\nfoj9DWOxK+QmFVIpJqeDxGC2oVbQJBNewlBDFCWEYjyKfE7UVk7BU2+1onH/KZxo12t065mgqX6G\nOB4+p95zUKvDK26ajEhcwCOvHkw6NpzpjGLtglL4XCzOhWNgGVrV+NYzQdWSIcwJuGnqaMydOkYX\nslZfFQAA67FFo/OpBHNlU+/TDU1TKPI68PSicvhcLFrPBLFl1wlUz7yIaBuB0E+s5qghjrelWyvT\nIWwAEBNEOGhaN9920DRigmjbFmN5ASqQYA0WGFSNBdmYa2bqPoJAsCLTmplu8v38QQFuh17P3Q46\ng/3oBEJqJJ1dSJJ0rSRJ1wK4GcBrAL4EcB5AY/fPBhW9hY8k61IKx3gIogiaAqpnjkdt42FMfnw7\nahsP45slI9XOJcVU+3w4jmW/7QlPW/fmEaxdUKo7dn11AB4HjWKfE6MKXYaAtvpq+YZ88fO7Memx\n7QjHBMNzyJ24ciLtiAKXmkireCOavea1C0qx4e1WNB06jfqqvoXCacmmGXomiwtDORwil8KDCAS7\nUW4q79m0R9X35TdORmXZWKzfcUSn0Wb6uWZ+KZ599zi+CHEQRFENofQ4aNR3P3aYx6kLYTAbG9Yu\nKMUwjwMUgAIXi1f2nISLpcEyFBb+ahduafgzznZxaKgOgKaAfwh8VRey9sGxdjy4pRm3Tv8qhnsd\nvYZ/mo+N8nMr4RB2h5plmggv4gcv7MGlj76OOevfwbodR4m2EQgDwONgDJpYXxWAx6bCo9JdZZgT\n2zhFEyQJ//elvbim7m1c+ujruKbubfzfl/ZCSMHebiA4uxf0WlbKIcpOZnDtJsjGXHMwLVIS8hOG\novCL7wTw9vJr8PETN+Pt5dfgF98JgMkTKxWGokw1OF/O3+1gsPK/PwLHiwAAjhex8r8/gnsQLZYR\nBgepLjE+D6ATQEP3v78DYBOA2+w4qWzRW7el1UrTifYwXCyNuCBhxSsHMLrQhXW3lWH0MDfCnACv\nyzgpSNzm2rj/FGgK2FhTAY+TMXQEuVkGcV7UrRLRFBDlBTkBXZQwwu/CQy8367qD65paVB8ts62m\nhtfc7Tu77vaAGmLW3+5TK/uJjHToptBRRhg4ZMJLGEqYdeyseOWA6k3rc7E6vfSwNJ6pKYfXyapd\ns437T+GDYx3qY7SBYk8vKjeMF2ZjQ11TC568LYBX9pxEzTcvxgPXTUR7kIPHQWNjTYW8yMjx8DpZ\nRHnB0rah0ONQ994k03l1nOh+7hPtYax67SO0dXJql9Rgs58h2kYgpJcYL8LloLFh4QwUehzojMRB\nUfLPWRsKkCwlz3e182a/iwVrYy0hGzYP4biAe0181gdTJ2k29JjcRxCyjctB41xYUndsKcGOw735\nsWDjdjKo+12LaV0iHxgqdjaE/CfVb+NUSZK+rvn3WxRF/dWOE8o2Vh57oihBFCWDl0rdgjKsbfob\n1t0ewHc37lIH/m3Np9TwhWDUWAg22+ba1snhiyCHa+reVn/2wbEObLxDDp4xm7A9U1OOJ749FYC8\nxddUeHrZXqZ7zZq/U3/WPdHuq3hF4kb7iW37TuLuKyfA77J3MBpsxYVchUx4CUMJq5vKklF+NFRP\nh5vtKYQq338fTRlScpXHjCxwgRckrLs9gKNtQTz33nHcfcUlpmPDuXAMU2vfAiDr+hdBDlUzx+O+\nF/fqxqN/+f1BNbySpim4HQy6Ir1ve+vNW5amKYCCbpwDoNuCOpjsZ4i2EQjpRZAk3PuC+TzWDmIi\nEI3zKPY7QVFAsd+JEBcHSzvhtOWI2dliPBQWn7Khx+Q+gpBtwjEBy7fu1zURLN+6vzvYMfeLuiGO\nN61L5IvlAtEAQr6Q6ii4l6Ko/yVJ0v8AAEVRMwEMjYjJbpQV8JEFLrVA+VlHGICEtk7Zp9BqVV4U\nJdRXB3QhCcO9DtRXBfDgFr3n1arXPjI8hzIpM5+wsTj9ZQQ/vuUyvHbwNJ68rQwPvbxfZ94tiCJE\nUcr4jbXXwaB65kVZEcLEzuNoXIAoAqDk8J58LjTkEmSwIwwGUg1bsb6p5C29/Kwe8/cvI1h+42Td\nDob1VQE4aMqg4/XVAWzZdQIsTaljhdvB6BLclYn+z/6xFNfWva2OG2FOwLEvugzjTX1V34N6eisc\n9CfULFdDFYm2EQjpJdPdq24HjXCMNniHux32FUIcNGWqtQ4bNS0cE7BkdokhSX4wLT5lQ4+HekYG\nIfv4XCxGF7p0oeob3m61teM/nTgt9NCZJ9dQvmtArs6vCemHklLwdaIo6iMAkwGc6P7ReAAtAHgA\nkiRJpbadIYCKigpp9+7s1o9FSTJ0WbE0hZaVc9HWGYUkSVje7XuoMGtCMTYsnIHaxsOYMMKHu664\nBD4XizAn4MtIDAdOnse3SkbC72YRjglgKeDvnZwaavPUW61qpxUkYPGm3YbnV7btrp43DQAwssCF\ns13mz5GNiV0uiAkJ7rKXXPiMBxkZe/NyQVuzTV/0wfxvA/B1h0yafe/NHrNmfikoCvjRfxrHjKcX\nlYOmgC+CMXWb8Ai/EwAFr4tBNCZAlCR4XazpmHRk1Vx8d+MueQLqYNAVjQMUhfdbz2LWpSPUrc4f\nfPwFrpg4sk9dEkGOx+LnjeNQf8eXXNdmom2DDqKtWSQY5U3nsdpg4XTSFY3rFr2U48ndbfZ0h4U5\nHnFRhCRBZyvhoGl4bZqD87yIjnDMUDQp8jptC2LLBkSPcxqirTYQ5nh0hGO6ncFrF5SiyOu0TU/S\nSTDK489HzxjmnldOHGVbMGW6yVfdyfX5NSFlUvqwUr2abhrAiQwKknVm+ZwMnn//E1O/2E3vf4Jl\nN0zGzr+1IRITdJ0Ca+aXyltsr5wAr4NBe4hTfXKWzC7Bv946FQVuFiGOV8MktBO2NfNLUfdGC/7y\nSQcuLPJC8Ri/ft2fDDf52dp6laxjK1MiSZJq7aU/XXkEQq7QF31IxUs2UcMSV/iDUR7PvXcc98+e\nqOtWqywbi/uvLYG/W/NHdYdYFvud8DgYUBSFrkgcXRyPh7fKvr1WCe5K55Kys+TF78/EDzc3mxZ/\ntfSmyenuksp1bSbaRiCkD5oCfvGdAIJRocfT1s3YFlKWDT9bAIgLIoJRAQVuB86H4/C7GTho+wqr\nEV5QQy8BqKGXz9SUo2AQFXSJHhOGGqIkqSG5gHxtP7z1ADbaZFOTbrwuBk2H2zBhZAEK3A60dXJo\nOtyGOVPHZPvUUiKfi6K5Pr8mpJeUPlFJkj61+0RyHasbWZ9TfgvvvnICPA7rAJwNC2fgPo0HrjZM\nR7l5XrJZnpBVlo3FrdPH4d4X9MXfw6fO4+lF5fC59M8/a0Kx3MVV4AKAvPD9y6RIDgV/MQKB0D/6\nqg+peMmaPUZ7I3r3lRMQ0SwSVpaNNdgvrF1QirrftajFYo+DwblwHI+8ehAfHGvHU2+1GhYRlQR3\nRUeV19Z6JtjruJCKJqd7+xnRZgJh6EBTgChBF/BTXx2wraCbDT9bt5PBo787iPuuKQEgp6Kv/+8j\ntoYAZatwTSAQ7MVrcW3nQ3cuAERjApbPmWzoMI7GhLx4DflcFCXz66HF4Fm6tRntjeyRVXOxsaYC\nPhcDUPIF73UwYGgaPheLyY9vx5z176Bx/ykAPWniVmE6IY7XXXj3X1uCFa/IK3K8KKnF3ysnjkIk\nLqA9xKG28TBeP3gasyYUY+2CUgz3OuB1MGrhedaEYrA0hVkTinUdVKIoIcjxECUJwSiPcKz7vzke\noti7/Ua60Iqk8hqXbN6HcFxI/7G6CydalGIGgUAY2vRHHwYyUVKKu14ng/rqAGZNKDbV/Ie3HsB9\n15So2uhx0hjhd+HF789E09KrAAB1b8jpwUdWzcXqedNQ4GLhZGiEunU9xPFYMrtELf5ajQtA6pqs\nnD9Ndf//AKoxRJsJhKEDLwIPdjcuKBrz4OZm8KI9x/M6GdQtKNPpXt2CMltvaLUhQJc++jrmrH8H\nbZ0cQhxv2zHDnIWOckRHCYR8Jt+vbW2HsXZuK6Zg95kL5HNRlMyvhxakoNsH1C4rCYjEBXzvud2Y\n9Nh2LH5+N9pDMXm7qsUF1BmJm/48xPF49t3jOH0+gh3LrsbHT9yMiaP9pgLicTJYuqUZLoZGQ3VA\nLSwX+ZwocDtA05Sx8HxHhdphJQgivghxWPx893lv2o2OUAzLftusew2ZIJMiKRe5AwnFjAAJtyEQ\nCL0ugpmRjokSTVMo9jrx9KJyS82fONqPpqVX4aapo9EeimHxpt2Y/Ph21DYexvIbJwMAahsP6yb3\nwRiPezbtwaTHtuOeTXtQdfl4zJ06GhQFvLR4Jpp/ciN+rRkX1PcyzPhAAAAgAElEQVQhiSbbtehH\ntJlAGDp4XRYa47LneufiItwOGqvnTUPLSnnRy+2gwcVtqiBD1tHExbM180ttLQDQFPDz2/U6+vPb\n7et8JhAImYGloS78K9d2fXUA+eKkku8dxvlcFO3PvQ0hf8mPKyrHSNaCb27NEICblYuwSzY367aa\nvdd6Fq1nQwAoPPKqvCVhx7KrTbeJtZ4J4oNj7bj3xb14pqYckigBFOB26Le+mvlMiaKEUExQuyOU\n83546wFsWDgDBW4HPusIw+dk4M6A4be1J3H6rSEkSYKDkSf1im+bg6EhBwKSGS+BMJTpj41Aurxk\nGYaGzykHR5jp4dG2IGobD+OXi8px7wt7DJY9q+dNg4Oh8ejvDqr2O6vnTdP93ZYPT6Bq5ng8qBl7\nGqoDhrEjHDfX5BPtYVy/7k+22eI4E7TZyeTJnQqBQOgTmbZAECUJD/xmn+54cgibff6T4ZiAw6fO\nY8PCGboQoOG+kShw26NtToZWC9eKjrodNNFSAiHPESV5wUZ7bSvWNflANmxv0km6cyMySbot0gi5\nzZAr6KYjiCtZJxNNWV9ATpbR/dzjoPHDzc14bcmVWL51vyp46948grULSnWeM0oAmnIsn4tFezB1\nD9pwTLD02Sp0O3D0TFC1fwjHeHidA9tK2xt9EcmBfmbhuKDzLwY0Scc2TXjzNRWTQBiK9DVsJZ0T\npQgv4rn3jpuGata90YIPjrXDb6Hd44u9WLqlWWfvc2GRV/d3c6aOMSzkLdncjA0LZ8DFMviii8Nw\nrwN+F2vQ5LULSvGzP7ToLBgG6h2m1cZQjMe9JtqcDn8yosEEQnIyfY2YhfvWVwXgsenmOBvdYR4H\ng6snjVIDKGmKwtWTRsFpY0tdhE8yx82XVj4CgWBAlIAf/qbZZFGqIotnlToeh2wt9mBCM5tdmp9u\naJrCcI8Dz9TI+UUhjoeHzZ+5JAmSHDoMqU83XUFcaekulYBoXMQ3Li5CySj9dtvG/adAU9AlqSsB\naMqxQhyfslG3KErwuhgcbTMPxumK8qhtPKybYMuBOqxtE/5UCyID+cyUc890YEQ+p2ISCITUSNdE\nyetk0LCzFa1nQ6itnIKJo/042hbUab5VqFlnJK57rm9cXITPOsK6nyWOL4CsfwVuByY/vl0t3LpY\nGh4Hg5cWz0SYE0BTUDt/tY8byNbhRG1sWTk3qfVOf8cfosEEQnKycY1E4gL2fNph6F69YuJIWxbX\nw5yAJbNLMGfqGJSM8qP1TBBNh04jzAnwu+2Z/wmCiGCMNxQwhlEsWJsaCEgoGoEwOMm0TU26ifEi\nvE4GTy8qh9/NIhjlQdPyz+3Sw3TC8yI6IjGDnhd5nGDJYhkhhxhS38Z0BXEl8yVRJsmqT223N60g\niPqfb9qNLyNxNFQFcKYzavBoaevkwIsiJAkY7nWiZKRPf6w+eNBGeQFdUR5Nh04bvL3qqwN4/v3j\n+pCKLc0QRVi+lnT5KKYSrtPfz0x77kohW4tSFLfDMziTgW8EAiG30QVRmmiOskDYuP8U5qx/R7VZ\n0BZSmw6dNviorZlfik3vf4If3TQZtwbGqv6zF3gdWHb9RDQtvQofP3EzwjE5GE2LYuGj6NOre06i\nk+OxeFPP+BSM8Zgwwmd4nJl3WG+vUX2tCdqoFKrNjjGQ8YdoMIGQnGxcI14ng4qLinA+HIckAefD\ncVRcVGSbvyxLA1WXj0dt42HVe7zq8vG2+k/GRMk0+C1m4x5pZVuzFmWOSyAQ8pd8v7ZpSp7j/uAF\nOdfhBy/sQTgm5I2/d4QXTPU8wpO5JCG3GFIF3XQEcSnBZ8V+JzbWVODAP9+IX9/ZEzCTbJKc+POH\nXt4PUQLcTgZrFyQUWqsC2PT+J5j8+Hbc+6IcbNOy8iZsrJGPFYmLWDK7RL1pb1p6FZbMLrG42Qae\nf+84bp0+Dtv2nURt5RS0rJyLpxeVo9jrRMPOVuN74mJy4qa4v5+Z9tzNEt7rqwJ49+hZW4Lg8jkV\nk0AgJCdZ8TLxd4aFPM0Cn/J3NAVdMJhZ8fbW6eOw55MOPL2oHC0r56K2cgrq3mjBuh1H8fDWA1h5\n6zSsnjcNLEPD72RRNbOniKEEoy27fqIu6d3vYtSx49szxplOWu/81iW9Bir0pfCaqI1m2qwcYyDj\nD9FgAiE52bhGuLgIEcAjrx7E5Me345FXD0Ls/rkdxEUJD25pNjQsxG0srmajW5ahKMM9xNoFpWCo\nPKmaEAgEU/L92o5bLHDZqcHphOx+IOQLQ+ob2VerhMTtnh6WRkc4pgs2W7ugFAUuFm5WngR7nQxG\nF7rQtPQqdYvXhrdbLUVhZKEL3924C3OmjFa3oUViAsIxHvfPnog5U8fgqbda8eCWZvz6jgo5w4sC\nIEmo+ebFuO/FvXovMpPWA6+rZ0vv/deWoGSUH13RODa9/wnmTB1j/p5wgvVWjxQm/OmyauivvYX2\nZkXpdFO2M3dG4ti273PU/v6vafNrTHbOlWVjseyGSQCAIMcTL0cCIU9Jtk0ZgOF3Ty8qx+Zdn6K2\ncoo6Hmze9Snu/NYl+MELe9S/++XCGarFjuKvXls5BZeO9CHYHR4RiQvwuRhMfnw7eFFCZdlYvLfi\nWgzzOOF1MWDCFF54/xPcdcUlBs/cB7fInrkPXDcRXVEecUHAD3+zXz3+S4tnmmq93832aotjFRKq\neI5pH5eojY37T6FkpM/0bwdScEqHLRLx4CUMZjIZTKsgShL2mlguXDlxpC3Hy8bNeJizeF9ttHlw\nOxnU/a5FN87UNbVg3e0BW44HEH0kEDJBNq7tdOJzsfjB1ZfoLBf2nsifgmg29DydEJ0eOuT+tzGN\npBLEpf3yBzkez713HA07W9Wb8yWbmzGywIXXllyJklF+fNYRRpQXwcTlSXA0LmD5nMm6QLO1C0oR\ntZo8cwJGF7ow+2ujcd+LezG60GV4/Jr5pXirpQ2hGG8oJo8scOk6D56pKYePonQXrCJISmHz/mtL\nMHG0H3OmjsEHH39hCONpqA6oF39/Jvz99WYzE57+JkyaFQ7OdnGorZyCOevfUf/Ojo4Ur4PBLxfO\nwLlwHOOGe9ARiulCQIiXI4GQn1gVLzfeIQdUaH83ssAFn4vBrdPHGcLOvE4GHxxrR2XZWNx/bQn8\nbgfCMR6QZD/eICdb5CQ+tr46gF9UB7D9UBt+fMtl4AQRizftVn9ft6DMtBA6utAFlu5Z7PvN/5zQ\nvYYT7eFetd5K860LrywmPbZdp3labVQSm4d7HfA5e6x31Pc6ZuF/mULBaaDJxMSDlzDYyUZ6t9vB\noPyiIkMjgtumY4Y43lRD7ExYZ2mYhgDZafMQ5gS0dXK6ue2sCcW2FR2IPhLyiXwuaoU43vTatlPD\n0kk8LuDrY4bpGhjqqwKIxwW4nLlfgqIpGELq1y4ozQvLCKLTQwtKknK/7b2iokLavXt3Wp4rmbCb\nffmVpPHG/afw8RM346GXm7HshsnYtu+kOkkMcTxYmoLbySDE8bhn0x7TtFmfi8WJ9jDW7ziCtk4O\ndQvKMMzD4otgDI+8ehAfHGtH09KrUNt42PD4DQtnmKbYaguULE2hZeVcdIRiugtWfl0cNu86YVpc\n2Pm3Nsy6dARKRvkRjvHyzTVN9VsMghyPxc/v7lNyeW+db30djM2er746gC27TmDdjqMpn1d/UN7v\nJZubUVs5xfTzTPcx+3OO+TrBGQJk7INIp7YOBURJwqTHtqsJ5oCsu0dWzQUA3e/eW3EtCtwO/OAF\n43iwYeEM/OS/DuPHt1yGUEzQFTeVSXqQ400fq4wlnZG46Zjw9KJy3eMqy8biRzcZFwm1oWu3Bsbi\nsVsu0y0Ypjrxs9J7ZWxSdiiML/YizAkQRBH3ago6DdUBFPtchuModhXaxbD6qgCKfU4wKYRpDETj\n+jOGEfICoq0aMj0P6IrGLefHdhQnojEenVHeoCGFbhZum4oJYY5HJC7nVii6XuBm4XEw8NqkHWGO\nR0c4Zig6FHmdthwzW/pI5q05TU5qa74XtTJ9baebTGt+uonGePCiBF6U1F0lLE1113xy+/0n89hB\nQ0pCNaQ8dIHkQVxmnn0rXjmAJ749DU1Lr8Lfv4xgxU1fw7Z9JzG//ELVo/AHL+xBRziGZb9thtdp\nvsVL6VZ65NWDeOyWy/DsnRVwshS8LhYj/C6MLnQBsE4kL/Q4TH9eMsqv/lsJujHzGfQ4WDxw3USI\nkqTr6l3xygHMunQEahsPoyMUU4u5yntV7HNi4x0VOLJqLjbeUZHSINifrbLJ/BJTCU9LxPTcvU5U\nz7yoV0/IgSK/FnnLs9XnmU0vR7vD7giEwYrS+a9QWTYWO5ZdDaCnG0z5uZOl4bfY8lvoduCf/89l\nkKD3k+zieER5AZIkwe+22C7cPZYUuM3HBL+L1XmuLbthEh7eesAwrt1/bU9QWlsnB5+LTUnrE32C\nPSxtCAldM78UT73VisqysVh+42Q88upBNWyti+N1Y9CSzc2mvrgRXjT1v4zwqflt9mfcUCAevISh\nwECukf7gc7GqJZni3z260GXb9ltehKmGpCgh/UKUJDzwm324pu5tXPro67im7m088Jt9EG1unnE7\naKyeNw0tK+di9bxpcDvsu73Lhj6SeSuhP+RCFsxAcDsZ/PGjNmxYOANHVs3FhoUz8MeP2uDOk7lI\nvnvQihLQGeVx34t7Memx7bjvxb3ojPLIB9kh89ihRX5cURnC6svvcTKobTyMn98ewMgCJ749YxyW\nb92v27L68NYDqK2coqZ2J25d1SaKb951AlUzx+u2ZK1dUApRguXjlaTLxJ9/1hEGS1O6rivtBavt\nFtVuywVkG4K/fNKBiaP9sk+iyWq3MuFXJ01U7z6w/bFqsEN4lHMHerYLK0VeO1f4ta/F6vO006eu\nN5JtGyerdgSCNdptymb2OA3VAdx++XjwgojNu07gjm9eYnr9BzkeDobBA7/ZYxhHfn1HBdpDMThZ\n2vSxndE4eFGy1JbPz0fw5BstqicvAMvFQGXsaKieDjfbo4WKDpj7yMcN3S5FXoeqqyGOx7PvHkfj\n/lNoWnoVVrxywPAaNyycgZ/fHlA95s10PpuT0Wz4ixIIg51oTMCPbvoaHnq5x7/7ydvKEI0JtnSb\nWeZAuOzTEK9FASMfuulSJRv6SOathP6Q70WtaEzAdZeN1tnUKDaO+aAp2bC9SSeiBDz0sr7e89DL\n+7GxpiLLZ9Y7ZB47tBhyHbrJSOy+AnqKsR8ca8f/+20zuqI8vjrcYzpATBztR8koP365sFyXKK50\nKynMmTrGkPr48NYDWHbDJGx4u9WQaFlfHYDHwRiSzxuqAxhV4NKlnjfuP6VesMprWpJwrOVb9+Ph\nOZPV16dc3IrNQmKCe19XxpWiR186Ya3ee+V1pItMdKRoX0uyFPdske8THAIhW2g7/1feOg0Pbz2g\neqq/+P2ZCHGyXjlZGt+eMQ7Pv3/ccP3LfoqUZeeCKMldZX4Xa3jsmvmlKHCzqCwba6otT95WBjdL\nY8IIH0ABNEUl0VZe7satqYDPpMBhpvuhmHm3S4QXVV31OVl1J4TVDoUCtwOTH9+O2sbDWD5nMqIm\n3TKZGhPM6M8YRiAQkiNKknpzrOjHQy/vt617NdzdCKFFzq7gbTmefEwL3eJs1C1K9icu8jlBUUCR\nzyn7EtvUcJ0NfSTzVkJ/yOY8Ih2IkmTYYfXw1gO2d/ynCw/LoOry8eqO5trGw6i6fDw8bH5ct9lY\nFEwXZB47tCAleg1mIRFK1yvQcyNqlXp4oj2M69f9SfVrvX92CYJRAc+/f1z1KgSsbRXGF3ux7vYA\nTp+P4Gf/WIqxF3gQiQn4j3eP4dgXITxy82U9SeicoE5k2kMx1DYexl8+6TBcsFZi9NXhHsPfWnkN\neRxMn1bGtUWPVDthsxHQkQr98ezSvpbXD55GyUifmvCZC75fZNWOQOg/6q4FScLoQheW3TDZECq5\nedcJPHDdRDTsbEXr2ZAuobjY5wRFUTjaZt5hq2j2x2dDaDp0WvfYbftOouabF+PHt1yGf33tI2zb\nd1L11D3byYEXRRT53bjrikvg6U7hMdPW+uoARFHCdzfusvSVM+uIsipCa2+qtfofsdAa7Y6Vh7ce\nMO12SHVMsMNXsT9jGIFASE6mu1dZmkJ9VcDgocvaeB3TFPCL7wQQjPZ4o/vdjK0hOk6aRjDG43w4\nDp+LRUcohgu8DvhtKnZmQx/JvJXQH6yCWLN9b5kq+d7xH+EF1fYGgC7AvcDOpMg0EbbYHR3mePhz\nvMOYzGOHFvmhCBki8ct/oj2sC45RLA6GeRyGSeLaBaX42R9aeny6Njdj9bxp8LsZVM0cjw+Odah/\nG4yaC8SJ9jA4XlRDzmZNKMbqedPQejaE5TdOxtIt+sAar5Pp9YK1Kj6HOcFgs2C1pemlxTP7vDJu\nZnfQl/c+F4Snv2b6lq8lIcU9W+Rq8ZxAyCfCMQFLr59ksBRQwhAV7W3cf0odQ5QwCEkCmg6dxpr5\npbpicH11QLXXeeqtViy/cbIhxHLT+59gXvk4PHbLZeDiIp599zjml4+DICKhsNyjVUVeB55eJBd+\nW88EIUnAvZpANbNFOrOOqFQtZLRWPckWSQHrbodUxgQ7A0/6OoYRCITkJJuP+t3pv8Z4Edjy4Qnd\notiWD0/g7ismpP1YCgwlb9N95NWDOl1nbJzKxgQRQY7XHXPtglI4GRqsTUWTTOsjmbcS+ktMEHXX\nRkN1INunlDJWdov5YlmQ7x66NEXhydvKDDZBNJUfRVEyjx065P7yiI2Y2QuoX34J8LkYnO3idNtZ\nL/A6EBNEFPudqK2cgpaV8pbVuqYWXRfuXz7pwIVFXvzwN83wOVmsu60MR1bJYQW/23cSdQvKdG3w\naxeU4gKvA02HTvdsza0K4E9HzuD+a0vUooGZqXsyGwGahsHCYe2CUtA0DH9rtaXJcguZzXYIAAyf\nT2+Yfab9ZSBm+pkOG+kL/Q27I6RGOr+DhNzF62AwvtibxJ8WqK/S2+TUVwXAUBSee+84qi8fDwdD\n4aXFM9H8kxvxq+7gRq+DQX1VAGe7OKx7swUbFs7Q2eqs23EUD289AJ+TRd0bLWjY2YphHqflGKF0\nsCrF3KfeasUIv6vXRTqzrYpNh06bWP9Y31Qnas0zNeXYtu+kbqxMNpb0pqP5HniSaxDtItgJTVnM\nR+2yBnAxOPZFSPezY1+EbN0uGxMlg6Xag5ubEbPxWhIlWGzLtu2QGSdb89ZMayLR4PSiDanuLYg1\nF/GwRrvF+upA3lgWKB662iDMJbNLELLR9iaduJ0M3vzr33WhdG/+9e95E0pHGDoM2XJ9Kp09bgeD\nlxbPRJgT8GUkhn/b/jeUjPThgesmoj3IwdW98s2LIto6Od3zK9tKlVC1sn95AzdPG4PHbrkMTYfb\n0PzZeay7rQyjh7kR5gTQFPCnI2cwZ+oY3D97otpJUBn4Ki4sMi8aeJ2MWoS2ws0yKHCxWD1vmrrd\npMDFwm0yGFhtaaJpZHxlvD+dV+nu1hrMnl1k1c4e7OwYJOQeym6LkQUu3H9tCUpG+fFZRxjRuABJ\nkrvD6haUYpjHCa+LQduXUbgcNI59EUJMkLB8635d1whFydpc7HOqVgoAMOmx7eA1N3ZKV2vj/lOY\nNaHY2ufLyaA9GDN0yJ46H+m109asI6p65kW6ALRUdlJotUbx19XuWBnIWDKYNTrTEO0i2I3LQcPj\nYHTzUY+DgcthT29JpkPYgOx0pOWzz2NfyPS8NdOaSDQ4/eT7HIFlaRR5euaDIY6Hh2Vs67xPNx6H\n7KGbaHvjyZPO+mhMwA1f/4oulM7uMYRA6A/5oQg2kKyzRxlU79m0Rw6D2bQbgig/rmFnK6IxAXFB\nwiOvHsTkx7dj0/ufGDqxlCA0ZWuEsvW22Cd39j55WwCdUR5LtzQj8NM34HLQmDCyQN0W9tRbrWjY\n2YrxxfKk16xD9kR7GO2hGITu7VZmK7o0TaHA7cCIAhcoChhR4EKB22E6ObAy0FYKDNqV8SKvQ36v\nbFpF7k/nVX+7taxWxPPdTJ+QeUjH4NAhHBfw3HvH0VAVwI9umqyGPjzy6kGEYzw8TrlwK4jA4k1y\nsNiyl/ejPRTDipu+huVb9xu6Rs50cmgPxQAAVPeWrpBFsM/n5yJqOKbV34Q4HsV+J372j6W4edoY\nfHCsHSteOWDaKZdYWLXqiGIYut+7D/raZdVbtxLR6PRBtItgN5GYiJf+51NwvDyh5nj535GYaMvx\nMh3CBljrtZ0dadkIfxsKZFoTiQann3yfI4iihHORnnrEPZv24FzEOpQ814jEezx01R0LW5oRyZPv\ntGAxhgh5EkpHGDoM2YJuslU7s0F1xSsHcP+1JfjGxUWGC3zdjqPY8uEJPFNTrtoqrHuzBWe7ODRU\ny1tsN9aU4+3l14CmKYwudEHSiMGS2SVqsJmSAvnj/30Z3vun2YjGBIwqdKGh2lgwXvfmESzZvA+h\nmKBLIm8PxQxF3VRuwJPdbGufw+tg0BGOJz2mnZ9POh9jluSuvBaSEEnoK/neDTCU6etWR6+TkRf4\neNGw3XXJ5maDx652++3oYW7T78mFRV6Dpj/77nHjlruqAMYMc6OhOgCvk4XHwWC9ib3Ds+8ex7Lf\nNkMQJayvCuDPP7oWowtd+MowDy7wONQx65machR5jQt9dljHpPqcybRZ/QyIRqcNol0Eu6EpYF75\nON1cd175OBstFzIfKMRQlKmtBGOj56IS/pao/3aGvw0FMq2JRIPTj4elTa8NT550uIZjvLllRCw/\nFmvy3UM338+fMHQYct9IxU8QEiy3nFoNqiWj/Gionm56gTfsbMUD103Ev//xKOZMHYMnbwsgyPHY\ntu8kvgzHUXX5eDQ2f445U8egZJQfkZiAIp8D66vk7qpn3z1uCNZZPW8aKAoo9jrh9rmwsaYCHieD\n1jNBNayNpSn4XGzScJu+kMqWJqvwtP4e0/QY/Ui07ddjenktuRbUlo/YkUKfq5Ak5vykt62OZt/h\ncFz+rMde4LGc8FlNBq3CgRSbHq2mr9txFACwYeEMFHocaPsyiide/wgTRvhQ882LcbaLw4VFXgDA\n04vK4XexCHI8nnvvuBqoqQ1LW7ugFHFegCBKOBeOw+tk0R6MQfQ6LHdvZAMrbX6mphw+J6suNBKN\nTg9Eu4YegiCq/tohjofXwYBhbCx0UIDbQessF9wOGrDpcs10CBsgey7W/a5FF8RW19SCdbfbF8SU\njfC3bJDpuWSmNZFocPqJ8KL5tXHlBPjt1Lo04XWxGF3oQtPSq9Tz3/B2a95s98+GBqeTfD9/wtAh\n99UsjWg7fh7fdtByy6n1Fg0eRV6H5Zaqzkgc63YcxZz17+DSR1/HvS/swf+aMAJzpo7Blg9P4Nbp\ncmfCQy8343wkhiWbm9UtFLdOH4fKsrHq8yndWg92m7fTNAVQwMJf7cKc9e+ooTJKEUCL1YruQMz2\ntY+FBIwudKV0zP7Sn86rfj2mlxXxdHaoDcWwg1S67AYTpGMwP0nFgifxO+xhaTRUT7e0xAnHBMtA\nyVCMx5r5pZY2PYma3rCzFQVuB767cRc8TgZP3hbA96+coCabT358O5ZuaUZM+P/svXt8FGWa9n9V\nVR+rOxETDgtiBAxkXSBpSJTX46DDGHF2sywMmsxidFxR+TkTGMTxVdl5s++AfBgjQ5j1g8IcFHGI\nMjJs3lWMwyjr6DgogXBaDQREQFgCiZj0qbrr8PujUpWu7qp0J+nqQ/J8/1GS7q7qStX1PM/93Pd1\nC+jmeLgdFpRPG4sn7yqKyRB+Yvsh+UMpClfnsWhr96Kx5St0czyCfOaUwRlrs0WjIXoaPRy1drAQ\n7RpeCIKosRZ7eEuzauFlFg4rg7cOnccI1gqKAkawVrx16DwcJt1jNAU8f4+2AbHcodyUwwGQLRcu\ndHHqOqB8/Qe40MWZarnA2uVqkchjbnivzVQP3XQ0C0v1XDLVmkg0OPkolVQxz0aWZD0HQwJW/v11\nas8eu4XGyr+/DsEssYxIdSPMZGPU3DhLErwJw4hhtb0QnfEjSsCa+dNRkM9qdntZq9xVcum2XhPv\ntQuK8dsPv8CDt04Ca2OwdkGxJutp7YLimB1UJasXADBtrLqwblp2m1qiC0C1dKitmBoTqI1M7ddr\nUlNf5UHD3tOa4+rt6A7GbF/vvc8tLIYoQXO+ydxFHkjm1UDek6od8eHa7CAV2dyZBMkYzE76tOAJ\nGd/D+S4bXHYGG6o8qIkYLyIXYdG/e25hMVa/9RkAqFkj3cEwahuP4mI3Z6jpyniQ47CiaOUutPz0\nTs04MirHjmBYxI9+F3kenpjNtzG5dnRxfMz4tqP5bEZldBlpc1u7F7WNRw01ZLhq7WAh2jW88Ed4\nGwJQvQ03VZcix6TMtWBYwLevG6NpMPPcwmIEwwJYW/LnAzQla3tkRjBrY0wNJhitD8wMICkeurFZ\nZDzcDmvSj5cOjU3HXDLVmkg0OPkMhaznYFjEUzsOazSTTf5jbQp2Kw07o63KsDO0aY0wkw0vSgbV\nDxPTfWoEggZKygJj57KyMmnfvn2D/hxRkmK6hVtoCsdWzwUd5W8liCLa2n2aJmVvHz6PY6vnwh8S\n8Js/n1TtE9ravWg6ch4Vnqswu24PKkrGqR3Pu4Nh+Dgef3OFE0Ur5WOfePZu9f8jz6N11VwUrdyl\nTgDr3pV9eDdVlyKnZ1IWXXLktNDo9Ic1E6sXF80EQ9Ng7dry4MWv7NMMajdOyk9oMuTleN33rpk/\nHXPW/VdWL5hTNTE1uoZDNbCp0J9njqCSsguTLG3Ndvp6PlkbE/ce7qt0mQvx4AQJbocFfk7Ayp2H\nsbPlXMxnAXJ5l4UGujleEwSuW1iCte98jovdHGorpqK28SheWzxLc15Ny25DbeNRXZ2eXbdH/dme\nFbPx1I7DMa+rrZiKyWPcGfNc6mmzMi4qY7HeuQ5XrSUkBOaF3y4AACAASURBVNHWHtIxNnuDPBZv\n0Xk2q8tMKV9N9fHkY4bR3h3EqByHOh5c7A5idI7DlOAqIPtsXvaH8fgbBzWd2EewVlMC5enQWDKX\nzHgyUluzfYM3HRqWTLqDYbS1d+PaUTlwOyzwBnmcuNiNwtE5alwjkyG6Q8gAErrRMl8Nkkh/duoC\nYVF3cawESKtmXRM1QHhgZWgsnzMZ82aM1+zOP39PCfyh3h30tnavwXnwOLZ6Lk53+NWmavWVnpjO\n49Eet5E7usGwAB/HoyYiA2JD1Qzku20DNts3yl4ryGfVAHe27iKnakd8uDY7GAq744Shj171Q7QF\nj9E9LIpSzKaasmCQJAnfBHksbZCDs7uXfwsXujjNsa+fkAdvkMcjrzbj01OdqLmjENU3TdBkNNgY\nCoWjXFhxZxF2HjiLDVUzYrKyCke7DXX6xkn56rkV5LOGHvGZ5AumaPOm6lKwNovGO14Zi/U0ZLhq\nLYHQH3wGWZ0+jjdtoc3aDZ5Nk6wBUn08ALDRFNx2Kx7e0txbSVfpgc3E+bGdoXUzke0mZVqnQ2PJ\nXJIwELI96zkdGpZM7DSFq0aw6vxW0UN7llz/VFc/EAgDJTty3pNEf/yJ+npt5ABxbPVcbL6/DC67\nBVv+cgr33zQxxrPw8TcOajrfbtzTpuvf67JZIIkS8t02rLvXg03Vpch32XSbVET6V/nDPQMURUGU\noNMR84Ch768/AR8eY09hIandz9OFGZ3co+nrGg5liCcYIRvQ03QlgyPePdyX/25kWTMvSlj3x2Mx\n2l9f5cHLH32hvqZ82lgs2bofs+v24Nqn38bsuj2oaWjBD26ZiKuudODBWyYh32UDa7NgQ1Wvt5eh\nly8naL6Xoa8vx2dc0JOmKbhsFnT6QqhtPIq3D5+PqyHDVWsJhP7AWhldb0Azx+ZUP5tGWufnzNMC\nTpQ0mq9YWXAmer0GeBFb/nIKHC/7H3M9/w7w5vghp0NjyVySMFBSscYzC6O1u5me3MkkHXqYTGia\n0vcAzqJ7iDA8GFaWC4Bxl1Q9K4NAWJRtCzgBNA04LMa7eqIkYfnrLVh3rwfLX2/BktmFmo6U0T//\nn28CECXgqiudhjuGfZ2rUQkJKOiWB7SuugudvnCM/26+yyZ/zz52LFNdspLqTrapINvLfgZDyjtp\nZz8ZWbo21OiPzvT1WqUk6+7pchOyK5y2nnGDh9PGoGjlOxo9nucZh1XzpquWOE4rrXlNX5Y8L7x3\nHA/cPFG2b1DGKV4Ea2MQCgsx3rh6GiNrEaexdKiv9IC1MWBtlpjXZoIW90dDhrPWEuJCtDWCVI/N\ngiCiwx/SaFR9lQf5rH7iwmDR07oNVR7ku+ymaYEoSXj141P4R89VyHVa0RUI4z9avsJ9N04wrUQ3\n1WXB6dJYMpfMaIi2mgDPi+gMxGpmntMGSxZ05hIlCZ+d+wYF+S71uT3d4cN1467ICsuCdOg5gRBF\nQjfasAvoRhM5QTjd4cf63cdwoUu2Omj45DQ2vNeW0GTFy/G41M0h12GBPyzgie29lgvPLSzGCNZm\n7NHY43GrWCaIolxm4Q3yePmjL2LOoS8/XAAJHcfoswcS0EgmQ3kxninBkVQylP+eJkImxiaTrPsy\ncvzwhwR0BbQ+hkqDs3W7j6vvuXFSPn59fxlEyKWrPo7Hbz/8Qn2NkRfuz79XDEmCaudTc0dhb3CX\nEyCIIl75yynV293H8XDZ9Be8/hCP9i4OV+exqkf8xW5O44GYKc/uQM5jOGotISGItqYRf4hHWBAh\nSVAXxxQFWBnaFK9XQRDRzcn+sooVwQjWihy7xbRAYDDEx2ys1Vd5kGu3wGHCdwTk9YdeX48Hb51k\nnqdtijU2U8YjgiFEW03AH5IrcUURqgctTQM0RZmimckmFOJxWUcPR9gtsGXB+ftDPDp9oZiYTl5P\nlRyBkAIS0tbM394xEUEQ0eEL4eEtzZjyzC48teMwln+nCKNy7Fja0ILyaWNjSmiNYK0MCvJZMAyt\ndh5X3vvE9kMQRQlrF8TaLDgtNDp8ISx+ZR+Wv96CTl8Ii7fsw5RnduGRV5sxb8Z43D19rOYc+vKv\nimcV4e4JPDzyajPW7T6e8PdLVclKX+XLySbStsLL8RBNLgHJ5rKfgZLKvyeBkCjJuC+jx49L3Rwe\nf+OgtrRsWwseuHmiRo83LpoJX0huKjPlmV14eEszKm8owPI5k2GhKTQdOa+WQs/zjMOeFbPx2uJZ\nyHVasfPAWXx8sgN3Tx+LeTPG45FX5WMv3rIP3RyPtos+lK//ANc+/TYeebXZsOTWYWUwZ91/4dqn\n30b5+g/QePBcjAdipjy7AzmP4ai1BELGIwHdQR5Ltu7HlGd2YcnW/egO8oBJUy9/WIixr1mydb+p\nGhYWJSyNsj1buq0FYRPnl04LjcobClDbeBRFK3ehtvEoKm8ogNPEDL5Ua2ymjEcEQkqRgK4Ar871\nHnm1GV0B8zQz2XAGepgtlguiCIOYTrrPjEDQMqy3FyL9DQHg45MdePLNQ6itmIrvbvgzCke71dfG\nM/ynaQp+jkeOw6IbbHU7LFi58whqK6aicLQbgVDPznbEJKVp2W2qcESfT+SCO15zgHgG8JncNCZV\n50Z2+1NDJt9rhOFLMu7L6PHj6jz9ZmNuuwWbqkvhslvQFQiD40Usixp3lja0YFN1KX747cnwhwQ4\nGBq/vr8MvhCvKRdeX+lBrtOKG68dqXq1K5/xxPbesSLe90mkwUymPLuZch4EAmFwiBJi5rhPbD+E\nzdVlphzPZdefj7tMbKKVjmMGeDFmLbO0oUWuuBgilgRkHCAMR0RJUhMFAKh9eTZXl6b5zBIjHXqY\nTLK9KR1h+DA0RvoBYiQ0haPduH5CHtraverP4xn+i6IEX4hHVyCsa2DeFQij8eA5lK//AIt+tReg\n5CBw5CTFqEu5ElhWziFec4B4O+eGjSIyoGlMqpotkN3+5BAvy5k0KCJkIsm4L6PHj7Z2r+5nnu70\n49zlICY99TY8//ePGOm2G09wJbna4+tAGO3dXEyDy2UNLfinGePjjhXq9zFo/pNIg5lMeXajz6Oi\nZBx2L/8WAKSksoJAICSHVC+O09FQKB2N2IZDsNPH8ai5oxBNy27DiWfvRtOy21BzR2HWNIciEAYC\naxCnYLMkIJoOPUwm/pCgqztkDUvINIZ1QNdosnem04/6Sg+ajpxPuJuqHCBswc4DX+l2Dv74xCXd\nz4o8B6OAQFu7V+2GrmTbGnVkj4coShBEMaZr44Yqc7sbJ0qqOtkOhwmw2ShZzkrp+OJX9qHDF9IE\nWEhnYkImkoz7Mnr8eOH9thhdff6eEoxgrZqxxGsw7pzu8MMfFtTNJsOMX4cF3UH9jcMznX5NJ15B\nFHUDnomMIZny7EaexzzPOPzkriI8teOwoeYQCITMJNUBVitN6c7HrSZWYdEU9Luim1j4lSmbb2bi\ntDL6thJkLkkYwmR7QDQdephM0mFnQyAMhLQ0RaMoagSAXwGYBtkJ5kFJkj42er1ZBuh+jkenX2t2\nvaHKA5fNAntE9/BEDP8ju8zW/sPfYd4MuSOij+PhtDAI8mJP53MBNA04LPLnCaKIr74O4sk3D2FM\nrh0ryos057O+0oN8lw3BsAinjQZDD05EvJzs3Tgqx47Hbi9E4Wg3znT6MTrXnpDBdyoaIaTiGMp1\n0GseZ1YTiaFGoteQNCjqN6S5RAoY7H2pN35sXDQTFpoCa7fAz/GgaQp2JmIs4QS8uf8M7p4+VmOl\nsHZBMdb9sRXr7vUAAKY8swtv1dyq2xxNtu1xocMX2/lYkoCRbrtho7P+XoNMeXaV84AELN5CdJsw\nYIi2phFBFPE/33BYsb23cWTdwhL8zRX2Qc9t9RAlCR8ca8fMgjy1odD+0524bcpo0zqU+zkegbCA\n7iCvNmLLcVjgtDKmZdUNBwux7mAYD29pjtH+TdWlyHFY03hmhB6ItpqA3jzzuYXFyGNtWZGlGwzx\n4EUJvCipjTAtNAULTZnWJDKZkFgBIQNISFvTdTfWA3hHkqTvURRlA8Cm4yQcNgZ1f2hVfW3b2r1Y\n/dZnWHevR7Yr6PGeSuShjfQk3H/6Mm68diRyeyYZFC1PLP/9T8ex4b02zWQrEBax88BZ/Px7xbjq\nSieCIQHr7inB6FyHej4XuzmsmT8dI3PscNsHN+lVMlN5UVK9Fi00hWOr58Z9byomjakKIChZX9Hf\nhWSOJk6iWc6KBQiQ2LNEIKSCwd6XeuNHbePR3vGjR/8js0clSPjGH0aIF7Fm/nRcnceird2L9z6/\ngGVzpgDoLS194f02rF1QjCffPKQJ/O48cBaYNhZNR87jpftK4XbIjS6dVhpFK98BH3G8eZ5xgCSP\nP9F6moieZ8KzGzkmACCVFQRClhIIiXiz+YxGM99sPoMHb5kEtyP5Ad1gSEDh6Bw88mqzJhgSDAmm\nBUMcNgafnOrAzII8UBSQ57KpQWSzoGkKeaxV9Wr3cfyQ2zjPdi9OAmGg5Dgs2LhophoQNWkvyhSs\nFhqXu0P48eu9yQe/uNeDUTm2dJ9aQpBqXkK2kPKRkKKoKwDcBuABAJAkKQQglOrzAOQg7IUuDuXr\nP1B/duOkfLUxTH+Ci0qAcNveLzFvxnjdRfi9NxTg/7u9ECcu+rBt75d48NZJYK0M7r9pAsKiiNMd\nflydx4IVJbzwXm/w95ff98DGMGBtDLwcD2c/s4ejv3PNHYUonzZWnVA3HTmvaYZj+N4I31kAqu9s\nsnaqUpllEFlynO7ss2wlkcZKBMJQJd74ASiaxmmyceurPPjqaz/yXQ4s+tVe3cqM+koPGj45jXV/\nbMWa+dNRkM/iq68D+MP+s5g3Yzzq3m1VN+R+cMtEdRFfc0chcp1WtULEy/H48/F2NB2VA8YF+Sy8\nQT6mISeQfD1PBtFjwu7l3yKaQyBkKTQN3H/zBEgSQFHAmFw77r95AkxIzgUACJKk24Rtk4kNhUJh\nAX839gpNELm+0oNQWDAtI03pZfC1PwzWZkGHNwSRtSLHYR0yc1oy3yQMR2gK4HgxphrLPsjkrlTh\nDwn48evaho0/fr2lJ7M+87+DYnkRozucALeD6A4hc0i55QJFUR4AmwD8N4ASAM0AlkqS5DN6j1nl\nFX0FEAH0O7ioNEbTKwuqrZiK2sajqFtYjLAgoSCfhZ/jwdosCIaFmJKK+koP8t02cGER3hCvFfOe\nxX50tm8iEzdBEOVS3Qbt5+WxNgSFvoPEkbYSCmp2r4RBZ9aS0obUkYxM6OFQ5pcmSOlaFpDI/e8N\n8roWAS/dVwrWxiAQFkCBUl9TUTJOtcLxh+TAayAkyjY9VgbHL8hWCo0Hz6GiZBxW3Fmk2TzcXF0K\nf0iI0XeGpvDD30Wdp9tmqOdSjz5EZnsxA+yWPhitiR4TKkpkD12tTRLRHELCEG1NI7wgolNv/umy\nwTJAfemLvuasZlkupMMawB/i0enTKct22RKyUssG9DZHN1R5kO+yE+3PDIi2moA3GMafj1+Uq357\nMnQ/PnEJt04epVaBZTLp0OBk4g/xuOwP4/E3em2ClN4YQ0VbCRlPxlouWADMBPAjSZL2UhRVD+B/\nA/jXyBdRFPUwgIcBoKCgwJQT6StL08vx/c5eomnKsCyocLQbY3LtsFlorNiu3Wlz2ywxWQRLG1qw\nuboMoICl21pifldbMRXrdh/vd1YVJ4jwhwRsfWiW6rPY8MlpVM4qiNkBzGdtmkW84Q45JyDQk+01\nmEU2KW1IDckKxJIs5+wkFdo6HEjk/jfq6q7YJOQ4rBAlCZ+e6tQN0NZX9Wy28SKCYUHjqftEeRF+\n8nvtuMGLEpY2xI4Xa+ZPjxnLNlWXGug5rxsUznfZ+h3UHazWRI8JjQfPgaaAzdVlsic90RxCBpFt\n2ppqj+xAWNDVp03VpcgxIaCrNGGL1jgfx5sWXE2HNYAoQjcTeXN1mWnHTAc2hlatis50+mEz4Z4h\nZCbZpq3JwmFlUHpNHpZs3a+ZjzmyxB7Qb6DBfo7PioC03ULDSlMa3bHSFOykKRohw0jHHXkWwFlJ\nkvb2/Pv3kAO8GiRJ2iRJUpkkSWWjRo0y7WQUj0Ca6vlvz2R2IMFFQRDhDep38W1r92LZnCmo6QnO\n8qIkT2a3tUCQJGx9aBaalt2GipJxvceyM4bnMXmMW319okFPUZTg43g8teOw2q1xxZ1F+KeZ49Wg\nceR5+UKCxv/RqOs5TUMNfivvr9l2QG5i0w+GUqdepQROlHr+m0Fd2CNLrQfz9wKMnx9C5pIqbR0O\nxLv/++pQzNoYVZOvn5CHJ8rlYG6MDnMCfvPnk/BxPDZXl2LPitk48ezduJK1YUyuXfPZuU6r7nhx\ndR4b8zPWpqfnsv+vEnRRz6OhZUD6YKQ1vlBi2qg3Jlzo4gAKRHMIGUc2aauy2bL4lX2Y8swuLH5l\nHzp8IVPnKqkOdjIUpdthnTExM0zR80iUILJZsHYGY3LtaFp2G048ezealt2GMbl2sPbsCPokgj8s\n4NGt+zG7bg+uffptzK7bg0e37h/QuETIPrJJW5NJ5CZY5HwskCX3PW2gwdmQnQv02FQ2tGh0p6ah\nJSvjEoShTcozdCVJ+h+Kos5QFFUkSVIrgG9Dtl9IC0YZCvH8mqLf57TQ8IUEvPzRF6hbWBLTxffN\n5jP44bcnGwSJLShauUv12wWAi90c/JwACZLueRy/IDfgWbugGIWjXOq595VtIS+utdkRT755CK8t\nnmU4yfaHe/2pjDLSQCWnUc1QaVSW6VYEJBOaQOgf/clki3wtTQHP31OiKdd6bmExfCF5cR8IC9i2\n90tsqPQgP8dumM1bPm0s9p2SG+uMdNtBUXJp78q/vw6iBNVPtysQ1h0vznT6NZ97/YQ8BMJijJ7T\nlJwRkqygi7HWWDDlmV1xtXGojAkEQqaRDg9tH8fjl1WemPJhszJm9RpX1jW1Yt29nqQfS4G1MbrN\nLM2cXwVDQowPu9nN31INmbcShiPZ3gzQYWPwp+YLmqZu/9HyFe67cUK6Ty0hsv36E4YP6bojfwTg\nNYqibABOAvhBOk6iL08mp4XGxkUzcdkfVtPsR7BWOC20YaObPNaGk5d8sDHa9Hy3ncG/3DoJF74J\n6i6229q96s7bk28ewpr50+Hqyc6VIOlODuvebVVf/9J9pXBaafhCPD48fhE/2taiu1A2mhAZmX63\ntXsxeYxb83q9rudeo5KKfjYrGCol/JnSbGigmxUEAqGXeF7r0Rt7nf6w5rUbF83EuntKMDrXgTOd\nfjisNBwWWdvzXDaUTxsLUMDpDr+hDl87yoXRuXZ8HeW1/vw9JXjmu9fh7cPncf2EPFAA6is9GruE\nuoUlsDFyZkR0UDRaz0VJMgwKGwVdRFFCkBcginKmmJJ93JfWRI55fWnjUBkTCIRMIx0BMqeFQemE\nqPLhKg+cFnOO6eN43caVZlouBEICdh44qwki7zxwFg/eMhFuk5oAidLQt1wg81bCcCQdtjHJJBgS\n8O3rxmg0P5s2m7L9+hOGD2kxAZEkqaWndKJYkqR5kiR9nY7z8IeEGAuEmm1yKr3iNavYEzy147Dm\n53rWCV6Ol20VotLzH926H4Io4QqnFfVVHk3pwdoFxXjh/Tb1nD491YmCfFY1+g+ERXVyeGz1XNRW\nTNV0OP/0VCfcdguKVr6Dh7c0o/SaPKz87nW6ZfRGlgY0Bd3zajpyPqGyAiMrhoFkUQ2FEv5MyCTo\nq5wymX8vAmGoYGSTYmQbEOSFmGfMF4p97ZKt++F2WEBRwEi3HW6bBYGwgIe3NKvWNwDwX8fasXZB\ncYwFQtOR8/ByPL7xh9VFu/LZj79xEC6bBa2r5LHhlb+cQq7Dgk3VpTi2ei7WzJ+Ote98jp+99Rlq\nK6aiddVcbKouRR6r3/3cHxLw8YlLqK/Ujgf1lR5dfRBFCd3BMDp9ISze0nMdtuxDh48z1Bq9Ma8v\nbRwKYwKBkGmkw+IqwAu69l4B3pxjOq2MrpY5TZzr0BSF+aXjUdt4VNX3+aXjTS0xNvJqH0qWC04L\nrf+3JF6WhCGMjaZ073tblsyDBEmKmbc+sf0QBClzbAj7Ih1jCIEwEDJ/e8RE+poE+TkBj79xULPj\n/fgbB9VmLHrvy3VYDT0M3XYLfCEe+S4bNlWXwmWzwMvxePmjL9TgLNA7oWatDLwcD6eVVhuW1VZM\n1TTFUV5/PCLbaWlDCzYumona//ffMQtlo/JVh5WB3ULjpftK4bJb1IyCqlnXJBTkI1lUWjIhkyBe\nljD5exEIvfSVhWu0QSOKiHnG+irPoikKbocF3iAfY31Ts01uXFb3bm958JlOP0QJmDdjPHIdVuQ4\n9McW1s5E2Bd4YLMwcNCUHFC1M7jYzeHTU5242M1h7YJiOVvs1klw6zSUYa0MyibkY9+pDrVEzsfx\ncFoY3YZo/rCAr/1hPLXjcMz30dMaH8fjtx/qj3kky4pASB3psDNJdflqICzgq8t+vHRfqaq9Jy52\nw2ljTGnCBqTH5sGoys7PCXA7hoauBngRDZ+c1lzXhk9OG45lBMJQICwCzV92aiwLPj5xCbdOHg17\n/LennWy3LAiEBV3d+cEtE00bQwiEgZAdT1QS0ZSh9zEJ6ivYqzQ+i36fl+Mh9fx/9O+6gzwe3dqs\ntWhw2lA5qwAfn+zUTKidFloTXKi5o7An2MqgvsqDpRFWD4r9QuQ55jqtvd8lFN8DVw7kUXDbKfjD\nAiaPceOqKyf1K8inZ8UwUFLdeTnZZILvY7ws4WT+vQiEbKevDRBAX9P1xoi2dm9c73WjsaUgn8XF\nbg7f3fBnNTjrslmQ76LRFQzjst/YCuHY6rnwcXyMVua7bNrAwrutePvwefzw25N1r4MyRtw6ZbRs\n48Pxspd6SABLUzE6zNoYXJ3HJqw1LpsFVbOuiRnzSHUAgZBaaJpCHmuVEwzsFl39SDapDjw6rQyu\nGsHikVcj5t4mZ1f5OR6TRro0P5s00mVqV3eaBp5bWBzjoUubGG9I9TydtTHY8F4b1u0+rv7MQlOG\nYxmBMBRg7Qx+tK0FfESzSgtN4djquWk8q8TxcTxq7ihE+bSx6jy06cj5rLEscNktRHcIWcGwiuRE\nZ2F99L/vMJwEGU08fT1ZtdG+tvVVHkiShFf+cirmd+srPXjlL19oggVLezKYRrrsMQHW6OBC20Uf\nuoJhPPJqM8bk2rFm/nQU5LOG2U5dgbBhGb1RIC9TgqiZ3lAsETIhYzkTsoQJhGyhzw0QCbobNHpj\nRNOR8zGbbhuqPKApQJQk+EI8Orwh3Wfzq68DavDVx/H4qO0ibp08Gicu+uAPhTFxpDtmvKqv8uC3\nH36BDe+1xWilPyzgUjcXU9WheEiqgdoobaJpCqyVSUiH/SH5GIlqTSZoI4FAkOda0X7fZs+1aMog\n8GjS4x/ZIR6AWsW2qbrUtOwqC02h8oYCjY95faUHFhM1zmFlUNeUuqzgdMzTyZyWMBzJdg9Xm4Ee\nZotlxHCofiAMDSgpC3xMysrKpH379g36c7wcj8Wv7FMfzIqScfjpP1wHb1BQG5hdyVphtdCw92TJ\nRi/M81w2FK18B3dPH4vHbi9UJ0+Fo11Y9KtP8PHJDtT+w99h3oyrkOu0whuUsx7+9qfv6O6wRftq\niaIkN9exMWhr9+KF99vw2O2FuovyTdWlCISEGKHMc9kQ5MWEF8qJTM7MDPhGfrYSpI7cDbtxUn7K\nG4plO0MhMD6MSdkfKFnamu1Ejw2AVnf09A8AuoNhfB3ROPNK1gq33YIAL6pVIIIo4tGehhCtq+bi\n8TdasPw7RbqNLhsPnoOFptC6ai68HI//8x9HMGmkCw/dOgmiJIFVMulsDNq7ONA0hVnP/kn/nCUJ\ny1+PPdaGKg+27T0dEwQGoNHhh7c0x16P6jLNJFbx0O3meE2QRmkuSrSGkGEQbe3By/H4zZ9PxmRO\nPXjrJNPmWn6ORyAsoDvIq5qZ47DAaWVMaZAjShKmPLMrobl3svAGeSzeojOWRGlnUo+Z4r9lOu4d\nMqfNeIi2moCf49EV5PHj13vX+b+414NchyUrmop5gzx+86GOVtwyKSsCov4Qj05fKGYTMs9lA2vL\n/PMnDAkS0tZhdTdGZ2E1HjwHmgJWzZuuNqwRRBH/8vI+jdWB22HRZM9ePyEPjQfPqZmxy+dMxrhb\nJmLrQ7Nw7nIADgsd08W35o5CTZBSb2dZb8KydkExxo1wGHrQsFYmpmSOYeh+eUrF81s1cyJl9J3b\nLvo0jd9S2VBsKEAy4QiExIlnk6JX2SCKEkKCiKd2HNYEMimq97WggEe37seoHDveqrkVFAUsmzMF\n731+AbUVUzF5jBunO/yaRpfXT8jr2SR0Q5SAqhsK0OnXTijrFpbguabP8fw92gysSK30hwRc6OJ0\nfXmVsUjR+l8/UAYfJ6jfv3XVXEPLIVGUVB2haQo5DnkTVPGX93NyUJhoDYGQuTitNObNGB+zseS0\nmlen77AxePoPh7FkdiEAgONFrP/P/zYtkzQd5b7paFDmtNC6WXBmNQxLx71D5rSE4YjdSoPhgDXz\np6ubYAwl/zwbcNoMtMKWHefvsDDIsVs01z/HboHDQmIShMwiO56oJKHX1fdCFwdQcmdaZfGtdGNc\nt/s4Hnm1GcFwTwdeSi4Z21DV2/Fw+ZzJqLyhQO1Y/pPfHwIniBiVY9d08X3g5olR3ctj7RD0uqk/\n+eYheHtKLiJRAsIMQyPHYQVNyQtrvcY18Yjnt2rU5d0fHnxnYqPv/NjtheprzO68PFQh3eEJhMSI\nXCweWz0Xm+8v63PDShRl+4SaqI7tNdta4AvxECVJbWo5JteOFXcWobbxKKY8swtP7TiMOX83Bhv3\ntOHf/3RcbVymjA1rFxSj6ch5tLV78djthfCFhJguwSu2H8SyOVNwptOvOa9IrVSC1Iov76Jf7YXL\nzmDN259p3hPd4I0XJdULOPqzT3f4Y3SfpimwNgvcKvga0QAAIABJREFUjt7Gb0RrCITMxh8S8OSb\nh2LmXmbOtXwcjwtdHMrXf4Brn34b5es/wIUuDj6ON+V41p5y39rGoyhauQu1jUdReUMBrKb6BBvM\n1036joDcMEyxlohskBzgRVOOl457ByBzWsLwwx8SUNPQgtl1e3Dt029jdt0e1DS0ZM2aOF1akSyU\npIWROXY58S/HLsdciPYQMoxhFdBVFrhGgVW9wOaYXDt8PeW4U57ZhX95eR+sDI1195SgddVc/OCW\niTETqSe2awOSn57qhNth6TNYIIqSYWA112lF3cKSuAHhgaIX6NYEBuIEfAeD0WcXjnab8l0JBAJB\nj0QXi0pVAWvT797L2iyY8swuLH5lHzp8ITx519/GTGif2H4Iq+ZNx4O3TkKe04aX7itF66q5qK2Y\nip0HzuJ7ZVfjhffbUDjabdh0rCCfxZWs1XBc0AtSu+wWeRMzAr0Gby+834a1C4o1n712QTHW7z5G\nqiUIhCFAOrqPszZGV1fM0hRehG6g06Q4JwA5o6s+Iunjxkn5qK/ymJrRZeYcXY9s71xPIGQL2f6s\nZfv5A2QjiZAdDKuAbrwsLL3A5rI5U2KysJZs3Q+X3YIznX64DBb1haPd6r8VA22nhVb9D30hHoIg\nzyqVAMHpDr9uYPX4BS/WvvM51syfrjlvQPayUrLBRHFgfsjxAt3xAr6Dweizu4Nh+btW950pRyAQ\nCKlEqSo4dzmgq11t7V5NdcaYK/Qtc1g7A9bKQLFypCjgqhFO3HtDAVgbg3X3lKA7GMaZTv1xwc8J\nyHFY+9wojJ6IOiz6Wq80flBoPHgOOw+cxcZFM9VAc927rbjQxWVNZgWBQDDGzHldX8fceeAsaium\najawzDpmOuwPAryAhr2nNd+xYe9pBHhzr2sq/5bR44V6PI6MDQRCMvEZZPybVdWQbIhWEAipYVg1\nRYuHnp/ra4tnGTZVAIDjF7yahmUVJeOw/DtTUJDP4vgF2a+ralYBcu0WfBPk0fDJadXPS/G8DQoi\nFr+yD6Ny5NLcSK+Z5xYWo65JXkhHNq8J8gLCvBjTkGegpQB9NT0bjIduvGZqRh66de+24mI3R5qh\nDVHMbLI3BCDNJTIYpdHOfz0xGxKg8bZdu6AY731+ATdeO1L1bLz6Sif+Rafh2pr503ElawUniJrm\nm2sXFGPngbMonzYWTUfOo/qmCfBGNR2rr/Ig126BlaF7G7Al+BwZNXjr8IWwbe+XmvGJpoDFW5oT\n1v3BPtdEFwgmQ7S1h3Q0mRIEER1+bbPh+ioP8lnbgOzC4tEdDOO3H34R46H7g1smmuahm45GbIIg\nopvjcTliPTCCtSLHbjHluqarURAZHzIaoq0mwPMivKHYZ9tts8Bikkd2Mkl1I0wzILpDSDMJ3Wwk\noBuFKEryA9vT3EWCpN/t+/4yAMBv/nxSNfwek2vHivKimIW322ZBSBCx5S+nsKD0aqzYflDz+zzW\nhr/913fAixIqSsbhsdsLUTjajUBIAE0DDmvswluUJIQFMWZCNdJlAy8h6cIzEEFLdMGgXHOnjUFb\nuxcvvN+mdns3cxJMSA+kW3FcyMQ4g/H2WPBsfWgWHn+jBUtmy3rdHQzj4xOXMHXcCM2mXH2VB/ku\nG850BrB+9zG5UdnCEgASrnDadDui11ZMxbWjXDhx0YfJo90IhgXwogSX3aIGJb5XdjVYG6NpwPni\noplgaFoev/qp/3rBlg1VHrjsFs0YFM+KYqDPNdEFQgog2hpBqheqoiihOxhOWiJCPLgQj2+CfEyz\nsCscFthNCjymI4jsD/HoCvD48eu93/MX93qQ67SYEmAVJQnLX+8d+9ravdi4pw3r7vWYNl8n40PG\nQ7TVBLI9IMqFeHzD8TGbeFfYzdPgZEJ0h5ABkIDuQIh+eGvuKIzpHruhagbyWCsCYVFtaBPiRVzJ\n6i/OX7yvFG47g3OXg/jJ7w/F/P6l+0rx8kdfqJ3HlZ/rZacqwYTN1WUxx1o+ZzIqZxXELMjzXfZB\nCc9AJ/3KueoFw42+VyKvJWQ35G8dFzIxzmCUMcLH8Xhqx2FNdcbP5k3Do6/GbgDWVkxFbeNRbKjq\n6eYuATUNLdj60CwUrYzN5vr8Z3ehwxfCsohx57mFxfj5O61oPHhO/dw186djdt0e9fg/uUu7oRg5\n8Yyn40bP5abqUrhs8X3DBvtcE10gpACirRGkOqCb6me8OxjWTcjYVF1qWnA1HUFkbzCMxXqJJ9Wl\ncJvwPdOh1WR8yHiItpqAn+PR6dfJhmdtWRHQTYcGJxOiO4QMICFtzfx8/RSj+CMqnrnrdh9Hwyen\nsam6VPV0zbEz6PSHsHiL3ChtWUML7Fba0K/LbbfAHxJw1ZVOQ3PwB26emFDTM6X5gd6xyqeNxVKd\nruuD8dFSghdKUzil0U8ifr39adQQz8fX6NyS4SFMSC2pbuBBICQTxYt9dK4dGyKa31zs5pDjMPZU\nV/TYZmHgCwnY+tAsdAfDht6HyxJotnl1Hqv++8m7isALErY+NAtv1dyKUTl21Gw7AH9YSEjHjZ5L\nl82iea2R7g72uSa6QCCkjsHM7QbKcGjeFRYl3UZsYTOvq8H3NCvgM5D5+qCPScYHwjBElGRbr+i5\nYLYsd7O9KRrRHUK2QAK6Ueg9vBvea5PFRwICYQH/08XFNEpbuq0F3UF98/K2di9cNouhOXhbuxdu\nh6XP5jbKIhoAPl05Bz6OR+uquWhadhsqSsYBAApHu5PeACI6wC0HJeQgQdz39qNRQ7yGddGkYzFC\nSA7paMZCICQTmqbA2izId9mxqboUravmom5hcZ9jACDrcY7Dgqd2HEbRyl3Y8pdTqK+M7YjuNpgE\nRzfbDIQF/Pknt+Pks3cj12nF+CudaonvijuLMCbXLmffJaDjRs03uoLhhALDg32uiS4QCKljMHO7\nAR8zJOCXVR60/PQ7OLnmbrT89Dv4ZZVnSDXvSnVwFZCz+PS/pzmNk/o7X08GZHwgDEfS0dgxmWR9\nUzeiO4QsgQR0o+jr4VUmwFfnsboCa6WpmMX52gXFaDpyHsfbvQiEecPf+0OCphu5XuOwxa/sw6sf\nn0KYF/HwlmYUrdyF2sajWHFnEZbPmQyvQTBhMJPXwexO9XcXP7oje1+Tw3QsRgjJIR3ZHQSCGdA0\nBZfdgqKVu+DlBLzy0RdYu6A4RuNfeL8NgKzHpzv8MRUgGxfNROuquVgzfzoYikK3wST4TKe/95mp\n9KArEMZPfn8IU1buwsNbmnHuchBNR85j3ozx2HngLJbNmSKXUieg46yN0T13JbgcLzA82Oea6AKB\nkDrSkXnkYGiUXpOHJVv3Y8ozu7Bk636UXpMHhwmNuwCApoDnFmo17bmFxTDT+jAdQWSaogy+p3lf\ntD/z9WRAxgfCcCTVmzXJRi8uUl/pgTVL/GeJ7hCyhezIeU8hysMbbYDNWhmAkie8be1eXD8hT+Op\ncv2EPDhsDLY3n8GLi0rhdsjNa3YeOIuqWQX42X9+BgD42T9O7fHU1f6+L3GIXERvXDQTS7buV4/9\n8ckOPPnmIWxcNBN/OHAWzy0s1njt/PL7HkiQIErSgDzSlAB39HdVAtB9EbmLn2yPNlIGkb2YeV8Q\nCKlG0cjC0W589702tF30obZiKgpHu+EN8njlL1/g7cPneyaCHqx+6zPN+09e8sFCywENjhfR+Ncv\nsaB0fIyWb6jyIMSLaF01F23tXgR5UePJrowFtRVT1f8W5LOAlJiOB8Iidh44q567Mj6VTxurvrYv\n3aWpwT3XRBcIhNQxmLndQAnwgmpHAEC1I9hUXYocEzq22600nFYGa+ZPVxsKOa0M7FbzclloCnj+\nnhI8/kZv8+Pn7ykxNYjssDGo+0OrRrvrmlqx7l6PeQdNMWR8IAxHaIrCv39/RkxTtGxpFi5Ksg5v\nXDQTuU4rugJhUBSyxjKC6A4hWyAB3Sj6eni9PTtlL7zfhrULijWdzNcuKMZXXwfwzpELaP7yMh67\nXe7+avdcBVGC2sgGAJ757nVgKAqTx7hx1YhJ8mK4D3GIXETnOq26C+ochxVNRy9g3oyrsO6eEozO\ndeCSl4MoAQ9vaR5wd8Y+A9wJXk+33dJrhUDJJuODFcR0LEYIyUO5LwCQvxchq1E08kynH9dPyEPj\nwXOq3i+fMxnVN03AY3dMhj/Eg6EoXOji1PdWlIzDivIiLN6yTzOWjMl1wMfx2FxdBtYuj0M0BdRs\n623OcOLZuw1tGZT/+jkBboclIR1nrQyqZl2jec3aBcXYeeCs+tp4ujvY59ro/alu3kQgDHUGO7cb\nCKn2U/SHBLz21y9RPm0sgN4Nsx/cMhE5DvOyglmbNogsb3iZcjgAclbwhS4O5es/UH9246R8Vf+H\nCmTeSBhu0BQgSBKe2nG4t8lilcdUPUkmDhuDp18/jCWzC5HjsOJCF4eNe9qyarOJ6A4hG6AkKfO3\nSczsaNmfhaJifVCz7QDG5NqxbM4UFOSz8AZ5vPzRFzh5yYcV5dFdxj2wMTQe3bpfI8b5rA1MVJmZ\n0blEdlls+el3NBm6gNLNtgyiJIG1MggKIlgbAx/H63aX7G93xsEupiOv20ADy/35TABk8U/IZki3\n4CxDFCUEeQE+jkfNtpaYgGjVrGtUbeoOhvG1P4yr81h0B8OGeg5IoGkKjp5AqtNCo9MfVjVv9/Jv\n4akdh2PeW1sxFbWNR/FSTyWIon196bjyO6eVhj8kwGW3wMfxYG0MAmFRfa0ZWp7ItU31MQlDFqKt\nEaR6oyTVHc8FQUSHL4SlDb2aXF/pQb4rdv6dLLzBMBbrzburS+E2qau7P8Tjsj8ckxU8grWCtZEA\nBCElEG01gVRrZrLJ9vMnEDKAhLR1WAd0E1koRk94nRYaAV7UTICB3gBiMCxAFKFmVUX/3mjSrHcu\nLy6aCYamwdoZNWh8BWvF3GljNRPUDVUeuGwWOKI+V5QkTHlmF/iI2gYLTeHY6rkpLdeIDEgrDCSw\nHI3eYgSAKYt/kiFGSCFkYpxB9HfTT3mtcUCUU4O+ravmomhlrEa3rpqLF947jvtvngi33YIznX5c\nyVrhslkQ4OWAazAkwBfSDyBXzirQ3TQ0Ouf+aGaqtdCs8YMwLCHamkZCIR6XOR5LIzSrvsqDEXYL\nbCYFHgVBhD8csUllZUwL5gLpmXcLgohuTg7qKlnBI1grcuwWU79rqiHz8IyGaKsJZMo6fqCkQ/OT\nDdEdQppJ6GbLjqfJJCK9aQGoDV6UhaIoSppsqkvdHK5krchxWNVGAArK/7M2i9rxO/Lhj5euH30u\no3Ls6OZ4TbZvfZUHeawN3wRCeHFRKXKcFnQFwnBYaDh0hDFTbAnM8rvVK4Pwcnyff9OBkOoMMTJ4\nEAiZQfQY4ON4iKIEt8Oi+2xGapKSfeC29y6oZZ3v9ZA08mM/dzmAeTPG49FXmzUZVxKAJRHVHi8u\nmqnaMigB5B/cMrFfQYt442A0qS4/S7dfOtFjAiE58JJcQhxpR0BT8s9tJh2ToihQPYGPyP83C6Wr\ne7Sm+zjetIy0QFg0rPRwmxTQTbUukkoNwkDJ5jE8HXqSTNKh+clEjufwPQF1Brwgwi9JYG3mN4Ik\nEPrD0Nm6HQDxFoohXgAoClfnsWhr96Kx5St0czyCvHG3WmXSsfiVfZjyzC4sfmUfOnwh1UNWFCV4\nOR6i1PPfnp9Hn8tjtxfiie2HNN3El25rQTAs4pI3jH/9jyOY9NTbKFu1GzYDz7NM6c6oBJYjUQLL\nycaMxX9fnd2TTbz7h0AgJAcjLY4kyAvo5ng8teMwHn+jBWFBxCOvNg/42VT0qaJkHJqW3YbC0W68\nuKgUy+dMVjV67QK5C/uTb2r1//E3DuKyP6z52aNb9wMAOrwhPLylGUUr38HDW5rR6Q8nfF7pDpjG\nI5XjRzREjwmE5CFKwO/+ehocLwKQPW1/99fTpjXIUSoitM8vZ+rzy9oY1C0s0cy76xaWmKqnrN1A\nw+3mHDMdupjKeThh6JDtYzhDUfjl9z3Ys2I2Tjx7N/asmI1fft8DJguycwFZ83/0uxbMrtuDa59+\nG7Pr9uBHv2vJmqZoIV6APyRgydb9mPLMLizZuh/+kCDHhwiEDGJYB3T7WiiKooQujsejrzajaOUu\n1DYexbwZ47Gj+SxEeS6qGxDoa9LR18ASfS7XjnKhtmIqTjx7N5qW3YaKknH49FQnnDYGtY1HseLO\nIlSUjOtzYRvZ4O3Y6rnYfH9ZWnazUxlYNmPxn0jAI5HgUCKQSSuBYD6JTvJFEerG2pLZsZtsNdsO\nwBdK/Ln3hwTU3FGIFXcWobbxKIpW7sKjW5tROasAravuwpr501H3biv+5gqnruZcncfG/Iy1M6jZ\ndgCjcux4q+ZWbH1oFnxxNh6jzyldAdNo9HQ0nRuTRI8JhOThtNGYN2O8qn3KvNppM2cp4g8J2Lb3\nNGorpqJ11VzUVkzFtr2nTdU2jhdht1JYM386WlfNxZr502G3UmoQ2wyULL5IlCw+M0iHLqZr4zFZ\nc3tCesj2MdxupUFFVVxToGC3Zkf4JtWbTckmLEpY2tCiTa5raEGY6AAhAVI5fgxrywW9Lr8vLpoJ\nSHKTgaURpbEfn+zAk28eQm3FVLB2BoIgwtfTPOb4BS+ajpyXG9+4bYaTDn9I0Cy8C0e7cabTD5ed\nAWtlUF/lwdJtLRiTa0enL4TaxqMab8TCUS60tXvVc1kzfzpcdkufC9u+ymNTVYYSGVg2+1hmdG6O\nZ12RzFKwTM+WIxCGAv6wgG17v0RtxVQUjnajrd2LbXu/xIO3TtLoZORktHC027BDuzIG3H/TBNX3\nXE/nWCuDB26eiEdebdaMLUu3teDn3ysGTVG42M1prBgqSsbhsdsLUTjaje5gGBUl49B48ByAHh3i\nBIzJtWP5d4rw5JvahpwOS3ydTUe3ez360tFUjR/RED0mEJKHPySolQdA77xabpCT/ACF00ZjQenV\nWLG9t1lY3cIS0wLIgLwJ+Lu/nkb5tLEA5ABv41+/woO3TDLtmKyNwYZKD3whQS1rdtkY03QqHbqY\nDgs5YvOQ/WT7GM6FRQTCAp7acVi9B59bWAxnmAFrz/ygro/j8csqD268diRynVZ0BcL4+MSlrLGM\ncNkthvN+AqEvUj1+ZL4amEh0BuuvHyhDSBDxmw9PgjV4iAtHuxEMCejwh/BIVPbutr1fGu6UK566\nY3Ltmuysp3YcVnfR8102bFw0E6vmTY/ZEXryzUN44OaJ+PjEJfVcCvLZAd8YZpah6O1IKIFlxXvY\nrMmQGVnJ8TLEkrkDnEnZcgTCUMVpNcgUi8p68HO9z+O5ywHdZ/OrrwOobTyKBaVXIyyKWLzFWFNp\nmoLboT+2jBvhRN27raitmIprR7lQX+XB8jmTNePFkq378ZO7ijDPM07VIZoGls2ZEmPRULOtJSEN\nSoZmJmMXui8dTdX4EXNORI8JhKSR6sWxPyRgxfaDGk1Zsf2gqc9vqrOQATnoExYlPLXjsLquCIsS\nuLA5WcGR46KCsrloFvI83BM1D/eYuvGY7dmdhN6qqKZlt6kVrzV3FGbNGC5KiKkMe2L7oayxLHBa\nGJRek6exLCi9Jg9OS3YE1FNd/UAYOqR6/BjWAV0AmoWiKAE121pQPm0sTnf4DR9iUYKavRsZcC2f\nNhZOK4P6yuhJhxz884eEPhfegbCIj09cMixRcNstuPHakeq5+EPCgBe2idxoA1mkZ4JfUbIX//EC\nHsncAc4U32MCYShgpGGRmWKRGh49yWdtvYtImgKeW1iseTafWyj73SqBAm9QUD9zVI5dnvRRiDm2\n3tjS1u5F48FzKF//Ae779ScI8yKqb5oQc55PbD+EVfOmY1N1KfJdNjgsDAry2UFp0GA0M1maPxAd\nNbuciegxgZA8fByvG1wxa3Gcjuwqf0jAzgNnNTYPOw+cNTWAJEoSHn9DG7h+/I2DECVz5t00bTAW\nmryitDG0xsrCZlLDN4Vsz+4kAE4LjcobCjQbLJU3FMBpyY7wR7ZbFgR4AQ2faG1vGj45jUCWeNAy\nFKWrddniYUxIH6keP4Z9znik7YBy8QtHu/H4Gy1Yu6A4poTVZWNA0RTG5NrVpjZt7V5s3NOGyaPd\nON4ul95uqi6Fy67thM5a+154i4KE0mvy1GBydGnR8XYvCke7ceOkfNUaQpSkAZWgxrvRBpoqrgSK\ndW0lbNl1u2ksKcI915iiYsq7klkKlkp7CgJhKKOnYS8umgmGphNe6MvPo119Hpe/3qLaNPzPNwGI\nEjDuSiealt2GjXvaVI/bipJxWHFntAWCrJ96Fgf1lR40fHIa8zzjsGzOFFyd54Q3KCDHqX+erJ0B\nJKi64A3qd0I2sxxVIXJzEIC6Obj5/jJ5IzNBW5/+6mgqypmIHhMIycNpZVB5QwGWNrRotM9p0gaJ\nkkkaoymcALfDHF1kbQz++X9dg+6gHKS2W2j88/+6xuSmaEbjhDnf0WFlUNfUqrEsqmtqxbp7PaYc\nD5DHmUe37tf8LW+clI/N95eZNsalw+aBkFwCvKgGFJV7teGT07K9lskbAsnA37MJVj5trHr+TUfO\nw8/xcGeBZQFrY3Rtb7JlU8RhY1D3h9RqHWFokOrxI/PVzESiM4uUQGpbuxcXuji1/FXZDXbZLWAY\nGsGQgBXlRZodvxXlRQj2LFwfu2MyKED9t9IQjaYpw1Kl0x1++MMClja0YN0fj2HtAu2O0NoFxWg6\nch6BkIBf318GTtAv7Y2XsaT8HgB2L/8WKkrGac5DySIYSKq4KEp92kpkWjOBvq5VX1ln0e9zWuik\nZnGlq7yYQBhKRGvYqBw7ujkei7fsw/ELXsOSUSO7GH9IwIUuDuXrP8CPX2+BIAJ/2H8Wxy/IG23/\n9o/T0BUMAwAeu71QpxKj1z4g32XD5uoytK6ai42LZiLPZcP9N0/Ev1VMRWPLV/jq6yAe3drcx3ny\nWm9em3Emqdx0k4c32PO9goPT4mj9M9ocdFrpfmXuRmfDLp8zGS/dVwrWxuiOZakqZyJ6TBiqpLrh\nU6BnjhvdYCZgUgmihUZMxVx9pQdmJudFel4q899AWDDN/gBIfeazj+MxaaRL87NJI12mliGnI1uW\nVGhkP04rjaobCmDveejtFvnf0fZamYqFplB90wTN+VffNAGWLJmHpMP2Jpn4OF6d91/79NsoX/8B\nLnRxxHKBEJdUjx+UZFJJTjIpKyuT9u3bl/TP9XI8Fr+yT42eV5SMw0/uKsKO5rOYN2O8bnaV8r7I\npjaAvFP80n2leOTVZo1xeV1TKy50cWpmGGtn4A3yePmjL7DhvTa14Vndu634xb0eFK3cBV6UNI1w\nAiEBv/nwJCpvKEDDJ3KzhdrGo7E71dVlCPQscvUylvQymiLPUfNaScKUZ+RzUbDQFI6tngtap9RA\n+WxF5J7acTilO+n9Re9a1Fd5kM/awDB0zL0B9H2N81grArxIsrgIySBlN45Z2poJRGtY07LbVN3U\nz6D1wMbQeHTr/rj6WVsxFU1HzseME/VVHjTsPY3H7pisarlCtH4GQzw4QYLbblGzLuaXjgcFYEWP\nZ1pFyTg8edffarIbnltYDLfdgpyeDUb1++o0uQSA7mAY3RyPJ7Zrv2u+y95vjdLTTWXci9bKTdWl\neHiL/s9dNv3AqPIdlGDw0m0thtm3/R2jCAQQbVVJR8OnVD+z3mAYv/nwi5jstgdvmWhadps3KG8a\n6s0dzcoK5nkRnQGtXtZXeZDntMFiQvQ6GOLRFeRjMq1zHRY4TKrES8d1BVLXPHoIkJHa6ud4XA6E\n8fgbvXOo5+8pwQin1bQM9mTi53h0+kOa+dtzC4uRx9qy4vyzfZ4miCK++jqomeevXVCMq650gDHb\nY4aQ9SRp/EjoDcP6boze8W08eA51Ta148JZJuOpKBzZVl8Z4pvrDQp/lutFeh0tmF2oyw6Y8swuP\nvNqMylkF+Pxnd6G2Yirq3m1F48Fzamdz5VzK13+ARb/aCwCovmkCGj45jXW7jxt2W2ftTJ8ZS5Gd\n3RUvmx3NZ7Fq3vQYX9j+NoNRsqXW/fHYoP0ck0Vf2Sd62V1Lt7XAFxLUTOP+XOMAL5IsLgIhg4jW\nsEjdbDx4Tq3AOLZ6rhpkVEo69fRTyazdVF2KyWPcKJ82NiYLd+m2FvzglokIxNFPUZTQxfF4NKqx\n5o7ms7iStaG2YipOPHs3Hru9EA4rpfoG1lZMxc/facWSrftVrVLQyyT1hwV87Q/HNNVItGFazDXV\nyXr2cTxeWzwLe1bM1jRrMxonWZvFMFNX+Q6BsBjjUx+dfUsalhEIAycdDZ9S3WCGtVtw8pJP87OT\nl3ymBkJYO6NasinZsmNy7aZ6Xgb5WL1cuq0FQd6crGBehG6mtUmHA5A+315SoZHdpNpfOtlke1M0\nv1Gj+CzJcE2HJzph6JDK8SPzt3eSTGS0XJlcRu74XujiAApgaBo5DnmmEJlVytoYtQw22hejrd2r\nOZbix/vY7YWqIANQJ1tr5k9H+foP1NefvNiN+iqPZpd97YJiPP2Hw/jFvR5seK8NANTAb/TxfRyP\nT091arJ7z10OgIY8qCmd3aN3mpw2OmanSc/nsa9UcSUAyosSnigvSrvvVLzsE+V8I69VW7tXtchQ\nzlnPey0TgtUEAqFvojXsTKfWm7zx4Dlc7OZQWzEVtY1H8driWXGfbZqm4LJbVJsFo429ZQ16Huy9\n+ukPC+oCHIDalO3n3yuGL8SjtvGoJpt2/e5j2NlyTj2Opec8grwAUULM7m/kODfSbceYXHuf3yvh\naxqx0aVkOS+PyHzZUOWBy26Bw8IY+ke1tXtR23i0z4qNRMpr+ztGEQiEXtJSwm5jYnRx7YJi046p\n2KNFZ7cFQ4JpQd10HDPVjZPS0ajJYWXwp88uYOOimch1WtEVCOM/Wr7CfTdOMO2YhOwn1f7SySbb\nm6LRFIVfft8Db1DA1XksznT64XYwWZGdC2TVW2CaAAAgAElEQVS/BzBh+DCsMnSjfVF/++EXMf5a\n8RaE/pCApiPnYzxu66s8aDpyXvNaZfFqtPAvyGc1foGl1+ShYW9vN8gXF5Xivc8voPHgOTUYAQAv\nvN+m67HL2hjU3FGo8a/9ye8PodMfwvLXW9Ad5BPq7K4EA/JcVt0sZaPropzfc02tMeeX6oV2vOwT\nf0iIuVa1jUfR4QvBaaUNvU9oGiQrjEDIAiIbWh1bPRejc+3YUOWJ0c0X3m/Dxyc7VA/1SPSebT8n\njwFKI7Lo13/1dQCNB89h54GzhvppFEy56konamIyU1uwbM4U9XUVJeOwe/m3QFEAL0j4zZ9Pajxq\nBUHUetdu2YcV5UWGfun9IVLn9X2CWyD2NGvT01DlescLHCWSfRv99403RhEIhF6M+jn4OfPmMoGw\nqJvtFDDJX1aQJN3sNsHE7DzR4JhmZgSm+m+Z6kxrQO5J8u3rxmDJ1v2Y8swuLNm6H9++bgyCJmaU\nE7KfdOhcMknHs5ZM7FYaXFjSeIpzYQn2LPEw5ngR9ogquTXzp8NupcCZWY5AIAyAYeWhq+eLunzO\nZPzglolw9TS9iedvoQSFt+39UvXl8gZ5HDjdiUmjcjSZB4o/7bI5U/Q9ZavLAApqtrCe36CSPfbi\nopkICSJqerJ3a+4oxP03TYTbYYnwBZsEUZJ0/QyVDo3xfB0H6qsW/b6aOwrxwM3y+aXDdyqeb49i\nx2Dk/ZjjsEIQRNViw8fxYK0MKIpKue8cYdiRkV5kQ4HIzNXjF7x44f02NB6UM1/necbhme9ep2qs\n0bPtD/Ho9IVw+OxlzLwmD8sifASfv6cEP3/n8xhP8mj68uj2/N93dXXrnzfvlRtORmV/KR7sjQfP\n9eldu2b+dMxZ91+D8tAVBBEdftmrcetDsxIaT3whHqxNHqeU6x3PUz0d/p6EYQHR1h4UHYvxZnTZ\nwJrkg5rq5zod/o3pOGaqfTYjxwG9HhRmkC4PXULCZKS2ZrsHbbZ7uHYHw4a9FHJM8jFPJkR3CBlA\nQto6rO5GvayoDe+14Yffnqz6W8SDpinksVY1COwN8njlL3KDs5o7CvHSfaVw2y3o5nhYaQrr7vXA\nGwzHWClsqJoB1tYb5DTyG5w8xo3N95ep2a2b7y+Tu35HNVZTPg8UdD9HsRSIZ4UQmdkKQM1sjdfQ\nLDJbSlP+m+B1TTZG5b7Kd6VpCm6HsReyKEro9Id1Fx6635MEGQiEjEfxM/JyfExjyQtdHFx2S9xn\n22FhkGO34LqxVyDfZVNLQP0cD7pH8+Ppgp5dwPpKDwLhWBsgJet3c3UZAGgml0qVRW3FVDQePKfq\nl1FFyLHVc+HnBM3YkyiKJipVJIE4Gqtcb1ePZ65iI5FIxYbheEJ0lkBICg4rg7qmVnWzv63di7qm\nVqy712PaMZX586bqUs1GuVnPtZKdp2edZdZi3K9j5aZ4RprViA0U4LDSWDN/ulrW7LDSpoXYGIZG\nPmuL+TuaFcwFsr/0nJAmUvxsJJtID1dFp3ceOIsf3DJRtYXMZPrqOZQNEN0hZAuZrwZJJBlNVJRF\n7cNbmjHlmV14+aMv8MDNE3Fs9Vw8eOsksFYGl7wcHn21GcX/9i7+/U/HEQyLGiuFl+4rRR5rjcr6\nMj43JQCplLH6QwJcdgYP3ToJLT+9E68tngWn4s1o8DlKdpTSVGCeZxz2rJiN1xbPAiSoDWoG46uW\nSc0DjCwTIoMIfV3zviwbMul7EgiE/mOkDw4LE/fZpmkKOQ4rRubIGa4WhgYkwO2wgrUlpgtKYOOl\n+2RLhjXzp2P1W5/hZ//5WYxdzfpKD458dRk0ZTy5LBztBtBbimekazRFqYEMo4aRRqiNL3cfR/n6\nD/D0Hw7HNKnRC9QO1BqB6CyBYB7+kIALXRzK13+Aa59+G+XrP8CFLs5U+yilMqrDG4IkAR3eUML6\nMxBoyqCRlolSYqEp1EdZ+9RXeWAx8aAOK4O3Dp3HCNYKigJGsFa8deg8HCbanDEMjRyHFTQlj4dm\nBnOB7C89J6QHh5XBqv/8TC2R53gRq/7zM1OfjWRipSlUzirQWANWziqANUvmQ9n+3JLmu4RsITu2\nSJLEQJqoRJboygtiaDJY1+0+jo9PdqoZrF6OV7u/AkD5tLHqv9ftPg4AarkpS1HqZ9MUsKHKE1Pu\nG3lukeVqeqW3G6pmII+1xnxHxfrhYjeHHLsFv76/DL4QH3UsT09mqkEzsIisq+hrkomZU4lkePV5\nPxhkOhMjdAIh++lPBqiR3il6ONAKhAAv4pFXm1E+dQzunj4WF7s5fHqqE3OnjcFL98mZT23tXrz+\nyWlU3lAAUZJUn1+9RmOKR+2Hxy/qVIR4QFO9G3cDKXuO3uxrPHgONAVsri4Da9e/hpHXDoAc+M6S\nzAwCYSiTjqaCQV5AWNR6D4ZFEUFeMMXmgaZkvYnMzpM3iZJ+KBVehJrAoWTUNew9jQdvmWTaMYOh\nXn/ZVDViSzWpbqhHGBr4ud6NK4UbJ+WbmqWfTCgANkabYWxj6GxJMAZDUXj+nhI8HtE89/l7SsBk\nS1M0K6MTm/GQ5ruEjGNYeegC/QtG6vt9ebD6rc9iOo6r3qxR/lknnr3b0GfwkpfTLLpfXDQTDE3D\naaN7snC1/rORvotNy25D05Hzqo+v6qPbkyWsfkdOAE3Lu5TKZ/nDgq5/45r503Ela9V49UYv9oea\nt6HR/WDocRnHeoJASAIZ6UXWX7Jh4yceZuqdMlbcPX0s/s8/XAebhVHLV/U8x15bPAvLX2/B8u8U\naRa19VUe5LE2nLjowwvvt+Htw+fRuuouBEIiWDuD0x1+rN99TPX1dVoZfU+wONrWX00camMFYUgw\nJLQ1WaRao/0cD3+Yj+l4zlot5ni9iiK8QR4SgFynFV2BMCgAbofFNP/JdHjoZrtPZSJ4OR6/+fNJ\n3TUPmZMPjiTpQEZqazq8wpNJtj/b6dDgZCKKErqDYXztD6tj1pWsVa5OIPNYQmogHroKmsEqnLi3\nq76fbAvWzJ+uCehGZrBGe7ca+dZ2BcJYuq1F89mPbt2PXz9Qhk6fvndrZIbUtaNcmDdjfMxutTO6\ncyQlez5Gfl8jW4Wr81gs+tVe/PqBMsPMNUOPXaXBW5YFbYwy7dKRvUIgDBWGSjAvUu8qSsbhsdsL\nkeeywRfi4bINzgbAHxJQc0ch5s0Yjx/+rncD7bXFs3T1Wck0ee/zC3hxUanaELNh72lUzSrQNBwL\nhEWAAv55817N2FOz7YDh58fLdOqvJg7Uj51AIKSGZFQa9JcQL3c8j8zWYk2KS/hDAh7dut8gGGJO\nMMFn4KHr43jTAjDZ7lOZCKyVQdWsa8icPMkMlbmaEenwCk8m2f5sc2ER3RwfE1C3MjRYe+YHdP1h\n/TGEzGMJmcaQvxsHM1jpBT7H5Nox0m3HiWfvVneIq2Zdo04qohe9TUfOo77Sg6U9ndBr7ijE/TdN\nRI7TgtqKqZou65+e6oQownARTFPA7uXfwtV5LHwcjyffPBTTHGdTdSmCYbHP72vUMKyt3YtPT3XC\n0RPwBmIn+UbBYKeNwaJf7R0yEwHSlIdAGDjpDuYlK/NM0buKknFYcac2M3ZDlQf5LnvczzU6F9bK\n4IGbJ+KRV5s118mokQ9NAfVVHvg5AY9u1WZsfHyyE7UVU3Gxm4trG2P0+d4g36dXraKJv36gDKIo\n+/n6OWMfscH4sRMIBPNJdYauKEl4/I2DGr17/I2D2Fxdasrx0hEMSYc1QDqCyKm+d8ic3BzSPVcz\nGx/H61oumPlsJJN0PNvJRJSAJ7ZrYxVPbD+kNvnNdFgbgzG5djQtu03dENi4p43MYwkZR/ardRwG\nM1gFw4IaQG1r9+LjE5cw5+/GYPGWfVHlrr2p93qTDqeFxub7y+C00rjkDeHRrc2aiR4g+xFePyHP\nuKOijUGHl0Njy1conzYWk8e4DSeqHV4/tj40S22EFv199TKt1i4oRt27rTF+uTHXs49g8FCbCKQj\ne4VAGAqkM5iXzIwTRe8eu70wZgOtZltLXK2Tz4WL8d9SAsFuh0WdLF47ygUvx8NpozWbgEpGAygg\nn7VhpJvSvbaTx7ixqbpUYxujGximEeOxu3ZBMV7+6IuEyld9nJDQtTUaK/oaXwgEQmpIR2YeaxBg\nNcvnVamCiC7TN1ODAiFRtyv9g7dMgtukrGArTcWMGfWVHtMaJ6Urq5PMyZPPUN94zXbv5VQ/28nG\nMKZhz47rHwwLMf2KnltYjGDYHN93AmGgZH6++yAZ6GAlihJ8HI+ndhxWO0v+08zx6k4TL0r4+GQH\nlm5rkctbI4juzs0wtGrHsKynQZry/iffPITHbi/EjZPy8eKimfBxPFpXzUXTsttQUTIOQO9u3La9\npzFvxnjUNh7F8Qte3c6L3qD2nFfcWYQxuXbN91WDztVlaof1dX9s1WZ3GV1Pne7waxcU44X32xK+\ntvGue3+7rxMIhMwinZ1hIzfxFJ2t2XYA/rD+sfvSHKUhQuFo/Q20eFrnDwmo2dYSdS4t6nUIhQX8\n2z9Ow+TRbvg4AVv+cgpFK99Bwyen8eJ9pWhdJetzjt0Ch4UBw9Bqhm0k10/Iw/ELXjy8pRmd/jBE\nUdLV6g1VM+CwMMh32VBbMRWtq+aitmIq6t5txYb34mcd9OfaGh2flMgSCOmnvzqZDFLd8dzB0Ki+\naQLsFnmpY7fI/3Yw5i19LDRQeUNUV/obCmAxcbVlszLYdeQ8Ni6aiWOr52LjopnYdeQ8bCZpbTru\nHYCsD8wgnXO1VBAI926wKPOdnQfOxqzbMxVeBBo+Oa05/4ZPToPPjtOH30Dz/SZpfrIRRcTEfZ7Y\nfghillx/wvBhyG8vDDRLSJ6waD1u3YbZBYlNmozKvyaPcePXD5TBx/Go2arNmioc5ZItHWwMyqeN\nVbPEXni/LWbXsb7Kg5c/+iLGhmHN/Okx31fJDhNFCSNz7Fh3ryehEqboDOTTHX7Uvduq2kYMJgNL\nEER0+EMx3dldPcGMTCqt+v/Ze/fwKsp7bfiew1qzDgmHhMMGIUoM0BZIFiSVC0WrFIvY903ZUDT5\nGuKhpepLN7IR69b69s3ehc2FpNmE/fZCpbaK+Abl1VK+TxRlq1WrG8shHNw2EBEjQkMgIFmnOX9/\nzJrJzJqZlZW4Jisrmfu6elFX1lrzzKyZ3/P87uf3u+/BYPjkwoVTyKYGdW828XqqNCJJAkGGRlec\n79M8kqo6QZJkXGEFU6Vsa0cEDftO4sNTndhaW4FR+Yx23bQKXosK2/o3WvDhqYto2v857pk7CUGG\nRpChsLW2QpFHMJg+iqjb/bFJF6zH8+nlBqnfQ+GF5bO7zTkHWBx34WKoIhuVef1dLceJEsKJwgx9\ndZWXIkE7xLDqCRi1QnfHR224d26xI8cDFKL89ePt+F+7/0t7bU5xIRbPmuBIW3Y22pB76nYZLOjv\n3GKw+4Uo2stFpvsmV84vwFA4dSFieO3UhUjOVLjSJGFarzZWh0DnyDOb6xXGLoYOBj2h29fJymqx\na2dwFmUFgCB6nIBTtX8BMBHIuw6f0RLzCCvg2tFBbUwqgVpXOQ2Tx+YhHBcQZChsfqvVcMy/nO5E\nUWEAcV6EJMmmcfWlhUn9jCTJCDI0OrpY0CRheW3TXZxIkowIJ5qM4lQTuiBDDxht3sFuIuDCxddF\nNvXuerOJl44kD0ORkGVgc3Wo10mBnV5tlBUBAqZ498jLR1FXOQ27j5zVFo0kQZhizsp5JXhyWTny\nfTSuxHjkMTRW3FKCWUUjcOu3/g4/3XbQOE4vZWn62LT/c20+irAC/D2QHOleW7sY6aO//iLY3Uxz\n4eLrIxuSKDFOtJEjmOSIHEE29BsDDIXSicMxbrgPBAGMG+5D6cThjhIAAQ+FLTWzcFnnxD4i4HGM\ntMpGG7K+2wXQyR7VViDP51wq25/zTTZyi6GgTeylSKxfPEN7NrwOVuhnGnFOxM9v+wYeeumIwUgy\nzomOSdVkEoIEHDzdiS01szDM78GVGI8PP72AGyePyfbQ0oIrHeYiVzDo78a+TlZWD/He4+dMO02/\nvqMMAHChi8XEggAudLEYGfAg3+cxHcNPk6i6rgg7PmoDEkn0uBsmwUeRICmjLmJl2XgsmjnBkJg3\nVoewcl4JGvadBKCQuh1dLBruKIMoy6BJwpIw/vJSDH84dAZ33zAJeT46YxN2T9e2N4uTKC/aVjBP\nLAig5rf7B4w272A3EXDhIhPIlt5dbzbxeqpSkyQZnVEOK5uaMXYYg/WLZygbZJwISZYBQqmatYun\nNAlL/TO/l0SMkzB2GGM6dsmYPADGRWNyzFHngKrZRcbKh6oQdnzU1mPSS5IECgIe0+d7Sh7TvbZO\nxUh3M82Fi8wgG5V5JEHghxUTTeSEasKbaQQYm0pSB8lVnhfxrXHDcd/zBw1xmedFMA6RnQRBIPkK\nEonXnYC+DRnoP6K8vyvl+nu+yVZuMZi1iaOciOc+OI0F08cBAFhBwnMfnE5oWg/8c+1vI8lMw+8l\nMW38CDyw/ZChK8PvzQ1S3U+TlptlPRU/uHDR3xj40SwD6MtkFfBQJvJ20cwJ2k5Tvs+DLzqjGBnw\n4GKEM7V0eWjStFMdEyTs+EjRwU2WSshjaAOBbGXC82BTM55aVo4PT3UajuXzUvjPTy/gO1PGoOq6\nIhN58F9nv8KimRMMC8y+Lkqsdqvtrm1vFicBL6XpAlsZrg0kkf7BbiLgwkUuozebeHa77xFWUDoj\nOMFQFbSr+SxWz59sirNW8VSSZHSxgqH9NsIK+HNrB/6hqVmL35IMg2RN6/mwSW/W7yENVW2/ebsV\nfz9rgqnC98EdzXjih6Ua4QvYJ70xQbLoiEidPNpdWyBBbKuvORQj3c00Fy4yg2xU5pGEsn7SV8sF\nvBScOmScs6kkdbC6jZNkPLjDHJefri0H08Nn+4q4IKLLQlrCKg/JBLJBrqbqdnGKmOvv+cbNLTIP\nv5c05dy5RCj2t5FkphHlRBOX8cjLR/F0bTnyHTKJzCRYUUKUEw2x9dd3lMFLkwjkUKW3i8EP9260\ngbrYTTaO+YemZgzze0AQwKh8BmIvBLOTdXD1xmqSBIOBjJ0JT56PxvrFM7QxPfF6C/7H9kO4/trR\nkOTuhaT23TuaESoaaTpmXwwM1N3q5c8dwJRfvIblzx3AxQhna0zQm8VJlBOx9/g5bFhSamm4NpBE\n+ge7iYALF7mOZGPKnipO9TGnsSqE37//Gab84jUEvObF9ILp40xx1iqeqjrsDftOYsGmd3HtY3tw\n3/MHUTw63zBXrL51iuHYJWOC2HpXhUYQq3FXb7Kz5ntTcdVIv2V8vWqk3/CaJvGQfO59TB6Try0A\n07wQjtsYYXzNGOkmvC5cZA7pxslMQZIBIWlxLEgSnPK2kmTZen0uO2emZddpFnSQgOlv4x47Y06r\neSZTIElg41JjfrBxaSlIB7PY/p5v3Nwi89ATinoz8ly5ptl41jKJbMTDTEKSoFVIq/fPQy8dcU3R\nXAw4uIRuCsR4CXW7P8a1j+3Bgk3vYveRs9rkqi6Ae7NTHeVEe7d0htKqJU6sW4goZ+cMKWJ+w5/w\njy82AwD+7c4Q6iqnIchQtjt5w/yejCxKeuts25vFiZ8mcfcNkzBhpB9P1pSjZe1tWL94BhrebEFH\nFzugRPpd93YXLgYH9FVqJ9YtxNO15djxURsa9p2EIMmabrqKyrLxmDzWJoYnxVO7ZFCVVFD/u6gw\noG3Q/eueT0AkkStRvltb3JCQpFjop5P0phOf03EVt5oXnv3zZ2isDmU8RroJrwsXmUM6z3dGjycD\n//B/mnFz/Tu49rE9uLn+HfzD/2l2jNDNRnVbxMbVPeKgq3t/V8xmg1z10RRG+D3YUjMLJ9YtxJaa\nWRjh92REl90O/T3fuLlF5pHrhCJJ2DxrOaIwleuEtGuK5iJXkBsRzUGkErxPR2OsN21AAQ+FcGKx\nZ2eSox9H8rE3LCnFVzEOK+eVmFpINlWFEPRSlt99JWbt0B6OC72qyujtbnU610+SZOV8GQqdEQ6/\n/ONxtF9h0Vgdwph8Bg13hgacSH9yq2JEbTXmB9Y4Xbhw0TP0kjxBhjYYS/7m7VbNlX3sMAZrFkxF\n28WovUyDV0fE2sg5tJ4PG/77ZHsYCza9C0BxJo+yYnc88VKADEutXXWhn9xOTBAwtDTnMTSYRGuY\nfr6DDDxZMwv367TN9PE5Xf1Aq3lh81utWDGvJKW+el+MZga7I7cLF/2FbOhR93dyHGVF/Ht1CHOu\nHWUw5HGyTd9DEpa66R4H14X9LUfg81D4j0/aDUZHf2z+EsvmXJPxY+kRS3S9GA0/nUtj+3u+GQoG\nZf2NbEh1ZBrDfLThWcul24EkgH//f0IIx8XuNanPOZmdTCNqy9kIyPN5sjgyFy6MIGQHW48yhYqK\nCvnAgQMZ/950FrQ9JZ7Kd7CmRUZhkDFNwpIkgxNEXGGFJCOaELwUmZRYhzCMocFJsqLlyAp4/2QH\n2q+wWFw+AUEvrWkp7j5yFnOKC/HMXRWIcKLhfBqrQygIeNEZ5QzH3LCkVHEXvrEYeQydVoIdZgUs\nf+6AIbDNKS5MqSeV6nutrv+GJaWof0OpynXavTYV0rkerkGPC4fQbzePU7F1oMPu+baKcavnT8Y9\ncyeBAIHl2w5gdD6DNd+batpQKwx6EeVEBL0UKIq0jA9bamYhxokYM8ynmSts++A0Nr/VqsV9JV7z\nhs9tXFqKJ15v0bR21XgvyTIESdYW+jRJ4L2THSgenW8wxrz3xmIEPJRFvAohyNDwecxxTr0Wo/MZ\nrLilBCVj8vBFZxRjhjGGJLq388LXjZv96TruYtDBja0J9GU993XRFefx020HTcdU9BQznxyznICv\n4oKJXB3uox0zKBMECXFBNMVlH02BdshIR5JkdMV5XNIZ99iZM2cCUU7AlZiAf3yx+7r+250hDPPT\njhGs2bhfAUAUJc20OZIwQKVc7UwrDMjY2pscfSCC5QR8lcQZNFaHMJxxLoZlEoIgIcwJJlOxPC/t\nWDzMJKKsgM4oZyqcKAh4c0bH2EXOI61ANaQJ3a+zQNAnlXFehCQp1QfpkH9jhzFYNX8KigoDiLIi\nSBL48bPmcTy5rBz364zMttTMQpyXDIsolQDdc+wcTqxbCMiwTHZFSULr+YjBVEf/mXQS7EwTmHbX\nv65yGr6/+T1tbP096aZ7ntlaYLoY9BiQC+PBglTPN2AdCwsCHhAkgSm/eA2CJKOybLxGcsY4Eb97\n/5RGyjZWhzAqkSwkzxMRVjAkFv92Zwj5Php+XbyO8qJlXFm/eAbmN/xJW1D+xyftuPVbf2dwjN9c\nHcK6Vz/Bruaz2mdpkkjI+Fh/ry3xKstY/WIzVt9qJK+Tk6Hezgtu3HSRRbixNQFJlrV4pkKNFSTh\nzGUSJQl/+4rFmp3dMat+aRn+bjgDyoF+/f4mkAElvr134rypKvjGKWMci2+iKOFiUtFGY3UIhQGv\nI+RjOM5jucV13Vpb7ljVWjbuV7doo1cYsLE1lzeBsxHDMolcJ0TVdfADN5do/MmWd1rRcGfIsbjj\nwkUS0rrRBv7T5CD6KnhvN8kHvJSWkCdPHMluqbuaz2pJrM9jPY48hjY4Q16O8nj0lWMmt8i6ymno\n6GIR5UTkMbS2aEwey97j57BA534+p7gQEVYAASItJ9dMtwOl0pj89jUFaLsYxah8JiOL4N5M6Ok6\n27oGPS5c5B56er6TY5yfJtEZ5TVtxA9PXcTuI2e1zoj1i2egIRFXVZNL9btUOQdJkiFKMlY2Gd3P\n//HFZqxfPMMQ5+ziSlFhACfWLUTbxSieeL0FK24p0cwaAGB0PoMIK6LhzhDWLpqBr2IcNrzeos0N\nvY1XUU7EqvlTTA7FK3XnB/R+XnDjpgsX2YedJIy6jnQCMU7C0TOXDO3DH356AcP9Y5DngON5NvQz\nA14Kez9uR/HofOT7PGi/wmLvx+1YMH2cY8fU66wD3fPQ07XlyHeA0M2GNnGUE7FyXgkWTB9n6D5x\n8n5NNxdw4cIp5LoGsCQDrxw8g7rKadpz+8rBM7h3bnG2h5YWIqyA9iusJosGdHMnuUCouxg6yI2I\n4BD6uqC1m+SfubsCEdYseVAY8KZMYu00fvRaiwAwsSBg+R2Tx+bhyZpZ8NMkwgk910jC4EIv49BY\nFQKA7kqyhJv7inmT006w9XqT6S5o7MhUu+v/RWcUG5aUouHNFjTcGUrrGD0dvze77OkSDk4kRLm8\nk+zCRS6gp+c7OcaFWQErmw5jdD6j6ekmV8TafRfQ3Q6bb2NOObEgAP1Gv11c+fJSDCMDXkwsCGDF\nLSW4dnRQWyRfjrIQJRhaizcuLcUv//s34SFJZXPPTkvOJl4FPBSKCq3nnICXMsYqVT+cIHqMfdkg\nkly4GOjo77k/G3rUNAmUX12AB5LWpU513kZs9A+dTMbjvIjH/9s3EY4rpj8MTeLx//ZNxHnRMTmC\n/iZ9sqFL6qdJVF1XZJLP8DvYth3wUhg7jMHeVTcZqvOc3Hx0c4DMQvFpERKV3RQEUUJUlhHwpu8f\nk03kugaw30uaPH82LCmF3zvw5RYAJQZsrgohwnVrAAe9lFuA4GLAITeeKIfQV0dRO0JAkmBy+36w\nqRkRTjQ4PVaWjcfeVTehZe1CxDkRoiSZXCwbq0LYe/yc4RhfdEYt3SLbLkYBAJ1RDsufO4Apv3gN\nP912EF2sgNH5TPdYdjTjnrmTTG7uqlFb8vf2xZU32TVZFCVcjHSPa/lzB3AxwkGSZJvrH4KHIlD/\nRgvar7Ap3WQNx4oLiHLWbs1xQWl13v6T2Xh15Y0Ync9gZdNhRHnr707X2TbTjrQq8Wx1rVy4cJEZ\n9Na5Wk3qVtxSgvEjfNhSMwstaxdia20FAl4a7VfYlN8VF0R0sYJmppb83q44b3i/nybx1LJyfPqv\nt2Pvqpuwev5k1C8tg48msXzbATz0Uqy+CcUAACAASURBVDOG+ZRkhKFJbP/P06ApCg/uaDbMPQ/v\nPIpwXNRama3mmc3VIZNJpRpTo7yoGUKYzo8V+xyrXCdvFy6MyMbcr6+sP7FuIbbeVeF4K7kgATs+\nakNd5TS0rF2Iuspp2PFRGwTJmeOpBmXJa2snDcogA3FewqOvHMPUx1/Do68cQ5yXAAeXcZEMruHT\ngWrIqb+uG5eWOmp0FOMly3snxjt080Ah59csmIq63R9j6uOvoW73x1izYCriNrnD14WbA2QenCAi\nyol4YPshTPnFa3hg+yFEORGc4MxvmGlk41nLJKKciF2Hzxie212Hz6TM7QcSWF4CL8mGeM5LMlgH\n444LF33BkNbQBfq2G2qnAfjC8tmWGk8taxeCFUREWBFN+z/HopkTsOvwGSyYPg6Tx+ah7WIU77Qo\nmluq8UyQocAKkkF3ZnNVCLwkGzQTVQ3d1bdOMcgxqGOqq5ymtQro9aZUParbZ4zDr34wDV2soB1r\n5bwS3HXDJOT76F7tEFtVwjZWh7Bjf5vWkqyOa2ttBUAAfg+pGAkxNMJxAc/++TOdQZB9Fa3VsTYu\nLUX9XoUINmpiGgXx9dW/Vho4vanoTdc8LZ17zNWWdJHAgNUiG6joTRzvbcV+lBPQGbHQAAt6wVCk\nSbsw+bvCccHWTK3hzjKMyWdAgNA0d5PHtilRwfbCf7ZhSfkEeGnSEM9UQ7Zv/M/XLecegoCmn5vK\n4Mzq2E/WzAInSkmGIjMRZChN912vJxzlBATTqHxxq5BcZAkDMrYOlblflCR8eSluqta6aqTPEQ3d\nKCuAlyTIMjSJB4KA0rXg0HUNxwX87v1TJmmAe+cWO1ZRJwiSYnycVL1aEPA6YjwkiBLCrIXREUOD\ndsgwrL/vHaB77jZrBTtj2JzjcWBAxtZc16DlOAGCDAiSjDyfkifTJAGaALw5YIqWjec2k8j1+8fF\noICroZsOSJJQ2lG59JNLu1a1VNIJk8fmwUdTuGfuJPz+/c8sWxDq31BczFXiNc6JeGpZOYIMjdbz\nYTR91Ia7rr8GW2sr4PdSaD0f1j7zb3eGbPVo9WNR21rVKrUVt5Tg/u2HMDqfQV3lNFw7OojOCGcw\nY0vXBMBKiuLBpmbUVU4zELqK1haFH23dbzIeuvfGYvzsu5PTIkcjSRXID+88qhHYqs4VAJNu5SMv\nH8X6xTNsW3x7ownZkwRFT+SR/nxcbUkXLnqP3hK0vdV8lSTg4Z1GHdmHdx7F1toKUF4So4KMSXPX\nQFYyynOtkq2qTEKME0EmyNZgQtqBJGCKoat2NOOZuyqwaOYE8KKMNTubTX/fUjPLVr5mVD6jxRZB\nkrH7iGKYps4z4YRzt1X8vn/7ITxzd4XpWoFQYlNl2XgTSZ3OfNEX6R4XLgYrhsrcH+VEkyb3Iy8f\nTSTHmU/ufV4Kj714DA/cXKLp2aqGNk4hGy3GMUHUqldVEnnHR224Z+4k5DtA6HKiBDGpalSUZHCi\n5Bih29/3DqAYXVtKLjDOPJdDJQ70J3JegxYK0Z+8WTMsB+QWgOw8t5lErt8/LoYOBv7T5DB60+Ki\ntqOCAPIZCk/XlmvyBQUBDwJeCo3VxvauDUtKNeF+kiQQZGgsmD5OC3AqGfnIy0ex4pYSVJaNx77V\n31GOJwNeikCMEzF5bB7unVus7AgRQM1v92PBpne15NxOjuGLzqhlW6tKSpeMycNfTndi95GzWLDp\nXXzaETG17qaSJ9AjlclZ8rjaLkZNx4gJkmIklNBhtCJz9b/Vo68cw5rvTUVl2XjTsdRFUCqDoVQt\nvirhkDyWZEmJnlqh9CRJ8vVMPh+7luxcaU1x4SIbsH3GONH2WbV7vq2gErJ6qJtS6oaM30Miwgrw\n0SQinAi/h8LJ9jB+994phOPd7bBqnK357X58FeMQ4QT8dNtBbe6JsALGDmNMx5Jk4JGXj9rqqA/z\nebBhiVm2Z2TAo21YWsWWk+1hbc6zi5U+D2W6VvoNweS5LHm+6G3MdOFiqKG3MjC5in7XeuVEzdDm\n2sf2YMGmd3uU8srEMa3W904eM8jQOHUhYnjt1IWIY9dVkoAX/vNzsAmtDFaQ8MJ/fg7JwS7kbBAr\n/S25MFTiQH+iv+VIMg1eki2lRvgcWUflOiGa6/ePi6GDIU/opiLc9NCTb6tfbMaFCIffv/8ZTraH\nEfDSiHAiZFlGYcCLp5aVG7RiqmdfjYBHSf7DcUEjUfX4y+lOXDs6iJ/fNhWPvnJMSfC3HcCFCIfH\ndx3Dj7buRywxJisNwpEBDzbryOTV8yfjyWXlKCoMoPmX38MzSfpoapValDMGK7uxpbNDbLcYibCC\nSbdx074TvT6G1W+lEuHqsVQjOXURZLtAYsVet/j2Rd8qpRle0vk0vHnCQuPS1ZZ04SIVbJ8xhsqI\nFp1dDInzio7s7947hS8vxfH79z/DxQiH+54/qCV/i2ZOwKcdXSZN3A1LSkEShNY90D33NGPV/Cmm\nY6mk8tnLMWsdXlYw6JQ9tawcw3w08n0erQslec7YsKQUv3m7VZvz7BauVslk8oag6donYrmrCejC\nRc8YKrrS/Z0c+2kSW2pm4Z01N+PTf70d76y5GVsSBsJOISvEIyfi57d9w0A8/vy2byDuEBGoViHr\nj7do5gRHq5CzQazou3P0nYBOEddDJQ70J/w0Zamj7adz45oGvJTls5YrVdu5TogGvJSpWGLDktKc\nuf4uhg5cDd2Elmyy9qCqNQsoSWmEExDwKtIHeQyFlw+eMbVVNVaHMCqoVFdZ6QOGWQG/e+8U7p1b\njAthFhMLAmg9H8Zv3m5FRxeLp2vLLbVaVBkBvZaSlQahely/h8TFSGpdR+38k9qV963+jqUWr6p5\na9WirB+LlQ5uQcCDmCAh4KUQ50WIkqxdy9+83YrdR86mpRNl91u1rF2Imt/uR2NVCK8dP4fXj7cn\naeim345tOmaSxIPV75Nq3Kk0sQJeynQ+i0LjsXbRDAQYV1tyCGNAapENVNg9Y+sXz8DN9e8YXkv1\nrNrputpJOvg9FJZvO4C6ymmo2/2x9m/yOJ6sKcf927slbDZVhXDo804smD7Odu5JlqPxeyj87v1T\nqL6uCKyYpK1eHUI+Q4OXZAQZGpGEhAKV1PqqP7+T7d2xVz1uy9rb0BnhTRq6FElaxiN1XkwVE3Nc\nE9DF4MOAja3Z0JXu72P2u4Yup+i86n0nfn1HGUYEPJp2eKaRDc3F/j5mNs4xG1qckizj+Q9P4weh\nqzQN5j82f4llc66x9N/IyDFzNw4MyNjaFefx/skOzLl2lPYbfvjpBcydPDonNFBzXcM11zV0Vd7G\npIl+Y7G7hnXRX3A1dNOBWn2VrD2o6qtaJ/Mh/P2sCfj5/zXqwjzY1Kwlqlb6gAEvhVMXIuhieTz6\nyjGDwU4+Q9vu7CfLCABmDcJk4vHBJN1YVVM2OQAl60nGeRGbq0NJJjghiJKE+7cfMpGigJkwbawO\nYcW8EsR4SVsY5FGkQgCwouG9G5aUomR0UKti7stvFeNErQ3l3rnFWFI+0aBjGWQoxZG+lyRp8m/f\nsnahbTWaJMuW322nt6xvg9afT/sVFiCgtTe7cOEiNayfsRDWvfqJ4X2pugCUZ501xb3CIGOruavq\nyKpVqnbVqnk+2qR5+9SycnRcYS3jWftXcdQvLcVwv1eJWawAv4fE3TdMwn3PH9T0zrsNNGn4vDR8\nie/QL/INiRqvjDvKiibiWYmjkmkuiLACVlrEfZJUTNyCXto2vgGuJqALF+miv3Wle6s9ngnEeEnr\nJFCT412HzySS48wn95IEPPTSEUP8feilI0qBgkPwe5SKwGTNS7+DVZb9XRWcjSrkGCda3ztzJyHP\nIS1OjhexcPo4PKCbAxurQuB4ET6HNgSGQhzoTwQZGv/Q1Gy5cZ4LyHXJgqjNc3vP3Ek5oaEb8FCo\nnn217RrXhYuBgoH/NDmMnlpcrCUZmnHVSH+vE9UoJ2LV/ClY/eIRUwsPRZK2rb3JMgLJSG5rDXit\nJwC7sen1JANeGoUJk58T6xZi610VCDI07t9+yFKWwur6PNjUjBgvmbRnI5xgKZlwz9xJaS0e7NqG\nH/vDMSzY9C42v6WYFQQ8FDqjvHY9fvzsAUWuQkaPepmG3yvp3FrPhy1/n7aLUdt2Yj0ZpF5P9Vzd\n9ioXLr4+rJ6xIEMrmyM6pNKii3KipfyB+n4rzV01XqtxoSvOp4zfKtTF+Lo9nxhkcuYUF2Lj0lLs\n/fhvECVg+baETMG2g+iM8sjz0Qa982sf24P5DX+CTxcv9Hq1UU7AxQhrkjugSZikXTYuLQVJGs9T\nkmFxTYxyRKnim3pdXU1AFy4GHtKVG8sk1ORY3z6czmZ+n4+XQv/cKcR40VLzMubgdVUNmfVQpcWc\nQDbaqGmSQNXsIsO9UzW7CLSDpCMvySZPkQd3NOeMfmk6yEYc6E9EWAEr55Vg76qbNNmrlfNKcqbl\nfzBIFuSyZERPa1wXLgYKcmOLx0H05HhuV2GkLqCSq5wirIAgQ9tWaxYVWpvaBBgKsiTjqWXlCDK0\nVta/uHwC6ve2mMg+Q0WujigFoBEMdlXH6VwTQ/WvLBvGXFk2HituKdECck8ainFBqfQqCDK2O43p\ntC8l/1ZtF6Oof6NFaxvWEwXJbu12FcqpkPzb/+btVmxYUmpoHdm4tBRPvN5iWAglH8dux72ne8+F\nCxfpQd0giXKJalRWxJM1s0xdBX6aRJgVTM9bXxJ/P02isTqEHfvbsGFJKfIY2hQf1L/roZpV7jl2\nDr/4/jextbYCfi+FGCfi8V2KI3uyK/DKpsN4ura8V90kyfI5WnyqrUD93hZDxUT93haT83u61bVW\n3SLqNSYJWHR8uJtWLlxkG9monu/vNY/dOj3KishzyCU+yNDY/FYrGvad1F6jSQI/++5kR44HACSh\nbNLppXg2Li2FU0tJD0lgS80sXI7ymFgQwBedUYwIeOBxcO0qSMCO/W2GeWvHfqUrzylkozqyvyUX\nBnsXjZ+mUHv9Nbgc5QEADE2i9vprckpDN3ldmUsartmorM80+rtq3oWLviArdyZBEKcBdAEQAQiy\nLDvX/5QGUj2sdm3+JAE0VocMOrWNVSG8f7IDpRNGYLjfCxBAOM4j4KW1FlXV8Tz5++KciDAnGL+v\nOoRhDI2GO0MpNR2TpQCsiMfN1SGQhLJY6It27Mp5JWjYdxKVZeOx5ntTte/et/o7tiRDwEPhYoRD\nhBXw6CvHUFc5zWZxLQAEkdbCRSVu4ryIIEOho4sFTRJGooBITTKnc85RTgRJwDDe3UfOomR0EE/X\nKqR7lFUIGJVQTvc4yefjThQuXHw92EnjPHN3BXwJotdHkbgY5UwxtjDgRYyXbBN/LbFiqISZIsBQ\nJDqjPHbsb8OC6eMwfoQPMV5ZuG6pmYV8nwet58PYc/QcFs2cgA9PdRrGFfTSaFl7G1hBgiDKqPnt\nftRVTkP7FTal0VgqeQN9pQ0ATCyw3zxUnd9VzCkuBMeLBh1eAkhJIKf7OzxZM6tPkjcuXLhwDj3J\njTmF/lzzKDHTLCHmJBmiVtRZFXs4pXnp81DIZ2isXzxDI1jzGdrQvZFpcKJkkI5rrA7B56DZXICh\n+p0o7+/fUpJkdMV5XEoQ5Re6WIwMeDRzUyeQrTjQX+AkCeFEDqrf7PDSJOgcaFKOcSI+PnsZW2pm\nGTSAC4Kjc4IQJQkCP6yYaNIxd0qD2glkQ9fahYveIiumaAlCt0KW5QvpvD+bxj2p9IUAGEjP1vNd\nmDxmGGK8YFpAFgS8iAkSfDSJziRSYePSUsgyDJq8gL15jGo0MzqfwYpbSjB5bB7aLkbR8OYJjVzc\n8qOZmDt5NIIMjbaLUWzadwLtV1iTNpKduZpJF7cqhB0fKeSFXn+xsmw8fn7bVJNJT2GQQZQXsfy5\nA9j+k9mY+vhruH3GOAMZrJ47Q5Fo+qgN1bOv7tXYxg5jsGr+FBQVBjTiRTWf660Rjx0h5KVIS+3g\nvh7HhYs0MCDNJQYyenoW1arR+543m0s8tawcNEmgM8qZKpxGBb3oYo3xfONSpRr3w08voHh0Pq4d\nHUSYFZDP0LgY5VAYZDD18W6zM7WjQY3TQS+Fpo/acNf118BDkeBFCV2sgFcOnkH1dUXw0CQeSEjc\n6Mf5dG05PCQBQYKmrUuShEZY+z0kpj7+unbcvatusjRp21pbgRhv1DLfWluOKCcadB+31MwCL0qm\n6tpU7WZuTHQxwOHG1gQGu3YmkFhDcgIESdbIEJoktCILJyBKEi5HeXTFhW5y1UdjRMDjmAmQIEgI\nJwzg9BWzeV4atAMkazaMmsJxAcu3WcwttRWOVVuznICv4oJJD3m4jwbjgIZulBPQGTGvQwqCXsdM\n/DIYBwZkbM3GfZNJxDkBVyzuwWE+2jEd50yC5QTEBMkUm/w06cgzlGkMhXnSxYBHWjeaS+imgXR2\nZ0RJwoUwhxgnGtpcgW7S4Nk/f4ZFMydg1+EzWDB9nIGI/bc7QwYSAOgWbk/eyZJkGatfbMbqW6dq\n31UyJg9hVsCuw2fwVZRH1XVFiNqMRU2uRVFChBMNEg/Vs6/W3NutFmtBhjY5sy8KjcfaRTO6K9gI\npWIABDDlF6/h1ZU3asSCSm6UjMlDV5zHL//4MTq6WM0hXk/AWAXRIEPhx8+mJgz6EoBtiYjaCoCA\n5W/vBnoXDmFALowHMiRZNsUlffwMswL8HsoyxrasXQhWEMELklYZ80VnFCMTCbhVLFy/eAZGBrx4\n7gMlpj/y8lGMHcbgkdu+gbHDfYYNtsqy8Vh9q7LxdLJdibNLyidABiCIMh595RhG5zN45Lap8NIk\nmva3ad+pb7HbdfiMJsFTPCqIquuKjIv8hLyDWsGkbra9crB7joiwAoJeCgRBGOY0WZYtE/Rn7qqA\nBOv415ffwYWLLMONrToM9sqjKCuAlyTIMjRClyAAD0ki4NAGU5wTcIW17rZzioDpb4I1G3E+miCs\nkyv9RgQ8jpGd/X3/ZIt8zFAcGJCxVZJlPP/hafwgdJX2G/6x+Ussm3NNTqxJcp2QDsd5/O79z7Q1\nqMo1KJILzmz+ZBJukYKLAYC0AlW27kYZwD6CIEQAT8my/HSWxpEW0mkRi3IiVu1oxvafzLbVXFow\nfZymjdiw7yQ+/dfbMb/hTxAkGStuKelR60s/6dZVTsO2D06bEv/G6hDyvDR+nKiMtWvdlSTZ1H68\nYUkpmvZ/jp99d7LtOZxsN+vztl9hIUoyfrR1v4l8/fY1BQYJiD3HzqGji8WGJaWa/i1NElqbsdoK\nl9w+rNd/7ElOoS86bVY6UmOHMdrfrL5jMGvgDvZEz8XgQk9tgwEvZRm7VNOyyWPz4KMpUBQJggAK\n87wIJIhPq3gzsSAAAFpMH53PYPWtU7Fal2xuXFqKxbPGY2ZRAfISsXPvcUWCYWTAa4jR6hxQt1OZ\nH1o7IgbpBjVWfniqE3WV0wBAM2sBoJlRPrWsXJN36OhiMcLvQdXsIkOcVzedUumkq+fp81KAjG5t\nYk6EnyYREyTL2DDY2zdduBhMGOySTyQBsLxkqm5jHGxV5iUZDzaZY/PTteXwOXTM/tZ6zYasBEOT\n8JCEQVbCQxJgHJR5kGTguT+fxoLp45Dv86D9Cpsgo5zR7Q0wFMYOY7B31U0a+bXlnVZHTfyAwR0H\nOF7Ewunj8ICu07KxKgSOF3OiwjUbxo6ZRIChcepCxPDaqQsRxzbUMo2A1+aZzBENYxdDB9kSYJkr\ny3IIwEIAKwiCuCn5DQRB/JQgiAMEQRzo6Ojo/xH2EuqCqvV82NJRs/V82KSNqJqXAd26t3rncVX3\nVhQlrRpUdSzP93kMBLHmwNrUDEmGNhY7h/EoL2qLTvWzj7x8FAumj7N1zI2wAvYeP6eNc1FoPN5Z\nczNeWD4bUU7A6HwGt88Yh7rKaSgIeiFKMp6smYWOLhYNb7Zg/eIZOLFuIRruKDOZmaljVU3NbIX6\nEySx1TnpYeVKnwrJbuyVZeOxZsHUbqf5hEO8lORu29vj5AKS7zW7c3eRm8i12GoFVUJBkpV//TSJ\nzdUzk+KnTl+WEw2xS31PY3UIIwIexHlR0+e+GObw+/c/Q+v5iG0s/KIzaojpDy+YaorFrxw8g2+N\nH477nz9ocPfddfiMZYzWzw+7j5xFvs+DqY+/hgWb3tVi5V9Od6JkTJ6tzm6ejza48QKEKc5bOVin\nclLWx4LfvXcqZWwIeKiUv4MLF4MZgyG2DibwkqxtfGlr5B3N4B1cy2TDSCtV/HYCHpJAY3XINJc6\naYoW5USs3NGMm+vfwbWP7cHN9e9g5Y5m09o/k/B7SSz99kTU7f5Ym8OXfnsi/F5nUuc4J2LNgqmG\n461ZMBVxB88xV9DX2JqNGJBJ9PeznWnEORE/v+0bhnv657d9I2fu6Tgn4p8WftMw/n9a+M2cGX9y\nruTm8YMXWZFcMAyAIOoAhGVZrrd7Ty60rqll+QumjcXtM8aZKgIOft6J4tH5KfVnV84rwV03TEK+\nz6h7q+rl3POsUTcXgG3b04+27sfofMakWdtYHcKoIKPJIdi1H0dY0SQlMNLv0ao247yY0GHUVX5V\nhcCJMtbsPKL7XAjBhDmDWt3VGeUN3622E991/TWgSBIBRtEk/v37nxkMEOz0HzMhc9CTQ7x2/CHQ\nZuG2mGQdA7J1baDATuqkIOCxrRxVP9O0/3OTRM3rx9sNut+/e++UQUZhzYKpJk27PIbGtg+Uyp29\nx8/hZ9+dbIqndhq2dZXTMHlsnilGq7IzqjTNv/xgmlah+5u3W7H7yFnt8wAsv/vp2nIEdfqQ6bbG\niqKEixHOUqftHp3Eja0ub5LkjVvd72KAwo2tQwjZkAboivP4vUWL8T1zJzlWvdrfOpscJyBqoYsZ\noEl4ndJ6zdJv2Z9SFjneXj8gY2uuy0BlQ8Ilk8iG3nYmEY7zWG4x/q215QNeMsKVhRw0GJiSCwRB\nBAGQsix3Jf7/9wD8S3+PI9NQK5MirGBuhd3RrGno1i8t0wjPji4WeQyNhjvKMGaYD63nw6AI4Edb\n9xuCx4M7lHatscOUtl6VoN23+ju2Mg2qs69aGVtUGMCXl2IGQy+7lqk8hoaPpgxSAnoiduwwBnWV\n07Ayqa0skqTZq1SDNWNrbYVWwQoABQGPpscbSWhb/uSmYkRYASuT2mIAYPNbrVog8nuUnfEXls/W\nHOd99NcnDJLlEwD0KO0wWGFbHT0Ezt3FwIetHIuOVEzeeFCf73tvLEbAS5lMJFc2NWvPvr7zAVDa\nLtUYqsYchiJRPftqNO3/HHddPwltF6OmeGpXRatq2T61TImBnREWTy4rRx5DaTq4i2ZOMLQIblhS\nipLRQYOGbmNVyJDAb1hSit+//5nBXDJdCQSKIlEY9BrjMk0hLkjY/pPZGqlsd07JkjeDtX3ThQsX\nuYNsSAP4acqsb14Vgp92bv0kQYm1Ty0rR56PRjgugCYJSA4dj5VkW+NOr0PHjLIiVs4rMRHlelm6\nTKO/q61zvb1+ICIbMSDTCHgobKmZZTB2zBVko2MhkwjYjD8XJCPSyZVcDB5kQ3JhLID3CYI4AuAj\nAK/Ksvx6FsaRUaiEQVFhwLYV9ic3FSOPobB+8Qy0rF2I9YtngACw4fW/4trH9mDBpndtg0eQobFq\n/hRDW2/DmyewcalZpiHgpRBkaKxfPAO/viMEVpCwakczfv5/jyLGK0s8q9bYxuoQgl6FHE2WEogJ\nElY2Hda0IvN9HtM4JxZYn7t+MSJJMjqjyo7dlF+8hp9uO4hLUR6SBI0g1rfF3DN3ktY+XBDwoDPK\nd8sgbDuACJt+20NPrQf6c06WYACspR0GI4byubsY+OjrhoOeaJzf8CeNzNV/PsqJJtJy95GzmN/w\nJ8gyEONF+GhFW9fvofCz705Gvp/Gpn0nTHIOqVrlJEnGfQkphpVNzeiKK2YvO/a34Z65k0zyDY+8\nfBT3zJ2EgqAXDXeGcO+NxRoB27J2Ieoqp6H+jRY07DupSSpIkgySADYntcbaSSBQFIl8nwckQSDo\npXEppsRarfXze1Nx9nLMjQ0uXLjICfg9FBqrkqQBqkLwOygBExckyxbvuOAUvapsMIY5Afc9r6yr\n73v+IMKcAIZyJsXLBklDk0DVdUWG1ueq64rgoISureRStBd5R6+O5669Mw4vSVjGAG+OkKJeD4WX\nD50xvPbyoTPw5oiMVX8/Q5lGLo/fLc4aWuh3QleW5VOyLJcl/jdNluV1/T0Gp6CviNJDnZAlGbh/\n+yGDBtT92w9h1fwp2nuvxHhbEiCZLN595Czq97Zga22FRhAHEzquPlohdWt+ux/f3/weOrpYQyKv\nr0hVCdNRQQaUzQJQDQwrbinBIy8ftdTn/aIz2mPg0+8Y6TUd7XamgwmCNeChEOVFFAS9qKuchttn\njLPVg7SCKEq4EGFttR/1ZG9XnIePJk0aYZnQgswFPRtXB9PFQMbXTXpSEa0BD2X799bzYaxsOoy4\nICo6somNpbaLUbRfYVH/RgvqKqdpsZgXRRPJu3FpKWiSwP2J6iY1Bj688ygeuLkEDftOpkyWA97u\nTTaKIhFkaIPObmXZeNRVToPfQ+JChMWPnz2Ada9+oumXb62tSKvdyipO7zp8BiOD3pQEcS7ENxcu\nXAwNxHgRBz/vxJaaWTixbiG21MzCwc87EUtjzdhXZKPKMiZI2LG/TZt/6iqnYcf+NsQcIpGzoesp\nSLAkyh3kyUESMBXNbFxaCqe4QHftnXnwEvDl5SieWlaOE+sW4qll5fjychS8g/dNJhFhBbx+vB2h\nf3kTxY/uQehf3sTrx9tzRkO3v5+hTCOXx+9uEA0tuDXXfYSdTqA6ISdrlgQ8FEBYt/EXFQYwp7gQ\nfzndiQ8/vWBqpW2sCimEJmtun22/wkKQJPzji8ew59g5nFi3EIBZQsBKy7A3rbFqYFCr11QTN70+\nb9BLGSQlVL1JUscR2+0YWZ2bH2yxggAAIABJREFUSgaTpDKp6fV6NywpBQDsOXaux90mSZIR4UST\n87DaehDwUCadmQ1LSvHx2ctaC1smtCBzRc8mnXvHhYtsIWWMTefzXsoUuzYsKVXudV5EMCF98GBT\nM8YOY7Bq/hRNsmbsMCbRTXBY07r1UIQmcfP9ze9p3/fP/+8nKBkdxJaaWZoWbv3eFjTcGbKVYgCA\nLy/FbGNhcmupXlKhsmy8pR4vAOxqPqtp3abzHCfH6cqy8Vg0cwKWP3cAY4cxmgRFOC5oXR25Et9c\nuHAxNBDwUrj+2lHQbytdf+0oRyuUUq1lnZIG8HtITfddP6epEmWZRqo51ClkgygnCSU3Wr94hqYV\nrHTxOXQ8d+2dcfi9JAqDPtz3/EHjs+GQsV2mEfBS2FwVQoQTtXsw6KVyqsoy30cbJCNyQLpYg5cm\nLWOA18nWgAzh6+ZKLnILQ4rQzZRZS0+Jq9WEDCiC93YLPf37fRRp0DIMeChQFIkAQZgezg1LSrHt\ng9NY872pKBkdNGgjfl0tQ0mSERdESJKymHpqWTkuhFl8+5oCrV1ZT1b86tVPAHTrTbZdjCI/ocer\nwk7TkSRhEXhCECUJF8K8SZtXJS06uliTHmQyFJLGRgfHqxDlyToz6vff9/zBjOnN5JKejauD6WIg\nQo3hBcFuHe7exvIYL2HX4TOoq5ymafF9fPYyRgZHI+Cl0Xo+jFMdXXjmrgpEOONG0salSiLwl9Od\nGoG6ZudRA8nZ/lUcG17/Kzq6WPz8tqn45R8/1uLl6vmTEWEFtKxdaDA7UyuAAeAPh85ohLI+Flot\n4PULtkdumwpelLH9J7NB2GweppsEJJMSamdGMkG8fvEMkASDPIrMqfjmwoWLwQ9ekMAmJBD0BRKM\nIIFyiNAhSaWiK9lIk3RSGoATDfFZXcMqxkOZP3CMk/Dx2csGkubDTy+gIDgGeQ4cD8iOFqrXQ+HF\nA1/gB6GrQBDAiIAHf2z+EsvmXOPI8VxkHv39bGQaLC+Z1rYkSYDlJQSYgT9+koASg5NM3ZgcGDug\ndHl8+OkFzLl2lBYDPvz0AuZOHo18hyRtMgV3g2hoYchkWZmsHuopcbUiw8KsgGf//JlpV7sxkair\nY1ClBVSiQnUsTyYyVOKh/o0W7D5yFh+e6sRTy8oztvMiSYr0QBcrGBamjdUhPFkzC/dvP4Q9x86h\nZHRQ07VS30NTBOKcIo8Q9BqDh92OkY+mTEZsJAH8+NkD2P6T2bYVbensNgW8FE62hy0Xg20Xo7a6\nx2o1cqZ2Ql09Gxcu+o7exvBUXRTVs6/WvmflvBJUXVeEn24zVnDEBclk/PjwTiUR+PY1BbYk59O1\n5Wi4M4QoK0CUZHR0saBJwvY4erOzOcWFqJ5dhIJAeoswdcH2zN0ViLAC1uxsTm2Y2cPml3rdREky\nkBJ2ZmgTCwJatYUb31y4cDGQwEuypUnx07Xl8Dl0TB9NIT+poiu5sCHT6G9NW5oEyq8uMBh3NlaF\nHNWzzUalYpQT8frxdvyv3f+lvTanuBBLyic6sknpdrlkHrluygUopOKjrxwzFhbkSJUlL8mm7tgH\nm5yNwZmE30NZxrpcuf5ucdbQwZD5dTNZPdSXxDXgpbD5rVa0dkRQv7QUw/1eBBgKUZ0OjjKZs4aK\nsM3VIRQEvOiM8tok37J2IaY+/hoEnT6harxGZqiXIcqLuBQ1V8Y+2NSMrbUV3WQDKwKQDYtXhiLx\nT68cQ0cXq1xf3S5WTztG+sAjyTL+crpT0+s1kxNCenqQnIi9x89ZtojVv9GC1bdOsfx+9bjpkCBp\nXdM0HedduHBhRm9iuHViFEKQoeHzUPB7KDxzdwV8Cc3cn247aKrgeGG59UZSkKGxuXomCoJe+0RB\nBkAQyPNRhm4Lq+M8WVOOMMvj13eElCQ1oY+rxs2eYgNJEpBkGMhn1TBTvxmXbqtVlBdx//ZDGJ3P\naFXMXXHeMnZ90RnFqDxGk6Vx45sLFy4GCrJB5mjmuiQBggAK87yOV0X1t8xDNohylpfAS7KB2Pr1\nHWWOVir2d8uy2+WSeWSjsjuTkGTg4Z1HTYUFW2srsjyy9JDrhHqMF7HjozZDR9+OjxTz4oFeoeti\naCE3nqgMIJPVQ31ZPOnFqUUJWL7tgGkHNsqJpoqwlYmdLP0kb09wZi5xDngpTCywrlwNMJRGHOf5\naEiSjFF5BGRZxoiAB/k+D1bcUoIt77RaXl91wasZ5hBKBXPyole9ZlZ6vWplc4QTEPBSiPGS5aJZ\nkmRABn723ckKwVxbDn9SdTNJAE/WzMKlKG/Y+W/6qC2jizdXz8aFi76jNzHcOjFqRsMdZfgiHkPJ\nmDxEWAEyJdsuOG3jPCeiIOBBlLNOFOIJA8wAQ+HLSzH84dAZVM++GoV51gRwno9G+do3Dd0KQM8S\nQfq/QwbGDmMAKHq3K24pwbjh/kQ3h318THWdBUnWpCIWhcabZCAaq0JgPKTWSuzGNxcuXAwkZIPM\nkSTZUIDRH1WWNAlL7w2nKmazQdJIsoyHXjpimNMfeukIttaWO3ZMkiQw0u8xbMr6aefIebfLJfOg\nCMJSAoXKESHXAEPhvu9M0vxcwnEBh9qc1Y7OJHKdUA8yNDa/1YqGfSe112iSwM++OzmLo3Lhwowh\nQ+hmsnqoJ40sq0RcTXYFUcLqpEWJZs5lI/qfvHiyIjgznThHOREXuti0rhlJEiBJ4G9X4qZrEudF\nBLzm6rk4L5r0KZMXvXqC4O2Wdjy5rBz5DI2uuIDnPvgMm99q1Sptdx0+g6rZRSgMejXyAoCpSq+x\nOoTfvf+ZITgXjwqCFSXDzv/m6hB+clMxfBlcvLl6Ni5c9B1xXsS+1d/BxIKApj9rp6FtlxiNHe7D\n6pf2G+JBHkNbxrmvYpxFnA1psji/f98sobOlZhYux3g89FK3MWT90jI07f8c98ydZNtpcGLdQkM8\nsOvWKAwytgZkG5eWIjRxBOZ9Y6xpbuiJTNDPWRFWwF9/dRs+7YhoGr/tV1gMZ2g8tUxJbNUqharZ\nRcjzdmu2u/HNhQsXAwUekrAkOj1OVsvyIg6cvmjSl71xyhjHqix5SbatInOiYjbKilg5rwQLpo/T\njrf3+DlHjd8Cdj4YDpLIgiChM8qZ7p+CgBe0A2y52+WSefi8FOr/0GJ4NlRz2lwAz4v41rjhBlO3\nxqoQeF4E4x349wRjE4OZHFkXRjkR/14dwpxrRxniuftMuhhoIGRZ7vldWUZFRYV84MCBr/UdmdQm\nkmQZq19sxgM3l2gTxJZ3WpUJQjaTiOpxZFkGQRKY8gujXAJNEkpCz4pYvu2AYTJXNRn1rbqAYq5z\nz9xJBlMgAF/L9M1ARCekFC7H+KS23W5SQf2M3jjty0sxbNyrVL7OKS7E1toKwwJPJSokGYgltLD0\n5Exya5E6JshKVXOye7t6jdTX1X83V8+E30NZXs+nlpUbJkf1v5Pf57Y5ucgS+m2lk4nY2h+wIjg3\nLi1FPkMj3+cx6Iz7PSQirGj5TK9fPAM3179jeG1rbQViiYpePdG7Y38bWjsiWHGLEue/6IxizDAG\nAS8NUZLQej6iyRHkMTTCrIAYJxo27CrLxmP1rVNQVBjQ5HUuhDmtE2BkoqMhOU6H44Jl7FLjaZgV\nsPw589+31MzCA9sP9SqWWc2N6ibZopkTsOuwUmGsxtPR+YzlNXHhIgfgxtYhBEmSEeUECJKsJeM0\nSSCQ8KZwAoIooTNiQQIGvaAdatFNlZNkSoZND5YT8FVcMJ3jcB/tGMnUFefx/skOE7Eyd/Joxyr9\nuuK8KfdSczInjpnjGroDMraG4zx+9/5nps2He+dOQl4OVIhm477PJHJ9/P29qePChQXSiq1DJgvL\nZPVQlBPRfoXFgk3vaq/NKS5ElBMBwFYDCUDKqleryt/N1SFQBKEZkamvV8++WjFMI7rlC77OQsBO\nc3JU0IuttRUJvV9Ra/0JswL8HhJdcQHhJOO0+qVlAIA9x86Z2kKivIim/W2onl1kqIjdsKQUDW+2\nmFqLNHmGhJ6unTmP+nrJmDztmttpYOb5aON94LY5uXAxoKFIKJgNyrbWVpgqVusqp1lqZm+uDmHd\nq58YvleVkAl4jYaMfprUjNO+v/k9gxyCeiy9EeSGJaUYP8KHfJ9HiyWVZeOx5ntTjVIxVSHsbv5S\n6y7YXG2sEtE21Wy6NdR4ahezhvk9vY5lVvIUj7x8FHWV0zQ36KCXBghF1mH1rVNN1zWTnQwuXLhw\nkQmQCfI2yitrc5oiHe8aiPGirb6sU5qLcU7EmgVTzR1ynOhIBSuXQkOXyfjRFPhpG3OiQWQ253a5\nZB40SaDquiILOZLcuKa5bsoV8FKYNn6EYfwblpTmTH4dE1LEc5fQdTGAMKTuRs2sgOg2LegL/DSJ\nxuoQ5hQXgiYJzCku1CQPUpGDAS+FTftOYMOS0qTPKm28qjtuwx1lOLFuIdYvnoF1r36CHz93AJwo\n4Zm7K3Bi3UJsra1AkKE07VmVBFCTckGSNVJTXcjaQZJkhFkhod+b/PlmCDI0s7U8Hw1ZlhNkLoWu\nuIDLUV4TbFc/t2bnEay4pUTTFQ6zAiTtcyQWTB+nkTPqZx55+ShWzZ9ier+qs6u2Iqn6wXroX289\nH9auuaqBmfxetVVCvQ/0+sbJ79Nfo+Qxpbqe6bzXhQsX6cE2rjLdnQlq/CoZk4fNb7Wi/g2lza5l\n7UI88cNSBLw02q+whu/o3kwzzg0URWqJ1Yl1C7H1rgptcyzKi5prrz5+xXgRX3RGtViy4pYSPPKy\nMTY+uKMZC6aPM8RYNUZLkoyuOI8LXawpdlWWjce+1d8BoMT8ON/998qy8di76ia0rF2ICCtg5bwS\ny3Ps7bVVN8mCibkyyolYNX+K6Zz05+DChQsXAwmZWveni2zpyyavwx/eeRSSQx2YQYbGbdPHovmX\nt+LU+tvR/Mtbcdv0sY6eo55Y0c+nMcG5uUfV/9RD1f90Cv19vw52CBIs7xtByvbI0oN+g8hw3+fI\nmivKidh1+Iy2Fq+rnIZdh8+kXJMOJOS6qZuLoQP3juwlVMODHfu79aoirICgV9lFDdsIgKvBq/0K\nqxENastqUDdpq+23P9q63/AdK5uasfWuCsiSjCgvGMxpnqyZBYok8MLy2YiyoqL9+HoLSAKArCz2\nknd6VamECKvo2G7/iXU1q34XTZJkXIxyOHi6U2ufSK5IU1txY5yIJ2tmQZQkQ2VxY3UIk5OqbNXP\nFRUGEI4LePb9z3QVbEqVsaqn27T/c1PlndoevGFJKerfaNGuOUkAm6tDJp3eZK1hOzMfP00iHBcQ\nYChc6GKxad8JtF9hbSufc7xdyoWLAYuetOUCXgpjhzHYu+omEASwb/V30PDmCa2LYvX8ybj7hkl4\nYflstF2MGp5lO+1xNbECYJArsCNAg4lOiV/fUYaHXjqSsptAxdhhjBajI4nW4N3NX+LvZ03Qxvqn\nE+fx/dJxCMdFyLLS5TEy4MGWmlnY9sFpLJo5wVQFDMAQQwMeytZkze7aqptk2jX2UCgqtDHKzJFq\nCxcuXAwt9GQumWlkwwSov/VlOV7ED0JX4XKUR77Pg8tRHj8IXQWOF+FzSHIhyNDaHK+XlXCSWPHT\nlKX+p5NVwf19vw52BBgKt00fa9C0/mPzlzljKpbrhGLAS6H6uiJEEhwIQ5Oovq4oZ9aMuW7q5mLo\nIDciwgCCvhKsYd9JTSMxzxdQKlBp0pYcZEVJIxi3vNOKVfNVbUURkiQnzMUI2wAe8FIIxwWtOgwA\nRucz6EqSPNi4tBS/+sE0CJKMC2EWE70BjQRQA1BXnIcMaJWyavJuFbSCCb2xKC/i4OlOrf2jrnIa\nGJrEt68pwOh8xtxenNChNLQqNCmtCuqxrNqSNywpRWtHBLuPnNXkKvIYGoVBL+69sRh+D2lwnQ14\nKdx9wyQ8++fPsOfYOcwpLsSGJaX47XuncNf113RLRtgsjpLbnCKJCuSLUc5AnKuEsX5MdveGer52\n73XhwkU3ekpi7DZdVDI2zlu3nJKEYnpYdV2RQTd7c3UIQS8Nn6dbQiadBEqSFOLVKla2XYxiTD6D\nYT4C6xfPQCwFUQokJBkWTMXybQe0cf36jjJUX1eElboE8rd3VeBSlDNI1Kj6wXffMMmgFaxWb2yp\nmYWffXcyoqyiKazfvEvebLK6tuommbaxlbg+dotb1yDChQsXAw1q18OlKI+JBcZ1sFMkmYckFHPM\nxDG/6IxiRMDjrBGbXVxmBUd0QiVZmTOT5yQv5c34sVT0t6wEAMRFCQc/77Q2uHOg3dotCsk8OF7E\nwunjTJIFTm4+ZBK5TiiyvGQyHd+4tBQsLyHADPwmcYogTFKYG5eWgnJAm9yFi6+DIWOK1lvYEQyS\nLGumZlZk5ObqmSgIeBATJIMeY2eUx8qmwxg7jMGjt38TJAHL5Fqt8rUyvHm6thwBL42pj3ebqu1d\ndZOlSdjW2nJEOdGws7xxaSkKgsqCqzPCYdxwv/ZddsTqrsNncNf114AiSQQYhVBWCYTKsvH4n9//\nJlhRgiDKePSVY5ZmZXqtYZok8Ndf3aaIjDc1pzQ5W7DpXc0wridjB5VoCXppXEmYFH3aEVHE728s\nTotsSNbhtBrXEz8sxS3171iOSX9v6M83nfG7cKHDgDSXcArpJjGpSN9UJmIyZEtjE0V/Fwaic+W8\nEtx9wyTk+WjLroaLEQ5N+z83VcVuqgqhMOhFnFfi/pTHX8PtM8ZZauju+KgNm99qxb7V37GMmcnG\nbcfrFtiem99LGeYDQIk5LWsXoua3+/HM3RWIsAqZa3Us9RpIkowAQyPKiiAJgPGQiPGSYe5Sr0+y\nHp2bcLrIIQyp2DrUEeUEdEY4UzJeEPQ6ZuQoiJK2vtUXNxQEnDNFi7ICLsd4PPTSEcPm4Ai/xxGy\nsyfTTifQ3wZlQP+v6e1yvxwpChmQsTUb900mIUoSvrwUN+XmV430gSIHPiGajViRSfS34aQLFxZw\nTdH6ilQEQ5TvrrrSayQC1hWZqvu5vnLzgZtLDGSh9rlaxTgtwFDYWluBCCdg/Z5PuluDvRRaz4ex\ncl6J5thJELBttVq+zVi5pZoIAcDDO4/iiR+Wauey+8hZAMD6xTNQVBjAl5diIAngf9xSknDsVXY3\nT6xbqB1P/cwjt021bcXVtxcDys7ipx0RlIwJahWxqT6n6vCCUFo34rwISYKp4lYxv6Bw5lLMNPH5\nPaknPT1RFGEFjM5nbNulrxrpx8p5JYiyomky6qkt3IULF2akW9luJ4EAoEcTMbu/dXSxWJXQJ6ss\nG49FMycYKnkbqxWilhUkiJKsjbO1I9ItucMZZWKeSnQgqPGxrnIarh0d1OJA7fXXYMW8EhAEYTmu\niQWBtM/tZLt1Z0Xr+TD+croTkqSYdNpJ6vi9FM59FUsy4pwJn4eynLsa9p0EAK1Dwm0JdeHCxUCF\nJEHTlgXM62AnENNprKvHVDvTnDJFYzwkPKzSGaJWBXtIAkwPa9++oqf51glko/U8yomGfKv1fBh7\nj59zbE3vGjRnHrkuWaDXoFXvwV2Hz+CeuZOQ7xv4hG6AoSylUnJF8iLCCmi/whoK0+YUF+ZMhbSL\noYOBHw2ygFQGY2p76pziQlvSL3nyTZ6kbT/HUFi+7QCm/OI1LN92ALwo4Z8rp+GZuxVDnhgv4VRH\nF6quK0Ld7o8x9fHX0Hax24RHhUqC2h1DXYyRBLBxabdBW0cXC5oicDHMQpaBNTuPov1KHFFOxPaf\nzMarK280mfXsPnIWa3YeRThubR4QZgWDAdyGJaXYe/wcYryU0pCs9XxYM4wTJQnLnzuA1S82ozPC\ndV+j5w7gYoQzGKclG/Y88vLRlOLrKnm//DnlOx995RjWfG8q/vZVzHJcbRejuOuGSbDaGNXfG8lm\neS5cuLBGJpIYuzjy5aUYumxiU9vFKEgioWMLGxOzpmb87as4OiMcAt7uxGD3kbNYsOldTH38NQS9\nNBr2ndQ+E/BQmvHlnmPnsPf4OXRGONz3/EFMefw1PLD9EDojnCbJkDyuLzqjhtfav4rbGrPsPX7O\nZLK5YUkpfvO2Qi6rsd7OTLIrbja11JtpWv02m99SdAtd0xYXLlwMZAwp4nFHM26ufwfXPrYHN9e/\ng5U7mh0zHsqGWVg2jumnSUO+Vbf7Y1RdVwS/Q+72PRk0u+g9snHfZBIBL4VFMycY7sFFMyfkDMmv\nSqXox79mwVTEc+SeVnW09Wtsp3W0XbjoC4YMoStJMsKsAElO/CvZS02kIhj0eqt2CXny5Js8Sdsl\n1+1fxU0utZeiPCRZqU4LeCjcUDLa4HjZ8OYJAymrJvRfxTjbSUwlCP5uuB/1e1sM7pP1e1tQmMdg\n1+EzqF9aCi9N4tFXjmmBOMoL2FIzyxjcqkMIeCg0VodM4zj8eSeerCk3uFtWz75aIzmtSNDG6hAm\njvTjheWz4fNQeO6D0/jw1EU8cHNJSvKhLwtpK/L+kZePIs/nwWaL89m07wTymG7tTT3098aJdQux\n9a4KtxXZhYsekIkkxiqObFxairf+2o44L1o+yw1vnsDKpmasmj8FgP1G23C/Fw/vPGobt1VNXBWf\ndkS0iooT6xai9vprTC7FK5uacSXGY1PSQrF+aRmCXsrwGk0SaLizzPS+1vNduOuGSRg/wocna8px\nYt1CrF88Aw1vtqCji8XGpaVaMvObt1stid+8FHrtmfptXLhw4SIbGCrEY3+TyB6SMK33G6tDjuoE\nB7yU5RzmJLEVEyTT3P3gjmbEBMmR4wU8FLbWlqP5l7fi1Prb0fzLW7G1ttwtCvkayMZ9k0nEOElb\nT+rz6BjnzD2YaYiybMrbH955FGIOyH0CQEwQseOjNsP13/FRG2KCuwZ2MbCQGz0HXxO9FZrvqXVe\nbf2VJDmlUY+KZNOZvcfPobE6ZNDY2ri0FDRJoLJsvNaqq7bfqjItJEkgz2dcuO0+chYkAc34q+1i\nFPVvtAAANiwpNZmUBbwUrsR4bK4O4YvOqGUrQZQVsGjmBPCijDU7rVvHVJOAcFzAs39W2o1XzivB\nU8vKFf1JVsRXMQ6vHD6LVw6fxYpbSjB5bB6uGllsaNFNNiSLsiJEScKPn+s2ClJN0nqqiO7pd7PS\n4LQj7/MYGjGyu4Wt9XwY9W8oRMkXnVGMymcsW65StYW7cOHCjJ4Mz9KBVRyhSeD2GeOwsqkZY4cx\nBjmZjXtbsPvIWdAkgaLCAOYUF9oaQ6pVXh9+esHseJ0wftRj7/FzqJpdhAcTx224M2QZY8YM80GW\nZWypmYV8nwet58PY8PpfAXRL35xsD+NXr34CQJFumDw2Dyfbw3jzv/6Ged8Yi/uT5CFGBj1ouDOk\nxV9/olr4kZePouHNFu1727+KY/1rf8WKW0pSxsxM/DYuXLhwkQ34PZQ5ZleF4HcwfqkEUrL0l5ME\nUjZM0RiaNJiFEYTyulPIRut5f0sgyLIMQZJxOcoj3+fB5SiPEQEPFK8btzCkL8h1yQKSABaXT7A0\n/M0F5LrkRZChsfmtVk1uDFB0tH/23clZHJULF2bkxhP1NZGuRqOKdJNYE4lgoydo9T4SMJCFT7yu\nkIV1ldOw+8hZ1P33b2HRzKtAEMqOf8BDgaJIS9Ky/QqrzPWyEnw6ulhFh3Z0UCNY9cTrt68pwNba\nCkgysLk6ZDBna6xWhL4fefmore5ikKEhy8CVGI8Hth8y6Ct+eKoT6xfPwPyGP2kL2fo3WlC3+2Pb\n660nyGXIuF/3nWrFbF3lNFvCJR3ywY7U93soWw1KlZip+e1+k7u8S2i4cJEZpBtH0/kebTPFp+q/\ndm9I7Wo+qxkuqptmqiyDqnObvNG2YUkpvrykyK/MuXaUtlOvJgYHT3ei6roifHiqU/tM1XVFGM7Q\n2iabnUtxV5yHKEngBBnD/B4wNAmSUOK530vhf//HSW0RWVk2HoyuzfO73xyLn//fo6bNtqeWleN/\n/8dJLc43Vofw8dnLmoFa6/kwtn1wGvO+MRYdXSy2vNNqcvDVz3WZ+m1cuHDhor8R40VTzN7xUZtC\n5jhlUMaJ+PjsZQPZ+eGnFzAyONoxAokkCPz6jjKTKZpTpj0+L4XHXjyGB24uQb7Pg/YrrGYU5BQo\ngrAktpx0m7ebu53Sz2QFCeGEgan+HL0UiYBD9+tgR8BLYUn5RKzZ2f1s1C8ty5kKXQDweUiDPrbP\nIW1sJ9Dfz1CmkevjT2Um7WJwYUgQur3dZSVJAgUBj2b8ohKqVg9BuhWZye+TZBnzG/5kck8tGZOH\nf678FhZOH4cHth8yVBUUBr0G0nLsMAar5k9BUWEAUVZAXBBREPSYDGuinIj7nu82SBudzyDCCSAJ\noGl/92I3wgr4tKMLZRNHGnQXkwNZ28Uo5jf8CS1rF1pe14kFAYN8wfrFMxDsgQRVCdeCoNfWJO2h\nl5r7TD4km/uopP4zd1WYSG2VhN5z7BxWzCvRiJkoK4IkAR/dc0B0g6gLF+nDicr2VIaLNEloz7q+\nWrdl7W1a/FA3wU5diGDj0lKMG+7H9y126lvW3oYnl5Ujj6E1wuCHFRMR9FKI88o4rGLMtg9Oo+q6\nIuw88IVGwG6uVhLiV4+eQ/V1RagMXYWJBQGEWQHP6TbkXlhuv9m2YPq4xOZaN8l7Iczi0VeOafHv\nUNtlrVo3zovdMc4iVrldBy5cuMhFBBkapy5EDK+duhBxtDrMT1Mov7rAtH53UnOxv03RojZGQU5V\nBAMKiVz/hxYDOV+/t8VREjngpbC5KoQIJ2rXNeilHCMDJbn/TfwGO1heQp6PMlWTs7yEADPwiVGf\nl8Jjf1A2TwCF9N/0/51w9L7PJBiSsOySYHIkH1Y1dE1dHjmgodvb7nQXuY0hkZ311IqfDEmS0Rnl\nHX0I7MckYPGsCfjptoPG6qsdCZdcnweFQS+eubsCkUQFmn5H/onX/4r2K6xhvHpio7JsPNZ8bypW\n7Wg2EMJfXorhD4fOYMGBep2sAAAgAElEQVT0cZrRmqq7qG8d27i0FE+83gJBkm0JX72m5F9Od6Ko\nMAAkdIBtr0eiirqucprtdWm4M9Rn8sHvIQ0Lwd+83Yo9x86B8SiTpb7tuf4NheCZU1yomLf5uqv+\n0oEbRF24yD5SxdgT6xZq8jT6al3VrBFQ4se9NxYj4KUQ50VEOZu2Vk7E/boNMwBapwIAzG/4E/78\nT/Pw1DJlo00fYz481Ym6ymkaAbuyqRlP1pQDAHhJNlTqqNIzu4+cRUcXi32rv6N1ePzm7VZ0dLFo\nPR9GyZg8bRx/Od2JPB8NmiIMpHJHF6uQGjIQ8HbHtXQIW3ezyoULF7mAOC/in38wDbIMEAkDzH/+\nwTTEedEQ9zJ6TEHUdFeB7vX71tpy5DloprVSd0xAIViVnCHzxyQJwlRcobSBOzcPRDkRxaOChteK\nRwVt87hMgOUl0zz86zvKHCMDs2HiNxQQYUVT9foI/8AncwGlQtRq8yRXKkRpD4WOjrChQK7tYgSj\n8odne2hpIS5Kll0e995Y7Fg8zxR6253uIrcxJH7R3uoA2j4EtRU9knrpJrtWY2qsCuH373+Gn313\nsm31VZgVEPBSECUZTfvbDGN86KUjeOKHpbjxibcND62e2NA7uQPGNuTNb7VixbzJeOilZi351+su\nRlkRj+86phEgVoTvr+8og/z/t3f3YVJUd77Av6ffp2fGF0YkIKIiQjboMDBElmi8xpggJg/rlRBn\ndhGMu2h8sosswfX6srvsXo2XwLJArtcXEleRBNQYDXcjQV01GjUqL8PbeoERCSgsIKMyMz3Tb3Xu\nH9VVU9VdNdM99Nvp/n6eh4eZnp7pc7qrflV16pzfT0p88KNr8V+f95gneJF4/xf+xqCz0998aPYk\nM3uUJvXnGhXWzfc8lnSdRZtMajgRiWHxht22wZExQ2v1fEpth9F07hm49pLh5nOmjm7odxvp73Nm\nEKVKpdJgnlvcrw34bOlpjNm61v3dqZ8AHP5ek+tM4HOHhAEACU2aN3PG3rPRtirjvQMduGhYHTYt\nuMK8yVQX8uG6iefY0tkYqx0emj0Jy787AR3dsYxlmUGvB+vePQhcPNz8+9YblyGf15x93J06jjjF\n5f4+Y96sIiJVeABIiYycpIW8DA8HfRh2WhCbFlxhDgA89Fo7wgU89yt2nspSzJat8Xkw5yvn47NI\nHICew3fOV85HTQEHVZJS4odPb8+4znp0TnNBXq/YuZCrgebyGa4u0GeYb8WeJZ5v3dEE/uP9Y5h2\n8XCMObsOhz/rxX+8fwyjGmqVGJAOB7zK5tAtdg5wKq2qGF3KNQ+g604Q1JfhhgPOv5vNxa71Yrk2\n6LXlWfy333+I5S/vw5yvnO+as+WWNVscZ2wZbTznzBoA+kwESP1g5hMwlwy4FRYbc3adObv26Mko\nwgEfHrj+Eow8swZdqeq8SU3q+XpTNmw/jDFDa/tSPEQTSGgSt63dimGnBbFo2jgstNwVzaYQndEX\nI6dlNKEhEkvgtrVtjn9Hf8+jtpnKRp5b42DRHUvi9nVtGYMjD89uxt//ehdmTBiBq744zHYXrjua\nQO0gP2cGUapEKg3mGXHWKQWN0Va3Y0J//XQq4HiooycjXs+/agw6e+M4rcaPTQuuQG88iU+6Yo5x\nfd/RLizesNu8yXT4sx6cc2aNYwwxVhEs3rA7Y1nm8u9OQOuUUVj3zkHHQWqPRyDs96KzJ45PI3GE\nh4TxSWcUZ9UFAAiEg/pM5PSVH9bPmDeriEgVmoRLTtJAwV6zN5bEomnjMmav9saSBRvUjURdVqNE\nk1mvLMvt9YqfcqEU+WWLPVBe4/dm5PFf2VrYIn6VLuzyGRbyBks+FXuWeL7V+L24+fILkNCkuUri\n5ssvQEiRbTrXFd7lROW2U+7KPxrkibEU35jZ2e/y/9ROYGUUz5m3ZjNOdMegOZRztV7sGjlk56/b\nhkg8CaBvQGTeE5sx9p6N+MvHN6MnnjRni616pR0A8Py2j7GypQlTRzfA5xGYOroBK1ua8Pt9x21/\n+85nd+AHXxtja2MkmsSMCSNw77f/BJ90RSElEE1KbPmjvrS3x6VvhzoiWDKzEZt2HcHK1ib87I39\neG3PMXRHk6gP+bHvaBfe+uB4Rruubx6pvxcSgBBY89YBLJ4xHvddd4mZC8rpvUhnzKabOroBL+w8\ngv3HO9ERieF4Z9QsbOT0d/T33P7zO57ZgU8jcUTiSUTiSdeTsvqQDxu2HzZnLS9/eR+mrXgdF979\nAm59cgt6Eprz9jHA5+y2/URizn0nUsFA2325sMbZHz69HSe6YvoP0kK22zGhv35afwcC+P7arVj+\n0l4smdloxsWFV1+ElktH4ba1WzH2no1YvGE3euJJ/G7vMdvzpo5uwJKZjXjw1XYzns+97AIEfB50\n9SYcY4iRUsEpng07PYRwwIfvXX4B9t4/HavnTs64mdgdS6C+Rr/g/uHTbdjQ9jEisSTmrdGPScdO\n9h9vVb1ZpWkSXdEENJn6v5Dl2IkUUen7hTUnqfX8sJDdTErp+JpJWbgX9XngeM1QqMmrPo/Ayta0\n12ttgq+AN3ZL8VkaBZGsjMk1hdCT0LA+Vddkz33TsXjGeKx/56DrtQgNrNifYb5ZZ4kb2/0Pn95e\n0HiST4mkhoQm8Vkkbq6WSGgSiaQa27R1bMKIdf2t3i0nKredcleRQ/SnuixY3wmaMmZ9Br0eDK0P\nus5IGuhi1zpQMGPCCPzga2MwpDaA7lgCAsK8k7L4//4nAJhJ3LujCdT4vBg9tB4f/OhaWw5Ya4Gf\npbMa8XlPDPd860/QG9cy8i8ue3EPAGSkNFjV2oTagF6o7a+/fhEi0QROD/txzcXD8f219hnBfl9f\ntc32Y1348W/34HhnFKvnTkaN34PrJo7Enc/uwNq/ci7a018huobaAB6d04wanxeRRBK1AR/C/qQ+\n29jl7/S33NlI57XvqHOu365oAlNHN2DM2XXOS+Rc2jrQ55xrig8iFagymGfE2aH1QSz8xri0WJc5\nozjjeJHqpxGjjZhQ4/eYKW+szzNSKBiz+yOxREYO9Pnr2vDA9Zdg2Yv6MtWLhtVh39EuWw7f9w50\noC7oQ33IBy0pM3IUGjH8B18b4xjPTvbE+wampR6HjH45zbxdMrMRXg9s+R7PHRLu/xg2yDv+pUzV\nodLMcqJicVrdtKq1CQ21wYrZL0qRk7TYszoBPa2PY57Hyy8oyOtpEgj5PLZCUx6Bgg6uluKz9LsU\ndPIXaP9QeXl3uQoHvBnXvEtmNpbdeaubUsSTfNKkft6YPsM4UKBZ9fnm8QicWeO35QCuyaIwejnI\ndXU6qU2NiJCDfFy8eTwCtamUA+kDl4tnjMe3Vr3heDAY6GLXOlCw6Jv2gYaHZ0+yDSJv2n0U32oc\noc/eDfjwSXfUMQdsZ28ce+6bjkMdEXMwoCemYd6azRkpBhbPGG8ukTLy4h48EcFvdhzBty4Zrufo\nCfjwSVcM/33iSNz65JaMv7F6zmRcvfx3tjyQvlSage5owszP61Ywrb8Lf2NJ8IlIzLbkaOmsRmgS\ntuJFxt9xe88PdURwVr0+ELxp15GMA/rK1ibUBvScktG4yxI5l8IZA33ODKJUiVRZvmPE2d/M/6ot\nX7hTegCn48UjNzbjJ61NmHTeECxYb1/6uP6dg1j1Srv5POP92LD9sFlI8efznG9mjWoI43hnFN9a\n9QZeXvjfbGkTgL64FfR5EE1oqA148fCNzagL+tDZG8eatw6YMTA9ni2bNQFr3jqAlktHQUK/2I4l\nNfN48vLC/4a7frXTMZ5b2zpQ3B7MzapSD6gyTQRRpkisb3UT0HfjKZtaEarodslJWsiCQqV4zXBq\nhV8xBwK7ilxoqhTva7EHylU5x1JJJJbE89s+sn2Gz2/7CN+7/IKCFAzMt1Js9/mkeg7jZFJDRySW\ncVOnoTYArwKD0m6F4qnylP/WmKN8LQsOBby4evnvcOHdL2DaitexYfthW65Zp+XzA01vNw7W1sJk\nRhu/v3YraoM+rJ47GXvvn45H5zRjSK1fTxtgyQFrTbcw97ILEE1oEAI4qz6I00J+eD0e1zvZxmxe\nvcK53qarl/8O3/jSMEST+ozecfduxF2/2om6UN9dwRkTRmDTgiuw9q+mANDzQ1oZ74f1TqJR3CzX\nqf49CS2jr3c8swMLvzHW8e8Ys6mtr7N0ViPODPsR9nsR9nvROuU884C+577peOTGZjSE9WBcF/S5\nL+VyWRGSzTKGXFJ8EKlAleU7RpxNT00wY8IILJ4xHuGA11xe7HS8eLP9OC4bMxQL1tvj0O3r2jDt\n4uHm94+/+WFG7FkysxEff9rjuMTv6Oe9ZgzyewX+9YbMuFUX0otd+L0C6949iPqQDz98ug3d0QRa\np4wyU9I8v+0jPDy7GXvum44Hrr8EgMTyl/fh9vVtiCU0fBqJ21InuM68DXptbX3w1XYsneUet603\nq5zSOjh+HiVO1aHKzHKiYirFjMdiCwe8WH7DBFs8W37DhILu+14hMmLo0lmN8IrCnQMWe1m5JiV+\nufmQLTXALzcfglbAZeB+lzQPhZotC/QNlBup2KateB2rXilcgTtVzrFUUuPzomXKKCzesBvj7tVT\nYLVMGYUanxrvaTjgxbJZ9hi2bFZhY1g+qZ7DOBJPmqvYzGuB9W1ll2qOSI09Kgf5unjrb+bnw7Mn\nmQXHrLMvjYvdn900GZqmnzBHon07vXGwHlIbyFji/4f9n+g5HlPnQ73xJE72xPGF02sgBJxzwAZ9\naP6fL2Hv/dNtM866YwnsuW+6mZphw/bD+PL5Q2yzeWsDPhw8EcGXzx+C02sCGTN6jZ8NrQ9mzCZe\n2aJXsjVmqhknHNbCDMZMMmMmcLazVN0+v1ENYey9fzoi0aStKJ3+ngfN4nKRaBIeDxDy9RU4qvF7\nU6kkkogmkhkDrLle2HAGLlUjVbZ7I84e6oiY8chpVYQei/3mqok7rxmH02v0Yjk1DnFo2GlBnHNG\njZn25qHX2m0rOf7r8x5oEjjnjBo8cmMzHn/zQ+z/pBsLrh6LUQ1hfPxpjxmPAeC6phFYPacZ4aBP\nj1sC6I4l0JvQMPyMGtxw6ShEovrqgUXP7MCw0/Q4VxPwov1YF/7+17uwYfth+DwCe+6bDkCPWyPO\nqDG/NrjNvD36ea9tSenxzijqg76+eOrwGed6x7/UA6qc9USUqdiFtEqhJ6ah7eCnttQAb3/wCb56\n0dmoK9DsvFDAi2XP7bHNCFy2aQ+W39BUkNcDUkv1W5r0VXZDwqlzfG/BYmxNwGumV7OuGqwpYEwP\n+L3YuPmQ7bP8ddvHuHHq+QV7zUg0iflXjcG0i4ebn+WmXUcKto+oco6lkoSmIez32rYbn0cgoWnw\nKTCnLRrXEPQL8zzzUEcEQb9QpihaJJrET1qbMPXCs2wxWJXjjOopL6h6VNwWma+LN+elpU2oD/rQ\nGU1g/tqtrstHu6NJ1+WlDbUB9Dos8X9o9iR81hPPWML0w6fbsHjGeOecib1xW9+clrYaqRmubx6J\nxRt24+jJqJ4P2O/Bipf1Yj5OA5orXt6LVa1N6I4mM5Yt376+DY/OadYHSW0D2sBP/rwJXb19J5V1\nIT1/Y7bvvdtFRnc0gbDf63gA8HiE+bj1525LfUNpd2YHs81wGQNVo3Le7q15Wmv8XtQH+3KhW1dF\nAH3L7h+d04z5V41B66WjEE3qqWrW/tWUjAHQGRNGYNG0cZi3ZrMtLUs0qcEjBB58ZV/GBe5Dsych\nbkl7YMRjQE8fc/RkFIDA2Hs2ZqSw2XPfNRAC6OyNm6sHAOC2K8c4pmpoP9Zl+zro89jab8y8tR5z\nVrU2oTboQ9Drcb2AzMdnXOoBVeY0J8rk8SAjJiyd1QhP+Y8RZK3G70HzeUNwm+V8fWVLE2r8hetk\nJJrA0ZNRM70ZAEwd3YBINIG6Ai2RjsY1xDWZkaeyUIM+kVjmdcGdz+7Ao3OaC7aMPRJN4re7juIf\nN/yn+djU0Q2YOencgg0M+TxAy6WjMpZbF6rYHOVfXJP4t99/iGkXD0d9yI+jJ6PYtOsIvnf5BQiV\nunFZSEqJv/lFm+38aeroBjyqSMoCvweOMbiAITivVE95QdVDSAUqJU6ePFlu3rw5q+fmM1+fUyGX\nSDyJeU9szgiuRj6+rmii358DQFdvwjYjdsaEEfjnPxuP29Zuzfi9xTPG48KhtTj8WW/G3fBzzgxB\nCGHOWnVr26NzmpHQJPypdAwff9qDgM+DBevbMLQ+iP953cX4fipfrrUQUE+q704DDnvvn65XerdI\nJrWM/LcrW5vM9AbZiMQS6OiO2S4yVrY04bSQDyGHfLYD/a1jJ6NmHuQHX203C7hZBxJKneORKE3R\nNrpcYms5c9uHh4T96Elo/cYxADjZEzfj76YFV2DTriO2Adr0HLSAHlt//J1GLN20JyN+DxTTF2/Y\njVUtTQj6vagN+sz4BAALvzHWzG9+7pAwxt3b126nmcZLZzVi2aY9OHoyihUtTXjq3YOY+5XzbTl0\njTztRkqeYs78KYf4WsqibFRWGFtTNE2iszeOTyNx8wb8mWE/6kP+itk3sjkfz7dINIGTvQn87VN9\nsfdfb9DPYQu1zLirN455lkKcQKqfc5oLMoisSZn1dUG+OF0bLJ3ViCG1AcdaF/nQ2Ru3FTgF+q6p\nCjGYUw7HylNQlrE1qWn4r8+jWPRM32SpZbMm4AunB+FV4O5VKfa1fCr2PpRvyaSGE93q5tClipDV\njl5eU6zyIJ9LVpxmow20fHSgn2uatM2INS7Q60N+17y3xjKf9KTuM5rOwdXLf9d30K8LuC4N+Kij\nJ6OIzuo5zfikK4a6oA8P39iMt9qPY/yIM+zF2iyFfwxus6us+W8BmHknV8+djLosA1/I78WyTXvw\n4+804pwza8ylyJrU3zsAWV2Ya5pEdzRhm7GwZGYjlr+0J2MZGpc5EanNtfDVnMmQkOh1mSV68EQE\nVy//HfbcN92W/3vRN8fZCmm4pb0ZcUYNNmw/jH+9oSnrmH7RsDqsaGlCQkrMf3KL7STR6xH4619s\nswzC2uPvhu2HMWZoLR6aPQn1IT8Of9YDAWD5DU3Yd7QLZ9UFcPPlo1Hj96AnruHn86ZkpKEBiju7\nuhziaznPLCcqBY9HoD7kh9frMeswVNp5TynSvQR8HoT8HtsS6ZDfg0ABp3UWO09lKWathXxenFHj\nz1g6n77iLp+KvdyaBTzzLxJLYtEz9qJci57ZXtDZ5PkUcdnXCjnjP59UT1ng9XowJBzAo3OaURv0\noTuaQI3Py8FcKjsVuUUWsiCVsXzUysg71hVNoDfu8vNY0pwRcbJHz2W7acEVuGOaPtvKWOKb/nvG\nzC0jZYKR1P365pFY/tJeW5GZ/gojpBdhe3bLIUTiSbMQ2vef3ILLxgzNeN4Tb36IFS1NAybp1zSZ\nl5PnSCyJ0WfVwiME/mL1O2j65xfxl09sRkckhkgsgRPdUcx7YjPG3rMR857YjBPdMXOg12hHVzRh\nq+BsLSS34OqxjgXtilnEzGijJqVZnImIBs819gS9ONEVQ088iZ/8eWYRMiOGWuPvhu2HsezFPZjR\ndA4uGqbfQDv6ea9rfJ4xYQS6evtir5HewS2mR6JJ1Pi9WPjU9oxCC529CXv8fSsz/l7fPBL/8Ovd\nuPDuF7B00x7Ek3r8CPo8+hLbgBcdkTjmrUnFyTWb0R099QIOpxK3WCSSqPxU+n7per7ucA6YLz3x\nJG5buxVXLnsNF979Aq5c9hpuW7sVPQUsolPsomgBlwJlgQJvP8Z7O/aejQV/TwH9fZ1/1RhsWnAF\nPvjRtdi04ArMv2pMwd7XUuebr0S1QZ9Zs8b4DIedFlRmQNHjUmRRhdm5QPFjU74ZdYlOdMUgJXCi\nK4buGK/bqfxU5IDuYGR7sepUhXTprEbc+/xOzHtiM7qjCTw8e5LjAGhvIonOaAK3rd1qDsyec2YN\n3jvQgQdfbceSmfag/S/fnYCHXmu3FarZe/90rJ4zGcs27TGL6wD6Qb/G7804yVoysxE1/syThGkX\nDzdn0xqDB0530la90o6G2oBZzXb1nMyq5sYyIaOQmlWuJ89hvxc3XXaBeUfVaNsdz+xAQpMZg7TW\naulGO+Y9sdmxqJFRXC3s95ZsUNXaRrdBaSLKjduF+76jXbjrVzvRE09CSr1I4977p+PROc34wmkh\n/OBrYzBjwoiM+Hu8M4qAz4OFT7Vh8YbdetEZhwvYM8J+rGhpQm8iaVYiHnN2nWtMX9U6EeGAngvc\nKT6dOyRse8yIv9Z2Dz89hIXfGIt/mvEl/N0143DXr3Zi7D0bcdevdqaKqvXN8nGKk4PBuEVEqnE6\nXy90/uxSzEjzugz6eAs06JOUgEfox9M9903HA9dfAo/QHy8Up0ka89e1FXRwvsbvRculo2yTaVou\nHYWaAm0/pbgBUel6Y3rNGutnuGjaOPQq8p6GAvqqVeMafPGM8Vi2aQ9Cigzy1/i9WJk2KUHPY65G\n+3vj+riNMfntrl/tRGdq8h5ROVHjFlWB5ZK3KH356METEfz4t32Dq/PXtWH1nMmOy0s1DbYCN2/v\nP2EOghq/n55q4F++OwE9cc2+FE4gVVCnz/yrxqAjEsOWAx3mkqSu3gS2/rEDPfHM5cbGoIOVMaNs\naH3QzKN7qCOCoyd7MW3F62busfT3xFgmNLQ+iCUzGzMqyTvN5o3Ek6jxexCJJVEb9NneJ7fBjtNq\nnJcwhwNedEUTgIQ5kOFW1T0STcDjEfqdd0t+yWLlqeKSKqL8cyp8tWRmI5a9uMe8IbR6zmS8sPMI\n/vvEkWbe2k27jmDRN8dh2Yt78Py2j8xlVUacWH5DkxmbAPTF9WgSCU2zFXpY1dKEH3+nET2pizIj\nphtpGyKxhH6BLdyXrB7qiNjymB/qiCCW0OD3Chz5rNeWB+6RG5tx65Nb0mKJfvwZKO1Prvlk+0tp\nAYGKW6pNROorRboXY1bntIuH21KmFTQdQcCLZc/tsaVlW7ZpD5bf0FSQ19MkHAs1rZ4zuSCvB8Cx\neLOxCqdQeuJJ3L4+LY1cqih0fQGWXLOAZ/4lpcy47r7jmR3KFBWLRJMuRRaTBSsGmE+xhIZw0ItH\nbmxGXciHrt4EPB79cZ8CaQs0mTluY1xPEJWT8o8GRZDrIJuxTE2TElcv/50tWblxgmEsh7D+vtMJ\nyYqX95qV2D0CEAL4i9Xv9DvQ6HTQv+myC/D4mx/iuokjbYMMK1qasPWPHVjZ2mQrVuY0oLBp1xE8\nNHsSuqIJW+GBZbMmYOHVF6F1ynmOJxbGMiHjfTBOKnti+qCBte1G4bT17xzMqApvVF3XNDgOdpzs\niTs+vu9oFxZv2I2fz5tiy4OZPrisz6TehQVXj7UVOMr3oGp/AyZcUkWUf+kX7vuOdmHZi3032t47\n0IGagAfTLx6OWy15a5fMbMTz2z7Cwm+Mhd/rwb/9/kPc/NXRttxk1phgfi1gK3j29v4TmL++DYtn\njMfSTTvN6vEv7DyC451RrGptgt/rwS1r9deef9UYrGxpshVaWDZrAupCXvzdNeMy4i+AjDxwbjPB\nwkGva97zsN87qKIr6XHLGHQOB/WbmpVWTImIKkOx82cHPAItl47KKKJTyHQExR70KcXgaiSadBwo\nL+TAVrFnW3s8AkPCflu+Tt4sPTXWlAvGdvPQa+0KpVyAeT5pvZZVaZP4LBLPLGYYDpS6WVkpRawj\nGgw1IlqB9TfI1hVNuN7Zj7gU2nEqGAboJyTpzz96MoragA+P3NgMjxCYt2bzgAONjrMOAl5Mu3i4\nmQPX+P0F69vwyI3NqA14bc+v8Xnw8OxJtgrHdSEvogkt426UkUC+NuCcY836PmzYfhgbth/G1NEN\neORG+x1QPRdNErev0wc+0ts6f10bHrj+EpwZ9puD3NYDgM8jMh7XBzsk1v7VFNuArzGQ88D1l5gV\n442Z1P/y3SbnwQnj887iBErTpP6+B722okMA+h0wyXWbIaomucwedXpuXVCfAbB4w+6Mfawrmjnb\n5s5nd2DxjPEY1RBGR3cUc79yflazYdyOGWPOrjNT5BgXZQdP6LNs51sKRi5/eR8A6LE5NVv42S2H\ncNNlFzjGX6dZt+6rEJLmDb9hpwWx4OqxGNUQRiSatKVjMP7+/HXb8LObJkOTer9640lomn4ia7yv\nkXjfxfSFQ2txojuGBevt8dnv8xSs2jgRkQriGhxnda6eMxnBAr2msZrvh0/3reD4l+9OKNigTylm\nIfs8QMuUUbaJKStbm1DAWnNFL/6maRIdkXjON1zJXW8sibuu/RPb+cqKlib0xpIFKxqYb/Uhn60Y\noCLpcwGoP8O1FAUgiQaj/Oe7F4Fb3qKu3kS/OQNzzc/llocx6POgLuhzvxPkMHszvZhFJJZ0TKPw\n3oEO1IV88Ho9tucLIRBLara8MNG4xFl1Qdc70m4nFGGH3L3LZk3QCxYImDlqI/Gkecfbra3nDgnj\n+2u3ojYtZ/CQ2gDCAR8aaoNYPVd//MffaUTAK7DomR0Yd+9GrHnrgC1Xz/HOKGqDPsjUTGpjkPfw\nZz3m521UpF+8YXfWuSH1FB1RW9Ghju4YOnvjqTxf7vkrS5HTjUgFueRp7e+5TnF2VWsT6l1SuYw5\nuw77jnbhb37RBq/Hk9WFk9sxoyeWxOo5k1Ef8kNAL+x45bLX8IXTaxzzk9elBnPHnF2HuZdd4Jpu\nxph1a7Vp15GMuGvk6G2oDeBnN03GPd/6EzPHrl4cLYFhp9mHFYadFkR3VD/WLXyqDR3dsb7Ylnpf\nQ16PmUvwg+PdWLDensvwjmd2QNMGfNuIiCpaKWZ0BXwehANeW07bcMCLQIFGO2t8LrllfYXrY0KT\nGXU/bl/XZlshmW/hgDcjD/6SmY0FW1FnXS2ar/z31U6TMuN8ZcH6NmhSjfz/gVSxW2sxwGhcK9i+\nnW+qz3Ct8WWOb2lmuxIAACAASURBVKxsbSporCMaDDUiQoE5DbKtbG3C429+2O+B1TpTdu/907F6\nbmbBMCv9+UHb88+qDcLr9dhmb1oZszcHKuIV9ntdq0k6JdTXTxzsB7lFz2xHZ288679htAkCtsJp\nxkDrgvVtGHvPRjz2xn5zpnNnbxzzrxrjWgG+/VgX3jvQgZDfCwgAEqgL+RBOzQ42BrIjsSSSmsR8\ny4F6+cv7sP7dg3h0TrPt8+iJa7bXMpawTB3dYFakz+YEyuivU3GGO57ZgU8j8QEH5XPdZoiqRS4X\nM/091ynONtQG+71x9+Cr7RknmdaY29kbR1LTzNhb4/PgkRubzarJC6++SD/J83tQF9JjlTUWuMW7\nzt6EOcPpH3+9y1zFkf68jz/tySh60zplFBrC9lgyJOzX3y8BPT46FJFZcPVY299fcPVYrHvnIBbP\nGI/7rrvEnE1hfV97En2zm91uxqlygk5EVChuMTwSLdygXE88iTVvHUA0od9ViyY0rHnrgD6pohCv\nZzkemIOr69vQkyhcH8OuKYYKN8uyJ5bE89s+shWken7bR+gpUEEtpmTLv1JsN/lkzeNs29cUGeSP\nuI1LRBMlalFuehJJrE+dHxsxYP07Bwsa64gGgwO6cB9kW/VKu+15TgfW9JmyevEz++BrMqmZ30fi\nqQI7xnisALp6E4jEEqjxexxnXNX4PAPOXPN4BGoDXjw0exJeW3QlPvjRtXht0ZV4ePakfvPepvev\nLuhzrszu7yuoY+3bY2/sx8Kn2tAVTWLxht248O4X9AHP1AHo2kuG47qJI3Hrk1vMu4stl47C/uOd\njne+H3y13cyL298MvbDfi1ENYcdZb7Vpn0f6gP0XTq8xq4ZeNMxlcCKQWcjN+AxqXN67c4eE3U/m\nY/YbAenbDFG1y+VipsbvweIZ480B1RkTRtgLJAIZ+5jTjbsVLU14bttH2LD9sG0/TZ8BfMuaLfj4\n01489sZ+dPbG0RGJ4dYnt/TNTkoNrnotRR6sJ7JGTu/0KuSxRBJ/+1Qbpq14HUdPRuHxIOMYsGRm\nI5Zu2oMf/3YPHrj+Euy9X5+BFfB6IFL9gwQgAeER+KQzioVPtSEccL6QGdUQth0jRp5Zg+smjsTi\nDbtdY5s1l6Db4HQhByyIiFTgtkKkkINy4YAXrZeOQjA1ay/o86D10lEFe81i55YFSjNQ7hEC35l8\nrm0m8ncmn2vWSMm3/ib10OCUYrvJp1Lsa/kUcpnhGlJkhmtt0IfTw34MOy0IIfQVbaeH/cq8/1Q9\nuEWmpBdO6HLJmzJQrlNjIMCaA2llSxPWv3sQq15pN4t/BbwefN9SvGzprEYs27QHo8+qNatB2vIX\nZlG0TQiBeCqNgrXQmBO3vDAfHO/Gpl1HMtpgDFSn923JzEZ4PcATb35oFiGzzuCyzoA12n77+jY8\nNHsS6tLyTC5/aQ+Od0Zt1endipV5PAJdvdl9Ruk5h7ujCbOARNs/fCOr/DjWz8Atd+WhjgjOPi3I\nKrVEg+CUY9w48bYWPTHi0OINu21xaMzQWrNAolPeufQ40NWbwONvfohVr7Rn3LhyirlGvt1ILImF\nT9sLlN2+Ts+RWGcZ0PV4hJnX8IWdRzBmaC0evrHZTLPw49/q8W7xjPGpwmkTEfJ5EfR6zPibXtzt\nhZ1HsOe+6bhy2Wt6VfG5kx0LnS2Z2WimlsnIJdybsB0jHrmx2YzRbrHNerxwKjhZ6AELIiIVWFeI\nZJMLPh+icQ3RtHP/pbMaEY1rCAfzP2+nFHklS1IcSgABn8AD119i1hoJ+IS+erAAnApe8/rh1Khe\nVEz1HK6xpAaPgG0f8gj9cZ8CaSNi8SSmXzzcVmx+ZUsTYvEkQqwZQWVESAXyyEyePFlu3ry5qK/p\nNHiZTXL6rlQuQmvwnTq6AYtnjDcr0E4d3YAHrr8EVy57zfE5xoW6MSipSYmx92y05YryeQT23j/d\ndrLo9tpOA6JJTcPHn/ZmXJTXpgJU0O9BJKbnvLUOLDv+/TmT0fTPL+LaS4bjB18bg3POqDGLu33w\no2sx7t7+2w7ALG6072gXHny13RzAMJ7rdEd8oM/IrcCS9fee/MtLcfgz+/uwZGYjzjkzBK+n72Bj\n/QxmTBiBO6/5IhY9s912glAf9JkH2GwLOxE5KNrGUorY6iYSS6CjO5ZZDTeVP9vgFucent2Mv//1\nLrMoo1Pcs+qvAJtbzN1z33QIASx8qg23XTnGVjV5+Q1NtjiV1DQc/qwX55xZoxdOFMDdz+3E822H\nbX8zPY4DQDKppdqmD/4aMXHq6AY8NHsS/uHXu/HCziPm7zq9H8tmNSKpISPGaxK4/zfvmzHWGqON\nnOL235mIIWG/rVjL/KvG4KZUzl/GOFJIVcbWapZLoU1VdfUmbAWVgb5zc+vN0Hxxun5wOm/Op2gs\ngZ6Ehs8shZzPCPtR4/MgWKCBFU1KvL73GCaNGoK6kF5wdevBDlwx9uyCzdJVeHsty9iaSGjoiiUy\ntpu6gE+JAcWkpuFYZxQLn+q73lx+wwScXR8s2L6WT129CTz2+/0ZBRRvvnx0QWJTvnX2xnHLmi0Z\nsfXROc1KDKhTRcgqtpb/3lQi6bO5sjmwGgV53ArvWL8/d0jY9Tnpy4yNZTjpd+jSZ6Nls2RZ0yR6\nE3r+2RFnhPDwjc2oD+kzZO//zfs4ejKKn/x5Ez6NSNuApTHYm15Qx1qwZ8P2w9iw/TBmTBhh3hF1\nrcSeNovWmBXtVJ3ebVZ0f5/RQIO9xu/1xjUzR5ZxsHl+20e4+aujUWeZ2WD9DIyBkOXfnYBhp4f0\nwRqPvrTE2D6ss72JaGAhv9dMhWLsi8s27cHyG+yrDFzTxYR85r6ZTd659FUZVm4xt/1YF849swaL\npo3LGHjujSURSt34qvHraXL+7pd9z1nR0oTRZ9XaXscpvjlVujZmIF83cSTWvHUAi745DmOG1uox\nz+X9+MLpNXjwlX22VRBGjF8ysxEAsGH7YVuMNt6/B66/BKMawraY6hhrhWCMI6KyZBSwnb+uzXYu\n21AbLOggWbEH5YpdeMiaW9Z23nz5BagLFWaQKSn1AlENdQEIATTUBeD1CCQLOCcpFk/iS8NPx61P\nbina7Lz+zksod70JDR9+0oULh9ZDCGBIbQAfHO/ERWefhjoFBnTjCQ01fq9thmuN34t4QoM3UP7t\nrwl4cN3EkRk3f2oUaDugfsoLqh5q7FElkkuuU2MA8eCJiGuxL+v3hzoirs9Jz5nklPvRyDdrLQY0\nUP4lTdOL+3R0x3DLmi344t//Fp92x8xK7M+3Hcbb+0+gqzeJRc9szyimc6wzikXTxmHGhBG2v59e\nsOd4ZxT1QR9Wz5mMMWfXOldid8rr69DPgZYbuX1GAxVYMn4vHPCidcp5thxZrVPOy3jN9LYd74zC\n5/VkFG0josGJxJJmKpQL737BzCubnj/OLc6lx9hTyTvnFnM37TqCpJQZhcPueGYHEqn4Ou+JzWg/\n1p1RkXvB+jbcdNkFA8Y3p9h157M7MOcr52PZi3uw/OV9uPPZHbjpsgv0lRMuOeKOnexF65TzICAy\nYvydz+7AD742BgCwadcRW4w+3hnVT1YlbDGVub+JSCVOBWznr2sraE7S9Pzr/dWCyBe3gsjdBSo8\n5BEC1zePtJ03X988smCzVgEg4PWgK5rALWu2mHntu6IJBLyFu4yNa9KxIFW8gJ8l5ZdHAEPrQ2Yd\nl1uf3IKh9SFlUi7ENYnb1m7Flctew4V3v4Arl72G29ZuVWYbjMSSGYXH73x2hzJ5oYsdW4kGi7cY\n8sS4CB9aH8zIL2jk0PV5hC2H7tTRDRk5dJ0u8tNnR6XnVTRno0n0m38pEk/i00gcd/1qpznz7Nwh\nmYXFnB4zZhXP/uk7eOD6S/DCziN631qb0FAbQDShYfWcyQgHM2cknJVlPrHBzIp2k22BpWxfM59t\nI6JM2eaPc3peZow9tbxzTnm3wwEvbv7qaNfYUhv04VBHBG/vP2HLI259Tl3IN2AMcfv79SG/LebX\nhfTBVY/HOUfc6TV+hPxeQMB11cjU0Q1onXIehoT9jG1EVFGKPXMVcM6/7lYLIl/CAW/GdceSmY0F\ny2seCnix7LmBV9PkU29SM2+SApbc9XMnF2ymJWfnVYaQ32Ob4RryqzOXTfVtUPX2+z0CK1uacPv6\nNtv1hp/nx1Rm1NijyozTcirjItzIuWic6PTE9OW3N391NP766xfZ8saaF9CpJfvLb2jqd0Axm7QE\n/Q06hgPejMFap5QIhzoirsuNjSrp6Xkfw5blE04FzLJdQpSv5UZuS6ad0jdk+5pcCkVUOKdyc6XG\n1xdje+NJaBoAoefbHezgpHV/N3Jl1QU9rgUz2491mWlzXFPNWAq8ucWQ/tI92P5WKpb1l6rCI4Rr\ne3tiSbOomscjzIJujG1EVAmyLbSZT9lOJsinnixTh+VLJNq3msYwdXRDxb2vqhekIv3mw93P7cRt\nV+orkqIJDSv+fW9Bbz7kk+rboOrtD/i92Lj5EB6aPQmn1fhxsieOX7d9jBunnl/qphHZqHObqky4\nLafqjfcte92w/TCmrXgds3/6DiAAr9djW6oKpBXNCnj1JftpS1k1TaIrmoAmU/9rcsC0BP0ti43E\nkuZgreHBV9tt6RKmjm5AXciLZbMmOKZ4MAYSyn3Z7WDSNxBRaWW7rD/9eUaMhQS6o0nMW9P/clen\n2JqtsN+bkUbGSMdgpNJ58NV2LJlpj6tLZzUimxoWTrFrZUsTNu064hjLBkpV4RoLA96yjuFERKfC\nWL0wmDg8WAOlPiuEGp8HLZeOsqVAaLl0FGoKNHO1Wt7XsN+LlS1NGcdiXkeoI9tUXuUqkJohmr4N\nBhQ5b1O9/ZFYEp9H4jh6MgopgaMno/g8Eldm+6HqIaQs/zws5VQt2K3C+uo5k9GTWmrlVITLMFCx\nrmyeByBjhrDTY+kX6kYO3c5owrY89+HZk+D1ePR0CanZwkGfB5FY0iyms+LlvTh6MurY1nKlcLVY\nqm5lWS1YBa7x2bLcNdsY3J9kUkN3Kj4aVXtbp4xCwOvB99duxXsHOjD/qjGYe9kFqEulYjgz7Ed9\nyJ/Va1hjV1dvAm+2H8foofUYc3YduqMJ1Aa88KZm1Fr7M+y0IBZcPVYvaBbVf98oEslYSMTYWk2M\nc95PLRXuc4nDqrxmsSvJJ5MaOqMJfGbp4xlhP+pTN1cLIR/H7cFIJjVE4vqxvju14qdQfVRcWcbW\nUm03+aJpEr3xJBKaRF3Ih67eBHwegZAi53BdvQm8se8Ypl54ljnD9e0PPsFXLzq7YLP58ymR0NAV\ny4x1dQEffAoU1aOKkNWOzgHdHGlSYuw9G83UCgDg8wjsvX86IAceVM1mwCGX5wG5HbA0TaI3oS9J\nNgZwjYt+x/5yIICo2MryxFgF/cVno2BLLrG139dyiI2A5RiQujkW8g8+dmbbViOud0cTaRXd1blw\nISoCxtYqU+xzWP18PJoWh5vQUBss3CByFse9fOqKJvDYGw4DyF8dXdCUPbweKWtlG1tV325Ubn+x\nY1O+RaIJdERiGTUqhoQDCDM9GRVHVjsKby/kqL9lP9ksF842D1Qu+aKcqqLPX7cNkXjmkgA9363P\nLKhTF+p/yS0rmxORKrJZlpmvXHxOsdH2WMjnmEonF7kUd9QkHCq6Ox8HiIiqQbHPYfXz8fQ43FbQ\nOGzkCrYycgUXQjjgxapX2m3L2Fe90l7QfLYAr0docFTfblRufylSpeSTJoE7ntlhi+d3PLMDOWRp\nIyoK3l7IUbaV2N1kW6wrl6JexkX/jAkj8IOvjTHvmNdkWclT5bt//anUfhGRs2zicy6xtdSybaum\nSUACa/9qCtqPdeHtDz7B1AvPMgtzaprMW+xjXCUiVRQ7XoUDXgw7LYhNC64wz8Ufeq2wg50eD/CT\nP29CV2/SXBZcF/IWLKetSsdQIqbNKJ2w34uHZ0/KSEGjSh7qcNBlUkVQjfZT9WDKhUHI5gTR7Tn5\nyKHrlMbhsTf247qJI3Hns33LAla2NuGsAZZ5qZ5fyE2l9ouqQtkuXcu3QlxsD/Q3VYoN2bTV6Tkr\nW5qw/t2DWPVKe177p9J7R+SgamIrlSZeRWIJdHQ7LNGtDSAcKMxgZzKp4UQkhtstaR5WtjahIRwo\nyMBVqY4DvJlY1soytiaTGk50x3D7+jbb+VFDbWH2DbIrRQqafOrqjeOx33/okJ/8AtSF/KVuHlUH\n5tAtlYFOdrI9KcnleV3RBG59ckvOeSHzlU+y3FRqv6gqlOWJcb6VcnBQpQvDgdrqFusWzxiPaSte\nN7/PR+xjXCXFVUVsJV0p4lVXbwLz1jgXTi5UEaBS9LM0uYl5M7GMlWVs7eyN45Y1mdfGj85pRj0H\n5ApO9XNG3hCgMsAcuqWSS07b/qTnzQH04KhJfQBXSyVx8Xj0fI2DyQuZr3yS5aZS+0WkOuMGVCSW\nnzg5GLnkJDPamx53i2WgtrrFujFn19m+z0fsY1wlIlWUIl6VYoluKfoppYQxIcj6daHk67qKqktt\n0PnauFaBwcRKoPo5Y09Cw/p3D2LxjPHYc990LJ4xHuvfPYiehFbqpmWl1NcvVDwc0C2A/gKYcZd5\n3hObMfaejZj3xGac6I4NuJMN9HuDTTyuesJyN5XaLyKVWeNYjQIneoON18XkFuvaj3XZvs9H7GNc\nJSJVlCJeVcNrGrPWblmzBWPv2Yhb1mzBie4YksnCDXKoPjBEpdEdTTjuG93RRIlaVF1UP2es8Xtw\n3cSRWLxhN8bduxGLN+zGdRNHZl2jqJRUuH6h/Cn/LbIMDXTHo78ANti7zAP9nlEMaOroBvg8AlNH\nN2RVrG2wv1fuKrVfRCqzxrH2Y10FOdHL1x1pTZPojiXKflaQU6xb2dKETbuO5D32Ma4SkSpKEa9K\n95pNaa/ZVLDXjMSTuH19m+24ePv6toIeF1UfGKLSCPu9WNnSlHF+xHOW4ih2bMq3SCyJO5/dYYt1\ndz67Q4m4w1UN1YU5dHOUTR6n/nKuCI/A2Hs2ImEZZPB5BPbePx0e0c+yXykH/L3B5rRSKZ9kLiq1\nX1TxyjIXWT5Y49iMCSOw6JvjbIUcTzUnXr7y7Bl/Z0htAOPuzT1eF1t6rKvxedCT0AoS+xhXSWEV\nG1vJWSniVbFfM5nU0BlN4DNLJfkzwn7UB32FKYqWxfVI3l+TOXTLXdnG1mRSQySeRG3Qh+5oAmG/\nl/lPi0TTJDp74/jUEpvODPtRH/Irsd+WItbli8ptJ5usPiwmkcmR9Y4HAPOOhzXBtzXnilEVcf27\nB3HzV0cD0O8qWxOEG3eZ+0sQHokmB/w9I9cigJySjQ/298pdpfaLSFXGLJu395/Ahu2HAQAPXH8J\nRjWEB3Xhm37hDIkB43NW7UzF+cUzxg8qXhebU6yrS12w5LudjKtEpIpSxKtiv2YknsRta7c6F34q\nwMCVsYw9/bjYHU0UrNCUxyMwJOzHo3OabQNzKgwKUWl5vR5zP2AhtOKKxJP4vkNsUqUoWiliXb5Y\nr7cM5Xj9QvnBW1Q5yiaPUzjgxapX2jFtxeu48O4XMG3F61j1SjvCAe+glmNpmkRS07B0VqOyyxaI\niNLj3/HOqF6cQmLA4mTpnPJDuRakyTHPnhHnH3y1HUtmpsddphggIqLyUOzCT6VYxq5pEh2RuC1v\nb0ckznyQRGVM9dzX4YA34xpgycxGJdrPFGnVhUP0OcrmjsdAz2moDWD13MlZL8cy7nANrQ+as34P\ndURQm+MACBFRKXk8Iuf458ZptcTBE5G83JE2Yrgxi9iIu5FYArUBxl0iIioPxZ5F5vV60FAbyJgt\nW8hl7NmsjiSi8qLyDFcA6IlreH7bR7YV189v+wg3f3U06oLlPScyn9dbVP7Ke2ssQ9nc8RjoOcZy\nLI8QWc1KM+5wbdh+2Jz1e/Xy3yHEuyxEpJhc458bpzv/K17e61CAIfc70tYY/sLOI1i8YTc6umMc\nzCUiorJSihmzXq9Hz4MpBOpD/oLnJFV9ph9RNVJ5hiugx9bWKedh8YbdGHfvRizesButU85TZpZr\nvq63qPzxtmaOsrnjke+7IsyDQkRk5xQXj57UUzicauzlnW0iIlJBKWbMFhuvg4jUo/IMV4DXAqSO\n8t+bylA2dzzyeVeEeVCIiOzc4mLI581L7OWdbSIiUkGxZ8wWG6+DiNSj+gxXgNcCpIaS3dYUQngB\nbAbwsZTy26VqR6GkV18/lTs6vENEhZTPbZUoF6ey7TEuElUPHqdoMLjdVAaPR2BI2J8xC5mfJVH5\nqoT9lscQUkEp16ncDuB9AKeVsA0FYVRfn79uG9470IEvnz8Eq1onoqE2cMqzxQD0u7yIgYdyUYht\nlSgb+dj2so2L5YZxmih7PE7RYFTTdlPpxxRNk+iIxKvisySqFPp+G8P8dW2W/bYJDbVBJfbbajqG\nkNpKsiZHCDESwLcA/LQUr19o1mqsCU2a1Vgj8WRBX9cIPPOe2Iyx92zEvCc240R3DJomC/q6pK5S\nbatE1brtMU4T5aZaYwWdmmrZbqrhmFItnyVRJYnEkpi/ri1tv21DJKbGfsu4Q6ooVZKlFQD+DoDm\n9gQhxC1CiM1CiM3Hjx8vXsvyoFTVWBl4KFesHFx9yiW2Vuu2xzhNlBtVYkW5xFbSqbLdnKpqOKZU\ny2dJzhhb1RQOuuy3QTX2W8YdUkXRB3SFEN8GcExKuaW/50kpH5VSTpZSTh46dGiRWpcfRjVWK6Ma\nayEx8FCuSrWtUumUS2yt1m2PcZooN6rEinKJraRTZbs5VdVwTKmWz5KcMbaqKRJ12W+jauy3jDuk\nilLM0L0MwAwhxAEA6wFcJYRYW4J2FEypqrEy8FCu3LbVGp8HXdEENCn1/yto6R6Vh2qtWl2JcVrT\nJOMFFUy1xgo6NdWy3VTiMSVdqT5LHtuIBs/jAZbOarTtt0tnNcJTqvXhOQr7vXh49iS8tuhKfPCj\na/Haoivx8OxJFXcMIfUJKUt3cBJCXAlgkZTy2/09b/LkyXLz5s3FaVSelKJAAZN302Ckb6s1Pg+L\nT5RW0d7kUsfWSi/k4qTS4nSl9YfKU55iRdXEVtJVwzGmWmJwsT/Lanlf84SxlTJomkRnbxyfRuI4\nd0gYhzoiODPsR33Ir8Q+pMeAqLJF3agiZLWhcUC3wlTDySsVVlc0gXlPbMbb+0+Yj00d3YDVcyej\nLugrYcuqBk+MK1wlxWnGC1IIYytVpEo6ppQLHttywthKjlSOTYwBVAay2llKujVKKV8D8Fop21Bp\nPB5hBhkGGxqMasjHRlRKlRSnGS+IiEqrko4p5YLHNqJTp3JsYgwgVSiSxYSIiqUa8rERUX4wXhAR\nUaXhsY2oujEGkCo4oEtENtVSSISITh3jBRERVRoe24iqG2MAqUKtue9EVHAej0BDbQCr505WMucR\nERUP4wUREVUaHtuIqhtjAKmCM3QprzRNoiuagCZT/2ulK7pHg2fkPPKI1P88eBGRC7d4weMBEVUb\nxr3KwXNhIiIqd5yhS3mjaRInumOYv24b3jvQgS+fPwSrWieioTbAkyAioirC4wERVRvGPSKiysB4\nTqrgDF3Km0g8ifnrtuHt/SeQ0CTe3n8C89dtQyTO5OFERNWExwMiqjaMe0RElYHxnFTBAV3Km3DA\ni/cOdNgee+9AB8IBJg8nIqomPB4QUbVh3CMiqgyM56QKDuhS3kRiSXz5/CG2x758/hBEYryTRURU\nTXg8IKJqw7hHRFQZGM9JFRzQpbwJ+71Y1ToRU0c3wOcRmDq6AataJyLs550sIqJqwuMBEVUbxj0i\nosrAeE6qYFE0yhuPR6ChNoDVcycjHPAiEksi7PcycTgRUZXh8YCIqg3jHhFRZWA8J1VwQJfyyuMR\nqAvqm5XxPxERVR8eD4io2jDuERFVBsZzUgFTLhAREREREREREREpggO6RERERERERERERIrggC4R\nERERERERERGRIjigS0RERERERERERKQIDugSERERERERERERKYIDukRERERERERERESK4IAuERER\nERERERERkSI4oEtERERERERERESkCA7oEhERERERERERESmCA7pEREREREREREREiuCALhERERER\nEREREZEiOKBLREREREREREREpAgO6BIREREREREREREpggO6RERERERERERERIrggC4RERERERER\nERGRIjigS0RERERERERERKQIDugSERERERERERERKYIDukRERERERERERESK4IAuERERERERERER\nkSKElLLUbRiQEOI4gD9m+fSzAHxSwOYUC/tRfiqlL+xHeUnvxydSymuK8cI5xlZA/fdc9fYD6veB\n7S+tam5/OcdWg+qfT7aqoZ/sY+Wohn4ytpY3tr+0VG6/ym0Hqrv9WcVWJQZ0cyGE2CylnFzqdpwq\n9qP8VEpf2I/yolI/VGqrE9XbD6jfB7a/tNj+8lbp/TNUQz/Zx8pRDf2s9D6q3j+2v7RUbr/KbQfY\n/mww5QIRERERERERERGRIjigS0RERERERERERKSIShzQfbTUDcgT9qP8VEpf2I/yolI/VGqrE9Xb\nD6jfB7a/tNj+8lbp/TNUQz/Zx8pRDf2s9D6q3j+2v7RUbr/KbQfY/gFVXA5dIiIiIiIiIiIiokpV\niTN0iYiIiIiIiIiIiCqSUgO6QohzhRCvCiH+UwixWwhxe+rxIUKIl4QQ+1L/n2n5nbuEEO1CiD1C\niGmla30fIURICPGuEGJ7qh//lHpcqX4YhBBeIcQ2IcS/p75XtR8HhBA7hRBtQojNqceU64sQ4gwh\nxC+FEP9PCPG+EGKqav0QQoxLfQ7Gv5NCiAWq9QMAhBB/m9rPdwkh1qX2fxX7cU2qTe1CiP9R6vbk\nwu3YoZr0WKsSp7hU6jblwmk/LnWbBiKEeEwIcUwIscvymGvsKTcu7V+a2oZ2CCGeE0KcUco25pPK\nMTYblRKHUK2MUgAAC8BJREFUs6FyrM6W6jE9GyrG/WyofmzIlcqx1emzUoXqMV+4jNeoRuXjkXAY\nm1FFMY+RSg3oAkgA+KGU8ksA/hTAD4QQXwLwPwD8h5TyIgD/kfoeqZ+1ABgP4BoA/0cI4S1Jy+2i\nAK6SUk4A0ATgGiHEn0K9fhhuB/C+5XtV+wEAX5NSNkkpJ6e+V7EvKwH8Vkr5RQAToH82SvVDSrkn\n9Tk0AWgGEAHwHBTrhxDiHADzAUyWUl4MwAu9nar1wwvgQQDTAXwJQGuqrapwO3aoJj3WqsQpLimh\nn/243D0OPY5YOcaeMvU4Mtv/EoCLpZSNAPYCuKvYjSqECoix2aiUOJwNlWN1tpSN6dlQOO5n43Go\nfWzIWgXE1seR+VmpQvWY7zZeoxrVj0fpYzOqKNoxUqkBXSnlESnl1tTXndDfmHMA/BmAJ1JPewLA\ndamv/wzAeillVEr5IYB2AJcWt9WZpK4r9a0/9U9CsX4AgBBiJIBvAfip5WHl+tEPpfoihDgdwBUA\nfgYAUsqYlPIzKNaPNF8H8IGU8o9Qsx8+ADVCCB+AMIDDUK8flwJol1Lul1LGAKyH3lYl9HPsUIZL\nrFVCP3FJJU77cVmTUr4OoCPtYbfYU3ac2i+lfFFKmUh9+wcAI4vesMJQOsZmoxLicDZUjtXZqpCY\nng3l4n42VD825Ejp2OryWSlB9Zjfz3iNMqrheFSOin2MVGpA10oIcT6AiQDeATBMSnkk9aP/AjAs\n9fU5AA5Zfu0jlEkgSU1/bwNwDMBLUkol+wFgBYC/A6BZHlOxH4AepF8WQmwRQtySeky1vlwA4DiA\nf0str/ipEKIW6vXDqgXAutTXSvVDSvkxgGUADgI4AuBzKeWLUKwfKN925Szt2KESp1irCre4pIR+\n9mMVucUeFd0MYGOpG5EnFRNjs6FwHM6GyrE6W0rH9GxUWNzPRiUdG6yqKraWK1Vjvst4jUpUPx45\njc2ooKjHSCUHdIUQdQCeBbBASnnS+jMppYQCd0+klMnUcvKRAC4VQlyc9vOy74cQ4tsAjkkpt7g9\nR4V+WFye+kymQ18WcoX1h4r0xQdgEoCHpJQTAXQjbdmUIv0AAAghAgBmAHgm/Wcq9COVg+zPoAf2\nEQBqhRCzrc9RoR+Vor9jRznLJtaWuQHjUjnLZj9WkcqxRwhxD/TlnD8vdVsoN6rG4WxUQKzOltIx\nPRuVGvezofKxgcqPyjF/oPGaclYhx6N+x2bKWFGPkcoN6Aoh/NCDws+llL9KPXxUCDE89fPh0O+i\nAMDHAM61/PrI1GNlIzX9+lXo+XFU68dlAGYIIQ5AX8JylRBiLdTrBwDzbjyklMeg52u9FOr15SMA\nH1nuIP4SekBRrR+G6QC2SimPpr5XrR9XA/hQSnlcShkH8CsAX4F6/SjXdmXN5dihCrdYqwq3uKQK\nt/1YRW6xRxlCiJsAfBvAX6QGHiqB8jE2G4rH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+ ""text/plain"": [ + """" + ] + }, + ""metadata"": {}, + ""output_type"": ""display_data"" + } + ], + ""source"": [ + ""# visualize the relationship between the features and the response using scatterplots\n"", + ""sns.pairplot(RO5, x_vars=['MW','LogP','nHAcc','nHDon'], y_vars='pIC50', size=7, aspect=0.7)"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 23, + ""metadata"": {}, + ""outputs"": [ + { + ""name"": ""stderr"", + ""output_type"": ""stream"", + ""text"": [ + ""/Users/zeromtmu/anaconda2/envs/ER_alpha/lib/python2.7/site-packages/ipykernel_launcher.py:1: SettingWithCopyWarning: \n"", + ""A value is trying to be set on a copy of a slice from a DataFrame.\n"", + ""Try using .loc[row_indexer,col_indexer] = value instead\n"", + ""\n"", + ""See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n"", + "" \""\""\""Entry point for launching an IPython kernel.\n"" + ] + } + ], + ""source"": [ + ""RO5['chemblId'] = RO5.index\n"", + ""ER_alpha_final = RO5[['SMILES_desalt','chemblId']]\n"", + ""\n"", + ""ER_alpha_final.to_csv('smiles/ER_alpha_Train_final.smi', sep='\\t' ,header=False ,index=False)"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 24, + ""metadata"": { + ""collapsed"": true + }, + ""outputs"": [], + ""source"": [ + ""RO5.to_csv ('model/ER_alpha_Train_final.csv' , sep=',' ,index=False)"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 25, + ""metadata"": {}, + ""outputs"": [ + { + ""data"": { + ""image/png"": 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ThQWqN9U+3WbAwIUmSJGmyPNRiBrR1CP7s2epZLyRJkiRpgIWHGbAyBH+tRdNh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\nQN5upBoA7wAAAABJRU5ErkJggg==\n"", + ""text/plain"": [ + """" + ] + }, + ""metadata"": {}, + ""output_type"": ""display_data"" + } + ], + ""source"": [ + ""%matplotlib inline\n"", + ""import matplotlib as mpl\n"", + ""import matplotlib.mlab as mlab\n"", + ""import matplotlib.pyplot as plt\n"", + ""import matplotlib.ticker as plticker\n"", + ""\n"", + ""figure, ((plt1,plt2), (plt3,plt4)) = plt.subplots(2, 2)\n"", + ""figure.set_size_inches(15,10)\n"", + ""\n"", + ""loc = plticker.MultipleLocator(base=50.0) # this locator puts ticks at regular intervals\n"", + ""\n"", + ""# Histogram for MW\n"", + ""hist, bins = np.histogram(RO5.MW, 50)\n"", + ""width = 1 * (bins[1] - bins[0])\n"", + ""center = (bins[:-1] + bins[1:]) / 2\n"", + ""plt1.bar(center, hist, align='center', width=width, color='blue',edgecolor='black',\\\n"", + "" error_kw=dict(ecolor='gray', lw=2, capsize=5, capthick=2,linestyle='-', linewidth=0.5))\n"", + ""plt1.yaxis.set_major_locator(loc)\n"", + ""plt1.set_xlabel('Molecular weight (Da)', fontsize=16, fontweight='bold')\n"", + ""plt1.set_ylabel('Frequency', fontsize=16, fontweight='bold')\n"", + ""plt1.tick_params(axis='both', which='major', labelsize=14)\n"", + ""#plt1.set_xlim(200,900)\n"", + ""plt1.set_ylim(0, 200)\n"", + ""#plt1.grid(True)\n"", + ""plt1.axvline(500, color='gray',linestyle='--', dashes=(5, 10), linewidth=0.5)\n"", + ""\n"", + ""# Histogram for LogP\n"", + ""hist, bins = np.histogram(RO5.LogP, 50)\n"", + ""width = 1 * (bins[1] - bins[0])\n"", + ""center = (bins[:-1] + bins[1:]) / 2\n"", + ""plt2.bar(center, hist, align='center', width=width, color='red',edgecolor='black',\\\n"", + "" error_kw=dict(ecolor='gray', lw=2, capsize=5, capthick=2,linestyle='-', linewidth=0.5))\n"", + ""plt2.yaxis.set_major_locator(loc)\n"", + ""plt2.set_xlabel('LogP', fontsize=16, fontweight='bold')\n"", + ""plt2.set_ylabel('Frequency', fontsize=16, fontweight='bold')\n"", + ""plt2.tick_params(axis='both', which='major', labelsize=14)\n"", + ""plt2.set_xlim(-1,10)\n"", + ""plt2.set_ylim(0,200)\n"", + ""#plt2.grid(True)\n"", + ""plt2.axvline(5, color='gray',linestyle='--', dashes=(5, 10), linewidth=0.5)\n"", + "" \n"", + ""# Histogram for nHAcc\n"", + ""hist, bins = np.histogram(RO5.nHAcc, 20)\n"", + ""width = 1 * (bins[1] - bins[0])\n"", + ""center = (bins[:-1] + bins[1:]) / 2\n"", + ""plt3.bar(center, hist, align='center', width=width, color='green',edgecolor='black',\\\n"", + "" error_kw=dict(ecolor='gray', lw=2, capsize=5, capthick=2, linestyle='-', linewidth=0.5))\n"", + ""#plt3.yaxis.set_major_locator(loc)\n"", + ""plt3.set_xlabel('Number of Hydrogen Bond Acceptors', fontsize=16, fontweight='bold')\n"", + ""plt3.set_ylabel('Frequency', fontsize=16, fontweight='bold')\n"", + ""plt3.tick_params(axis='both', which='major', labelsize=14)\n"", + ""plt3.set_xlim(-1,10)\n"", + ""plt3.set_ylim(0,700)\n"", + ""#plt3.grid(True)\n"", + ""plt3.axvline(10, color='gray',linestyle='--', dashes=(5, 10), linewidth=0.5)\n"", + ""\n"", + ""# Histogram for nHDon\n"", + ""hist, bins = np.histogram(RO5.nHDon, 20)\n"", + ""width = 1 * (bins[1] - bins[0])\n"", + ""center = (bins[:-1] + bins[1:]) / 2\n"", + ""plt4.bar(center, hist, align='center', width=width, color='orange',edgecolor='black',\\\n"", + "" error_kw=dict(ecolor='gray', lw=2, capsize=5, capthick=2, linestyle='-', linewidth=0.5))\n"", + ""#plt4.yaxis.set_major_locator(loc)\n"", + ""plt4.set_xlabel('Number of Hydrogen Bond Donors', fontsize=16, fontweight='bold')\n"", + ""plt4.set_ylabel('Frequency', fontsize=16, fontweight='bold')\n"", + ""plt4.tick_params(axis='both', which='major', labelsize=14)\n"", + ""plt4.set_xlim(-1,10)\n"", + ""plt4.set_ylim(0,700)\n"", + ""#plt4.grid(True)\n"", + ""plt4.axvline(5, color='gray',linestyle='--', dashes=(5, 10), linewidth=0.5)\n"", + ""\n"", + ""plt.tight_layout(pad=2.0, w_pad=2.0, h_pad=2.0)\n"", + ""plt.savefig('Result/ER_Alpha-the histogram plots of the descriptors select.pdf', dpi=300)"" + ] + }, + { + ""cell_type"": ""code"", + 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chemblId
CHEMBL370037active252.2693.2248327.69NaN0.330.503.31...Bioorg. Med. Chem. Lett., (2005) 15:12:3137CHEMBL2069Estrogen receptor alphanM6.0activeCC1=C(c2ccc(O)cc2)C(=O)c2ccc(O)cc218.221849CHEMBL370037
CHEMBL189073intermediate277.2794.0592428.15NaN4.064.123.86...J. Med. Chem., (2004) 47:21:5021CHEMBL2069Estrogen receptor alphanM1727.0intermediateOc1ccc2c(-c3cccc4ccc(O)cc34)noc2c15.762708CHEMBL189073
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"" + ], + ""text/plain"": [ + "" STATUS MW LogP nHAcc nHDon acdAcidicPka \\\n"", + ""chemblId \n"", + ""CHEMBL370037 active 252.269 3.2248 3 2 7.69 \n"", + ""CHEMBL189073 intermediate 277.279 4.0592 4 2 8.15 \n"", + ""\n"", + "" acdBasicPka acdLogd acdLogp alogp ... \\\n"", + ""chemblId ... \n"", + ""CHEMBL370037 NaN 0.33 0.50 3.31 ... \n"", + ""CHEMBL189073 NaN 4.06 4.12 3.86 ... \n"", + ""\n"", + "" reference target_chemblid \\\n"", + ""chemblId \n"", + ""CHEMBL370037 Bioorg. Med. Chem. Lett., (2005) 15:12:3137 CHEMBL206 \n"", + ""CHEMBL189073 J. Med. Chem., (2004) 47:21:5021 CHEMBL206 \n"", + ""\n"", + "" target_confidence target_name units value \\\n"", + ""chemblId \n"", + ""CHEMBL370037 9 Estrogen receptor alpha nM 6.0 \n"", + ""CHEMBL189073 9 Estrogen receptor alpha nM 1727.0 \n"", + ""\n"", + "" STATUS SMILES_desalt pIC50 \\\n"", + ""chemblId \n"", + ""CHEMBL370037 active CC1=C(c2ccc(O)cc2)C(=O)c2ccc(O)cc21 8.221849 \n"", + ""CHEMBL189073 intermediate Oc1ccc2c(-c3cccc4ccc(O)cc34)noc2c1 5.762708 \n"", + ""\n"", + "" chemblId \n"", + ""chemblId \n"", + ""CHEMBL370037 CHEMBL370037 \n"", + ""CHEMBL189073 CHEMBL189073 \n"", + ""\n"", + ""[2 rows x 40 columns]"" + ] + }, + ""execution_count"": 26, + ""metadata"": {}, + ""output_type"": ""execute_result"" + } + ], + ""source"": [ + ""RO5.head(2)"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 27, + ""metadata"": {}, + ""outputs"": [ + { + ""data"": { + ""text/plain"": [ + ""0.0 817\n"", + ""1.0 313\n"", + ""2.0 98\n"", + ""3.0 1\n"", + ""Name: numRo5Violations, dtype: int64"" + ] + }, + ""execution_count"": 27, + ""metadata"": {}, + ""output_type"": ""execute_result"" + } + ], + ""source"": [ + ""RO5.numRo5Violations.value_counts()"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 28, + ""metadata"": {}, + ""outputs"": [ + { + ""data"": { + ""text/plain"": [ + ""1229"" + ] + }, + ""execution_count"": 28, + ""metadata"": {}, + ""output_type"": ""execute_result"" + } + ], + ""source"": [ + ""df2 = RO5[np.isfinite(RO5['numRo5Violations'])]\n"", + ""len(df2)"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 29, + ""metadata"": {}, + ""outputs"": [ + { + ""data"": { + ""text/plain"": [ + ""2"" + ] + }, + ""execution_count"": 29, + ""metadata"": {}, + ""output_type"": ""execute_result"" + } + ], + ""source"": [ + ""#check missing data\n"", + ""\n"", + ""df1 = RO5[pd.isnull(RO5['numRo5Violations'])]\n"", + ""len(df1)"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 30, + ""metadata"": {}, + ""outputs"": [ + { + ""data"": { + ""text/html"": [ + ""
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CHEMBL3235685active272.4202.7006022NaNNaNNaNNaNNaN...Bioorg. Med. Chem., (2014) 22:7:2244CHEMBL2069Estrogen receptor alphanM115.0activeCC[Si](CC)(c1ccc(O)cc1)c1ccc(O)cc16.939302CHEMBL3235685
CHEMBL3235686active328.5284.0976422NaNNaNNaNNaNNaN...Bioorg. Med. Chem., (2014) 22:7:2244CHEMBL2069Estrogen receptor alphanM26.2activeCCC[Si](CCC)(c1ccc(O)c(C)c1)c1ccc(O)c(C)c17.581699CHEMBL3235686
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"" + ], + ""text/plain"": [ + "" STATUS MW LogP nHAcc nHDon acdAcidicPka \\\n"", + ""chemblId \n"", + ""CHEMBL3235685 active 272.420 2.70060 2 2 NaN \n"", + ""CHEMBL3235686 active 328.528 4.09764 2 2 NaN \n"", + ""\n"", + "" acdBasicPka acdLogd acdLogp alogp ... \\\n"", + ""chemblId ... \n"", + ""CHEMBL3235685 NaN NaN NaN NaN ... \n"", + ""CHEMBL3235686 NaN NaN NaN NaN ... \n"", + ""\n"", + "" reference target_chemblid \\\n"", + ""chemblId \n"", + ""CHEMBL3235685 Bioorg. Med. Chem., (2014) 22:7:2244 CHEMBL206 \n"", + ""CHEMBL3235686 Bioorg. Med. Chem., (2014) 22:7:2244 CHEMBL206 \n"", + ""\n"", + "" target_confidence target_name units value \\\n"", + ""chemblId \n"", + ""CHEMBL3235685 9 Estrogen receptor alpha nM 115.0 \n"", + ""CHEMBL3235686 9 Estrogen receptor alpha nM 26.2 \n"", + ""\n"", + "" STATUS SMILES_desalt pIC50 \\\n"", + ""chemblId \n"", + ""CHEMBL3235685 active CC[Si](CC)(c1ccc(O)cc1)c1ccc(O)cc1 6.939302 \n"", + ""CHEMBL3235686 active CCC[Si](CCC)(c1ccc(O)c(C)c1)c1ccc(O)c(C)c1 7.581699 \n"", + ""\n"", + "" chemblId \n"", + ""chemblId \n"", + ""CHEMBL3235685 CHEMBL3235685 \n"", + ""CHEMBL3235686 CHEMBL3235686 \n"", + ""\n"", + ""[2 rows x 40 columns]"" + ] + }, + ""execution_count"": 30, + ""metadata"": {}, + ""output_type"": ""execute_result"" + } + ], + ""source"": [ + ""df1"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 31, + ""metadata"": { + ""collapsed"": true + }, + ""outputs"": [], + ""source"": [ + ""from rdkit.Chem import Descriptors\n"", + ""from rdkit.Chem import MolFromSmiles\n"", + ""\n"", + ""#path = r'/Users/zeromtmu/Desktop/Adjust_ER_alpha/smiles/'\n"", + ""#mols = SmilesMolSupplier(path +\""Train_smi_only.smi\"")\n"", + ""#mols = SDMolSupplier(\""molecule.sdf\"")\n"", + ""mols = []\n"", + ""\n"", + ""for i in df1.SMILES_desalt:\n"", + "" mol = MolFromSmiles(i)\n"", + "" mols.append(mol)\n"", + ""MW = [Descriptors.MolWt(n) for n in mols]\n"", + ""LogP = [Descriptors.MolLogP(o) for o in mols]\n"", + ""nHAcc = [Descriptors.NumHAcceptors(p) for p in mols]\n"", + ""nHDon = [Descriptors.NumHDonors(q) for q in mols]\n"", + ""\n"", + ""data2 = pd.DataFrame(\n"", + "" {'chemblId': df1.chemblId,\n"", + "" 'STATUS' : 'active',\n"", + "" 'IC50' : df1.value,\n"", + "" 'MW': MW, \n"", + "" 'LogP': LogP,\n"", + "" 'nHAcc': nHAcc,\n"", + "" 'nHDon': nHDon\n"", + "" })\n"", + ""data2 = data2[['chemblId','STATUS','IC50','MW','LogP','nHAcc','nHDon']]"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 32, + ""metadata"": {}, + ""outputs"": [ + { + ""data"": { + ""text/html"": [ + ""
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CHEMBL3235686CHEMBL3235686active26.2328.5284.0976422
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CHEMBL3235685CHEMBL3235685active272.4202.70060226.939302
CHEMBL3235686CHEMBL3235686active328.5284.09764227.581699
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"" + ], + ""text/plain"": [ + "" chemblId STATUS MW LogP nHAcc nHDon pIC50\n"", + ""chemblId \n"", + ""CHEMBL3235685 CHEMBL3235685 active 272.420 2.70060 2 2 6.939302\n"", + ""CHEMBL3235686 CHEMBL3235686 active 328.528 4.09764 2 2 7.581699"" + ] + }, + ""execution_count"": 34, + ""metadata"": {}, + ""output_type"": ""execute_result"" + } + ], + ""source"": [ + ""data2 = pIC50(data2)\n"", + ""data2"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 35, + ""metadata"": { + ""collapsed"": true + }, + ""outputs"": [], + ""source"": [ + ""# fillin missing data\n"", + ""\n"", + ""data2['numRo5Violations'] = [0,0]"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 36, + ""metadata"": {}, + ""outputs"": [ + { + ""data"": { + ""text/html"": [ + ""
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CHEMBL370037active252.2693.2248327.69NaN0.330.503.31...Bioorg. Med. Chem. Lett., (2005) 15:12:3137CHEMBL2069Estrogen receptor alphanM6.0activeCC1=C(c2ccc(O)cc2)C(=O)c2ccc(O)cc218.221849CHEMBL370037
CHEMBL189073intermediate277.2794.0592428.15NaN4.064.123.86...J. Med. Chem., (2004) 47:21:5021CHEMBL2069Estrogen receptor alphanM1727.0intermediateOc1ccc2c(-c3cccc4ccc(O)cc34)noc2c15.762708CHEMBL189073
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Chem., (2004) 47:21:5021 CHEMBL206 \n"", + ""\n"", + "" target_confidence target_name units value \\\n"", + ""chemblId \n"", + ""CHEMBL370037 9 Estrogen receptor alpha nM 6.0 \n"", + ""CHEMBL189073 9 Estrogen receptor alpha nM 1727.0 \n"", + ""\n"", + "" STATUS SMILES_desalt pIC50 \\\n"", + ""chemblId \n"", + ""CHEMBL370037 active CC1=C(c2ccc(O)cc2)C(=O)c2ccc(O)cc21 8.221849 \n"", + ""CHEMBL189073 intermediate Oc1ccc2c(-c3cccc4ccc(O)cc34)noc2c1 5.762708 \n"", + ""\n"", + "" chemblId \n"", + ""chemblId \n"", + ""CHEMBL370037 CHEMBL370037 \n"", + ""CHEMBL189073 CHEMBL189073 \n"", + ""\n"", + ""[2 rows x 40 columns]"" + ] + }, + ""execution_count"": 36, + ""metadata"": {}, + ""output_type"": ""execute_result"" + } + ], + ""source"": [ + ""RO5.head(2)"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 37, + ""metadata"": {}, + ""outputs"": [ + { + ""name"": ""stderr"", + ""output_type"": ""stream"", + ""text"": [ + ""/Users/zeromtmu/anaconda2/envs/ER_alpha/lib/python2.7/site-packages/ipykernel_launcher.py:1: SettingWithCopyWarning: \n"", + ""A value is trying to be set on a copy of a slice from a DataFrame.\n"", + ""Try using .loc[row_indexer,col_indexer] = value instead\n"", + ""\n"", + ""See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n"", + "" \""\""\""Entry point for launching an IPython kernel.\n"" + ] + }, + { + ""data"": { + ""text/plain"": [ + ""0"" + ] + }, + ""execution_count"": 37, + ""metadata"": {}, + ""output_type"": ""execute_result"" + } + ], + ""source"": [ + ""RO5['numRo5Violations']=RO5['numRo5Violations'].fillna(0)\n"", + ""len(RO5[pd.isnull(RO5['numRo5Violations'])])"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 39, + ""metadata"": {}, + ""outputs"": [ + { + ""data"": { + ""image/png"": 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+ ""text/plain"": [ + """" + ] + }, + ""metadata"": {}, + ""output_type"": ""display_data"" + } + ], + ""source"": [ + ""import matplotlib \n"", + ""\n"", + ""def bins_labels(bins, **kwargs):\n"", + "" bin_w = (max(bins) - min(bins)) / (len(bins) - 1)\n"", + "" plt.xticks(np.arange(min(bins)+bin_w/2, max(bins), bin_w), bins, **kwargs)\n"", + "" plt.xlim(bins[0], bins[-1])\n"", + "" \n"", + ""bins = range(10)\n"", + ""\n"", + ""ax = RO5.plot(x='numRo5Violations', \n"", + "" y='pIC50', \n"", + "" kind='hist',\n"", + "" alpha=0.7,\n"", + "" figsize=(5,5),\n"", + "" edgecolor='black', \n"", + "" legend=False,\n"", + "" bins=bins)\n"", + ""\n"", + ""bins_labels(bins, fontsize=12)\n"", + ""\n"", + ""plt.rcParams[\""axes.labelweight\""] = \""bold\""\n"", + ""ax.set_xlabel('pIC$_{50}$', fontsize=13)\n"", + ""ax.set_ylabel('Frequency', fontsize=13)\n"", + ""\n"", + ""plt.xlim(xmin=2, xmax = 10)\n"", + ""plt.savefig('Result/RO5 pIC50 Violations.pdf', dpi=300, bbox_inches='tight')\n"", + ""plt.show()"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 40, + ""metadata"": {}, + ""outputs"": [ + { + ""data"": { + ""image/png"": 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qp/mWZmY08tRzlnwK8Ko3PBC6tVzFmZiNZpRtMm5lZ/xyoZmaZ1HpQ6p2SbgbeAJDGe0RE\nzM9emQ07F33wErZ17arrNiZPOJU7b/tSXbdhVi+1BurvpkFpel56FcWpVAcNVElHAg8A49I274iI\nqyWdBPwrxe+0TwMXpycBIGlxWu9+4GMR8a0aa7U62da1izMvu7au23j8lqvqun6zeqolUB9g6Oef\n7gXeHhEvpButPCjp/1LcG2B1RFwnaRGwCLhS0nSgHZgBnAZ8R9JrI2L/EOswM6ubAQM1Is4d6kbS\n46ZfSJOHpyGAOUDP+pdRXNJ6ZWpfnh5dvUXSJoozDB4aai1mZvXSsINSksZIWgvsAVZFxMNAa0Ts\nTF12AT2PpJ4AbC8tviO19V7nAkmdkjq7u7vrWL2Z2cAaFqgRsT8izgYmAjMlndFrflDxp4WIWBoR\nbRHR1tLSkrFaM7PqGn7aVEQ8D9xH8Xyq3ZLGA6TXPalbFy9/murE1GZmNmw1JFAltUg6IY0fBVwA\n/BBYyYEzBuYBK9L4SqBd0jhJU4FpwJpG1GpmNlhV7jY1FOOBZZLGUIR4R0TcLekhoEPSfGArcDFA\nRKyT1AGsB/YBC32E38yGu4YEakQ8DpzTR/uzwPn9LLMEWFLn0szMsvGlp2ZmmThQzcwycaCamWXi\nQDUzy8SBamaWiQPVzCwTB6qZWSYOVDOzTByoZmaZOFDNzDJxoJqZZeJANTPLxIFqZpaJA9XMLBMH\nqplZJg5UM7NMHKhmZpk4UM3MMnGgmpll4kA1M8vEgWpmlokD1cwsk4YEqqRJku6TtF7SOkmXp/aT\nJK2S9FR6PbG0zGJJmyRtlDSrEXWamQ1Fo/ZQ9wEfj4jpwBuBhZKmA4uA1RExDVidpknz2oEZwGzg\nRkljGlSrmdmgNCRQI2JnRDyaxn8BbAAmAHOAZanbMuDCND4HWB4ReyNiC7AJmNmIWs3MBqvhv6FK\nmgKcAzwMtEbEzjRrF9CaxicA20uL7UhtZmbDVkMDVdIxwJ3AFRHx8/K8iAggKq5vgaROSZ3d3d0Z\nKzUzq65hgSrpcIow/UpE3JWad0san+aPB/ak9i5gUmnxiantZSJiaUS0RURbS0tL/Yo3M6tBo47y\nC7gJ2BARnynNWgnMS+PzgBWl9nZJ4yRNBaYBaxpRq5nZYI1t0HbeAlwCPCFpbWq7CrgO6JA0H9gK\nXAwQEeskdQDrKc4QWBgR+xtUq5nZoDQkUCPiQUD9zD6/n2WWAEvqVpSZWWa+UsrMLBMHqplZJg5U\nM7NMHKhmZpk4UM3MMnGgmpll4kA1M8vEgWpmlokD1cwsEweqmVkmDlQzs0wcqGZmmThQzcwycaCa\nmWXiQDUzy8SBamaWiQPVzCwTB6qZWSYOVDOzTByoZmaZOFDNzDJxoJqZZeJANTPLpCGBKulmSXsk\nPVlqO0nSKklPpdcTS/MWS9okaaOkWY2o0cxsqBq1h/p/gNm92hYBqyNiGrA6TSNpOtAOzEjL3Chp\nTIPqNDMbtIYEakQ8ADzXq3kOsCyNLwMuLLUvj4i9EbEF2ATMbESdZmZD0czfUFsjYmca3wW0pvEJ\nwPZSvx2p7RUkLZDUKamzu7u7fpWamdVgbLMLAIiIkBSDWG4psBSgra2t8vJm9XTRBy9hW9euum9n\n8oRTufO2L9V9OzawZgbqbknjI2KnpPHAntTeBUwq9ZuY2sxGlG1duzjzsmvrvp3Hb7mq7tuw2jTz\nK/9KYF4anwesKLW3SxonaSowDVjThPrMzCppyB6qpNuBc4GTJe0ArgauAzokzQe2AhcDRMQ6SR3A\nemAfsDAi9jeiTjOzoWhIoEbE3H5mnd9P/yXAkvpVZGaWn6+UMjPLxIFqZpaJA9XMLBMHqplZJg5U\nM7NMHKhmZpk4UM3MMnGgmpll4kA1M8vEgWpmlokD1cwsEweqmVkmDlQzs0wcqGZmmThQzcwycaCa\nmWXiQDUzy8SBamaWiQPVzCwTB6qZWSYOVDOzTByoZmaZDOtAlTRb0kZJmyQtanY9ZmYHM2wDVdIY\n4F+AdwHTgbmSpje3KjOz/g3bQAVmApsiYnNEvAgsB+Y0uSYzs36NbXYBBzEB2F6a3gG8oUm1mFmd\nXfTBS9jWtavu25k84VTuvO1LdVm3IqIuKx4qSX8MzI6I/5ymLwHeEBF/XuqzAFiQJk8HNlbczMnA\nMxnKtQP8meblzzO/qp/pqyOipZaOw3kPtQuYVJqemNr+Q0QsBZYOdgOSOiOibbDL2yv5M83Ln2d+\n9fxMh/NvqN8HpkmaKukIoB1Y2eSazMz6NWz3UCNin6Q/B74FjAFujoh1TS7LzKxfwzZQASLiHuCe\nOm5i0D8XWL/8meblzzO/un2mw/aglJnZSDOcf0M1MxtRRmWg+pLWvCTdLGmPpCebXcuhQtIkSfdJ\nWi9pnaTLm13TSCbpSElrJD2WPs9P1WU7o+0rf7qk9UfABRQXC3wfmBsR65ta2Agm6a3AC8CtEXFG\ns+s5FEgaD4yPiEclHQs8Alzov9PBkSTg6Ih4QdLhwIPA5RHxvZzbGY17qL6kNbOIeAB4rtl1HEoi\nYmdEPJrGfwFsoLh60AYhCi+kycPTkH1vcjQGal+XtPoP1YYtSVOAc4CHm1vJyCZpjKS1wB5gVURk\n/zxHY6CajRiSjgHuBK6IiJ83u56RLCL2R8TZFFddzpSU/eep0RioA17SajYcpN/67gS+EhF3Nbue\nQ0VEPA/cB8zOve7RGKi+pNWGvXQQ5SZgQ0R8ptn1jHSSWiSdkMaPojgo/cPc2xl1gRoR+4CeS1o3\nAB2+pHVoJN0OPAScLmmHpPnNrukQ8BbgEuDtktam4d3NLmoEGw/cJ+lxip2qVRFxd+6NjLrTpszM\n6mXU7aGamdWLA9XMLBMHqplZJg5UM7NMHKhmZpk4UK1mkp6WFJIurbDMNWmZ+zNsf0paV6TLMYc9\nSZdK2ixpf6rbN485hDlQRzlJ96Z/6K84J0/Sl9O8R1PTzcANQF3veHSQ4Px52v4NaXxYk9QK/G9g\nKvB1irpf8bTNXu83JL0kqTv9tzl3ENuNfoZjhvqe7OCG9SNQrCG+CJwHzJJ0akTsApB0HPC+Uh8i\n4m+bU2IhIp4DrmhmDRVN48C/sfdHxEs1LHMnxc07/pDiv8tMSRMi4meD2P4NvaZfHMQ6rIqI8DCK\nB+BIilvvBfCJUvuC1PbvwPGp7enUdmmp34XA/wN+CnQDq4G3leZfk5a5v9T2DYq7fP06DU8Af5rm\nnZv69x6uAaaUpqek/kcAnwCeBH4JbANuA6aWtnd/WuZG4I7UbwewoNTnD4DvAs+nmrYAKwb47Pp9\n78Clfb2PftZTfl/nltbd09ZW6nsWxaXSPwF+ATwKzAcOK/Xpd1se6vzvqdkFeGj+APzP9I9wfant\nodR2a6ntZYEK/Nc0vR/4V4oHKgbwEvCu1KevQF0PfAX4HHBXWj6AtwO/TfHTQk+Y3AxcT3Eji74C\ndXmafp7i2ve1afoZ4LTU5/7Sct8GVqXxfT3BC2xNbfcAn6cI/ecP8pkd9L1T3Hf3jtJ2rweu72dd\nLwtUiv9JXJumf8aB/6GdDfwqtT8A3ArsTdP/UFpfz7qeS6HbCXyg2X9no2FoegEemj8AZ5b+Eb4B\n+J3S9FtL/XoH6o/T9GdLfb6R2r6TpvsK1InAwhQa1wO7Up/r0vxXBGdf7RR3DeuZvij1OSqFaQB/\nndp6ArWnpsNSUJWX66nhCuD3gWOAsQf5zGp57+f21DfA519+X+XhR8DMUr+bUvvjpbZPcOCbxBGl\n99IBfIFiz71nfXOa/bd2qA/+DdWIiMclfR94PXAZRdgA/CiKu/H3Z3J6LT9L6gng3aV5LyNpJsXe\n1bg+ZrdWqbvXNp4EiIhfSdoEvKqPGh5OfV6S9DxwHHBsmvcnwKeBzwCi2NO8R9LFEfGrg2y75vde\noztJQU/xG+zbgDW9tlm+mc8T6fUo4GSKnwLGR0rWdEe1HwGvpriz2ooh1GYD8FF+6/HF9NoOfDiN\n3zTAMtvS64xS2xm95vU2lyJMNwOnUPwNbkjzlF73lfof7G+0vI0ZUDyMDXhNPzX8pjQevebdGxG/\nRxGyr6fYA30vRbAdbNtV3nstPhcR7weuS9NLJJ3ea73T+9jmr4BnJJ0GHF2aLw58rkcOoS6rgQPV\netxOcbDmeOBUivBZNsAy/5ReF0pank69eg9FWP1zP8v8JL1OTH3u50AA9thNcWAI4POSrpc0vVcf\nImI78NU0eZOkmygOEp1McaDolgHqL1sraVWq6SOpPuj/WVmDee9VXEtxoOtw4O9S279Q/GZ6pqR/\nk7QM+Ps077NRPCPtnUCXpK9L+gLFw/169mwH+u9pQ+RANeA/HgTXUWq6OyJ2D7DMjcD7Ke4vORt4\nI0VAviMivtHPYp8FvkwRmLOAeykOgJXX+xvgYxRPUrgAuBz4T/2s7xJgMUVQzwVaKA4SzYyIKk9i\n+CZF8Myl2EvfDlwZEff01XmQ771mUTxQ7to0+ceSzonioX1vAu4GTqc4rW0DxQGyxalvZ3ovr6M4\n0+AUis/4XRHx9aHWZQfn+6GamWXiPVQzs0wcqGZmmThQzcwycaCamWXiQDUzy8SBamaWiQPVzCwT\nB6qZWSYOVDOzTP4/p1x8DZilRLAAAAAASUVORK5CYII=\n"", + ""text/plain"": [ + """" + ] + }, + ""metadata"": {}, + ""output_type"": ""display_data"" + } + ], + ""source"": [ + ""import matplotlib.pyplot as plt\n"", + ""\n"", + ""ax = RO5.hist(column='numRo5Violations',\n"", + "" grid=False, \n"", + "" alpha=0.7,\n"", + "" figsize=(5,5),\n"", + "" sharey=True, \n"", + "" sharex=True,\n"", + "" edgecolor='black')\n"", + ""\n"", + ""plt.title('')\n"", + ""plt.xlabel('Violations of Ro5', fontsize=13, weight='bold')\n"", + ""plt.ylabel('Frequency', fontsize=13, weight='bold')\n"", + ""plt.xticks(np.arange(min(RO5['numRo5Violations']), max(RO5['numRo5Violations'])+1, 1.0))\n"", + ""plt.savefig('Result/RO5 Violations.pdf', dpi=300, bbox_inches='tight')\n"", + ""plt.show()"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 49, + ""metadata"": { + ""collapsed"": true + }, + ""outputs"": [], + ""source"": [ + ""Ro5_0 = (RO5[RO5.numRo5Violations == 0])\n"", + ""Ro5_1 = (RO5[RO5.numRo5Violations == 1])\n"", + ""Ro5_2 = (RO5[RO5.numRo5Violations == 2])\n"", + ""Ro5_3 = (RO5[RO5.numRo5Violations == 3])"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 119, + ""metadata"": {}, + ""outputs"": [ + { + ""data"": { + ""image/png"": 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DRbr7ppvzoTz5r8v1s23pELr/4PRv+uwIaALB07t3VecLpb5p8P1dfcPYkv2uIEwBgMAIa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\n1v5kRgAAAABJRU5ErkJggg==\n"", + ""text/plain"": [ + """" + ] + }, + ""metadata"": {}, + ""output_type"": ""display_data"" + } + ], + ""source"": [ + ""fig, axs = plt.subplots(2,2,figsize=(10,10), subplot_kw={'ylim': (0,260)}, sharey=True)\n"", + ""\n"", + ""Ro5_0.plot(ax=axs[0, 0], x='numRo5Violations', \n"", + "" y='pIC50', \n"", + "" kind='hist',\n"", + "" alpha=0.7,\n"", + "" edgecolor='black', \n"", + "" legend=False,\n"", + "" bins=bins)\n"", + ""Ro5_1.plot(ax=axs[0, 1], x='numRo5Violations', \n"", + "" y='pIC50', \n"", + "" kind='hist',\n"", + "" alpha=0.7,\n"", + "" edgecolor='black', \n"", + "" legend=False,\n"", + "" bins=bins)\n"", + ""Ro5_2.plot(ax=axs[1, 0], x='numRo5Violations', \n"", + "" y='pIC50', \n"", + "" kind='hist',\n"", + "" alpha=0.7b\n"", + "" edgecolor='black', \n"", + "" legend=False,\n"", + "" bins=bins, label='ylabel')\n"", + ""Ro5_3.plot(ax=axs[1, 1], x='numRo5Violations', \n"", + "" y='pIC50', \n"", + "" kind='hist',\n"", + "" alpha=0.7,\n"", + "" edgecolor='black', \n"", + "" legend=False,\n"", + "" bins=bins)\n"", + ""\n"", + ""plt.xlabel('pIC$_{50}$', fontsize=13, ha='center')\n"", + ""plt.ylabel('Frequency', fontsize=13)\n"", + ""\n"", + ""\n"", + "" \n"", + ""plt.savefig('Result/RO5 pIC50 Violations.pdf', dpi=300, bbox_inches='tight')\n"", + ""fig.show()"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 42, + ""metadata"": { + ""collapsed"": true + }, + ""outputs"": [], + ""source"": [ + ""MW_0_100= (RO5[(0 < RO5.MW) & (RO5.MW <= 100 )])\n"", + ""MW_100_200= (RO5[(100 < RO5.MW) & (RO5.MW <= 200 )])\n"", + ""MW_200_300= (RO5[(200 < RO5.MW) & (RO5.MW <= 300 )])\n"", + ""MW_300_400= (RO5[(300 < RO5.MW) & (RO5.MW <= 400 )])\n"", + ""MW_400_500= (RO5[(400 < RO5.MW) & (RO5.MW <= 500 )])\n"", + ""MW_500_600= (RO5[(500 < RO5.MW) & (RO5.MW <= 600 )])\n"", + ""MW_600_700= (RO5[(600 < RO5.MW) & (RO5.MW <= 700 )])\n"", + ""MW_700_800= (RO5[(700 < RO5.MW) & (RO5.MW <= 800 )])\n"", + ""MW_800_900= (RO5[(800 < RO5.MW) & (RO5.MW <= 900 )])\n"", + ""MW_900_1000=(RO5[(900 < RO5.MW) & (RO5.MW <= 1000)])\n"", + ""\n"", + ""mean0_100 = (RO5[(0 < RO5.MW) & (RO5.MW <= 100 )]['pIC50'].mean())\n"", + ""mean100_200 = (RO5[(100 < RO5.MW) & (RO5.MW <= 200 )]['pIC50'].mean())\n"", + ""mean200_300 = (RO5[(200 < RO5.MW) & (RO5.MW <= 300 )]['pIC50'].mean())\n"", + ""mean300_400 = (RO5[(300 < RO5.MW) & (RO5.MW <= 400 )]['pIC50'].mean())\n"", + ""mean400_500 = (RO5[(400 < RO5.MW) & (RO5.MW <= 500 )]['pIC50'].mean())\n"", + ""mean500_600 = (RO5[(500 < RO5.MW) & (RO5.MW <= 600 )]['pIC50'].mean())\n"", + ""mean600_700 = (RO5[(600 < RO5.MW) & (RO5.MW <= 700 )]['pIC50'].mean())\n"", + ""mean700_800 = (RO5[(700 < RO5.MW) & (RO5.MW <= 800 )]['pIC50'].mean())\n"", + ""mean800_900 = (RO5[(800 < RO5.MW) & (RO5.MW <= 900 )]['pIC50'].mean())\n"", + ""mean900_1000= (RO5[(900 < RO5.MW) & (RO5.MW <= 1000)]['pIC50'].mean())\n"", + ""\n"", + ""std0_100 = (RO5[(0 < RO5.MW) & (RO5.MW <= 100)]['pIC50'].std())\n"", + ""std100_200 = (RO5[(100 < RO5.MW) & (RO5.MW <= 200)]['pIC50'].std())\n"", + ""std200_300 = (RO5[(200 < RO5.MW) & (RO5.MW <= 300)]['pIC50'].std())\n"", + ""std300_400 = (RO5[(300 < RO5.MW) & (RO5.MW <= 400)]['pIC50'].std())\n"", + ""std400_500 = (RO5[(400 < RO5.MW) & (RO5.MW <= 500)]['pIC50'].std())\n"", + ""std500_600 = (RO5[(500 < RO5.MW) & (RO5.MW <= 600)]['pIC50'].std())\n"", + ""std600_700 = (RO5[(600 < RO5.MW) & (RO5.MW <= 700)]['pIC50'].std())\n"", + ""std700_800 = (RO5[(700 < RO5.MW) & (RO5.MW <= 800)]['pIC50'].std())\n"", + ""std800_900 = (RO5[(800 < RO5.MW) & (RO5.MW <= 900)]['pIC50'].std())\n"", + ""std900_1000 =(RO5[(900 < RO5.MW) & (RO5.MW <= 1000)]['pIC50'].std())\n"", + ""\n"", + ""\n"", + ""outfile = open('binning.csv', 'a')\n"", + ""\n"", + ""print >> outfile, 'MW'\n"", + ""print >> outfile, 'n 0-100, mean 0-100 , std 0-100,' + \\\n"", + "" 'n 100-200, mean 100-200, std 100-200,' + \\\n"", + "" 'n 200-300, mean 200-300, std 200-300,' + \\\n"", + "" 'n 300-400, mean 300-400, std 300-400,' + \\\n"", + "" 'n 400-500, mean 400-500, std 400-500,' + \\\n"", + "" 'n 500-600, mean 500-600, std 500-600,' + \\\n"", + "" 'n 600-700, mean 600-700, std 600-700,' + \\\n"", + "" 'n 700-800, mean 700-800, std 700-800,' + \\\n"", + "" 'n 800-900, mean 800-900, std 800-900,' + \\\n"", + "" 'n 900-1000,mean 900-1000,std 900-1000'\n"", + "" \n"", + ""print >> outfile, '%d,%0.4f,%0.4f,%d,%0.4f,%0.4f,%d,%0.4f,%0.4f, \\\n"", + "" %d,%0.4f,%0.4f,%d,%0.4f,%0.4f, \\\n"", + "" %d,%0.4f,%0.4f,%d,%0.4f,%0.4f, \\\n"", + "" %d,%0.4f,%0.4f,%d,%0.4f,%0.4f,%d,%0.4f,%0.4f' \\\n"", + "" % (len(MW_0_100), mean0_100, std0_100,\n"", + "" len(MW_100_200), mean100_200, std100_200,\n"", + "" len(MW_200_300), mean200_300, std200_300,\n"", + "" len(MW_300_400), mean300_400, std300_400,\n"", + "" len(MW_400_500), mean400_500, std400_500,\n"", + "" len(MW_500_600), mean500_600, std500_600,\n"", + "" len(MW_600_700), mean600_700, std600_700,\n"", + "" len(MW_700_800), mean700_800, std700_800,\n"", + "" len(MW_800_900), mean800_900, std800_900,\n"", + "" len(MW_900_1000), mean900_1000, std900_1000)\n"", + "" \n"", + ""outfile.close() "" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 43, + ""metadata"": { + ""collapsed"": true + }, + ""outputs"": [], + ""source"": [ + ""LogP_0_1= (RO5[(0 < RO5.LogP) & (RO5.LogP <= 1 )])\n"", + ""LogP_1_2= (RO5[(1 < RO5.LogP) & (RO5.LogP <= 2 )])\n"", + ""LogP_2_3= (RO5[(2 < RO5.LogP) & (RO5.LogP <= 3 )])\n"", + ""LogP_3_4= (RO5[(3 < RO5.LogP) & (RO5.LogP <= 4 )])\n"", + ""LogP_4_5= (RO5[(4 < RO5.LogP) & (RO5.LogP <= 5 )])\n"", + ""LogP_5_6= (RO5[(5 < RO5.LogP) & (RO5.LogP <= 6 )])\n"", + ""LogP_6_7= (RO5[(6 < RO5.LogP) & (RO5.LogP <= 7 )])\n"", + ""LogP_7_8= (RO5[(7 < RO5.LogP) & (RO5.LogP <= 8 )])\n"", + ""LogP_8_9= (RO5[(8 < RO5.LogP) & (RO5.LogP <= 9 )])\n"", + ""LogP_9_10=(RO5[(9 < RO5.LogP) & (RO5.LogP <= 10)])\n"", + ""\n"", + ""mean0_1 = (RO5[(0 < RO5.LogP) & (RO5.LogP <= 1 )]['pIC50'].mean())\n"", + ""mean1_2 = (RO5[(1 < RO5.LogP) & (RO5.LogP <= 2 )]['pIC50'].mean())\n"", + ""mean2_3 = (RO5[(2 < RO5.LogP) & (RO5.LogP <= 3 )]['pIC50'].mean())\n"", + ""mean3_4 = (RO5[(3 < RO5.LogP) & (RO5.LogP <= 4 )]['pIC50'].mean())\n"", + ""mean4_5 = (RO5[(4 < RO5.LogP) & (RO5.LogP <= 5 )]['pIC50'].mean())\n"", + ""mean5_6 = (RO5[(5 < RO5.LogP) & (RO5.LogP <= 6 )]['pIC50'].mean())\n"", + ""mean6_7 = (RO5[(6 < RO5.LogP) & (RO5.LogP <= 7 )]['pIC50'].mean())\n"", + ""mean7_8 = (RO5[(7 < RO5.LogP) & (RO5.LogP <= 8 )]['pIC50'].mean())\n"", + ""mean8_9 = (RO5[(8 < RO5.LogP) & (RO5.LogP <= 9 )]['pIC50'].mean())\n"", + ""mean9_10= (RO5[(9 < RO5.LogP) & (RO5.LogP <= 10)]['pIC50'].mean())\n"", + ""\n"", + ""std0_1 = (RO5[(0 < RO5.LogP) & (RO5.LogP <= 1)]['pIC50'].std())\n"", + ""std1_2 = (RO5[(1 < RO5.LogP) & (RO5.LogP <= 2)]['pIC50'].std())\n"", + ""std2_3 = (RO5[(2 < RO5.LogP) & (RO5.LogP <= 3)]['pIC50'].std())\n"", + ""std3_4 = (RO5[(3 < RO5.LogP) & (RO5.LogP <= 4)]['pIC50'].std())\n"", + ""std4_5 = (RO5[(4 < RO5.LogP) & (RO5.LogP <= 5)]['pIC50'].std())\n"", + ""std5_6 = (RO5[(5 < RO5.LogP) & (RO5.LogP <= 6)]['pIC50'].std())\n"", + ""std6_7 = (RO5[(6 < RO5.LogP) & (RO5.LogP <= 7)]['pIC50'].std())\n"", + ""std7_8 = (RO5[(7 < RO5.LogP) & (RO5.LogP <= 8)]['pIC50'].std())\n"", + ""std8_9 = (RO5[(8 < RO5.LogP) & (RO5.LogP <= 9)]['pIC50'].std())\n"", + ""std9_10 = (RO5[(9 < RO5.LogP) & (RO5.LogP <= 10)]['pIC50'].std())\n"", + ""\n"", + ""\n"", + ""outfile = open('binning.csv', 'a')\n"", + ""\n"", + ""print >> outfile, 'LogP'\n"", + ""print >> outfile, 'n 0-1, mean 0-1, std 0-1,' + \\\n"", + "" 'n 1-2, mean 1-2, std 1-2,' + \\\n"", + "" 'n 2-3, mean 2-3, std 2-3,' + \\\n"", + "" 'n 3-4, mean 3-4, std 3-4,' + \\\n"", + "" 'n 4-5, mean 4-5, std 4-5,' + \\\n"", + "" 'n 5-6, mean 5-6, std 5-6,' + \\\n"", + "" 'n 6-7, mean 6-7, std 6-7,' + \\\n"", + "" 'n 7-8, mean 7-8, std 7-8,' + \\\n"", + "" 'n 8-9, mean 8-9, std 8-9,' + \\\n"", + "" 'n 9-10, mean 9-10, std 9-10'\n"", + "" \n"", + ""print >> outfile, '%d,%0.4f,%0.4f,%d,%0.4f,%0.4f,%d,%0.4f,%0.4f, \\\n"", + "" %d,%0.4f,%0.4f,%d,%0.4f,%0.4f, \\\n"", + "" %d,%0.4f,%0.4f,%d,%0.4f,%0.4f, \\\n"", + "" %d,%0.4f,%0.4f,%d,%0.4f,%0.4f,%d,%0.4f,%0.4f' \\\n"", + "" % (len(LogP_0_1), mean0_1, std0_1,\n"", + "" len(LogP_1_2), mean1_2, std1_2,\n"", + "" len(LogP_2_3), mean2_3, std2_3,\n"", + "" len(LogP_3_4), mean3_4, std3_4,\n"", + "" len(LogP_4_5), mean4_5, std4_5,\n"", + "" len(LogP_5_6), mean5_6, std5_6,\n"", + "" len(LogP_6_7), mean6_7, std6_7,\n"", + "" len(LogP_7_8), mean7_8, std7_8,\n"", + "" len(LogP_8_9), mean8_9, std8_9,\n"", + "" len(LogP_9_10),mean9_10, std9_10)\n"", + "" \n"", + ""outfile.close() "" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 44, + ""metadata"": { + ""collapsed"": true + }, + ""outputs"": [], + ""source"": [ + ""nHAcc_1 = (RO5[RO5.nHAcc== 1 ])\n"", + ""nHAcc_2 = (RO5[RO5.nHAcc== 2 ])\n"", + ""nHAcc_3 = (RO5[RO5.nHAcc== 3 ])\n"", + ""nHAcc_4 = (RO5[RO5.nHAcc== 4 ])\n"", + ""nHAcc_5 = (RO5[RO5.nHAcc== 5 ])\n"", + ""nHAcc_6 = (RO5[RO5.nHAcc== 6 ])\n"", + ""nHAcc_7 = (RO5[RO5.nHAcc== 7 ])\n"", + ""nHAcc_8 = (RO5[RO5.nHAcc== 8 ])\n"", + ""nHAcc_9 = (RO5[RO5.nHAcc== 9 ])\n"", + ""nHAcc_10= (RO5[RO5.nHAcc== 10])\n"", + ""\n"", + ""mean_1 = (RO5[RO5.nHAcc== 1 ]['pIC50'].mean())\n"", + ""mean_2 = (RO5[RO5.nHAcc== 2 ]['pIC50'].mean())\n"", + ""mean_3 = (RO5[RO5.nHAcc== 3 ]['pIC50'].mean())\n"", + ""mean_4 = (RO5[RO5.nHAcc== 4 ]['pIC50'].mean())\n"", + ""mean_5 = (RO5[RO5.nHAcc== 5 ]['pIC50'].mean())\n"", + ""mean_6 = (RO5[RO5.nHAcc== 6 ]['pIC50'].mean())\n"", + ""mean_7 = (RO5[RO5.nHAcc== 7 ]['pIC50'].mean())\n"", + ""mean_8 = (RO5[RO5.nHAcc== 8 ]['pIC50'].mean())\n"", + ""mean_9 = (RO5[RO5.nHAcc== 9 ]['pIC50'].mean())\n"", + ""mean_10= (RO5[RO5.nHAcc== 10]['pIC50'].mean())\n"", + ""\n"", + ""std_1 = (RO5[RO5.nHAcc== 1 ]['pIC50'].std())\n"", + ""std_2 = (RO5[RO5.nHAcc== 2 ]['pIC50'].std())\n"", + ""std_3 = (RO5[RO5.nHAcc== 3 ]['pIC50'].std())\n"", + ""std_4 = (RO5[RO5.nHAcc== 4 ]['pIC50'].std())\n"", + ""std_5 = (RO5[RO5.nHAcc== 5 ]['pIC50'].std())\n"", + ""std_6 = (RO5[RO5.nHAcc== 6 ]['pIC50'].std())\n"", + ""std_7 = (RO5[RO5.nHAcc== 7 ]['pIC50'].std())\n"", + ""std_8 = (RO5[RO5.nHAcc== 8 ]['pIC50'].std())\n"", + ""std_9 = (RO5[RO5.nHAcc== 9 ]['pIC50'].std())\n"", + ""std_10 =(RO5[RO5.nHAcc== 10]['pIC50'].std())\n"", + ""\n"", + ""\n"", + ""outfile = open('binning.csv', 'a')\n"", + ""\n"", + ""print >> outfile, 'nHAcc'\n"", + ""print >> outfile, 'n 1, mean 1, std 1,' + \\\n"", + "" 'n 2, mean 2, std 2,' + \\\n"", + "" 'n 3, mean 3, std 3,' + \\\n"", + "" 'n 4, mean 4, std 4,' + \\\n"", + "" 'n 5, mean 5, std 5,' + \\\n"", + "" 'n 6, mean 6, std 6,' + \\\n"", + "" 'n 7, mean 7, std 7,' + \\\n"", + "" 'n 8, mean 8, std 8,' + \\\n"", + "" 'n 9, mean 9, std 9,' + \\\n"", + "" 'n 10,mean 10,std 10'\n"", + "" \n"", + ""print >> outfile, '%d,%0.4f,%0.4f,%d,%0.4f,%0.4f,%d,%0.4f,%0.4f, \\\n"", + "" %d,%0.4f,%0.4f,%d,%0.4f,%0.4f, \\\n"", + "" %d,%0.4f,%0.4f,%d,%0.4f,%0.4f, \\\n"", + "" %d,%0.4f,%0.4f,%d,%0.4f,%0.4f,%d,%0.4f,%0.4f' \\\n"", + "" % (len(nHAcc_1), mean_1, std_1,\n"", + "" len(nHAcc_2), mean_2, std_2,\n"", + "" len(nHAcc_3), mean_3, std_3,\n"", + "" len(nHAcc_4), mean_4, std_4,\n"", + "" len(nHAcc_5), mean_5, std_5,\n"", + "" len(nHAcc_6), mean_6, std_6,\n"", + "" len(nHAcc_7), mean_7, std_7,\n"", + "" len(nHAcc_8), mean_8, std_8,\n"", + "" len(nHAcc_9), mean_9, std_9,\n"", + "" len(nHAcc_10),mean_10,std_10)\n"", + "" \n"", + ""outfile.close()"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 45, + ""metadata"": { + ""collapsed"": true + }, + ""outputs"": [], + ""source"": [ + ""nHDon_1 = (RO5[RO5.nHDon== 1 ])\n"", + ""nHDon_2 = (RO5[RO5.nHDon== 2 ])\n"", + ""nHDon_3 = (RO5[RO5.nHDon== 3 ])\n"", + ""nHDon_4 = (RO5[RO5.nHDon== 4 ])\n"", + ""nHDon_5 = (RO5[RO5.nHDon== 5 ])\n"", + ""nHDon_6 = (RO5[RO5.nHDon== 6 ])\n"", + ""nHDon_7 = (RO5[RO5.nHDon== 7 ])\n"", + ""nHDon_8 = (RO5[RO5.nHDon== 8 ])\n"", + ""nHDon_9 = (RO5[RO5.nHDon== 9 ])\n"", + ""nHDon_10= (RO5[RO5.nHDon== 10])\n"", + ""\n"", + ""mean_1_D = (RO5[RO5.nHDon== 1 ]['pIC50'].mean())\n"", + ""mean_2_D = (RO5[RO5.nHDon== 2 ]['pIC50'].mean())\n"", + ""mean_3_D = (RO5[RO5.nHDon== 3 ]['pIC50'].mean())\n"", + ""mean_4_D = (RO5[RO5.nHDon== 4 ]['pIC50'].mean())\n"", + ""mean_5_D = (RO5[RO5.nHDon== 5 ]['pIC50'].mean())\n"", + ""mean_6_D = (RO5[RO5.nHDon== 6 ]['pIC50'].mean())\n"", + ""mean_7_D = (RO5[RO5.nHDon== 7 ]['pIC50'].mean())\n"", + ""mean_8_D = (RO5[RO5.nHDon== 8 ]['pIC50'].mean())\n"", + ""mean_9_D = (RO5[RO5.nHDon== 9 ]['pIC50'].mean())\n"", + ""mean_10_D= (RO5[RO5.nHDon== 10]['pIC50'].mean())\n"", + ""\n"", + ""std_1_D = (RO5[RO5.nHDon== 1 ]['pIC50'].std())\n"", + ""std_2_D = (RO5[RO5.nHDon== 2 ]['pIC50'].std())\n"", + ""std_3_D = (RO5[RO5.nHDon== 3 ]['pIC50'].std())\n"", + ""std_4_D = (RO5[RO5.nHDon== 4 ]['pIC50'].std())\n"", + ""std_5_D = (RO5[RO5.nHDon== 5 ]['pIC50'].std())\n"", + ""std_6_D = (RO5[RO5.nHDon== 6 ]['pIC50'].std())\n"", + ""std_7_D = (RO5[RO5.nHDon== 7 ]['pIC50'].std())\n"", + ""std_8_D = (RO5[RO5.nHDon== 8 ]['pIC50'].std())\n"", + ""std_9_D = (RO5[RO5.nHDon== 9 ]['pIC50'].std())\n"", + ""std_10_D =(RO5[RO5.nHDon== 10]['pIC50'].std())\n"", + ""\n"", + ""outfile = open('binning.csv', 'a')\n"", + ""\n"", + ""print >> outfile, 'nHDon'\n"", + ""print >> outfile, 'n 1, mean 1, std 1,' + \\\n"", + "" 'n 2, mean 2, std 2,' + \\\n"", + "" 'n 3, mean 3, std 3,' + \\\n"", + "" 'n 4, mean 4, std 4,' + \\\n"", + "" 'n 5, mean 5, std 5,' + \\\n"", + "" 'n 6, mean 6, std 6,' + \\\n"", + "" 'n 7, mean 7, std 7,' + \\\n"", + "" 'n 8, mean 8, std 8,' + \\\n"", + "" 'n 9, mean 9, std 9,' + \\\n"", + "" 'n 10,mean 10,std 10'\n"", + "" \n"", + ""print >> outfile, '%d,%0.4f,%0.4f,%d,%0.4f,%0.4f,%d,%0.4f,%0.4f, \\\n"", + "" %d,%0.4f,%0.4f,%d,%0.4f,%0.4f, \\\n"", + "" %d,%0.4f,%0.4f,%d,%0.4f,%0.4f, \\\n"", + "" %d,%0.4f,%0.4f,%d,%0.4f,%0.4f,%d,%0.4f,%0.4f' \\\n"", + "" % (len(nHDon_1), mean_1_D, std_1_D,\n"", + "" len(nHDon_2), mean_2_D, std_2_D,\n"", + "" len(nHDon_3), mean_3_D, std_3_D,\n"", + "" len(nHDon_4), mean_4_D, std_4_D,\n"", + "" len(nHDon_5), mean_5_D, std_5_D,\n"", + "" len(nHDon_6), mean_6_D, std_6_D,\n"", + "" len(nHDon_7), mean_7_D, std_7_D,\n"", + "" len(nHDon_8), mean_8_D, std_8_D,\n"", + "" len(nHDon_9), mean_9_D, std_9_D,\n"", + "" len(nHDon_10),mean_10_D,std_10_D)\n"", + "" \n"", + ""outfile.close()"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 46, + ""metadata"": {}, + ""outputs"": [ + { + ""name"": ""stderr"", + ""output_type"": ""stream"", + ""text"": [ + ""/Users/zeromtmu/anaconda2/envs/ER_alpha/lib/python2.7/site-packages/matplotlib/cbook.py:2649: UserWarning: Saw kwargs [u'lw', u'linewidth'] which are all aliases for u'linewidth'. Kept value from u'linewidth'\n"", + "" seen=seen, canon=canonical, used=seen[-1]))\n"" + ] + }, + { + ""data"": { + ""image/png"": 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RJs+iysbGO32Kr6f7tFmNjzQUkaSD1mng4DjgpM89vIhhJkiRJkjS39Ho5zbf2I+kQEQ+M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+ ""text/plain"": [ + """" + ] + }, + ""metadata"": {}, + ""output_type"": ""display_data"" + } + ], + ""source"": [ + ""import matplotlib.ticker as plticker\n"", + ""\n"", + ""figure, ((plt1,plt2), (plt3,plt4)) = plt.subplots(2, 2)\n"", + ""figure.set_size_inches(15,10)\n"", + ""\n"", + ""loc = plticker.MultipleLocator(base=1.0) # this locator puts ticks at regular intervals\n"", + ""\n"", + ""# Histogram for MW\n"", + ""mean1 = ((mean0_100),(mean100_200),(mean200_300),(mean300_400),(mean400_500), \\\n"", + "" (mean500_600),(mean600_700),(mean700_800),(mean800_900),(mean900_1000))\n"", + ""std1 = ((std0_100),(std100_200),(std200_300),(std300_400),(std400_500), \\\n"", + "" (std500_600),(std600_700),(std700_800),(std800_900),(std900_1000))\n"", + ""index = np.arange(len(mean1)) # the x locations for the groups\n"", + ""\n"", + ""\n"", + ""plt1.bar(index, mean1, align='center', width=width, color='blue',edgecolor='black', yerr=std1,\\\n"", + "" error_kw=dict(ecolor='gray', lw=2, capsize=5, capthick=2, linestyle='-', linewidth=0.5))\n"", + ""\n"", + ""labels1 = ('0-100','100-200','200-300','300-400','400-500','500-600','600-700','700-800','800-900','900-1000')\n"", + ""plt1.set_xticklabels(labels1, fontdict=None, minor=False)\n"", + ""plt1.tick_params(axis='both', which='major', labelsize=14)\n"", + ""#plt1.set_xlim(0,10)\n"", + ""plt1.set_ylim(0,10)\n"", + ""plt1.set_xlabel('Range of molecular weight (Da)', fontsize=16, fontweight='bold')\n"", + ""plt1.set_ylabel('Average $pIC_{50}$ values', fontsize=16, fontweight='bold')\n"", + ""plt1.grid(True)\n"", + ""\n"", + ""\n"", + ""# Histogram for LogP\n"", + ""mean2 = ((mean0_1),(mean1_2),(mean2_3),(mean3_4),(mean4_5), \\\n"", + "" (mean5_6),(mean6_7),(mean7_8),(mean8_9),(mean9_10))\n"", + ""std2 = ((std0_1),(std1_2),(std2_3),(std3_4),(std4_5), \\\n"", + "" (std5_6),(std6_7),(std7_8),(std8_9),(std9_10))\n"", + ""index = np.arange(len(mean2)) # the x locations for the groups\n"", + ""\n"", + ""plt2.bar(index, mean2, align='center', width=width, color='red',edgecolor='black', yerr=std1,\\\n"", + "" error_kw=dict(ecolor='gray', lw=2, capsize=5, capthick=2, linestyle='-', linewidth=0.5))\n"", + ""\n"", + ""labels2 = ('','','0-1','1-2','2-3','3-4','4-5','5-6','6-7','7-8','8-9','9-10')\n"", + ""plt2.xaxis.set_major_locator(loc)\n"", + ""\n"", + ""plt2.set_xticklabels(labels2, minor=False)\n"", + ""plt2.set_xlim(-1,10)\n"", + ""plt2.set_ylim(0,10)\n"", + ""plt2.tick_params(axis='both', which='major', labelsize=14)\n"", + ""plt2.set_xlabel('Range of LogP', fontsize=16, fontweight='bold')\n"", + ""#plt2.set_ylabel('Average $pIC_{50}$ values', fontsize=16, fontweight='bold')\n"", + ""plt2.grid(True)\n"", + ""\n"", + ""\n"", + ""# Histogram for nHAcc\n"", + ""mean3 = ((mean_1),(mean_2),(mean_3),(mean_4),(mean_5),(mean_6),(mean_7),(mean_8),(mean_9),(mean_10))\n"", + ""std3 = ((std_1),(std_2),(std_3),(std_4),(std_5),(std_6),(std_7),(std_8),(std_9),(std_10))\n"", + ""index = np.arange(len(mean3)) # the x locations for the groups\n"", + ""\n"", + ""plt3.bar(index, mean3, align='center', width=width, color='green',edgecolor='black', yerr=std1,\\\n"", + "" error_kw=dict(ecolor='gray', lw=2, capsize=5, capthick=2, linestyle='-', linewidth=0.5))\n"", + ""\n"", + ""labels = ('','','0','1','2','3','4','5','6','7','8','9','10')\n"", + ""plt3.xaxis.set_major_locator(loc)\n"", + ""plt3.set_xlim(-1,9.9)\n"", + ""plt3.set_ylim(0,10)\n"", + ""plt3.set_xticklabels(labels, fontdict=None, minor=False)\n"", + ""plt3.tick_params(axis='both', which='major', labelsize=14)\n"", + ""plt3.set_xlabel('Number of Hydrogen Bond Acceptors', fontsize=16, fontweight='bold')\n"", + ""plt3.set_ylabel('Average $pIC_{50}$ values', fontsize=16, fontweight='bold')\n"", + ""plt3.grid(True)\n"", + ""\n"", + ""\n"", + ""# Histogram for nHDon\n"", + ""mean4 = ((mean_1_D),(mean_2_D),(mean_3_D),(mean_4_D),(mean_5_D),(mean_6_D),(mean_7_D),(mean_8_D),(mean_9_D),(mean_10_D))\n"", + ""std4 = ((std_1_D),(std_2_D),(std_3_D),(std_4_D),(std_5_D),(std_6_D),(std_7_D),(std_8_D),(std_9_D),(std_10_D))\n"", + ""index = np.arange(len(mean4)) # the x locations for the groups\n"", + ""\n"", + ""plt4.bar(index, mean4, align='center', width=width, color='orange',edgecolor='black', yerr=std1,\\\n"", + "" error_kw=dict(ecolor='gray', lw=2, capsize=5, capthick=2, linestyle='-', linewidth=0.5))\n"", + ""\n"", + ""labels = ('','','0','1','2','3','4','5','6','7','8','9','10')\n"", + ""plt4.xaxis.set_major_locator(loc)\n"", + ""\n"", + ""plt4.set_xlim(-1,9.9)\n"", + ""plt4.set_ylim(0,10)\n"", + ""plt4.set_xticklabels(labels, fontdict=None, minor=False)\n"", + ""plt4.tick_params(axis='both', which='major', labelsize=14)\n"", + ""plt4.set_xlabel('Number of Hydrogen Bond Donors', fontsize=16, fontweight='bold')\n"", + ""plt4.set_ylabel('Average $pIC_{50}$ values', fontsize=16, fontweight='bold')\n"", + ""plt4.grid(True)\n"", + ""\n"", + ""plt.tight_layout(pad=2.0, w_pad=2.0, h_pad=2.0)\n"", + ""plt.savefig('Result/ER_Alpha-Average pIC50 plots of the descriptors by range.pdf', dpi=300)"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": null, + ""metadata"": { + ""collapsed"": true + }, + ""outputs"": [], + ""source"": [] + }, + { + ""cell_type"": ""code"", + ""execution_count"": null, + ""metadata"": { + ""collapsed"": true + }, + ""outputs"": [], + ""source"": [] + }, + { + ""cell_type"": ""code"", + ""execution_count"": null, + ""metadata"": { + ""collapsed"": true + }, + ""outputs"": [], + ""source"": [] + } + ], + ""metadata"": { + ""kernelspec"": { + ""display_name"": ""Python 2"", + ""language"": ""python"", + ""name"": ""python2"" + }, + ""language_info"": { + ""codemirror_mode"": { + ""name"": ""ipython"", + ""version"": 2 + }, + ""file_extension"": "".py"", + ""mimetype"": ""text/x-python"", + ""name"": ""python"", + ""nbconvert_exporter"": ""python"", + ""pygments_lexer"": ""ipython2"", + ""version"": ""2.7.13"" + } + }, + ""nbformat"": 4, + ""nbformat_minor"": 2 +} +","Unknown" +"Inhibition","chaninlab/estrogen-receptor-alpha-qsar","06_ER_alpha_preparation-test.ipynb",".ipynb","269938","5315","{ + ""cells"": [ + { + ""cell_type"": ""code"", + ""execution_count"": 71, + ""metadata"": {}, + ""outputs"": [ + { + ""name"": ""stdout"", + ""output_type"": ""stream"", + ""text"": [ + ""Entry name\tLength\tEntry\tGene names\n"", + ""ESR1_HUMAN\t595\tP03372\tESR1 ESR NR3A1\n"", + ""ANDR_HUMAN\t920\tP10275\tAR DHTR NR3C4\n"", + ""EGFR_HUMAN\t1210\tP00533\tEGFR ERBB ERBB1 HER1\n"", + ""\n"" + ] + } + ], + ""source"": [ + ""from bioservices import UniProt\n"", + ""u = UniProt(verbose=False)\n"", + ""data = u.search(\""Estrogen receptor+and+human+and+alpha\"", frmt=\""tab\"", limit=3,\n"", + "" columns=\""entry name,length,id, genes\"") # zap70 specy in human organism\n"", + ""print(data)"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 72, + ""metadata"": { + ""collapsed"": true + }, + ""outputs"": [], + ""source"": [ + ""import numpy as np\n"", + ""import pandas as pd\n"", + ""from time import time\n"", + ""\n"", + ""#from IPython.display import Image\n"", + ""from sklearn.metrics import f1_score\n"", + ""from sklearn.metrics import confusion_matrix\n"", + ""import seaborn as sns\n"", + ""%matplotlib inline\n"", + ""\n"", + ""# Read ER data\n"", + ""#df = pd.read_csv(\""ER_Alpha.csv\"")\n"", + ""#df_for_analysis=df.copy(deep=True)\n"", + ""#print \""ER alpha data import successfully!\"""" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 73, + ""metadata"": {}, + ""outputs"": [ + { + ""name"": ""stdout"", + ""output_type"": ""stream"", + ""text"": [ + ""True\n"" + ] + } + ], + ""source"": [ + ""# Target object handler for API access and check connection to the db\n"", + ""from chembl_webresource_client import *\n"", + ""\n"", + ""targets = TargetResource()\n"", + ""print targets.status()"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 74, + ""metadata"": {}, + 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bioactivityCountchemblIdcompoundCountdescriptiongeneNamesorganismpreferredNameproteinAccessionsynonymstargetType
010936CHEMBL2065942Estrogen receptor alphaUnspecifiedHomo sapiensEstrogen receptor alphaP03372ESR1,Estradiol receptor,ER,ER-alpha,ESR ,Estro...SINGLE PROTEIN
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"" + ], + ""text/plain"": [ + "" bioactivityCount chemblId compoundCount description \\\n"", + ""0 10936 CHEMBL206 5942 Estrogen receptor alpha \n"", + ""\n"", + "" geneNames organism preferredName proteinAccession \\\n"", + ""0 Unspecified Homo sapiens Estrogen receptor alpha P03372 \n"", + ""\n"", + "" synonyms targetType \n"", + ""0 ESR1,Estradiol receptor,ER,ER-alpha,ESR ,Estro... SINGLE PROTEIN "" + ] + }, + ""execution_count"": 74, + ""metadata"": {}, + ""output_type"": ""execute_result"" + } + ], + ""source"": [ + ""# Load targets in data frame\n"", + ""\n"", + ""targetsDF = pd.DataFrame.from_dict(targets.get(uniprot=['P03372'])) #ESR1_HUMAN\n"", + ""targetsDF"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 75, + ""metadata"": {}, + ""outputs"": [ + { + ""data"": { + ""text/plain"": [ + ""10936"" + ] + }, + ""execution_count"": 75, + ""metadata"": {}, + ""output_type"": ""execute_result"" + } + ], + ""source"": [ + ""# Download all bioactivities for targets\n"", + ""\n"", + ""bioactsDF = pd.DataFrame.from_dict(targets.bioactivities('CHEMBL206'))\n"", + ""len(bioactsDF)"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 76, + ""metadata"": {}, + ""outputs"": [ + { + ""data"": { + ""text/html"": [ + ""
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activity_commentassay_chemblidassay_descriptionassay_typebioactivity_typeingredient_cmpd_chemblidname_in_referenceoperatororganismparent_cmpd_chemblidreferencetarget_chemblidtarget_confidencetarget_nameunitsvalue
0UnspecifiedCHEMBL677870Binding affinity for estrogen receptor alphaBIC50CHEMBL2197634=Homo sapiensCHEMBL219763Bioorg. Med. Chem. Lett., (2003) 13:22:4089CHEMBL2069Estrogen receptor alphanM9.57
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"" + ], + ""text/plain"": [ + "" activity_comment assay_chemblid \\\n"", + ""0 Unspecified CHEMBL677870 \n"", + ""\n"", + "" assay_description assay_type bioactivity_type \\\n"", + ""0 Binding affinity for estrogen receptor alpha B IC50 \n"", + ""\n"", + "" ingredient_cmpd_chemblid name_in_reference operator organism \\\n"", + ""0 CHEMBL219763 4 = Homo sapiens \n"", + ""\n"", + "" parent_cmpd_chemblid reference \\\n"", + ""0 CHEMBL219763 Bioorg. Med. Chem. Lett., (2003) 13:22:4089 \n"", + ""\n"", + "" target_chemblid target_confidence target_name units value \n"", + ""0 CHEMBL206 9 Estrogen receptor alpha nM 9.57 "" + ] + }, + ""execution_count"": 76, + ""metadata"": {}, + ""output_type"": ""execute_result"" + } + ], + ""source"": [ + ""bioactsDF.head(1)"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 77, + ""metadata"": {}, + ""outputs"": [ + { + ""data"": { + ""text/plain"": [ + ""3641"" + ] + }, + ""execution_count"": 77, + ""metadata"": {}, + ""output_type"": ""execute_result"" + } + ], + ""source"": [ + ""bioactsDF = bioactsDF[(bioactsDF['bioactivity_type'] == 'IC50')] # keep ony IC50\n"", + ""len(bioactsDF)"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 78, + ""metadata"": {}, + ""outputs"": [ + { + ""data"": { + ""text/plain"": [ + ""(3641,)"" + ] + }, + ""execution_count"": 78, + ""metadata"": {}, + ""output_type"": ""execute_result"" + } + ], + ""source"": [ + ""bioactsDF.units.shape"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 79, + ""metadata"": {}, + ""outputs"": [ + { + ""data"": { + ""text/plain"": [ + ""nM 2686\n"", + ""Unspecified 955\n"", + ""Name: units, dtype: int64"" + ] + }, + ""execution_count"": 79, + ""metadata"": {}, + ""output_type"": ""execute_result"" + } + ], + ""source"": [ + ""bioactsDF['units'].value_counts()"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 80, + ""metadata"": {}, + ""outputs"": [ + { + ""data"": { + ""text/plain"": [ + ""2686"" + ] + }, + ""execution_count"": 80, + ""metadata"": {}, + ""output_type"": ""execute_result"" + } + ], + ""source"": [ + ""bioactsDF = bioactsDF[(bioactsDF['units'] == 'nM')]\n"", + ""len(bioactsDF)"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 81, + ""metadata"": { + ""collapsed"": true + }, + ""outputs"": [], + ""source"": [ + ""bioactsDF['value'] = bioactsDF['value'].astype('float64') "" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 82, + ""metadata"": { + ""collapsed"": true + }, + ""outputs"": [], + ""source"": [ + ""# define active, inactive, intermediate compounds\n"", + ""\n"", + ""STATUS = []\n"", + ""\n"", + ""for i in bioactsDF.value:\n"", + "" if i <=1000:\n"", + "" STATUS.append(\""active\"") #active\n"", + "" \n"", + "" elif i >=10000:\n"", + "" STATUS.append(\""inactive\"") #inactive\n"", + "" \n"", + "" else:\n"", + "" STATUS.append(\""intermediate\"") #intermediate"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 83, + ""metadata"": { + ""scrolled"": true + }, + ""outputs"": [ + { + ""data"": { + ""text/html"": [ + ""
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activity_commentassay_chemblidassay_descriptionassay_typebioactivity_typeingredient_cmpd_chemblidname_in_referenceoperatororganismparent_cmpd_chemblidreferencetarget_chemblidtarget_confidencetarget_nameunitsvalueSTATUS
0UnspecifiedCHEMBL677870Binding affinity for estrogen receptor alphaBIC50CHEMBL2197634=Homo sapiensCHEMBL219763Bioorg. Med. Chem. Lett., (2003) 13:22:4089CHEMBL2069Estrogen receptor alphanM9.57active
9UnspecifiedCHEMBL873603Displacement of [3H]17-beta-estradiol from ful...BIC50CHEMBL9272019=Homo sapiensCHEMBL92720Bioorg. Med. Chem. Lett., (2004) 14:11:2741CHEMBL2068Estrogen receptor alphanM41.10active
10UnspecifiedCHEMBL679886Potency in cellular transactivation assay util...FIC50CHEMBL9272019=Homo sapiensCHEMBL92720Bioorg. Med. Chem. Lett., (2004) 14:11:2741CHEMBL2068Estrogen receptor alphanM274.00active
17UnspecifiedCHEMBL679891In vitro concentration required to inhibit [3H...BIC50CHEMBL33030711f=Homo sapiensCHEMBL330307Bioorg. Med. Chem. Lett., (2004) 14:11:2729CHEMBL2068Estrogen receptor alphanM24.80active
22UnspecifiedCHEMBL829337Inhibition of human estrogen receptor alphaBIC50CHEMBL37003730=Homo sapiensCHEMBL370037Bioorg. Med. Chem. Lett., (2005) 15:12:3137CHEMBL2069Estrogen receptor alphanM6.00active
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"" + ], + ""text/plain"": [ + "" activity_comment assay_chemblid \\\n"", + ""0 Unspecified CHEMBL677870 \n"", + ""9 Unspecified CHEMBL873603 \n"", + ""10 Unspecified CHEMBL679886 \n"", + ""17 Unspecified CHEMBL679891 \n"", + ""22 Unspecified CHEMBL829337 \n"", + ""\n"", + "" assay_description assay_type \\\n"", + ""0 Binding affinity for estrogen receptor alpha B \n"", + ""9 Displacement of [3H]17-beta-estradiol from ful... B \n"", + ""10 Potency in cellular transactivation assay util... F \n"", + ""17 In vitro concentration required to inhibit [3H... B \n"", + ""22 Inhibition of human estrogen receptor alpha B \n"", + ""\n"", + "" bioactivity_type ingredient_cmpd_chemblid name_in_reference operator \\\n"", + ""0 IC50 CHEMBL219763 4 = \n"", + ""9 IC50 CHEMBL92720 19 = \n"", + ""10 IC50 CHEMBL92720 19 = \n"", + ""17 IC50 CHEMBL330307 11f = \n"", + ""22 IC50 CHEMBL370037 30 = \n"", + ""\n"", + "" organism parent_cmpd_chemblid \\\n"", + ""0 Homo sapiens CHEMBL219763 \n"", + ""9 Homo sapiens CHEMBL92720 \n"", + ""10 Homo sapiens CHEMBL92720 \n"", + ""17 Homo sapiens CHEMBL330307 \n"", + ""22 Homo sapiens CHEMBL370037 \n"", + ""\n"", + "" reference target_chemblid \\\n"", + ""0 Bioorg. Med. Chem. Lett., (2003) 13:22:4089 CHEMBL206 \n"", + ""9 Bioorg. Med. Chem. Lett., (2004) 14:11:2741 CHEMBL206 \n"", + ""10 Bioorg. Med. Chem. Lett., (2004) 14:11:2741 CHEMBL206 \n"", + ""17 Bioorg. Med. Chem. Lett., (2004) 14:11:2729 CHEMBL206 \n"", + ""22 Bioorg. Med. Chem. Lett., (2005) 15:12:3137 CHEMBL206 \n"", + ""\n"", + "" target_confidence target_name units value STATUS \n"", + ""0 9 Estrogen receptor alpha nM 9.57 active \n"", + ""9 8 Estrogen receptor alpha nM 41.10 active \n"", + ""10 8 Estrogen receptor alpha nM 274.00 active \n"", + ""17 8 Estrogen receptor alpha nM 24.80 active \n"", + ""22 9 Estrogen receptor alpha nM 6.00 active "" + ] + }, + ""execution_count"": 83, + ""metadata"": {}, + ""output_type"": ""execute_result"" + } + ], + ""source"": [ + ""bioactsDF['STATUS'] = STATUS\n"", + ""bioactsDF.head(5)"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 84, + ""metadata"": {}, + ""outputs"": [ + { + ""data"": { + ""text/plain"": [ + ""= 2237\n"", + ""> 404\n"", + ""< 29\n"", + ""Unspecified 16\n"", + ""Name: operator, dtype: int64"" + ] + }, + ""execution_count"": 84, + ""metadata"": {}, + ""output_type"": ""execute_result"" + } + ], + ""source"": [ + ""bioactsDF['operator'].value_counts()"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 85, + ""metadata"": {}, + ""outputs"": [ + { + ""data"": { + ""text/plain"": [ + ""PUBCHEM_BIOASSAY: Estrogen Receptor-alpha Coactivator Binding Inhibitors Dose Response Confirmation. (Class of assay: confirmatory) [Related pubchem assays: 629 (Primary screen preceding this dose response confirmation assay.)] 439\n"", + ""Inhibitory concentration against human ER alpha expressed in Escherichia coli was determined using [3H]17-beta-estradiol as radio ligand 107\n"", + ""Binding affinity to human ERalpha 79\n"", + ""Inhibition of [3H]17-beta-estradiol binding to human estrogen receptor alpha expressed in Escherichia coli 70\n"", + ""Binding affinity to ERalpha 67\n"", + ""Displacement of radiolabeled estrogen from estrogen receptor alpha by scintillation counting 60\n"", + ""Antagonist activity at estrogen receptor in human Ishikawa cells assessed as 17-beta-estradiol-induced alkaline phosphatase activity after 3 days by chemiluminescence assay 60\n"", + ""Antagonist activity at estrogen receptor in human MCF7 cells assessed as 17-beta-estradiol-induced cell proliferation after 24 hrs by [14C]thymidine incorporation assay 60\n"", + ""Binding affinity for human Estrogen receptor alpha 54\n"", + ""BindingDB_Patents: Binding Assay. The competition assay was performed in a 96-well plate (polystyrene*) which binds <2.0% of the total input [3H]-17beta-estradiol and each data point was gathered in triplicate. 100 uG/100 uL of the receptor preparation was aliquoted per well. A saturating dose of 2.5 nM [3H]17beta-estradiol+competitor (or buffer) in a 50 uL volume was added in the preliminary competition when 100x and 500x competitor were evaluated, only 0.8 nM [3H]17beta-estradiol was used. The plate was incubated at room temperature for 2.5 h. At the end of this incubation period 150 uL of ice-cold dextran coated charcoal (5% activated charcoal coated with 0.05% 69K dextran) was added to each well and the plate was immediately centrifuged at 99 g for 5 minutes at 4 C. 200 uL of the supernatant solution was then removed for scintillation counting. Samples were counted to 2% or 10 minutes, whichever occurs first. 54\n"", + ""Antagonist activity at ERalpha in human HEC1 cells assessed as inhibition of estrogen-induced transcriptional activity after 24 hrs by reporter gene assay 51\n"", + ""Binding affinity to human recombinant ERalpha by scintillation proximity assay 51\n"", + ""Inhibition of estrogen receptor alpha 43\n"", + ""Displacement of [3H]estrone from ER alpha 40\n"", + ""Suppression of ER alpha in human MCF7 cells after 24 hrs by immunostaining analysis 37\n"", + ""Binding affinity to ERalpha by scintillation proximity assay 37\n"", + ""Binding affinity for human estrogen receptor alpha 36\n"", + ""Binding affinity to ER alpha (unknown origin) by LanthaScreen TR-FRET competitive binding assay 36\n"", + ""Binding potency for human ER alpha 34\n"", + ""Displacement of [3H]17-beta-estradiol from full length human estrogen receptor alpha 33\n"", + ""Inhibition of binding to ER in Ad5-wt-ER-infected HAECT1 cells by NF-kappaB-mediated luciferase reporter 31\n"", + ""In vitro concentration required to inhibit [3H]estradiol binding to human estrogen receptor alpha expressed in 293T cells 30\n"", + ""Inhibition of bindign to recombinant human estrogen receptor alpha 30\n"", + ""Binding affinity against human estrogen receptor alpha in competitive binding assay 27\n"", + ""Displacement of fluormone ES2 from GST-tagged recombinant human ERalpha LBD after 1 hr by Lanthascreen TR-FRET assay 26\n"", + ""Activity at human ERalpha expressed in HAECT1 cells assessed as inhibition of NF-kappaB transcription 26\n"", + ""Inhibitory concentration against estrogen receptor alpha using radioligand binding assay. 26\n"", + ""Displacement of [3H]estradiol from full length human recombinant ERalpha after 3 hrs by scintillation proximity assay 25\n"", + ""Downregulation of ERalpha in human MCF7 cells incubated for 18 to 22 hrs by immunofluorescence assay 25\n"", + ""Inhibitory concentration against estrogen receptor alpha using estrogen response element (ERE) assay. 24\n"", + "" ... \n"", + ""Agonist activity at ERalpha (unknown origin) by cell-based assay 1\n"", + ""Antagonist activity at ERalpha receptor in human MCF7 cells in presence of 0.25 uM tamoxifen 1\n"", + ""Binding affinity to human estrogen receptor assessed as decrease in estradiol-dependent beta-galactosidase activity 1\n"", + ""Displacement of [3H]Estradiol from human recombinant estrogen receptor alpha expressed in Sf9 cells after 2 hrs 1\n"", + ""Antagonist activity at human ERalpha expressed in human MCF7 cells by luciferase reporter gene assay 1\n"", + ""Antagonist activity at ERalpha by TR-FRET assay 1\n"", + ""Displacement of fluorescent labeled ES2 from recombinant ERalpha 1\n"", + ""Antagonist activity at human ERalpha LBD in human MCF7 cells assessed as inhibition of estradiol-induced cell proliferation after up to 6 days by celltiter-glo assay 1\n"", + ""In vitro binding affinity for Estrogen receptor alpha in HEK 293 cells 1\n"", + ""Antagonist activity at human ER ligand binding domain expressed in african green monkey COS7 cells in presence of 17-beta-estradiol by Gal4 hybrid assay 1\n"", + ""Inhibition of ERalpha expressed in human tumor cells 1\n"", + ""Binding affinity to ERalpha by fluorescence polarization assay 1\n"", + ""Binding affinity to human recombinant ERalpha receptor by liquid scintillation counter 1\n"", + ""Mixed-type inhibition of ER-alpha (unknown origin) expressed in ER-negative human SKBR3 cells coexpressing ERE using estradiol as substrate 1\n"", + ""Inhibition of estrogen receptor alpha mediated transcriptional activation 1\n"", + ""Antagonist activity at estrogen receptor ligand binding domain by two hybrid assay 1\n"", + ""Binding affinity to estrogen receptor 1\n"", + ""Antagonist activity at gal-ERalpha expressed in african green monkey CV1 cells co-expressing MH100 by luciferase reporter gene assay 1\n"", + ""Displacement of fluorescent estrogen ES2 from human recombinant ERalpha by fluorescence polarization assay 1\n"", + ""Inhibition of estrogen receptor alpha in MCF-7-2a cells at 10 uM 1\n"", + ""Binding affinity to estrogen receptor (unknown origin) incubated for 4 hrs 1\n"", + ""Inhibition of estradiol-induced estrogen receptor-alpha (unknown origin)-mediated transcription expressed in HEK293T cells after 16 hrs by luciferase reporter gene assay 1\n"", + ""Antagonist activity at estrogen receptor in human MCF7 cells assessed as inhibition of estradiol-induced cell proliferation after 5 days by WST-8 assay 1\n"", + ""Antagonist activity at estrogen receptor by cellular reporter gene assay 1\n"", + ""Antagonist activity at human recombinant ERalpha by FRET assay 1\n"", + ""Displacement of [3H]E2 from ERalpha ligand binding domain 1\n"", + ""Binding affinity at ERalpha 1\n"", + ""Binding affinity to ERalpha assessed as inhibition of steroid receptor coactivator interaction in african green monkey COS7 cells by mammalian 2 hybrid assay 1\n"", + ""Inhibitory activity against Estrogen receptor alpha 1\n"", + ""Displacement of [3H]-17beta-estradiol from human recombinant ERalpha expressed in rabbit reticulocytes 1\n"", + ""Name: assay_description, Length: 174, dtype: int64"" + ] + }, + ""execution_count"": 85, + ""metadata"": {}, + ""output_type"": ""execute_result"" + } + ], + ""source"": [ + ""bioactsDF['assay_description'].value_counts()"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 86, + ""metadata"": {}, + ""outputs"": [ + { + ""data"": { + ""text/plain"": [ + ""2377"" + ] + }, + ""execution_count"": 86, + ""metadata"": {}, + ""output_type"": ""execute_result"" + } + ], + ""source"": [ + ""bioactsDF = bioactsDF[(bioactsDF['assay_type'] == 'B')] # only binding data\n"", + ""len(bioactsDF)"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 87, + ""metadata"": { + ""collapsed"": true + }, + ""outputs"": [], + ""source"": [ + ""bioactsDF = bioactsDF.rename(columns={'ingredient_cmpd_chemblid': 'chemblId'})"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 88, + ""metadata"": { + ""scrolled"": false + }, + ""outputs"": [ + { + ""name"": ""stdout"", + ""output_type"": ""stream"", + ""text"": [ + ""1708 294 19\n"" + ] + } + ], + ""source"": [ + ""# Filter bioactivities\n"", + ""bioactsDF_Train = bioactsDF[(bioactsDF['operator'] == '=') & # only exact measurements\n"", + "" (bioactsDF['assay_type'] == 'B') & # only binding data\n"", + "" (bioactsDF['target_confidence'] == 9)] # only high target confidence\n"", + ""bioactsDF_Test_gra = bioactsDF[(bioactsDF['operator'] == '>') & # only exact measurements\n"", + "" (bioactsDF['assay_type'] == 'B') & # only binding data\n"", + "" (bioactsDF['target_confidence'] == 9)] # only high target confidence\n"", + ""bioactsDF_Test_les = bioactsDF[(bioactsDF['operator'] == '<') & # only exact measurements\n"", + "" (bioactsDF['assay_type'] == 'B') & # only binding data\n"", + "" (bioactsDF['target_confidence'] == 9)] # only high target confidence\n"", + ""\n"", + ""print len(bioactsDF_Train), len(bioactsDF_Test_gra), len(bioactsDF_Test_les)"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 89, + ""metadata"": {}, + ""outputs"": [ + { + ""data"": { + ""text/html"": [ + ""
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activity_commentassay_chemblidassay_descriptionassay_typebioactivity_typechemblIdname_in_referenceoperatororganismparent_cmpd_chemblidreferencetarget_chemblidtarget_confidencetarget_nameunitsvalueSTATUS
9601UnspecifiedCHEMBL2445311Inhibition of estrogen receptor alpha-mediated...BIC50CHEMBL2441817S58=Homo sapiensCHEMBL2441817UnspecifiedCHEMBL2069Estrogen receptor alphanM64908.26inactive
9608UnspecifiedCHEMBL2445311Inhibition of estrogen receptor alpha-mediated...BIC50CHEMBL2441818S59=Homo sapiensCHEMBL2441818UnspecifiedCHEMBL2069Estrogen receptor alphanM64908.26inactive
9609UnspecifiedCHEMBL2445311Inhibition of estrogen receptor alpha-mediated...BIC50CHEMBL2441819S60=Homo sapiensCHEMBL2441819UnspecifiedCHEMBL2069Estrogen receptor alphanM64908.26inactive
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"" + ], + ""text/plain"": [ + "" activity_comment assay_chemblid \\\n"", + ""9601 Unspecified CHEMBL2445311 \n"", + ""9608 Unspecified CHEMBL2445311 \n"", + ""9609 Unspecified CHEMBL2445311 \n"", + ""\n"", + "" assay_description assay_type \\\n"", + ""9601 Inhibition of estrogen receptor alpha-mediated... B \n"", + ""9608 Inhibition of estrogen receptor alpha-mediated... B \n"", + ""9609 Inhibition of estrogen receptor alpha-mediated... B \n"", + ""\n"", + "" bioactivity_type chemblId name_in_reference operator organism \\\n"", + ""9601 IC50 CHEMBL2441817 S58 = Homo sapiens \n"", + ""9608 IC50 CHEMBL2441818 S59 = Homo sapiens \n"", + ""9609 IC50 CHEMBL2441819 S60 = Homo sapiens \n"", + ""\n"", + "" parent_cmpd_chemblid reference target_chemblid target_confidence \\\n"", + ""9601 CHEMBL2441817 Unspecified CHEMBL206 9 \n"", + ""9608 CHEMBL2441818 Unspecified CHEMBL206 9 \n"", + ""9609 CHEMBL2441819 Unspecified CHEMBL206 9 \n"", + ""\n"", + "" target_name units value STATUS \n"", + ""9601 Estrogen receptor alpha nM 64908.26 inactive \n"", + ""9608 Estrogen receptor alpha nM 64908.26 inactive \n"", + ""9609 Estrogen receptor alpha nM 64908.26 inactive "" + ] + }, + ""execution_count"": 89, + ""metadata"": {}, + ""output_type"": ""execute_result"" + } + ], + ""source"": [ + ""bioactsDF_Train[(bioactsDF_Train['assay_description'] == 'Inhibition of estrogen receptor alpha-mediated human MCF7 cell growth inhibition')]"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 90, + ""metadata"": {}, + ""outputs"": [ + { + ""data"": { + ""text/plain"": [ + ""Inhibition of estrogen receptor alpha-mediated human MCF7 cell growth inhibition 16\n"", + ""PUBCHEM_BIOASSAY: Estrogen Receptor-alpha Coactivator Binding Inhibitors Dose Response Confirmation. (Class of assay: confirmatory) [Related pubchem assays: 629 (Primary screen preceding this dose response confirmation assay.)] 2\n"", + ""Binding affinity to ER alpha (unknown origin) by LanthaScreen TR-FRET competitive binding assay 1\n"", + ""Name: assay_description, dtype: int64"" + ] + }, + ""execution_count"": 90, + ""metadata"": {}, + ""output_type"": ""execute_result"" + } + ], + ""source"": [ + ""bioactsDF_Test_les['assay_description'].value_counts()"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 91, + ""metadata"": { + ""scrolled"": true + }, + ""outputs"": [ + { + ""data"": { + ""text/plain"": [ + ""PUBCHEM_BIOASSAY: Estrogen Receptor-alpha Coactivator Binding Inhibitors Dose Response Confirmation. (Class of assay: confirmatory) [Related pubchem assays: 629 (Primary screen preceding this dose response confirmation assay.)] 157\n"", + ""Binding affinity to human ERalpha 16\n"", + ""Binding affinity to ERalpha 11\n"", + ""Binding affinity to human recombinant ERalpha by scintillation proximity assay 11\n"", + ""Displacement of [3H]estradiol from human estrogen receptor alpha 8\n"", + ""Inhibition of estrogen receptor alpha (unknown origin)-SRC1 coactivator interaction incubated for 1 hr by receptor cofactor assay system based method 7\n"", + ""Binding affinity for human Estrogen receptor alpha 7\n"", + ""Binding affinity to human ER alpha 6\n"", + ""Binding affinity for human estrogen receptor alpha 6\n"", + ""Inhibition of human LBD of of ERalpha 5\n"", + ""Inhibition of estrogen receptor (unknown origin) by Gal4-based luciferase assay 5\n"", + ""Binding affinity to ERalpha by scintillation proximity assay 5\n"", + ""Agonist activity at ER alpha in human MCF7 cells assessed as PR gene expression after 24 hrs by laser scanning imaging cytometer analysis 5\n"", + ""Displacement of fluormone from human estrogen receptor by fluorescence binding assay 5\n"", + ""Binding affinity at human recombinant ERalpha 4\n"", + ""Inhibitory concentration against human ER alpha expressed in Escherichia coli was determined using [3H]17-beta-estradiol as radio ligand 4\n"", + ""Inhibition of human estrogen receptor alpha 4\n"", + ""Binding affinity to ER-alpha (unknown origin) 3\n"", + ""Suppression of ER alpha in human MCF7 cells after 24 hrs by immunostaining analysis 3\n"", + ""Binding affinity to human ER-alpha 2\n"", + ""Inhibition of estrogen receptor (unknown origin) 2\n"", + ""Binding affinity for human Estrogen receptor Alpha 2\n"", + ""Antagonist activity at estrogen receptor alpha (unknown origin) by NH Pro assay 2\n"", + ""Antiestrogenic activity in human T47D cells expressing estrogen receptor assessed as estrogen-dependent transcription by luciferase reporter gene assay 2\n"", + ""Displacement of fluorescein-labeled estrogen ligand from recombinant ER-alpha (unknown origin) incubated for 2 hrs by fluorescence polarization assay 2\n"", + ""Binding affinity to estrogen receptor (unknown origin) incubated for 4 hrs 1\n"", + ""Antagonist activity at human estrogen receptor alpha expressed in CHOK1 cells assessed as inhibition of beta-estradiol-induced protein interaction with steroid receptor co-activator peptide after overnight incubation by beta-galactosidase reporter gene assay 1\n"", + ""Displacement of fluorescein-labeled estradiol from human recombinant ERalpha by fluorescence polarization assay 1\n"", + ""Binding affinity to human recombinant ERalpha 1\n"", + ""Inhibition of [3H]17-beta-estradiol binding to human estrogen receptor alpha expressed in Escherichia coli 1\n"", + ""Binding affinity for Human Estrogen receptor-alpha 1\n"", + ""Displacement of [3H]17beta-estradiol from recombinant human ERalpha expressed in 293T cells 1\n"", + ""Displacement of [3H]17-beta-estradiol from human estrogen receptor alpha 1\n"", + ""PUBCHEM_BIOASSAY: Estrogen Receptor-alpha Coactivator Binding Inhibitors ELISA Secondary Assay. (Class of assay: confirmatory) [Related pubchem assays: 713 (Primary dose response assay preceding this ELISA secondary assay.), 629 (Primary screen preceding this dose response confirmation assay.)] 1\n"", + ""Displacement of fluorescein labeled estradiol from human recombinant ERalpha expressed in baculovirus infected insect cells by fluorescence polarization assay 1\n"", + ""Name: assay_description, dtype: int64"" + ] + }, + ""execution_count"": 91, + ""metadata"": {}, + ""output_type"": ""execute_result"" + } + ], + ""source"": [ + ""bioactsDF_Test_gra['assay_description'].value_counts()"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 92, + ""metadata"": {}, + ""outputs"": [ + { + ""data"": { + ""text/plain"": [ + ""(1708, 1424)"" + ] + }, + ""execution_count"": 92, + ""metadata"": {}, + ""output_type"": ""execute_result"" + } + ], + ""source"": [ + ""len(bioactsDF_Train), len(bioactsDF_Train['chemblId'].unique())"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 93, + ""metadata"": {}, + ""outputs"": [ + { + ""data"": { + ""text/html"": [ + ""
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activity_commentassay_chemblidassay_descriptionassay_typebioactivity_typechemblIdname_in_referenceoperatororganismparent_cmpd_chemblidreferencetarget_chemblidtarget_confidencetarget_nameunitsvalueSTATUS
10934UnspecifiedCHEMBL3829134Displacement of fluorescein-labeled estrogen l...BIC50CHEMBL382811720d=Homo sapiensCHEMBL3828117UnspecifiedCHEMBL2069Estrogen receptor alphanM38300.0inactive
10935UnspecifiedCHEMBL3825990Inhibition of ER-alpha (unknown origin) by Lan...BIC50CHEMBL382319286=Homo sapiensCHEMBL3823192Bioorg. Med. Chem., (2016) 24:18:4075CHEMBL2069Estrogen receptor alphanM574.0active
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"" + ], + ""text/plain"": [ + "" activity_comment assay_chemblid \\\n"", + ""10934 Unspecified CHEMBL3829134 \n"", + ""10935 Unspecified CHEMBL3825990 \n"", + ""\n"", + "" assay_description assay_type \\\n"", + ""10934 Displacement of fluorescein-labeled estrogen l... B \n"", + ""10935 Inhibition of ER-alpha (unknown origin) by Lan... B \n"", + ""\n"", + "" bioactivity_type chemblId name_in_reference operator \\\n"", + ""10934 IC50 CHEMBL3828117 20d = \n"", + ""10935 IC50 CHEMBL3823192 86 = \n"", + ""\n"", + "" organism parent_cmpd_chemblid \\\n"", + ""10934 Homo sapiens CHEMBL3828117 \n"", + ""10935 Homo sapiens CHEMBL3823192 \n"", + ""\n"", + "" reference target_chemblid \\\n"", + ""10934 Unspecified CHEMBL206 \n"", + ""10935 Bioorg. Med. Chem., (2016) 24:18:4075 CHEMBL206 \n"", + ""\n"", + "" target_confidence target_name units value STATUS \n"", + ""10934 9 Estrogen receptor alpha nM 38300.0 inactive \n"", + ""10935 9 Estrogen receptor alpha nM 574.0 active "" + ] + }, + ""execution_count"": 93, + ""metadata"": {}, + ""output_type"": ""execute_result"" + } + ], + ""source"": [ + ""bioactsDF_Train.tail(2)"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 94, + ""metadata"": {}, + ""outputs"": [ + { + ""data"": { + ""text/plain"": [ + ""(294, 290)"" + ] + }, + ""execution_count"": 94, + ""metadata"": {}, + ""output_type"": ""execute_result"" + } + ], + ""source"": [ + ""len(bioactsDF_Test_gra), len(bioactsDF_Test_gra['chemblId'].unique())"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 95, + ""metadata"": {}, + ""outputs"": [ + { + ""data"": { + ""text/plain"": [ + ""(19, 19)"" + ] + }, + ""execution_count"": 95, + ""metadata"": {}, + ""output_type"": ""execute_result"" + } + ], + ""source"": [ + ""len(bioactsDF_Test_les), len(bioactsDF_Test_les['chemblId'].unique())"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 96, + ""metadata"": {}, + ""outputs"": [ + { + ""name"": ""stdout"", + ""output_type"": ""stream"", + ""text"": [ + ""('1259', '449', '1708')\n"" + ] + } + ], + ""source"": [ + ""bioactsDF_Train_dup = pd.concat(g for _, \n"", + "" g in bioactsDF_Train.groupby(\""chemblId\"") \n"", + "" if len(g) > 1)\n"", + ""bioactsDF_Train_non = bioactsDF_Train.loc[~bioactsDF_Train.index.isin(bioactsDF_Train_dup.index)]\n"", + ""\n"", + ""print (str(len(bioactsDF_Train_non)), \n"", + "" str(len(bioactsDF_Train_dup)), \n"", + "" str(len(bioactsDF_Train_dup)+len(bioactsDF_Train_non)))"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 97, + ""metadata"": {}, + ""outputs"": [ + { + ""data"": { + ""text/plain"": [ + ""(1259, 1259)"" + ] + }, + ""execution_count"": 97, + ""metadata"": {}, + ""output_type"": ""execute_result"" + } + ], + ""source"": [ + ""len(bioactsDF_Train_non), len(bioactsDF_Train_non['chemblId'].unique())"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 98, + ""metadata"": {}, + ""outputs"": [ + { + ""data"": { + ""text/html"": [ + ""
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\n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + "" \n"", + ""
activity_commentassay_chemblidassay_descriptionassay_typebioactivity_typechemblIdname_in_referenceoperatororganismparent_cmpd_chemblidreferencetarget_chemblidtarget_confidencetarget_nameunitsvalueSTATUS
6530UnspecifiedCHEMBL831663Inhibition of transcriptional activation by hu...BIC50CHEMBL10023113=Homo sapiensCHEMBL100231Bioorg. Med. Chem. Lett., (2005) 15:4:957CHEMBL2069Estrogen receptor alphanM5.2000active
6531UnspecifiedCHEMBL831661Inhibition of binding to human estrogen recept...BIC50CHEMBL10023113=Homo sapiensCHEMBL100231Bioorg. Med. Chem. Lett., (2005) 15:4:957CHEMBL2069Estrogen receptor alphanM70.9600active
3114UnspecifiedCHEMBL3736957Antagonist activity at ERalpha in human T47D-K...BIC50CHEMBL100414-OHT=Homo sapiensCHEMBL10041Bioorg. Med. Chem., (2015) 23:24:7597CHEMBL2069Estrogen receptor alphanM10.0000active
3115UnspecifiedCHEMBL3736956Antagonist activity at luciferase-fused ERalph...BIC50CHEMBL100414-OHT=Homo sapiensCHEMBL10041Bioorg. Med. Chem., (2015) 23:24:7597CHEMBL2069Estrogen receptor alphanM70.0000active
6798UnspecifiedCHEMBL831663Inhibition of transcriptional activation by hu...BIC50CHEMBL10059519=Homo sapiensCHEMBL100595Bioorg. Med. Chem. Lett., (2005) 15:4:957CHEMBL2069Estrogen receptor alphanM16.1100active
6799UnspecifiedCHEMBL831661Inhibition of binding to human estrogen recept...BIC50CHEMBL10059519=Homo sapiensCHEMBL100595Bioorg. Med. Chem. Lett., (2005) 15:4:957CHEMBL2069Estrogen receptor alphanM46.0300active
1739UnspecifiedCHEMBL831663Inhibition of transcriptional activation by hu...BIC50CHEMBL10061715=Homo sapiensCHEMBL100617Bioorg. Med. Chem. Lett., (2005) 15:4:957CHEMBL2069Estrogen receptor alphanM0.6998active
1740UnspecifiedCHEMBL831661Inhibition of binding to human estrogen recept...BIC50CHEMBL10061715=Homo sapiensCHEMBL100617Bioorg. Med. Chem. Lett., (2005) 15:4:957CHEMBL2069Estrogen receptor alphanM33.9600active
7559UnspecifiedCHEMBL831663Inhibition of transcriptional activation by hu...BIC50CHEMBL10076310=Homo sapiensCHEMBL100763Bioorg. Med. Chem. Lett., (2005) 15:4:957CHEMBL2069Estrogen receptor alphanM3.3040active
7560UnspecifiedCHEMBL831661Inhibition of binding to human estrogen recept...BIC50CHEMBL10076310=Homo sapiensCHEMBL100763Bioorg. Med. Chem. Lett., (2005) 15:4:957CHEMBL2069Estrogen receptor alphanM26.9800active
1282UnspecifiedCHEMBL831663Inhibition of transcriptional activation by hu...BIC50CHEMBL1010839=Homo sapiensCHEMBL101083Bioorg. Med. Chem. Lett., (2005) 15:4:957CHEMBL2069Estrogen receptor alphanM1.9010active
1283UnspecifiedCHEMBL831661Inhibition of binding to human estrogen recept...BIC50CHEMBL1010839=Homo sapiensCHEMBL101083Bioorg. Med. Chem. Lett., (2005) 15:4:957CHEMBL2069Estrogen receptor alphanM38.0200active
985UnspecifiedCHEMBL831007Inhibition of 17-beta-estradiol mediated lucif...BIC50CHEMBL10138260=Homo sapiensCHEMBL101382J. Med. Chem., (2005) 48:2:364CHEMBL2069Estrogen receptor alphanM16.5000active
986UnspecifiedCHEMBL831528Inhibition of [3H]estradiol binding to human e...BIC50CHEMBL10138260=Homo sapiensCHEMBL101382J. Med. Chem., (2005) 48:2:364CHEMBL2069Estrogen receptor alphanM19.0000active
987UnspecifiedCHEMBL831663Inhibition of transcriptional activation by hu...BIC50CHEMBL1013822=Homo sapiensCHEMBL101382Bioorg. Med. Chem. Lett., (2005) 15:4:957CHEMBL2069Estrogen receptor alphanM16.4800active
988UnspecifiedCHEMBL831661Inhibition of binding to human estrogen recept...BIC50CHEMBL1013822=Homo sapiensCHEMBL101382Bioorg. Med. Chem. Lett., (2005) 15:4:957CHEMBL2069Estrogen receptor alphanM19.0100active
1399UnspecifiedCHEMBL831663Inhibition of transcriptional activation by hu...BIC50CHEMBL1018074=Homo sapiensCHEMBL101807Bioorg. Med. Chem. Lett., (2005) 15:4:957CHEMBL2069Estrogen receptor alphanM2.6000active
1400UnspecifiedCHEMBL831661Inhibition of binding to human estrogen recept...BIC50CHEMBL1018074=Homo sapiensCHEMBL101807Bioorg. Med. Chem. Lett., (2005) 15:4:957CHEMBL2069Estrogen receptor alphanM28.9700active
2241UnspecifiedCHEMBL831663Inhibition of transcriptional activation by hu...BIC50CHEMBL1019978=Homo sapiensCHEMBL101997Bioorg. Med. Chem. Lett., (2005) 15:4:957CHEMBL2069Estrogen receptor alphanM1.6000active
2242UnspecifiedCHEMBL831661Inhibition of binding to human estrogen recept...BIC50CHEMBL1019978=Homo sapiensCHEMBL101997Bioorg. Med. Chem. Lett., (2005) 15:4:957CHEMBL2069Estrogen receptor alphanM30.9700active
7518UnspecifiedCHEMBL831663Inhibition of transcriptional activation by hu...BIC50CHEMBL10329411=Homo sapiensCHEMBL103294Bioorg. Med. Chem. Lett., (2005) 15:4:957CHEMBL2069Estrogen receptor alphanM3.0970active
7519UnspecifiedCHEMBL831661Inhibition of binding to human estrogen recept...BIC50CHEMBL10329411=Homo sapiensCHEMBL103294Bioorg. Med. Chem. Lett., (2005) 15:4:957CHEMBL2069Estrogen receptor alphanM34.9900active
1470UnspecifiedCHEMBL831663Inhibition of transcriptional activation by hu...BIC50CHEMBL10330518=Homo sapiensCHEMBL103305Bioorg. Med. Chem. Lett., (2005) 15:4:957CHEMBL2069Estrogen receptor alphanM4.4980active
1471UnspecifiedCHEMBL831661Inhibition of binding to human estrogen recept...BIC50CHEMBL10330518=Homo sapiensCHEMBL103305Bioorg. Med. Chem. Lett., (2005) 15:4:957CHEMBL2069Estrogen receptor alphanM48.9800active
6569UnspecifiedCHEMBL3736957Antagonist activity at ERalpha in human T47D-K...BIC50CHEMBL12220354; ICI-164,384=Homo sapiensCHEMBL1222035Bioorg. Med. Chem., (2015) 23:24:7597CHEMBL2069Estrogen receptor alphanM50.0000active
6570UnspecifiedCHEMBL3736956Antagonist activity at luciferase-fused ERalph...BIC50CHEMBL12220354; ICI-164,384=Homo sapiensCHEMBL1222035Bioorg. Med. Chem., (2015) 23:24:7597CHEMBL2069Estrogen receptor alphanM340.0000active
3878UnspecifiedCHEMBL3825990Inhibition of ER-alpha (unknown origin) by Lan...BIC50CHEMBL135Estradiol=Homo sapiensCHEMBL135Bioorg. Med. Chem., (2016) 24:18:4075CHEMBL2069Estrogen receptor alphanM1.7000active
3885UnspecifiedCHEMBL3599826Inhibition of estrogen receptor alpha (unknown...BIC50CHEMBL135E2=Homo sapiensCHEMBL135Bioorg. Med. Chem., (2015) 23:15:4132CHEMBL2069Estrogen receptor alphanM0.9000active
3892UnspecifiedCHEMBL3382763Displacement of fluorescein-labeled estrogen f...BIC50CHEMBL1351=Homo sapiensCHEMBL135J. Med. Chem., (2014) 57:22:9370CHEMBL2069Estrogen receptor alphanM5.7000active
3902UnspecifiedCHEMBL3102026Agonist activity at ERalpha (unknown origin) b...BIC50CHEMBL135E2=Homo sapiensCHEMBL135Bioorg. Med. Chem., (2014) 22:1:303CHEMBL2069Estrogen receptor alphanM1.3000active
......................................................
3691UnspecifiedCHEMBL863236Inhibition of binding to recombinant human ERa...BIC50CHEMBL812=Homo sapiensCHEMBL81Bioorg. Med. Chem. Lett., (2005) 15:23:5124CHEMBL2069Estrogen receptor alphanM1.8000active
3697UnspecifiedCHEMBL1909145DRUGMATRIX: Estrogen ERalpha radioligand bindi...BIC50CHEMBL81RALOXIFENE=Homo sapiensCHEMBL81UnspecifiedCHEMBL2069Estrogen receptor alphanM0.5530active
3708UnspecifiedCHEMBL904964Displacement of [3H]estradiol from human recom...BIC50CHEMBL81raloxifene=Homo sapiensCHEMBL81J. Med. Chem., (2007) 50:11:2682CHEMBL2069Estrogen receptor alphanM20.6000active
3713UnspecifiedCHEMBL867524Binding affinity to ERalphaBIC50CHEMBL81raloxifene=Homo sapiensCHEMBL81J. Med. Chem., (2006) 49:11:3056CHEMBL2069Estrogen receptor alphanM1.8000active
3715UnspecifiedCHEMBL831072Binding potency for human ER alphaBIC50CHEMBL81Raloxifene=Homo sapiensCHEMBL81Bioorg. Med. Chem. Lett., (2004) 14:15:3865CHEMBL2069Estrogen receptor alphanM1.8000active
3716UnspecifiedCHEMBL831071Binding potency for human ER alphaBIC50CHEMBL81Raloxifene=Homo sapiensCHEMBL81Bioorg. Med. Chem. Lett., (2004) 14:15:3861CHEMBL2069Estrogen receptor alphanM1.8000active
3717UnspecifiedCHEMBL829466Inhibition of [3H]17-beta-estradiol binding to...BIC50CHEMBL81Raloxifene=Homo sapiensCHEMBL81Bioorg. Med. Chem. Lett., (2005) 15:11:2891CHEMBL2069Estrogen receptor alphanM0.8900active
3718UnspecifiedCHEMBL832667Inhibition of bindign to recombinant human est...BIC50CHEMBL81Raloxifene=Homo sapiensCHEMBL81Bioorg. Med. Chem. Lett., (2005) 15:1:107CHEMBL2069Estrogen receptor alphanM1.8000active
3719UnspecifiedCHEMBL831007Inhibition of 17-beta-estradiol mediated lucif...BIC50CHEMBL817=Homo sapiensCHEMBL81J. Med. Chem., (2005) 48:2:364CHEMBL2069Estrogen receptor alphanM2.4000active
3720UnspecifiedCHEMBL831528Inhibition of [3H]estradiol binding to human e...BIC50CHEMBL817=Homo sapiensCHEMBL81J. Med. Chem., (2005) 48:2:364CHEMBL2069Estrogen receptor alphanM22.0000active
3721UnspecifiedCHEMBL833547Inhibition of estrogen receptor alphaBIC50CHEMBL811=Homo sapiensCHEMBL81Bioorg. Med. Chem. Lett., (2005) 15:5:1505CHEMBL2069Estrogen receptor alphanM0.7300active
3727UnspecifiedCHEMBL679892In vitro inhibition of transcriptional activat...BIC50CHEMBL81Raloxifene=Homo sapiensCHEMBL81J. Med. Chem., (2002) 45:25:5492CHEMBL2069Estrogen receptor alphanM0.7000active
3728UnspecifiedCHEMBL679888In vitro binding affinity for estrogen recepto...BIC50CHEMBL811 (Raloxifene)=Homo sapiensCHEMBL81Bioorg. Med. Chem. Lett., (2003) 13:11:1919CHEMBL2069Estrogen receptor alphanM7.0000active
3734UnspecifiedCHEMBL681157In vitro inhibition of [3H]17-beta-estradiol b...BIC50CHEMBL812, Raloxifene=Homo sapiensCHEMBL81J. Med. Chem., (2001) 44:11:1654CHEMBL2069Estrogen receptor alphanM4.0000active
3735UnspecifiedCHEMBL677871Binding affinity for Human Estrogen receptor-a...BIC50CHEMBL811=Homo sapiensCHEMBL81Bioorg. Med. Chem. Lett., (2003) 13:3:479CHEMBL2069Estrogen receptor alphanM1.8000active
279UnspecifiedCHEMBL3829134Displacement of fluorescein-labeled estrogen l...BIC50CHEMBL83Tamoxifen=Homo sapiensCHEMBL83UnspecifiedCHEMBL2069Estrogen receptor alphanM1000.0000active
280UnspecifiedCHEMBL3777355Displacement of [3H]-E2 from human ER-alpha in...BIC50CHEMBL83TAM=Homo sapiensCHEMBL83UnspecifiedCHEMBL2069Estrogen receptor alphanM8.0000active
281UnspecifiedCHEMBL3624759Antagonist activity at ERalpha receptor in hum...BIC50CHEMBL831a=Homo sapiensCHEMBL83J. Med. Chem., (2015) 58:20:8128CHEMBL2069Estrogen receptor alphanM10000.0000inactive
282UnspecifiedCHEMBL3624758Binding affinity to ERalpha receptor (unknown ...BIC50CHEMBL831a=Homo sapiensCHEMBL83J. Med. Chem., (2015) 58:20:8128CHEMBL2069Estrogen receptor alphanM24.5500active
283UnspecifiedCHEMBL3382767Binding affinity to human ERalphaBIC50CHEMBL832=Homo sapiensCHEMBL83J. Med. Chem., (2014) 57:22:9370CHEMBL2069Estrogen receptor alphanM60.9000active
284UnspecifiedCHEMBL3382763Displacement of fluorescein-labeled estrogen f...BIC50CHEMBL832=Homo sapiensCHEMBL83J. Med. Chem., (2014) 57:22:9370CHEMBL2069Estrogen receptor alphanM61.0000active
286UnspecifiedCHEMBL2406604Antagonist activity at Gal4 DBD-fused human ER...BIC50CHEMBL83Tamoxifen=Homo sapiensCHEMBL83J. Med. Chem., (2013) 56:14:5782CHEMBL2069Estrogen receptor alphanM39.0000active
306UnspecifiedCHEMBL1023895Antiestrogenic activity in human T47D cells ex...BIC50CHEMBL83Tamoxifen=Homo sapiensCHEMBL83Bioorg. Med. Chem. Lett., (2009) 19:4:1218CHEMBL2069Estrogen receptor alphanM100.0000active
307UnspecifiedCHEMBL940315Displacement of fluorescein labeled estradiol ...BIC50CHEMBL831a=Homo sapiensCHEMBL83Bioorg. Med. Chem., (2008) 16:21:9554CHEMBL2069Estrogen receptor alphanM70.0000active
310UnspecifiedCHEMBL831007Inhibition of 17-beta-estradiol mediated lucif...BIC50CHEMBL836=Homo sapiensCHEMBL83J. Med. Chem., (2005) 48:2:364CHEMBL2069Estrogen receptor alphanM622.0000active
313UnspecifiedCHEMBL682292Displacement of [3H]17-beta-estradiol from hum...BIC50CHEMBL831=Homo sapiensCHEMBL83J. Med. Chem., (1999) 42:16:3126CHEMBL2069Estrogen receptor alphanM42.0000active
5548UnspecifiedCHEMBL831663Inhibition of transcriptional activation by hu...BIC50CHEMBL9847012=Homo sapiensCHEMBL98470Bioorg. Med. Chem. Lett., (2005) 15:4:957CHEMBL2069Estrogen receptor alphanM2.6000active
5549UnspecifiedCHEMBL831661Inhibition of binding to human estrogen recept...BIC50CHEMBL9847012=Homo sapiensCHEMBL98470Bioorg. Med. Chem. Lett., (2005) 15:4:957CHEMBL2069Estrogen receptor alphanM43.9500active
7480UnspecifiedCHEMBL831663Inhibition of transcriptional activation by hu...BIC50CHEMBL985583=Homo sapiensCHEMBL98558Bioorg. Med. Chem. Lett., (2005) 15:4:957CHEMBL2069Estrogen receptor alphanM1.7990active
7481UnspecifiedCHEMBL831661Inhibition of binding to human estrogen recept...BIC50CHEMBL985583=Homo sapiensCHEMBL98558Bioorg. Med. Chem. Lett., (2005) 15:4:957CHEMBL2069Estrogen receptor alphanM39.8100active
\n"", + ""

449 rows × 17 columns

\n"", + ""
"" + ], + ""text/plain"": [ + "" activity_comment assay_chemblid \\\n"", + ""6530 Unspecified CHEMBL831663 \n"", + ""6531 Unspecified CHEMBL831661 \n"", + ""3114 Unspecified CHEMBL3736957 \n"", + ""3115 Unspecified CHEMBL3736956 \n"", + ""6798 Unspecified CHEMBL831663 \n"", + ""6799 Unspecified CHEMBL831661 \n"", + ""1739 Unspecified CHEMBL831663 \n"", + ""1740 Unspecified CHEMBL831661 \n"", + ""7559 Unspecified CHEMBL831663 \n"", + ""7560 Unspecified CHEMBL831661 \n"", + ""1282 Unspecified CHEMBL831663 \n"", + ""1283 Unspecified CHEMBL831661 \n"", + ""985 Unspecified CHEMBL831007 \n"", + ""986 Unspecified CHEMBL831528 \n"", + ""987 Unspecified CHEMBL831663 \n"", + ""988 Unspecified CHEMBL831661 \n"", + ""1399 Unspecified CHEMBL831663 \n"", + ""1400 Unspecified CHEMBL831661 \n"", + ""2241 Unspecified CHEMBL831663 \n"", + ""2242 Unspecified CHEMBL831661 \n"", + ""7518 Unspecified CHEMBL831663 \n"", + ""7519 Unspecified CHEMBL831661 \n"", + ""1470 Unspecified CHEMBL831663 \n"", + ""1471 Unspecified CHEMBL831661 \n"", + ""6569 Unspecified CHEMBL3736957 \n"", + ""6570 Unspecified CHEMBL3736956 \n"", + ""3878 Unspecified CHEMBL3825990 \n"", + ""3885 Unspecified CHEMBL3599826 \n"", + ""3892 Unspecified CHEMBL3382763 \n"", + ""3902 Unspecified CHEMBL3102026 \n"", + ""... ... ... \n"", + ""3691 Unspecified CHEMBL863236 \n"", + ""3697 Unspecified CHEMBL1909145 \n"", + ""3708 Unspecified CHEMBL904964 \n"", + ""3713 Unspecified CHEMBL867524 \n"", + ""3715 Unspecified CHEMBL831072 \n"", + ""3716 Unspecified CHEMBL831071 \n"", + ""3717 Unspecified CHEMBL829466 \n"", + ""3718 Unspecified CHEMBL832667 \n"", + ""3719 Unspecified CHEMBL831007 \n"", + ""3720 Unspecified CHEMBL831528 \n"", + ""3721 Unspecified CHEMBL833547 \n"", + ""3727 Unspecified CHEMBL679892 \n"", + ""3728 Unspecified CHEMBL679888 \n"", + ""3734 Unspecified CHEMBL681157 \n"", + ""3735 Unspecified CHEMBL677871 \n"", + ""279 Unspecified CHEMBL3829134 \n"", + ""280 Unspecified CHEMBL3777355 \n"", + ""281 Unspecified CHEMBL3624759 \n"", + ""282 Unspecified CHEMBL3624758 \n"", + ""283 Unspecified CHEMBL3382767 \n"", + ""284 Unspecified CHEMBL3382763 \n"", + ""286 Unspecified CHEMBL2406604 \n"", + ""306 Unspecified CHEMBL1023895 \n"", + ""307 Unspecified CHEMBL940315 \n"", + ""310 Unspecified CHEMBL831007 \n"", + ""313 Unspecified CHEMBL682292 \n"", + ""5548 Unspecified CHEMBL831663 \n"", + ""5549 Unspecified CHEMBL831661 \n"", + ""7480 Unspecified CHEMBL831663 \n"", + ""7481 Unspecified CHEMBL831661 \n"", + ""\n"", + "" assay_description assay_type \\\n"", + ""6530 Inhibition of transcriptional activation by hu... B \n"", + ""6531 Inhibition of binding to human estrogen recept... B \n"", + ""3114 Antagonist activity at ERalpha in human T47D-K... B \n"", + ""3115 Antagonist activity at luciferase-fused ERalph... B \n"", + ""6798 Inhibition of transcriptional activation by hu... B \n"", + ""6799 Inhibition of binding to human estrogen recept... B \n"", + ""1739 Inhibition of transcriptional activation by hu... B \n"", + ""1740 Inhibition of binding to human estrogen recept... B \n"", + ""7559 Inhibition of transcriptional activation by hu... B \n"", + ""7560 Inhibition of binding to human estrogen recept... B \n"", + ""1282 Inhibition of transcriptional activation by hu... B \n"", + ""1283 Inhibition of binding to human estrogen recept... B \n"", + ""985 Inhibition of 17-beta-estradiol mediated lucif... B \n"", + ""986 Inhibition of [3H]estradiol binding to human e... B \n"", + ""987 Inhibition of transcriptional activation by hu... B \n"", + ""988 Inhibition of binding to human estrogen recept... B \n"", + ""1399 Inhibition of transcriptional activation by hu... B \n"", + ""1400 Inhibition of binding to human estrogen recept... B \n"", + ""2241 Inhibition of transcriptional activation by hu... B \n"", + ""2242 Inhibition of binding to human estrogen recept... B \n"", + ""7518 Inhibition of transcriptional activation by hu... B \n"", + ""7519 Inhibition of binding to human estrogen recept... B \n"", + ""1470 Inhibition of transcriptional activation by hu... B \n"", + ""1471 Inhibition of binding to human estrogen recept... B \n"", + ""6569 Antagonist activity at ERalpha in human T47D-K... B \n"", + ""6570 Antagonist activity at luciferase-fused ERalph... B \n"", + ""3878 Inhibition of ER-alpha (unknown origin) by Lan... B \n"", + ""3885 Inhibition of estrogen receptor alpha (unknown... B \n"", + ""3892 Displacement of fluorescein-labeled estrogen f... B \n"", + ""3902 Agonist activity at ERalpha (unknown origin) b... B \n"", + ""... ... ... \n"", + ""3691 Inhibition of binding to recombinant human ERa... B \n"", + ""3697 DRUGMATRIX: Estrogen ERalpha radioligand bindi... B \n"", + ""3708 Displacement of [3H]estradiol from human recom... B \n"", + ""3713 Binding affinity to ERalpha B \n"", + ""3715 Binding potency for human ER alpha B \n"", + ""3716 Binding potency for human ER alpha B \n"", + ""3717 Inhibition of [3H]17-beta-estradiol binding to... B \n"", + ""3718 Inhibition of bindign to recombinant human est... B \n"", + ""3719 Inhibition of 17-beta-estradiol mediated lucif... B \n"", + ""3720 Inhibition of [3H]estradiol binding to human e... B \n"", + ""3721 Inhibition of estrogen receptor alpha B \n"", + ""3727 In vitro inhibition of transcriptional activat... B \n"", + ""3728 In vitro binding affinity for estrogen recepto... B \n"", + ""3734 In vitro inhibition of [3H]17-beta-estradiol b... B \n"", + ""3735 Binding affinity for Human Estrogen receptor-a... B \n"", + ""279 Displacement of fluorescein-labeled estrogen l... B \n"", + ""280 Displacement of [3H]-E2 from human ER-alpha in... B \n"", + ""281 Antagonist activity at ERalpha receptor in hum... B \n"", + ""282 Binding affinity to ERalpha receptor (unknown ... B \n"", + ""283 Binding affinity to human ERalpha B \n"", + ""284 Displacement of fluorescein-labeled estrogen f... B \n"", + ""286 Antagonist activity at Gal4 DBD-fused human ER... B \n"", + ""306 Antiestrogenic activity in human T47D cells ex... B \n"", + ""307 Displacement of fluorescein labeled estradiol ... B \n"", + ""310 Inhibition of 17-beta-estradiol mediated lucif... B \n"", + ""313 Displacement of [3H]17-beta-estradiol from hum... B \n"", + ""5548 Inhibition of transcriptional activation by hu... B \n"", + ""5549 Inhibition of binding to human estrogen recept... B \n"", + ""7480 Inhibition of transcriptional activation by hu... B \n"", + ""7481 Inhibition of binding to human estrogen recept... B \n"", + ""\n"", + "" bioactivity_type chemblId name_in_reference operator organism \\\n"", + ""6530 IC50 CHEMBL100231 13 = Homo sapiens \n"", + ""6531 IC50 CHEMBL100231 13 = Homo sapiens \n"", + ""3114 IC50 CHEMBL10041 4-OHT = Homo sapiens \n"", + ""3115 IC50 CHEMBL10041 4-OHT = Homo sapiens \n"", + ""6798 IC50 CHEMBL100595 19 = Homo sapiens \n"", + ""6799 IC50 CHEMBL100595 19 = Homo sapiens \n"", + ""1739 IC50 CHEMBL100617 15 = Homo sapiens \n"", + ""1740 IC50 CHEMBL100617 15 = Homo sapiens \n"", + ""7559 IC50 CHEMBL100763 10 = Homo sapiens \n"", + ""7560 IC50 CHEMBL100763 10 = Homo sapiens \n"", + ""1282 IC50 CHEMBL101083 9 = Homo sapiens \n"", + ""1283 IC50 CHEMBL101083 9 = Homo sapiens \n"", + ""985 IC50 CHEMBL101382 60 = Homo sapiens \n"", + ""986 IC50 CHEMBL101382 60 = Homo sapiens \n"", + ""987 IC50 CHEMBL101382 2 = Homo sapiens \n"", + ""988 IC50 CHEMBL101382 2 = Homo sapiens \n"", + ""1399 IC50 CHEMBL101807 4 = Homo sapiens \n"", + ""1400 IC50 CHEMBL101807 4 = Homo sapiens \n"", + ""2241 IC50 CHEMBL101997 8 = Homo sapiens \n"", + ""2242 IC50 CHEMBL101997 8 = Homo sapiens \n"", + ""7518 IC50 CHEMBL103294 11 = Homo sapiens \n"", + ""7519 IC50 CHEMBL103294 11 = Homo sapiens \n"", + ""1470 IC50 CHEMBL103305 18 = Homo sapiens \n"", + ""1471 IC50 CHEMBL103305 18 = Homo sapiens \n"", + ""6569 IC50 CHEMBL1222035 4; ICI-164,384 = Homo sapiens \n"", + ""6570 IC50 CHEMBL1222035 4; ICI-164,384 = Homo sapiens \n"", + ""3878 IC50 CHEMBL135 Estradiol = Homo sapiens \n"", + ""3885 IC50 CHEMBL135 E2 = Homo sapiens \n"", + ""3892 IC50 CHEMBL135 1 = Homo sapiens \n"", + ""3902 IC50 CHEMBL135 E2 = Homo sapiens \n"", + ""... ... ... ... ... ... \n"", + ""3691 IC50 CHEMBL81 2 = Homo sapiens \n"", + ""3697 IC50 CHEMBL81 RALOXIFENE = Homo sapiens \n"", + ""3708 IC50 CHEMBL81 raloxifene = Homo sapiens \n"", + ""3713 IC50 CHEMBL81 raloxifene = Homo sapiens \n"", + ""3715 IC50 CHEMBL81 Raloxifene = Homo sapiens \n"", + ""3716 IC50 CHEMBL81 Raloxifene = Homo sapiens \n"", + ""3717 IC50 CHEMBL81 Raloxifene = Homo sapiens \n"", + ""3718 IC50 CHEMBL81 Raloxifene = Homo sapiens \n"", + ""3719 IC50 CHEMBL81 7 = Homo sapiens \n"", + ""3720 IC50 CHEMBL81 7 = Homo sapiens \n"", + ""3721 IC50 CHEMBL81 1 = Homo sapiens \n"", + ""3727 IC50 CHEMBL81 Raloxifene = Homo sapiens \n"", + ""3728 IC50 CHEMBL81 1 (Raloxifene) = Homo sapiens \n"", + ""3734 IC50 CHEMBL81 2, Raloxifene = Homo sapiens \n"", + ""3735 IC50 CHEMBL81 1 = Homo sapiens \n"", + ""279 IC50 CHEMBL83 Tamoxifen = Homo sapiens \n"", + ""280 IC50 CHEMBL83 TAM = Homo sapiens \n"", + ""281 IC50 CHEMBL83 1a = Homo sapiens \n"", + ""282 IC50 CHEMBL83 1a = Homo sapiens \n"", + ""283 IC50 CHEMBL83 2 = Homo sapiens \n"", + ""284 IC50 CHEMBL83 2 = Homo sapiens \n"", + ""286 IC50 CHEMBL83 Tamoxifen = Homo sapiens \n"", + ""306 IC50 CHEMBL83 Tamoxifen = Homo sapiens \n"", + ""307 IC50 CHEMBL83 1a = Homo sapiens \n"", + ""310 IC50 CHEMBL83 6 = Homo sapiens \n"", + ""313 IC50 CHEMBL83 1 = Homo sapiens \n"", + ""5548 IC50 CHEMBL98470 12 = Homo sapiens \n"", + ""5549 IC50 CHEMBL98470 12 = Homo sapiens \n"", + ""7480 IC50 CHEMBL98558 3 = Homo sapiens \n"", + ""7481 IC50 CHEMBL98558 3 = Homo sapiens \n"", + ""\n"", + "" parent_cmpd_chemblid reference \\\n"", + ""6530 CHEMBL100231 Bioorg. Med. Chem. Lett., (2005) 15:4:957 \n"", + ""6531 CHEMBL100231 Bioorg. Med. Chem. Lett., (2005) 15:4:957 \n"", + ""3114 CHEMBL10041 Bioorg. Med. Chem., (2015) 23:24:7597 \n"", + ""3115 CHEMBL10041 Bioorg. Med. Chem., (2015) 23:24:7597 \n"", + ""6798 CHEMBL100595 Bioorg. Med. Chem. Lett., (2005) 15:4:957 \n"", + ""6799 CHEMBL100595 Bioorg. Med. Chem. Lett., (2005) 15:4:957 \n"", + ""1739 CHEMBL100617 Bioorg. Med. Chem. Lett., (2005) 15:4:957 \n"", + ""1740 CHEMBL100617 Bioorg. Med. Chem. Lett., (2005) 15:4:957 \n"", + ""7559 CHEMBL100763 Bioorg. Med. Chem. Lett., (2005) 15:4:957 \n"", + ""7560 CHEMBL100763 Bioorg. Med. Chem. Lett., (2005) 15:4:957 \n"", + ""1282 CHEMBL101083 Bioorg. Med. Chem. Lett., (2005) 15:4:957 \n"", + ""1283 CHEMBL101083 Bioorg. Med. Chem. Lett., (2005) 15:4:957 \n"", + ""985 CHEMBL101382 J. Med. Chem., (2005) 48:2:364 \n"", + ""986 CHEMBL101382 J. Med. Chem., (2005) 48:2:364 \n"", + ""987 CHEMBL101382 Bioorg. Med. Chem. Lett., (2005) 15:4:957 \n"", + ""988 CHEMBL101382 Bioorg. Med. Chem. Lett., (2005) 15:4:957 \n"", + ""1399 CHEMBL101807 Bioorg. Med. Chem. Lett., (2005) 15:4:957 \n"", + ""1400 CHEMBL101807 Bioorg. Med. Chem. Lett., (2005) 15:4:957 \n"", + ""2241 CHEMBL101997 Bioorg. Med. Chem. Lett., (2005) 15:4:957 \n"", + ""2242 CHEMBL101997 Bioorg. Med. Chem. Lett., (2005) 15:4:957 \n"", + ""7518 CHEMBL103294 Bioorg. Med. Chem. Lett., (2005) 15:4:957 \n"", + ""7519 CHEMBL103294 Bioorg. Med. Chem. Lett., (2005) 15:4:957 \n"", + ""1470 CHEMBL103305 Bioorg. Med. Chem. Lett., (2005) 15:4:957 \n"", + ""1471 CHEMBL103305 Bioorg. Med. Chem. Lett., (2005) 15:4:957 \n"", + ""6569 CHEMBL1222035 Bioorg. Med. Chem., (2015) 23:24:7597 \n"", + ""6570 CHEMBL1222035 Bioorg. Med. Chem., (2015) 23:24:7597 \n"", + ""3878 CHEMBL135 Bioorg. Med. Chem., (2016) 24:18:4075 \n"", + ""3885 CHEMBL135 Bioorg. Med. Chem., (2015) 23:15:4132 \n"", + ""3892 CHEMBL135 J. Med. Chem., (2014) 57:22:9370 \n"", + ""3902 CHEMBL135 Bioorg. Med. Chem., (2014) 22:1:303 \n"", + ""... ... ... \n"", + ""3691 CHEMBL81 Bioorg. Med. Chem. Lett., (2005) 15:23:5124 \n"", + ""3697 CHEMBL81 Unspecified \n"", + ""3708 CHEMBL81 J. Med. Chem., (2007) 50:11:2682 \n"", + ""3713 CHEMBL81 J. Med. Chem., (2006) 49:11:3056 \n"", + ""3715 CHEMBL81 Bioorg. Med. Chem. Lett., (2004) 14:15:3865 \n"", + ""3716 CHEMBL81 Bioorg. Med. Chem. Lett., (2004) 14:15:3861 \n"", + ""3717 CHEMBL81 Bioorg. Med. Chem. Lett., (2005) 15:11:2891 \n"", + ""3718 CHEMBL81 Bioorg. Med. Chem. Lett., (2005) 15:1:107 \n"", + ""3719 CHEMBL81 J. Med. Chem., (2005) 48:2:364 \n"", + ""3720 CHEMBL81 J. Med. Chem., (2005) 48:2:364 \n"", + ""3721 CHEMBL81 Bioorg. Med. Chem. Lett., (2005) 15:5:1505 \n"", + ""3727 CHEMBL81 J. Med. Chem., (2002) 45:25:5492 \n"", + ""3728 CHEMBL81 Bioorg. Med. Chem. Lett., (2003) 13:11:1919 \n"", + ""3734 CHEMBL81 J. Med. Chem., (2001) 44:11:1654 \n"", + ""3735 CHEMBL81 Bioorg. Med. Chem. Lett., (2003) 13:3:479 \n"", + ""279 CHEMBL83 Unspecified \n"", + ""280 CHEMBL83 Unspecified \n"", + ""281 CHEMBL83 J. Med. Chem., (2015) 58:20:8128 \n"", + ""282 CHEMBL83 J. Med. Chem., (2015) 58:20:8128 \n"", + ""283 CHEMBL83 J. Med. Chem., (2014) 57:22:9370 \n"", + ""284 CHEMBL83 J. Med. Chem., (2014) 57:22:9370 \n"", + ""286 CHEMBL83 J. Med. Chem., (2013) 56:14:5782 \n"", + ""306 CHEMBL83 Bioorg. Med. Chem. Lett., (2009) 19:4:1218 \n"", + ""307 CHEMBL83 Bioorg. Med. Chem., (2008) 16:21:9554 \n"", + ""310 CHEMBL83 J. Med. Chem., (2005) 48:2:364 \n"", + ""313 CHEMBL83 J. Med. Chem., (1999) 42:16:3126 \n"", + ""5548 CHEMBL98470 Bioorg. Med. Chem. Lett., (2005) 15:4:957 \n"", + ""5549 CHEMBL98470 Bioorg. Med. Chem. Lett., (2005) 15:4:957 \n"", + ""7480 CHEMBL98558 Bioorg. Med. Chem. Lett., (2005) 15:4:957 \n"", + ""7481 CHEMBL98558 Bioorg. Med. Chem. Lett., (2005) 15:4:957 \n"", + ""\n"", + "" target_chemblid target_confidence target_name units \\\n"", + ""6530 CHEMBL206 9 Estrogen receptor alpha nM \n"", + ""6531 CHEMBL206 9 Estrogen receptor alpha nM \n"", + ""3114 CHEMBL206 9 Estrogen receptor alpha nM \n"", + ""3115 CHEMBL206 9 Estrogen receptor alpha nM \n"", + ""6798 CHEMBL206 9 Estrogen receptor alpha nM \n"", + ""6799 CHEMBL206 9 Estrogen receptor alpha nM \n"", + ""1739 CHEMBL206 9 Estrogen receptor alpha nM \n"", + ""1740 CHEMBL206 9 Estrogen receptor alpha nM \n"", + ""7559 CHEMBL206 9 Estrogen receptor alpha nM \n"", + ""7560 CHEMBL206 9 Estrogen receptor alpha nM \n"", + ""1282 CHEMBL206 9 Estrogen receptor alpha nM \n"", + ""1283 CHEMBL206 9 Estrogen receptor alpha nM \n"", + ""985 CHEMBL206 9 Estrogen receptor alpha nM \n"", + ""986 CHEMBL206 9 Estrogen receptor alpha nM \n"", + ""987 CHEMBL206 9 Estrogen receptor alpha nM \n"", + ""988 CHEMBL206 9 Estrogen receptor alpha nM \n"", + ""1399 CHEMBL206 9 Estrogen receptor alpha nM \n"", + ""1400 CHEMBL206 9 Estrogen receptor alpha nM \n"", + ""2241 CHEMBL206 9 Estrogen receptor alpha nM \n"", + ""2242 CHEMBL206 9 Estrogen receptor alpha nM \n"", + ""7518 CHEMBL206 9 Estrogen receptor alpha nM \n"", + ""7519 CHEMBL206 9 Estrogen receptor alpha nM \n"", + ""1470 CHEMBL206 9 Estrogen receptor alpha nM \n"", + ""1471 CHEMBL206 9 Estrogen receptor alpha nM \n"", + ""6569 CHEMBL206 9 Estrogen receptor alpha nM \n"", + ""6570 CHEMBL206 9 Estrogen receptor alpha nM \n"", + ""3878 CHEMBL206 9 Estrogen receptor alpha nM \n"", + ""3885 CHEMBL206 9 Estrogen receptor alpha nM \n"", + ""3892 CHEMBL206 9 Estrogen receptor alpha nM \n"", + ""3902 CHEMBL206 9 Estrogen receptor alpha nM \n"", + ""... ... ... ... ... \n"", + ""3691 CHEMBL206 9 Estrogen receptor alpha nM \n"", + ""3697 CHEMBL206 9 Estrogen receptor alpha nM \n"", + ""3708 CHEMBL206 9 Estrogen receptor alpha nM \n"", + ""3713 CHEMBL206 9 Estrogen receptor alpha nM \n"", + ""3715 CHEMBL206 9 Estrogen receptor alpha nM \n"", + ""3716 CHEMBL206 9 Estrogen receptor alpha nM \n"", + ""3717 CHEMBL206 9 Estrogen receptor alpha nM \n"", + ""3718 CHEMBL206 9 Estrogen receptor alpha nM \n"", + ""3719 CHEMBL206 9 Estrogen receptor alpha nM \n"", + ""3720 CHEMBL206 9 Estrogen receptor alpha nM \n"", + ""3721 CHEMBL206 9 Estrogen receptor alpha nM \n"", + ""3727 CHEMBL206 9 Estrogen receptor alpha nM \n"", + ""3728 CHEMBL206 9 Estrogen receptor alpha nM \n"", + ""3734 CHEMBL206 9 Estrogen receptor alpha nM \n"", + ""3735 CHEMBL206 9 Estrogen receptor alpha nM \n"", + ""279 CHEMBL206 9 Estrogen receptor alpha nM \n"", + ""280 CHEMBL206 9 Estrogen receptor alpha nM \n"", + ""281 CHEMBL206 9 Estrogen receptor alpha nM \n"", + ""282 CHEMBL206 9 Estrogen receptor alpha nM \n"", + ""283 CHEMBL206 9 Estrogen receptor alpha nM \n"", + ""284 CHEMBL206 9 Estrogen receptor alpha nM \n"", + ""286 CHEMBL206 9 Estrogen receptor alpha nM \n"", + ""306 CHEMBL206 9 Estrogen receptor alpha nM \n"", + ""307 CHEMBL206 9 Estrogen receptor alpha nM \n"", + ""310 CHEMBL206 9 Estrogen receptor alpha nM \n"", + ""313 CHEMBL206 9 Estrogen receptor alpha nM \n"", + ""5548 CHEMBL206 9 Estrogen receptor alpha nM \n"", + ""5549 CHEMBL206 9 Estrogen receptor alpha nM \n"", + ""7480 CHEMBL206 9 Estrogen receptor alpha nM \n"", + ""7481 CHEMBL206 9 Estrogen receptor alpha nM \n"", + ""\n"", + "" value STATUS \n"", + ""6530 5.2000 active \n"", + ""6531 70.9600 active \n"", + ""3114 10.0000 active \n"", + ""3115 70.0000 active \n"", + ""6798 16.1100 active \n"", + ""6799 46.0300 active \n"", + ""1739 0.6998 active \n"", + ""1740 33.9600 active \n"", + ""7559 3.3040 active \n"", + ""7560 26.9800 active \n"", + ""1282 1.9010 active \n"", + ""1283 38.0200 active \n"", + ""985 16.5000 active \n"", + ""986 19.0000 active \n"", + ""987 16.4800 active \n"", + ""988 19.0100 active \n"", + ""1399 2.6000 active \n"", + ""1400 28.9700 active \n"", + ""2241 1.6000 active \n"", + ""2242 30.9700 active \n"", + ""7518 3.0970 active \n"", + ""7519 34.9900 active \n"", + ""1470 4.4980 active \n"", + ""1471 48.9800 active \n"", + ""6569 50.0000 active \n"", + ""6570 340.0000 active \n"", + ""3878 1.7000 active \n"", + ""3885 0.9000 active \n"", + ""3892 5.7000 active \n"", + ""3902 1.3000 active \n"", + ""... ... ... \n"", + ""3691 1.8000 active \n"", + ""3697 0.5530 active \n"", + ""3708 20.6000 active \n"", + ""3713 1.8000 active \n"", + ""3715 1.8000 active \n"", + ""3716 1.8000 active \n"", + ""3717 0.8900 active \n"", + ""3718 1.8000 active \n"", + ""3719 2.4000 active \n"", + ""3720 22.0000 active \n"", + ""3721 0.7300 active \n"", + ""3727 0.7000 active \n"", + ""3728 7.0000 active \n"", + ""3734 4.0000 active \n"", + ""3735 1.8000 active \n"", + ""279 1000.0000 active \n"", + ""280 8.0000 active \n"", + ""281 10000.0000 inactive \n"", + ""282 24.5500 active \n"", + ""283 60.9000 active \n"", + ""284 61.0000 active \n"", + ""286 39.0000 active \n"", + ""306 100.0000 active \n"", + ""307 70.0000 active \n"", + ""310 622.0000 active \n"", + ""313 42.0000 active \n"", + ""5548 2.6000 active \n"", + ""5549 43.9500 active \n"", + ""7480 1.7990 active \n"", + ""7481 39.8100 active \n"", + ""\n"", + ""[449 rows x 17 columns]"" + ] + }, + ""execution_count"": 98, + ""metadata"": {}, + ""output_type"": ""execute_result"" + } + ], + ""source"": [ + ""bioactsDF_Train_dup"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 99, + ""metadata"": {}, + ""outputs"": [ + { + ""data"": { + ""text/html"": [ + ""
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chemblIdvalue
meanstd
0CHEMBL10023138.0846.499342
1CHEMBL1004140.0042.426407
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"" + ], + ""text/plain"": [ + "" chemblId value \n"", + "" mean std\n"", + ""0 CHEMBL100231 38.08 46.499342\n"", + ""1 CHEMBL10041 40.00 42.426407"" + ] + }, + ""execution_count"": 99, + ""metadata"": {}, + ""output_type"": ""execute_result"" + } + ], + ""source"": [ + ""# mean and std of all duplicae\n"", + ""\n"", + ""mean_std = bioactsDF_Train_dup.groupby(['chemblId'], as_index=False).agg(\n"", + "" {'value':['mean','std']})\n"", + ""mean_std.head(2)"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 100, + ""metadata"": {}, + ""outputs"": [ + { + ""data"": { + ""text/plain"": [ + ""165"" + ] + }, + ""execution_count"": 100, + ""metadata"": {}, + ""output_type"": ""execute_result"" + } + ], + ""source"": [ + ""len(mean_std)"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 101, + ""metadata"": {}, + ""outputs"": [ + { + ""data"": { + ""text/plain"": [ + ""449"" + ] + }, + ""execution_count"": 101, + ""metadata"": {}, + ""output_type"": ""execute_result"" + } + ], + ""source"": [ + ""bioactsDF_Train_dup = bioactsDF_Train_dup.merge(mean_std, on='chemblId', how='inner')\n"", + ""len(bioactsDF_Train_dup)"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 102, + ""metadata"": {}, + ""outputs"": [ + { + ""data"": { + ""text/html"": [ + ""
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activity_commentassay_chemblidassay_descriptionassay_typebioactivity_typechemblIdname_in_referenceoperatororganismparent_cmpd_chemblidreferencetarget_chemblidtarget_confidencetarget_nameunitsvalueSTATUS(value, mean)(value, std)
0UnspecifiedCHEMBL831663Inhibition of transcriptional activation by hu...BIC50CHEMBL10023113=Homo sapiensCHEMBL100231Bioorg. Med. Chem. Lett., (2005) 15:4:957CHEMBL2069Estrogen receptor alphanM5.20active38.0846.499342
1UnspecifiedCHEMBL831661Inhibition of binding to human estrogen recept...BIC50CHEMBL10023113=Homo sapiensCHEMBL100231Bioorg. Med. Chem. Lett., (2005) 15:4:957CHEMBL2069Estrogen receptor alphanM70.96active38.0846.499342
2UnspecifiedCHEMBL3736957Antagonist activity at ERalpha in human T47D-K...BIC50CHEMBL100414-OHT=Homo sapiensCHEMBL10041Bioorg. Med. Chem., (2015) 23:24:7597CHEMBL2069Estrogen receptor alphanM10.00active40.0042.426407
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"" + ], + ""text/plain"": [ + "" activity_comment assay_chemblid \\\n"", + ""0 Unspecified CHEMBL831663 \n"", + ""1 Unspecified CHEMBL831661 \n"", + ""2 Unspecified CHEMBL3736957 \n"", + ""\n"", + "" assay_description assay_type \\\n"", + ""0 Inhibition of transcriptional activation by hu... B \n"", + ""1 Inhibition of binding to human estrogen recept... B \n"", + ""2 Antagonist activity at ERalpha in human T47D-K... B \n"", + ""\n"", + "" bioactivity_type chemblId name_in_reference operator organism \\\n"", + ""0 IC50 CHEMBL100231 13 = Homo sapiens \n"", + ""1 IC50 CHEMBL100231 13 = Homo sapiens \n"", + ""2 IC50 CHEMBL10041 4-OHT = Homo sapiens \n"", + ""\n"", + "" parent_cmpd_chemblid reference \\\n"", + ""0 CHEMBL100231 Bioorg. Med. Chem. Lett., (2005) 15:4:957 \n"", + ""1 CHEMBL100231 Bioorg. Med. Chem. Lett., (2005) 15:4:957 \n"", + ""2 CHEMBL10041 Bioorg. Med. Chem., (2015) 23:24:7597 \n"", + ""\n"", + "" target_chemblid target_confidence target_name units value \\\n"", + ""0 CHEMBL206 9 Estrogen receptor alpha nM 5.20 \n"", + ""1 CHEMBL206 9 Estrogen receptor alpha nM 70.96 \n"", + ""2 CHEMBL206 9 Estrogen receptor alpha nM 10.00 \n"", + ""\n"", + "" STATUS (value, mean) (value, std) \n"", + ""0 active 38.08 46.499342 \n"", + ""1 active 38.08 46.499342 \n"", + ""2 active 40.00 42.426407 "" + ] + }, + ""execution_count"": 102, + ""metadata"": {}, + ""output_type"": ""execute_result"" + } + ], + ""source"": [ + ""bioactsDF_Train_dup.head(3)"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 103, + ""metadata"": {}, + ""outputs"": [ + { + ""data"": { + ""text/plain"": [ + ""(80, 31)"" + ] + }, + ""execution_count"": 103, + ""metadata"": {}, + ""output_type"": ""execute_result"" + } + ], + ""source"": [ + ""#keep only SD less thab 2SD\n"", + ""\n"", + ""bioactsDF_Train_dup = bioactsDF_Train_dup[(bioactsDF_Train_dup[('value', 'std')] < 2)]\n"", + ""len(bioactsDF_Train_dup), len(bioactsDF_Train_dup['chemblId'].unique())"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 104, + ""metadata"": {}, + ""outputs"": [ + { + ""data"": { + ""text/html"": [ + ""
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activity_commentassay_chemblidassay_descriptionassay_typebioactivity_typechemblIdname_in_referenceoperatororganismparent_cmpd_chemblidreferencetarget_chemblidtarget_confidencetarget_nameunitsvalueSTATUS(value, mean)(value, std)select
0UnspecifiedCHEMBL831007Inhibition of 17-beta-estradiol mediated lucif...BIC50CHEMBL10138260=Homo sapiensCHEMBL101382J. Med. Chem., (2005) 48:2:364CHEMBL2069Estrogen receptor alphanM16.50active17.74751.4520651.2475
1UnspecifiedCHEMBL831528Inhibition of [3H]estradiol binding to human e...BIC50CHEMBL10138260=Homo sapiensCHEMBL101382J. Med. Chem., (2005) 48:2:364CHEMBL2069Estrogen receptor alphanM19.00active17.74751.4520651.2525
2UnspecifiedCHEMBL831661Inhibition of binding to human estrogen recept...BIC50CHEMBL1013822=Homo sapiensCHEMBL101382Bioorg. Med. Chem. Lett., (2005) 15:4:957CHEMBL2069Estrogen receptor alphanM19.01active17.74751.4520651.2625
3UnspecifiedCHEMBL831663Inhibition of transcriptional activation by hu...BIC50CHEMBL1013822=Homo sapiensCHEMBL101382Bioorg. Med. Chem. Lett., (2005) 15:4:957CHEMBL2069Estrogen receptor alphanM16.48active17.74751.4520651.2675
4UnspecifiedCHEMBL832667Inhibition of bindign to recombinant human est...BIC50CHEMBL18079219=Homo sapiensCHEMBL180792Bioorg. Med. Chem. Lett., (2005) 15:1:107CHEMBL2069Estrogen receptor alphanM1.70active1.50000.2828430.2000
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"" + ], + ""text/plain"": [ + "" activity_comment assay_chemblid \\\n"", + ""0 Unspecified CHEMBL831007 \n"", + ""1 Unspecified CHEMBL831528 \n"", + ""2 Unspecified CHEMBL831661 \n"", + ""3 Unspecified CHEMBL831663 \n"", + ""4 Unspecified CHEMBL832667 \n"", + ""\n"", + "" assay_description assay_type \\\n"", + ""0 Inhibition of 17-beta-estradiol mediated lucif... B \n"", + ""1 Inhibition of [3H]estradiol binding to human e... B \n"", + ""2 Inhibition of binding to human estrogen recept... B \n"", + ""3 Inhibition of transcriptional activation by hu... B \n"", + ""4 Inhibition of bindign to recombinant human est... B \n"", + ""\n"", + "" bioactivity_type chemblId name_in_reference operator organism \\\n"", + ""0 IC50 CHEMBL101382 60 = Homo sapiens \n"", + ""1 IC50 CHEMBL101382 60 = Homo sapiens \n"", + ""2 IC50 CHEMBL101382 2 = Homo sapiens \n"", + ""3 IC50 CHEMBL101382 2 = Homo sapiens \n"", + ""4 IC50 CHEMBL180792 19 = Homo sapiens \n"", + ""\n"", + "" parent_cmpd_chemblid reference \\\n"", + ""0 CHEMBL101382 J. Med. Chem., (2005) 48:2:364 \n"", + ""1 CHEMBL101382 J. Med. Chem., (2005) 48:2:364 \n"", + ""2 CHEMBL101382 Bioorg. Med. Chem. Lett., (2005) 15:4:957 \n"", + ""3 CHEMBL101382 Bioorg. Med. Chem. Lett., (2005) 15:4:957 \n"", + ""4 CHEMBL180792 Bioorg. Med. Chem. Lett., (2005) 15:1:107 \n"", + ""\n"", + "" target_chemblid target_confidence target_name units value \\\n"", + ""0 CHEMBL206 9 Estrogen receptor alpha nM 16.50 \n"", + ""1 CHEMBL206 9 Estrogen receptor alpha nM 19.00 \n"", + ""2 CHEMBL206 9 Estrogen receptor alpha nM 19.01 \n"", + ""3 CHEMBL206 9 Estrogen receptor alpha nM 16.48 \n"", + ""4 CHEMBL206 9 Estrogen receptor alpha nM 1.70 \n"", + ""\n"", + "" STATUS (value, mean) (value, std) select \n"", + ""0 active 17.7475 1.452065 1.2475 \n"", + ""1 active 17.7475 1.452065 1.2525 \n"", + ""2 active 17.7475 1.452065 1.2625 \n"", + ""3 active 17.7475 1.452065 1.2675 \n"", + ""4 active 1.5000 0.282843 0.2000 "" + ] + }, + ""execution_count"": 104, + ""metadata"": {}, + ""output_type"": ""execute_result"" + } + ], + ""source"": [ + ""bioactsDF_Train_dup['select'] = (bioactsDF_Train_dup['value']- bioactsDF_Train_dup[('value', 'mean')]).abs()\n"", + ""#value_median = keep.groupby('chemblId')['value'].transform('median')\n"", + ""\n"", + ""bioactsDF_Train_dup = bioactsDF_Train_dup.groupby([\""chemblId\""]).apply(lambda x: x.sort_values([\""select\""], ascending = True)).reset_index(drop=True)\n"", + ""bioactsDF_Train_dup.head(5)"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 105, + ""metadata"": {}, + ""outputs"": [ + { + ""data"": { + ""text/html"": [ + ""
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activity_commentassay_chemblidassay_descriptionassay_typebioactivity_typechemblIdname_in_referenceoperatororganismparent_cmpd_chemblidreferencetarget_chemblidtarget_confidencetarget_nameunitsvalueSTATUS(value, mean)(value, std)select
0UnspecifiedCHEMBL831007Inhibition of 17-beta-estradiol mediated lucif...BIC50CHEMBL10138260=Homo sapiensCHEMBL101382J. Med. Chem., (2005) 48:2:364CHEMBL2069Estrogen receptor alphanM16.5active17.74751.4520651.2475
4UnspecifiedCHEMBL832667Inhibition of bindign to recombinant human est...BIC50CHEMBL18079219=Homo sapiensCHEMBL180792Bioorg. Med. Chem. Lett., (2005) 15:1:107CHEMBL2069Estrogen receptor alphanM1.7active1.50000.2828430.2000
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"" + ], + ""text/plain"": [ + "" activity_comment assay_chemblid \\\n"", + ""0 Unspecified CHEMBL831007 \n"", + ""4 Unspecified CHEMBL832667 \n"", + ""\n"", + "" assay_description assay_type \\\n"", + ""0 Inhibition of 17-beta-estradiol mediated lucif... B \n"", + ""4 Inhibition of bindign to recombinant human est... B \n"", + ""\n"", + "" bioactivity_type chemblId name_in_reference operator organism \\\n"", + ""0 IC50 CHEMBL101382 60 = Homo sapiens \n"", + ""4 IC50 CHEMBL180792 19 = Homo sapiens \n"", + ""\n"", + "" parent_cmpd_chemblid reference \\\n"", + ""0 CHEMBL101382 J. Med. Chem., (2005) 48:2:364 \n"", + ""4 CHEMBL180792 Bioorg. Med. Chem. Lett., (2005) 15:1:107 \n"", + ""\n"", + "" target_chemblid target_confidence target_name units value \\\n"", + ""0 CHEMBL206 9 Estrogen receptor alpha nM 16.5 \n"", + ""4 CHEMBL206 9 Estrogen receptor alpha nM 1.7 \n"", + ""\n"", + "" STATUS (value, mean) (value, std) select \n"", + ""0 active 17.7475 1.452065 1.2475 \n"", + ""4 active 1.5000 0.282843 0.2000 "" + ] + }, + ""execution_count"": 105, + ""metadata"": {}, + ""output_type"": ""execute_result"" + } + ], + ""source"": [ + ""bioactsDF_Train_dup = bioactsDF_Train_dup.drop_duplicates(subset='chemblId', keep='first')\n"", + ""bioactsDF_Train_dup.head(2)"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 106, + ""metadata"": {}, + ""outputs"": [ + { + ""data"": { + ""text/plain"": [ + ""(31, 31)"" + ] + }, + ""execution_count"": 106, + ""metadata"": {}, + ""output_type"": ""execute_result"" + } + ], + ""source"": [ + ""len(bioactsDF_Train_dup), len(bioactsDF_Train_dup['chemblId'].unique())"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 107, + ""metadata"": {}, + ""outputs"": [ + { + ""data"": { + ""text/html"": [ + ""
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activity_commentassay_chemblidassay_descriptionassay_typebioactivity_typechemblIdname_in_referenceoperatororganismparent_cmpd_chemblidreferencetarget_chemblidtarget_confidencetarget_nameunitsvalueSTATUS(value, mean)(value, std)select
76UnspecifiedCHEMBL3776153Downregulation of ERalpha in human MCF7 cells ...BIC50CHEMBL377590822=Homo sapiensCHEMBL3775908ACS Med. Chem. Lett., (2016) 7:1:94CHEMBL2069Estrogen receptor alphanM0.370active0.8350.6576090.465
78UnspecifiedCHEMBL1909145DRUGMATRIX: Estrogen ERalpha radioligand bindi...BIC50CHEMBL691ETHINYLESTRADIOL=Homo sapiensCHEMBL691UnspecifiedCHEMBL2069Estrogen receptor alphanM0.448active1.2241.0974300.776
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"" + ], + ""text/plain"": [ + "" activity_comment assay_chemblid \\\n"", + ""76 Unspecified CHEMBL3776153 \n"", + ""78 Unspecified CHEMBL1909145 \n"", + ""\n"", + "" assay_description assay_type \\\n"", + ""76 Downregulation of ERalpha in human MCF7 cells ... B \n"", + ""78 DRUGMATRIX: Estrogen ERalpha radioligand bindi... B \n"", + ""\n"", + "" bioactivity_type chemblId name_in_reference operator organism \\\n"", + ""76 IC50 CHEMBL3775908 22 = Homo sapiens \n"", + ""78 IC50 CHEMBL691 ETHINYLESTRADIOL = Homo sapiens \n"", + ""\n"", + "" parent_cmpd_chemblid reference target_chemblid \\\n"", + ""76 CHEMBL3775908 ACS Med. Chem. Lett., (2016) 7:1:94 CHEMBL206 \n"", + ""78 CHEMBL691 Unspecified CHEMBL206 \n"", + ""\n"", + "" target_confidence target_name units value STATUS \\\n"", + ""76 9 Estrogen receptor alpha nM 0.370 active \n"", + ""78 9 Estrogen receptor alpha nM 0.448 active \n"", + ""\n"", + "" (value, mean) (value, std) select \n"", + ""76 0.835 0.657609 0.465 \n"", + ""78 1.224 1.097430 0.776 "" + ] + }, + ""execution_count"": 107, + ""metadata"": {}, + ""output_type"": ""execute_result"" + } + ], + ""source"": [ + ""bioactsDF_Train_dup.tail(2)"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 108, + ""metadata"": { + ""collapsed"": true + }, + ""outputs"": [], + ""source"": [ + ""bioactsDF_Train_dup = bioactsDF_Train_dup.drop('select', 1)\n"", + ""bioactsDF_Train_dup = bioactsDF_Train_dup.drop(('value', 'mean'), 1)\n"", + ""bioactsDF_Train_dup = bioactsDF_Train_dup.drop(('value', 'std'), 1)"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 109, + ""metadata"": {}, + ""outputs"": [ + { + ""data"": { + ""text/plain"": [ + ""(31, 1259, 1290)"" + ] + }, + ""execution_count"": 109, + ""metadata"": {}, + ""output_type"": ""execute_result"" + } + ], + ""source"": [ + ""bioactsDF_Train_final = pd.concat([bioactsDF_Train_non, bioactsDF_Train_dup])\n"", + ""len(bioactsDF_Train_dup), len(bioactsDF_Train_non), len(bioactsDF_Train_final)"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 110, + ""metadata"": {}, + ""outputs"": [ + { + ""data"": { + ""text/html"": [ + ""
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activity_commentassay_chemblidassay_descriptionassay_typebioactivity_typechemblIdname_in_referenceoperatororganismparent_cmpd_chemblidreferencetarget_chemblidtarget_confidencetarget_nameunitsvalueSTATUS
76UnspecifiedCHEMBL3776153Downregulation of ERalpha in human MCF7 cells ...BIC50CHEMBL377590822=Homo sapiensCHEMBL3775908ACS Med. Chem. Lett., (2016) 7:1:94CHEMBL2069Estrogen receptor alphanM0.370active
78UnspecifiedCHEMBL1909145DRUGMATRIX: Estrogen ERalpha radioligand bindi...BIC50CHEMBL691ETHINYLESTRADIOL=Homo sapiensCHEMBL691UnspecifiedCHEMBL2069Estrogen receptor alphanM0.448active
\n"", + ""
"" + ], + ""text/plain"": [ + "" activity_comment assay_chemblid \\\n"", + ""76 Unspecified CHEMBL3776153 \n"", + ""78 Unspecified CHEMBL1909145 \n"", + ""\n"", + "" assay_description assay_type \\\n"", + ""76 Downregulation of ERalpha in human MCF7 cells ... B \n"", + ""78 DRUGMATRIX: Estrogen ERalpha radioligand bindi... B \n"", + ""\n"", + "" bioactivity_type chemblId name_in_reference operator organism \\\n"", + ""76 IC50 CHEMBL3775908 22 = Homo sapiens \n"", + ""78 IC50 CHEMBL691 ETHINYLESTRADIOL = Homo sapiens \n"", + ""\n"", + "" parent_cmpd_chemblid reference target_chemblid \\\n"", + ""76 CHEMBL3775908 ACS Med. Chem. Lett., (2016) 7:1:94 CHEMBL206 \n"", + ""78 CHEMBL691 Unspecified CHEMBL206 \n"", + ""\n"", + "" target_confidence target_name units value STATUS \n"", + ""76 9 Estrogen receptor alpha nM 0.370 active \n"", + ""78 9 Estrogen receptor alpha nM 0.448 active "" + ] + }, + ""execution_count"": 110, + ""metadata"": {}, + ""output_type"": ""execute_result"" + } + ], + ""source"": [ + ""bioactsDF_Train_final.tail(2)"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 111, + ""metadata"": {}, + ""outputs"": [ + { + ""name"": ""stdout"", + ""output_type"": ""stream"", + ""text"": [ + ""('286', '8', '294')\n"" + ] + } + ], + ""source"": [ + ""bioactsDF_Test_gra_dup = pd.concat(g for _, \n"", + "" g in bioactsDF_Test_gra.groupby(\""chemblId\"") \n"", + "" if len(g) > 1)\n"", + ""bioactsDF_Test_gra_non = bioactsDF_Train.loc[~bioactsDF_Test_gra.index.isin(bioactsDF_Test_gra_dup.index)]\n"", + ""\n"", + ""print (str(len(bioactsDF_Test_gra_non)), \n"", + "" str(len(bioactsDF_Test_gra_dup)), \n"", + "" str(len(bioactsDF_Test_gra_dup)+len(bioactsDF_Test_gra_non)))"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 112, + ""metadata"": {}, + ""outputs"": [ + { + ""data"": { + ""text/plain"": [ + ""(8, 286)"" + ] + }, + ""execution_count"": 112, + ""metadata"": {}, + ""output_type"": ""execute_result"" + } + ], + ""source"": [ + ""len(bioactsDF_Test_gra_dup), len(bioactsDF_Test_gra_non)"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 113, + ""metadata"": {}, + ""outputs"": [ + { + ""data"": { + ""text/html"": [ + ""
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chemblIdvalue
7577CHEMBL1031000.0
7578CHEMBL1031000.0
8294CHEMBL24614010000.0
8297CHEMBL24614010000.0
5597CHEMBL33899100000.0
5598CHEMBL338991270.0
4200CHEMBL38663010000.0
4201CHEMBL38663010000.0
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"" + ], + ""text/plain"": [ + "" chemblId value\n"", + ""7577 CHEMBL103 1000.0\n"", + ""7578 CHEMBL103 1000.0\n"", + ""8294 CHEMBL246140 10000.0\n"", + ""8297 CHEMBL246140 10000.0\n"", + ""5597 CHEMBL33899 100000.0\n"", + ""5598 CHEMBL33899 1270.0\n"", + ""4200 CHEMBL386630 10000.0\n"", + ""4201 CHEMBL386630 10000.0"" + ] + }, + ""execution_count"": 113, + ""metadata"": {}, + ""output_type"": ""execute_result"" + } + ], + ""source"": [ + ""check = bioactsDF_Test_gra_dup[['chemblId','value']]\n"", + ""check"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 114, + ""metadata"": {}, + ""outputs"": [ + { + ""data"": { + ""text/plain"": [ + ""292"" + ] + }, + ""execution_count"": 114, + ""metadata"": {}, + ""output_type"": ""execute_result"" + } + ], + ""source"": [ + ""# drop variable ID \n"", + ""bioactsDF_Test_gra = bioactsDF_Test_gra[~bioactsDF_Test_gra['chemblId'].isin(['CHEMBL33899'])]\n"", + ""len(bioactsDF_Test_gra)"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 115, + ""metadata"": {}, + ""outputs"": [ + { + ""name"": ""stdout"", + ""output_type"": ""stream"", + ""text"": [ + ""('1290', '292', '19')\n"" + ] + } + ], + ""source"": [ + ""print (str(len(bioactsDF_Train_final)), \n"", + "" str(len(bioactsDF_Test_gra)), \n"", + "" str(len(bioactsDF_Test_les)))"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 64, + ""metadata"": {}, + ""outputs"": [ + { + ""name"": ""stdout"", + ""output_type"": ""stream"", + ""text"": [ + ""('1290', '289', '19')\n"" + ] + } + ], + ""source"": [ + ""# Load the compounds in the dataframe\n"", + ""\n"", + ""compounds = CompoundResource()\n"", + ""\n"", + ""cpdsDF_Train_final = pd.DataFrame.from_dict(compounds.get(\n"", + "" list(bioactsDF_Train_final['chemblId'])))\n"", + ""\n"", + ""cpdsDF_Test_gra = pd.DataFrame.from_dict(compounds.get(\n"", + "" list(bioactsDF_Test_gra['chemblId'].unique())))\n"", + ""\n"", + ""cpdsDF_Test_les = pd.DataFrame.from_dict(compounds.get(\n"", + "" list(bioactsDF_Test_les['chemblId'])))\n"", + ""\n"", + ""print (str(len(cpdsDF_Train_final)), \n"", + "" str(len(cpdsDF_Test_gra)), \n"", + "" str(len(cpdsDF_Test_les)))"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 65, + ""metadata"": {}, + ""outputs"": [ + { + ""name"": ""stderr"", + ""output_type"": ""stream"", + ""text"": [ + ""/Users/zeromtmu/anaconda2/envs/ER_alpha/lib/python2.7/site-packages/ipykernel_launcher.py:5: SettingWithCopyWarning: \n"", + ""A value is trying to be set on a copy of a slice from a DataFrame.\n"", + ""Try using .loc[row_indexer,col_indexer] = value instead\n"", + ""\n"", + ""See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n"", + "" \""\""\""\n"" + ] + } + ], + ""source"": [ + ""# making sure everything is float\n"", + ""\n"", + ""bioactsDF_Train_final['value'] = bioactsDF_Train_non['value'].astype(float)\n"", + ""bioactsDF_Test_gra ['value'] = bioactsDF_Test_gra ['value'].astype(float)\n"", + ""bioactsDF_Test_les ['value'] = bioactsDF_Test_les ['value'].astype(float)"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 66, + ""metadata"": {}, + ""outputs"": [ + { + ""data"": { + ""text/html"": [ + ""
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activity_commentassay_chemblidassay_descriptionassay_typebioactivity_typechemblIdname_in_referenceoperatororganismparent_cmpd_chemblidreferencetarget_chemblidtarget_confidencetarget_nameunitsvalueSTATUS
76UnspecifiedCHEMBL3776153Downregulation of ERalpha in human MCF7 cells ...BIC50CHEMBL377590822=Homo sapiensCHEMBL3775908ACS Med. Chem. Lett., (2016) 7:1:94CHEMBL2069Estrogen receptor alphanMNaNactive
78UnspecifiedCHEMBL1909145DRUGMATRIX: Estrogen ERalpha radioligand bindi...BIC50CHEMBL691ETHINYLESTRADIOL=Homo sapiensCHEMBL691UnspecifiedCHEMBL2069Estrogen receptor alphanMNaNactive
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"" + ], + ""text/plain"": [ + "" activity_comment assay_chemblid \\\n"", + ""76 Unspecified CHEMBL3776153 \n"", + ""78 Unspecified CHEMBL1909145 \n"", + ""\n"", + "" assay_description assay_type \\\n"", + ""76 Downregulation of ERalpha in human MCF7 cells ... B \n"", + ""78 DRUGMATRIX: Estrogen ERalpha radioligand bindi... B \n"", + ""\n"", + "" bioactivity_type chemblId name_in_reference operator organism \\\n"", + ""76 IC50 CHEMBL3775908 22 = Homo sapiens \n"", + ""78 IC50 CHEMBL691 ETHINYLESTRADIOL = Homo sapiens \n"", + ""\n"", + "" parent_cmpd_chemblid reference target_chemblid \\\n"", + ""76 CHEMBL3775908 ACS Med. Chem. Lett., (2016) 7:1:94 CHEMBL206 \n"", + ""78 CHEMBL691 Unspecified CHEMBL206 \n"", + ""\n"", + "" target_confidence target_name units value STATUS \n"", + ""76 9 Estrogen receptor alpha nM NaN active \n"", + ""78 9 Estrogen receptor alpha nM NaN active "" + ] + }, + ""execution_count"": 66, + ""metadata"": {}, + ""output_type"": ""execute_result"" + } + ], + ""source"": [ + ""bioactsDF_Train_final.tail(2)"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 131, + ""metadata"": {}, + ""outputs"": [ + { + ""data"": { + ""text/html"": [ + ""
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acdAcidicPkaacdBasicPkaacdLogdacdLogpalogpchemblIdknownDrugmolecularFormulamolecularWeightnumRo5ViolationspassesRuleOfThreepreferredCompoundNamerotatableBondssmilesspeciesstdInChiKeysynonyms
0NaNNaNNaNNaNNaNCHEMBL219763NoC8H5B10ONaNNaNNoNaNNaNNaNNaNNaNNaN
17.69NaN0.330.53.31CHEMBL370037NoC16H12O3252.260.0NoNaN1.0CC1=C(C(=O)c2ccc(O)cc12)c3ccc(O)cc3NEUTRALIOAPPLGKQYAVIJ-UHFFFAOYSA-NNaN
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"" + ], + ""text/plain"": [ + "" acdAcidicPka acdBasicPka acdLogd acdLogp alogp chemblId knownDrug \\\n"", + ""0 NaN NaN NaN NaN NaN CHEMBL219763 No \n"", + ""1 7.69 NaN 0.33 0.5 3.31 CHEMBL370037 No \n"", + ""\n"", + "" molecularFormula molecularWeight numRo5Violations passesRuleOfThree \\\n"", + ""0 C8H5B10O NaN NaN No \n"", + ""1 C16H12O3 252.26 0.0 No \n"", + ""\n"", + "" preferredCompoundName rotatableBonds smiles \\\n"", + ""0 NaN NaN NaN \n"", + ""1 NaN 1.0 CC1=C(C(=O)c2ccc(O)cc12)c3ccc(O)cc3 \n"", + ""\n"", + "" species stdInChiKey synonyms \n"", + ""0 NaN NaN NaN \n"", + ""1 NEUTRAL IOAPPLGKQYAVIJ-UHFFFAOYSA-N NaN "" + ] + }, + ""execution_count"": 131, + ""metadata"": {}, + ""output_type"": ""execute_result"" + } + ], + ""source"": [ + ""cpdsDF_Train_final.head(2)"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 86, + ""metadata"": { + ""collapsed"": true + }, + ""outputs"": [], + ""source"": [ + ""#set(cpdsDF_Test_gra['chemblId']).difference(set(bioactsDF_Test_gra['chemblId']))"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 68, + ""metadata"": {}, + ""outputs"": [ + { + ""name"": ""stdout"", + ""output_type"": ""stream"", + ""text"": [ + ""('1290', '1290')\n"", + ""('292', '289')\n"", + ""('19', '19')\n"" + ] + } + ], + ""source"": [ + ""print (str(len(bioactsDF_Train_final)), str(len(cpdsDF_Train_final)))\n"", + ""print (str(len(bioactsDF_Test_gra)), str(len(cpdsDF_Test_gra)))\n"", + ""print (str(len(bioactsDF_Test_les)), str(len(cpdsDF_Test_les)))"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 116, + ""metadata"": {}, + ""outputs"": [ + { + ""name"": ""stdout"", + ""output_type"": ""stream"", + ""text"": [ + ""1290\n"", + ""292\n"", + ""19\n"" + ] + } + ], + ""source"": [ + ""# merge cpd to bio activity\n"", + ""\n"", + ""Train_ER_alpha_RAW = cpdsDF_Train_final.merge(bioactsDF_Train_final, on='chemblId', how='inner')\n"", + ""Test_gra_ER_alpha_RAW = cpdsDF_Test_gra.merge (bioactsDF_Test_gra , on='chemblId', how='inner')\n"", + ""Test_les_ER_alpha_RAW = cpdsDF_Test_les.merge (bioactsDF_Test_les , on='chemblId', how='inner')\n"", + ""\n"", + ""print len(Train_ER_alpha_RAW)\n"", + ""print len(Test_gra_ER_alpha_RAW)\n"", + ""print len(Test_les_ER_alpha_RAW)"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 117, + ""metadata"": {}, + ""outputs"": [ + { + ""data"": { + ""text/html"": [ + ""
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acdAcidicPkaacdBasicPkaacdLogdacdLogpalogpchemblIdknownDrugmolecularFormulamolecularWeightnumRo5Violations...operatororganismparent_cmpd_chemblidreferencetarget_chemblidtarget_confidencetarget_nameunitsvalueSTATUS
12884.066.57-0.542.452.11CHEMBL3775908NoC23H24F3NO3419.440.0...=Homo sapiensCHEMBL3775908ACS Med. Chem. Lett., (2016) 7:1:94CHEMBL2069Estrogen receptor alphanM0.370active
128910.24NaN4.114.114.89CHEMBL691YesC20H24O2296.400.0...=Homo sapiensCHEMBL691UnspecifiedCHEMBL2069Estrogen receptor alphanM0.448active
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2 rows × 33 columns

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"" + ], + ""text/plain"": [ + "" acdAcidicPka acdBasicPka acdLogd acdLogp alogp chemblId \\\n"", + ""1288 4.06 6.57 -0.54 2.45 2.11 CHEMBL3775908 \n"", + ""1289 10.24 NaN 4.11 4.11 4.89 CHEMBL691 \n"", + ""\n"", + "" knownDrug molecularFormula molecularWeight numRo5Violations ... \\\n"", + ""1288 No C23H24F3NO3 419.44 0.0 ... \n"", + ""1289 Yes C20H24O2 296.40 0.0 ... \n"", + ""\n"", + "" operator organism parent_cmpd_chemblid \\\n"", + ""1288 = Homo sapiens CHEMBL3775908 \n"", + ""1289 = Homo sapiens CHEMBL691 \n"", + ""\n"", + "" reference target_chemblid target_confidence \\\n"", + ""1288 ACS Med. Chem. Lett., (2016) 7:1:94 CHEMBL206 9 \n"", + ""1289 Unspecified CHEMBL206 9 \n"", + ""\n"", + "" target_name units value STATUS \n"", + ""1288 Estrogen receptor alpha nM 0.370 active \n"", + ""1289 Estrogen receptor alpha nM 0.448 active \n"", + ""\n"", + ""[2 rows x 33 columns]"" + ] + }, + ""execution_count"": 117, + ""metadata"": {}, + ""output_type"": ""execute_result"" + } + ], + ""source"": [ + ""Train_ER_alpha_RAW.tail(2)"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 118, + ""metadata"": {}, + ""outputs"": [ + { + ""data"": { + ""text/plain"": [ + ""8"" + ] + }, + ""execution_count"": 118, + ""metadata"": {}, + ""output_type"": ""execute_result"" + } + ], + ""source"": [ + ""# check missing SMILES\n"", + ""\n"", + ""missing = Train_ER_alpha_RAW[Train_ER_alpha_RAW['smiles'].isnull()]\n"", + ""len(missing)"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 128, + ""metadata"": { + ""collapsed"": true + }, + ""outputs"": [], + ""source"": [ + ""cpdsDF_missing = pd.DataFrame.from_dict(compounds.get(\n"", + "" list(missing['chemblId'])))"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 130, + ""metadata"": {}, + ""outputs"": [ + { + ""data"": { + ""text/html"": [ + ""
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chemblIdknownDrugmolecularFormulapassesRuleOfThree
0CHEMBL219763NoC8H5B10ONo
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\n"", + ""
"" + ], + ""text/plain"": [ + "" chemblId knownDrug molecularFormula passesRuleOfThree\n"", + ""0 CHEMBL219763 No C8H5B10O No\n"", + ""1 CHEMBL219003 No C8H5B10O No"" + ] + }, + ""execution_count"": 130, + ""metadata"": {}, + ""output_type"": ""execute_result"" + } + ], + ""source"": [ + ""cpdsDF_missing.head(2)"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 137, + ""metadata"": { + ""collapsed"": true + }, + ""outputs"": [], + ""source"": [ + ""dup = pd.concat(g for _, \n"", + "" g in Train_ER_alpha_RAW.groupby(\""molecularFormula\"") \n"", + "" if len(g) > 1)\n"", + ""non = Train_ER_alpha_RAW.loc[~Train_ER_alpha_RAW.index.isin(dup.index)]"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 139, + ""metadata"": {}, + ""outputs"": [ + { + ""data"": { + ""text/plain"": [ + ""(1290, 585, 705)"" + ] + }, + ""execution_count"": 139, + ""metadata"": {}, + ""output_type"": ""execute_result"" + } + ], + ""source"": [ + ""len(Train_ER_alpha_RAW), len(dup), len(non)"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 126, + ""metadata"": { + ""collapsed"": true + }, + ""outputs"": [ + { + ""ename"": ""KeyError"", + ""evalue"": ""'chemblId'"", + ""output_type"": ""error"", + ""traceback"": [ + ""\u001b[0;31m---------------------------------------------------------------------------\u001b[0m"", + ""\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)"", + ""\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mTrain_Missing\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcpdsDF_missing\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmerge\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmissing\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mon\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'chemblId'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhow\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'inner'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m"", + ""\u001b[0;32m/Users/zeromtmu/anaconda2/envs/ER_alpha/lib/python2.7/site-packages/pandas/core/frame.pyc\u001b[0m in \u001b[0;36mmerge\u001b[0;34m(self, right, how, on, left_on, right_on, left_index, right_index, sort, suffixes, copy, indicator)\u001b[0m\n\u001b[1;32m 4818\u001b[0m \u001b[0mright_on\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mright_on\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mleft_index\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mleft_index\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4819\u001b[0m \u001b[0mright_index\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mright_index\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msort\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msort\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msuffixes\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msuffixes\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 4820\u001b[0;31m copy=copy, indicator=indicator)\n\u001b[0m\u001b[1;32m 4821\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4822\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mround\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdecimals\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n"", + ""\u001b[0;32m/Users/zeromtmu/anaconda2/envs/ER_alpha/lib/python2.7/site-packages/pandas/core/reshape/merge.pyc\u001b[0m in \u001b[0;36mmerge\u001b[0;34m(left, right, how, on, left_on, right_on, left_index, right_index, sort, suffixes, copy, indicator)\u001b[0m\n\u001b[1;32m 51\u001b[0m \u001b[0mright_on\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mright_on\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mleft_index\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mleft_index\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 52\u001b[0m \u001b[0mright_index\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mright_index\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msort\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msort\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msuffixes\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msuffixes\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 53\u001b[0;31m copy=copy, indicator=indicator)\n\u001b[0m\u001b[1;32m 54\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mop\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_result\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 55\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n"", + ""\u001b[0;32m/Users/zeromtmu/anaconda2/envs/ER_alpha/lib/python2.7/site-packages/pandas/core/reshape/merge.pyc\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, left, right, how, on, left_on, right_on, axis, left_index, right_index, sort, suffixes, copy, indicator)\u001b[0m\n\u001b[1;32m 556\u001b[0m (self.left_join_keys,\n\u001b[1;32m 557\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mright_join_keys\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 558\u001b[0;31m self.join_names) = self._get_merge_keys()\n\u001b[0m\u001b[1;32m 559\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 560\u001b[0m \u001b[0;31m# validate the merge keys dtypes. We may need to coerce\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n"", + ""\u001b[0;32m/Users/zeromtmu/anaconda2/envs/ER_alpha/lib/python2.7/site-packages/pandas/core/reshape/merge.pyc\u001b[0m in \u001b[0;36m_get_merge_keys\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 821\u001b[0m \u001b[0mright_keys\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrk\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 822\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mlk\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 823\u001b[0;31m \u001b[0mleft_keys\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mleft\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mlk\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_values\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 824\u001b[0m \u001b[0mjoin_names\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlk\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 825\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n"", + ""\u001b[0;32m/Users/zeromtmu/anaconda2/envs/ER_alpha/lib/python2.7/site-packages/pandas/core/frame.pyc\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m 2060\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_multilevel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2061\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2062\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_column\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2063\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2064\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_getitem_column\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n"", + ""\u001b[0;32m/Users/zeromtmu/anaconda2/envs/ER_alpha/lib/python2.7/site-packages/pandas/core/frame.pyc\u001b[0m in \u001b[0;36m_getitem_column\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m 2067\u001b[0m \u001b[0;31m# get column\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2068\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_unique\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2069\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_item_cache\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2070\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2071\u001b[0m \u001b[0;31m# duplicate columns & possible reduce dimensionality\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n"", + ""\u001b[0;32m/Users/zeromtmu/anaconda2/envs/ER_alpha/lib/python2.7/site-packages/pandas/core/generic.pyc\u001b[0m in \u001b[0;36m_get_item_cache\u001b[0;34m(self, item)\u001b[0m\n\u001b[1;32m 1532\u001b[0m \u001b[0mres\u001b[0m \u001b[0;34m=\u001b[0m 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\u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3592\u001b[0m \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0misnull\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n"", + ""\u001b[0;32m/Users/zeromtmu/anaconda2/envs/ER_alpha/lib/python2.7/site-packages/pandas/core/indexes/base.pyc\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m 2393\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2394\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2395\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_maybe_cast_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2396\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2397\u001b[0m \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmethod\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtolerance\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtolerance\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n"", + ""\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc (pandas/_libs/index.c:5239)\u001b[0;34m()\u001b[0m\n"", + ""\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc (pandas/_libs/index.c:5169)\u001b[0;34m()\u001b[0m\n"", + ""\u001b[0;31mKeyError\u001b[0m: 'chemblId'"" + ] + } + ], + ""source"": [ + ""Train_Missing = cpdsDF_missing.merge(missing, on='chemblId', how='inner')\n"", + ""missing2 = cpdsDF_missing[Train_Missing['smiles'].isnull()]\n"", + ""len(missing2)"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 118, + ""metadata"": {}, + ""outputs"": [ + { + ""name"": ""stdout"", + ""output_type"": ""stream"", + ""text"": [ + ""Train_ER_alpha of 1290 after drop duplicate SMILEs are reduce to 1282\n"", + ""Test_gra_ER_alpha of 292 after drop duplicate SMILEs are reduce to 292\n"", + ""Test_les_ER_alpha of 19 after drop duplicate SMILEs are reduce to 19\n"" + ] + } + ], + ""source"": [ + ""# drop Nan in SMILES\n"", + ""\n"", + ""Train_ER_alpha_RAW1 = Train_ER_alpha_RAW [pd.notnull(Train_ER_alpha_RAW ['smiles'])]\n"", + ""Test_gra_ER_alpha_RAW1 = Test_gra_ER_alpha_RAW[pd.notnull(Test_gra_ER_alpha_RAW['smiles'])]\n"", + ""Test_les_ER_alpha_RAW1 = Test_les_ER_alpha_RAW[pd.notnull(Test_les_ER_alpha_RAW['smiles'])]\n"", + ""\n"", + ""print 'Train_ER_alpha of '+str(len(Train_ER_alpha_RAW))+' after drop duplicate SMILEs are reduce to '+ str(len(Train_ER_alpha_RAW1)) \n"", + ""print 'Test_gra_ER_alpha of '+str(len(Test_gra_ER_alpha_RAW))+' after drop duplicate SMILEs are reduce to '+ str(len(Test_gra_ER_alpha_RAW1)) \n"", + ""print 'Test_les_ER_alpha of '+str(len(Test_les_ER_alpha_RAW))+' after drop duplicate SMILEs are reduce to '+ str(len(Test_les_ER_alpha_RAW1))"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 119, + ""metadata"": {}, + ""outputs"": [ + { + ""data"": { + ""text/html"": [ + ""
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acdAcidicPkaacdBasicPkaacdLogdacdLogpalogpchemblIdknownDrugmolecularFormulamolecularWeightnumRo5Violations...operatororganismparent_cmpd_chemblidreferencetarget_chemblidtarget_confidencetarget_nameunitsvalueSTATUS
12884.066.57-0.542.452.11CHEMBL3775908NoC23H24F3NO3419.440.0...=Homo sapiensCHEMBL3775908ACS Med. Chem. Lett., (2016) 7:1:94CHEMBL2069Estrogen receptor alphanM0.370active
128910.24NaN4.114.114.89CHEMBL691YesC20H24O2296.400.0...=Homo sapiensCHEMBL691UnspecifiedCHEMBL2069Estrogen receptor alphanM0.448active
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2 rows × 33 columns

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"" + ], + ""text/plain"": [ + "" acdAcidicPka acdBasicPka acdLogd acdLogp alogp chemblId \\\n"", + ""1288 4.06 6.57 -0.54 2.45 2.11 CHEMBL3775908 \n"", + ""1289 10.24 NaN 4.11 4.11 4.89 CHEMBL691 \n"", + ""\n"", + "" knownDrug molecularFormula molecularWeight numRo5Violations ... \\\n"", + ""1288 No C23H24F3NO3 419.44 0.0 ... \n"", + ""1289 Yes C20H24O2 296.40 0.0 ... \n"", + ""\n"", + "" operator organism parent_cmpd_chemblid \\\n"", + ""1288 = Homo sapiens CHEMBL3775908 \n"", + ""1289 = Homo sapiens CHEMBL691 \n"", + ""\n"", + "" reference target_chemblid target_confidence \\\n"", + ""1288 ACS Med. Chem. Lett., (2016) 7:1:94 CHEMBL206 9 \n"", + ""1289 Unspecified CHEMBL206 9 \n"", + ""\n"", + "" target_name units value STATUS \n"", + ""1288 Estrogen receptor alpha nM 0.370 active \n"", + ""1289 Estrogen receptor alpha nM 0.448 active \n"", + ""\n"", + ""[2 rows x 33 columns]"" + ] + }, + ""execution_count"": 119, + ""metadata"": {}, + ""output_type"": ""execute_result"" + } + ], + ""source"": [ + ""Train_ER_alpha_RAW1.tail(2)"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 120, + ""metadata"": {}, + ""outputs"": [ + { + ""data"": { + ""text/plain"": [ + ""(292, 289)"" + ] + }, + ""execution_count"": 120, + ""metadata"": {}, + ""output_type"": ""execute_result"" + } + ], + ""source"": [ + ""len(Test_gra_ER_alpha_RAW1), len(Test_gra_ER_alpha_RAW1['chemblId'].unique())"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 121, + ""metadata"": {}, + ""outputs"": [ + { + ""data"": { + ""text/plain"": [ + ""(292, 289)"" + ] + }, + ""execution_count"": 121, + ""metadata"": {}, + ""output_type"": ""execute_result"" + } + ], + ""source"": [ + ""Test_gra_ER_alpha_RAW2 = Test_gra_ER_alpha_RAW1.drop_duplicates(subset='chemblId', keep='last')\n"", + ""\n"", + ""len(Test_gra_ER_alpha_RAW1), len(Test_gra_ER_alpha_RAW2)"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 122, + ""metadata"": {}, + ""outputs"": [ + { + ""name"": ""stdout"", + ""output_type"": ""stream"", + ""text"": [ + ""RAW data of 1282 Train compounds has been reduced to 1282 Compounds.\n"", + ""RAW data of 289 Test_gracompounds has been reduced to 289 Compounds.\n"", + ""RAW data of 19 Test_les compounds has been reduced to 19 Compounds.\n"" + ] + } + ], + ""source"": [ + ""# only smiles exist\n"", + ""\n"", + ""Train_ER_alpha = Train_ER_alpha_RAW1[pd.notnull(\n"", + "" Train_ER_alpha_RAW1['smiles'])]\n"", + ""\n"", + ""Test_gra_ER_alpha = Test_gra_ER_alpha_RAW2[pd.notnull(\n"", + "" Test_gra_ER_alpha_RAW2['smiles'])]\n"", + ""\n"", + ""\n"", + ""Test_les_ER_alpha = Test_les_ER_alpha_RAW1[pd.notnull(\n"", + "" Test_les_ER_alpha_RAW1['smiles'])]\n"", + ""\n"", + ""\n"", + ""print \""RAW data of \"" + str(len(Train_ER_alpha_RAW1)) + \\\n"", + "" \"" Train compounds has been reduced to \"" \\\n"", + "" + str(len(Train_ER_alpha)) + \"" Compounds.\""\n"", + ""\n"", + ""print \""RAW data of \"" + str(len(Test_gra_ER_alpha_RAW2)) + \\\n"", + "" \"" Test_gracompounds has been reduced to \"" \\\n"", + "" + str(len(Test_gra_ER_alpha)) + \"" Compounds.\""\n"", + "" \n"", + ""print \""RAW data of \"" + str(len(Test_les_ER_alpha_RAW1)) + \\\n"", + "" \"" Test_les compounds has been reduced to \"" \\\n"", + "" + str(len(Test_les_ER_alpha)) + \"" Compounds.\"""" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 123, + ""metadata"": { + ""collapsed"": true + }, + ""outputs"": [], + ""source"": [ + ""# clean smiles\n"", + ""\n"", + ""from rdkit.Chem.SaltRemover import SaltRemover\n"", + ""from rdkit.Chem import MolFromSmiles,MolToSmiles\n"", + ""\n"", + ""def clean_smiles (ListSMILEs):\n"", + "" remover = SaltRemover()\n"", + "" len(remover.salts)\n"", + ""\n"", + "" SMILES_desalt = []\n"", + ""\n"", + "" for i in ListSMILEs:\n"", + "" mol = MolFromSmiles(i) \n"", + "" mol_desalt = remover.StripMol(mol)\n"", + "" mol_SMILES = MolToSmiles(mol_desalt)\n"", + "" SMILES_desalt.append(mol_SMILES)\n"", + "" return SMILES_desalt"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 124, + ""metadata"": { + ""collapsed"": true + }, + ""outputs"": [], + ""source"": [ + ""Train_ER_alpha['SMILES_desalt'] = clean_smiles(Train_ER_alpha.smiles)"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 125, + ""metadata"": { + ""collapsed"": true + }, + ""outputs"": [], + ""source"": [ + ""Test_gra_ER_alpha['SMILES_desalt'] = clean_smiles(Test_gra_ER_alpha.smiles)"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 126, + ""metadata"": { + ""collapsed"": true + }, + ""outputs"": [], + ""source"": [ + ""Test_les_ER_alpha['SMILES_desalt'] = clean_smiles(Test_les_ER_alpha.smiles)"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 127, + ""metadata"": {}, + ""outputs"": [ + { + ""name"": ""stdout"", + ""output_type"": ""stream"", + ""text"": [ + ""RAW data of 1282 SMILES has been reduced to 1238 SMILES.\n"" + ] + } + ], + ""source"": [ + ""Train_ER_alpha2 = Train_ER_alpha.drop_duplicates(\\\n"", + "" subset='SMILES_desalt', keep='last')\n"", + ""\n"", + ""print \""RAW data of \"" + str(len(Train_ER_alpha)) + \\\n"", + "" \"" SMILES has been reduced to \"" \\\n"", + "" + str(len(Train_ER_alpha2)) + \"" SMILES.\"""" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 128, + ""metadata"": {}, + ""outputs"": [ + { + ""name"": ""stdout"", + ""output_type"": ""stream"", + ""text"": [ + ""RAW data of 289 SMILES has been reduced to 283 SMILES.\n"" + ] + } + ], + ""source"": [ + ""Test_gra_ER_alpha2 = Test_gra_ER_alpha.drop_duplicates(\\\n"", + "" subset='SMILES_desalt', keep='last')\n"", + ""\n"", + ""print \""RAW data of \"" + str(len(Test_gra_ER_alpha)) + \\\n"", + "" \"" SMILES has been reduced to \"" \\\n"", + "" + str(len(Test_gra_ER_alpha2)) + \"" SMILES.\"""" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 129, + ""metadata"": {}, + ""outputs"": [ + { + ""name"": ""stdout"", + ""output_type"": ""stream"", + ""text"": [ + ""RAW data of 19 SMILES has been reduced to 19 SMILES.\n"" + ] + } + ], + ""source"": [ + ""Test_les_ER_alpha2 = Test_les_ER_alpha.drop_duplicates(\\\n"", + "" subset='SMILES_desalt', keep='last')\n"", + ""\n"", + ""print \""RAW data of \"" + str(len(Test_les_ER_alpha)) + \\\n"", + "" \"" SMILES has been reduced to \"" \\\n"", + "" + str(len(Test_les_ER_alpha2)) + \"" SMILES.\"""" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 130, + ""metadata"": { + ""collapsed"": true + }, + ""outputs"": [], + ""source"": [ + ""# save model\n"", + ""Train_ER_alpha2.to_csv ('model/Train_ER_alpha.csv' , sep=',' ,index=False)\n"", + ""Test_gra_ER_alpha2.to_csv('model/Test_gra_ER_alpha.csv', sep=',' ,index=False)\n"", + ""Test_les_ER_alpha2.to_csv('model/Test_les_ER_alpha.csv', sep=',' ,index=False)"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 131, + ""metadata"": { + ""collapsed"": true + }, + ""outputs"": [], + ""source"": [ + ""Train_smiles = Train_ER_alpha2[['SMILES_desalt','chemblId']]\n"", + ""Train_smiles.to_csv('smiles/Train_ER_alpha.smi', sep='\\t' ,header=False ,index=False)"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 132, + ""metadata"": { + ""collapsed"": true + }, + ""outputs"": [], + ""source"": [ + ""Test_gra = Test_gra_ER_alpha2[['SMILES_desalt','chemblId']]\n"", + ""Test_gra.to_csv('smiles/Test_gra_ER_alpha.smi', sep='\\t' ,header=False ,index=False)"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 133, + ""metadata"": { + ""collapsed"": true + }, + ""outputs"": [], + ""source"": [ + ""Test_les = Test_les_ER_alpha2[['SMILES_desalt','chemblId']]\n"", + ""Test_les.to_csv('smiles/Test_les_ER_alpha.smi', sep='\\t' ,header=False ,index=False)"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 134, + ""metadata"": { + ""collapsed"": true + }, + ""outputs"": [], + ""source"": [ + ""Train_QSAR = Train_ER_alpha2[['chemblId','value']]\n"", + ""\n"", + ""Train_QSAR.to_csv('Train_QSAR.csv', sep=',' ,index=False)"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 135, + ""metadata"": {}, + ""outputs"": [ + { + ""data"": { + ""text/html"": [ + ""
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acdAcidicPkaacdBasicPkaacdLogdacdLogpalogpchemblIdknownDrugmolecularFormulamolecularWeightnumRo5Violations...organismparent_cmpd_chemblidreferencetarget_chemblidtarget_confidencetarget_nameunitsvalueSTATUSSMILES_desalt
12874.067.140.313.112.60CHEMBL3775766NoC24H27F2NO3415.470.0...Homo sapiensCHEMBL3775766ACS Med. Chem. Lett., (2016) 7:1:94CHEMBL2069Estrogen receptor alphanM1.800activeCC(C)CN1C(c2c(F)cc(C=CC(=O)O)cc2F)c2ccc(O)cc2C...
12884.066.57-0.542.452.11CHEMBL3775908NoC23H24F3NO3419.440.0...Homo sapiensCHEMBL3775908ACS Med. Chem. Lett., (2016) 7:1:94CHEMBL2069Estrogen receptor alphanM0.370activeCC(CF)CN1C(C)Cc2cc(O)ccc2C1c1c(F)cc(C=CC(=O)O)...
128910.24NaN4.114.114.89CHEMBL691YesC20H24O2296.400.0...Homo sapiensCHEMBL691UnspecifiedCHEMBL2069Estrogen receptor alphanM0.448activeC#CC1(O)CCC2C3CCc4cc(O)ccc4C3CCC21C
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3 rows × 34 columns

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in 2D and 3D\n"", + "" # Apply Scaling \n"", + "" from sklearn.decomposition import PCA\n"", + "" from mpl_toolkits.mplot3d import Axes3D\n"", + "" from sklearn.preprocessing import StandardScaler\n"", + "" from matplotlib import pyplot as plt\n"", + "" data_pca = data\n"", + "" \n"", + "" # Apply Scaling \n"", + "" X = data_pca.drop('model', axis=1).as_matrix().astype(np.float)\n"", + "" y = data_pca['model'].values\n"", + "" \n"", + "" # Formatting\n"", + "" target_names = ['Train','Test']\n"", + "" colors = ['blue','red']\n"", + "" lw = 0\n"", + "" alpha = 0.3\n"", + ""\n"", + "" # 2 Components PCA\n"", + "" plt.style.use('seaborn-whitegrid')\n"", + "" plt.figure(2, figsize=(20, 8))\n"", + ""\n"", + "" plt.subplot(1, 2, 1)\n"", + "" pca = PCA(n_components=2)\n"", + "" X_std = StandardScaler().fit_transform(X)\n"", + "" X_r = pca.fit_transform(X_std)\n"", + "" #X_r = pca.fit(X).transform(X)\n"", + "" \n"", + "" for color, i, target_name in zip(colors, ['Train','Test'], target_names):\n"", + "" plt.scatter(X_r[y == i, 0], X_r[y == i, 1], \n"", + "" color=color, \n"", + "" alpha=alpha, \n"", + "" lw=lw,\n"", + "" label=target_name)\n"", + "" plt.xlabel(\""PC1\"", weight= 'bold')\n"", + "" plt.ylabel(\""PC2\"", weight= 'bold')\n"", + "" #plt.legend(loc='best', shadow=False, scatterpoints=1)\n"", + "" #plt.title('First two PCA directions');\n"", + ""\n"", + "" # 3 Components PCA\n"", + "" ax = plt.subplot(1, 2, 2, projection='3d')\n"", + ""\n"", + "" pca = PCA(n_components=3)\n"", + "" X_reduced = pca.fit_transform(X_std)\n"", + "" for color, i, target_name in zip(colors, ['Train','Test'], target_names):\n"", + "" ax.scatter(X_reduced[y == i, 0], X_reduced[y == i, 1], X_reduced[y == i, 2], \n"", + "" color=color,\n"", + "" alpha=alpha,\n"", + "" lw=lw, \n"", + "" label=target_name)\n"", + "" plt.legend(loc='best', shadow=False, scatterpoints=1)\n"", + "" ax.set_xlabel(\""PC1\"", weight= 'bold')\n"", + "" ax.set_ylabel(\""PC2\"", weight= 'bold')\n"", + "" ax.set_zlabel(\""PC3\"", weight= 'bold')\n"", + ""\n"", + "" # rotate the axes\n"", + "" ax.view_init(30, 10)\n"", + "" plt.savefig('applicability_domain.pdf', dpi=300)"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 83, + ""metadata"": {}, + ""outputs"": [ + { + ""data"": { + ""image/png"": 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rAcMmMczLv4rbVlDdPP3++F93dHdo8nYiIpCaEQCaTqboEK5FIYGBgoCScsds/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zIxk58715x/7yfyfuXRxyueDT3/awfy84Xj62Md0hobgyhWZeG+JgO4yYtUxZLuKNg9bGNoK1HJS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+ ""text/plain"": [ + """" + ] + }, + ""metadata"": {}, + ""output_type"": ""display_data"" + } + ], + ""source"": [ + ""PCA_plot(pca)\n"", + ""pca .to_csv('SubFiles/applicability_domain_data.csv' , sep=',' ,index=False)"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 84, + ""metadata"": {}, + ""outputs"": [ + { + ""data"": { + ""image/png"": 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mJahmkE6tP1JWMSl7T/gSCUZ3wu91u3HDDDTh06BBWrFiBxx57DBVZdlUR6iHd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\n/IQQQkgC+f+JGP5tlTD3TwAAAABJRU5ErkJggg==\n"", + ""text/plain"": [ + """" + ] + }, + ""metadata"": {}, + ""output_type"": ""display_data"" + } + ], + ""source"": [ + ""import matplotlib\n"", + ""\n"", + ""def PCA_plot2d(data):\n"", + "" # PCA's components graphed in 2D and 3D\n"", + "" # Apply Scaling \n"", + "" from sklearn.decomposition import PCA\n"", + "" from mpl_toolkits.mplot3d import Axes3D\n"", + "" from sklearn.preprocessing import StandardScaler\n"", + "" from matplotlib import pyplot as plt\n"", + "" data_pca = data\n"", + "" \n"", + "" font = {'family' : 'normal',\n"", + "" #'weight' : 'bold',\n"", + "" 'size' : 20}\n"", + ""\n"", + "" matplotlib.rc('font', **font)\n"", + ""\n"", + ""\n"", + "" # Apply Scaling \n"", + "" X = data_pca.drop('model', axis=1).as_matrix().astype(np.float)\n"", + "" y = data_pca['model'].values\n"", + "" \n"", + "" # Formatting\n"", + "" target_names = ['Train','Test']\n"", + "" colors = ['blue','red']\n"", + "" lw = 0\n"", + "" alpha = 0.3\n"", + ""\n"", + "" # 2 Components PCA\n"", + "" plt.style.use('seaborn-whitegrid')\n"", + "" plt.figure(2, figsize=(8, 8))\n"", + ""\n"", + ""\n"", + "" plt.subplot(1, 1, 1)\n"", + "" pca = PCA(n_components=2)\n"", + "" X_std = StandardScaler().fit_transform(X)\n"", + "" X_r = pca.fit_transform(X_std)\n"", + "" #X_r = pca.fit(X).transform(X)\n"", + "" \n"", + "" for color, i, target_name in zip(colors, ['Train','Test'], target_names):\n"", + "" plt.scatter(X_r[y == i, 0], X_r[y == i, 1], \n"", + "" color=color, \n"", + "" alpha=alpha, \n"", + "" lw=lw,\n"", + "" label=target_name)\n"", + "" \n"", + "" #plt.legend(loc='best', shadow=False, scatterpoints=1)\n"", + "" plt.xlabel(\""PC1\"", weight= 'bold')\n"", + "" plt.ylabel(\""PC2\"", weight= 'bold')\n"", + "" \n"", + "" plt.savefig('applicability_domain_2D.pdf', dpi=300)\n"", + "" \n"", + ""PCA_plot2d(pca)"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 85, + ""metadata"": {}, + ""outputs"": [ + { + ""data"": { + ""image/png"": 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qPkRQPF5Q1wmIn+ZZEOp2Goijwer0FzzEKygcCgYJpjEZCkd+B2CouEwqF0N3d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\n3b375yeU0VFlvTs25iYSERw8WExdnTihLHMBrl6FgwfV7M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xaLodmGAvOt8fprDiSLqVk\nYsJgeFhis+Xo6FBtVWogNkYgkCWbFdhsJokEXLqUZM2a9CLfj1OnXMTjauYomdQZHKwys6PryH37\nCLS08oE3NmGarhW3AjscSqvLSsMJsaRB5zXVNoSwmt2u/fpeLcpfP6wSygpgGfAspeZbHiFYnWDV\nopnStYlEApfLVVGLq9Jxg8EgU2dnOPngSP63c7Ttc+E7WLwti0aj+P3+ql/IeBz6+lStortbUlWI\n+coVZZsrJbKjA7lr14I/Syk4elQQiWhoGuzebXLoEBw6tDBaa+lycde9ER7/YRyRG+eGNaPs6ZiG\nRx+l/pOb2bEjwPCwklNpa1OEUb4XG4akr0+l1NraYPNmE00ThYxPpfeQTsNjjwkuXtSoq1N3w8eP\nqzbdlhbJ+LgaOty+PQo4cbnggQcMHv1uErfdTTKrPhMBvPG1UYyMbUFkA+ruPpuFhrZObJqmHBeD\nQejsJJbXcFx0xywEb/1UBz/46xDRmMa2A3523ph/I04n0u9H5PXupd3OU08LJqdnuOUuD2s2Fsks\nHBacPOnIN5U5SacdtLVFac7XRu64Q/DMM+qatdngrruytLQs9n9PJpNEo7KQ2rXZMjid6UXSOxYR\nnevVicVUF9rgoODAgeUbO3QdbrpJ0turWqI3bJBLEsorNR+yWpS/flgllBUgl8vxxS9+kc9//vNV\n15RGEpW6tcrXlpNENQOt0rWlg4z9Z8cXrJu5GGP9rXkjqfwgZWnRvhSGoabNLX/y6Wk4fLhCLtsw\nCmQCqiAr29spdVGanLQzO6s2ddOEs2fVJHglvO/TTdx780WMbz9BZ0O+amqakErR2RkgFFIzKGMX\nojz4Gy9Rm5lix3aDrb+/j1xOSa5ksyKvQiJ53/tMHn1UI5VSmmFbty5+zpMnBRMTKk0WjQocDkk0\nKlizhsJ/5cXhjfZBNu/s5db/FuRX/3g7oZSHdXWzOOeTPP2lUW5+30bcdSry/OlPBadeMrFHZtm6\nLsntD9Rhy0+Lnj1b9KPv7FQ1oURC4PGo4KOu2c77PlPhcxdCTcNfvgyGwVe/E+SJpwVg8uOHovzn\n/0/StVXtxGXuwczOKkK2cOfeMG3ZeWaSHjbd2kBTkwZoi7r+DhyAY8dUysvtVikvl6tIMBYBWV2O\nExNg5p0xTdPgpZcydHVlFrValxNRTY2NW29dWTTwSkUOqxHK9cMqoawATzzxBPX19UtKkJSmsZZz\nTiwln1RKDYhVk1kpJZTSLjB30AEUh9PcAVthXaWifSmSSQpkAmqTjkYlZWUZtdnnjzkTcRBJ2AjM\nSmbmBNmsajdWS0rTd1VOUB4tt3aj9fphUhGK7OwsEFS+cYljXz7O2JhGwuYlc3Qe8YfPUvsn78Ju\nFyUT+Uqd9jd/U53Hm7rmeGCiFq8jxZMvehgaglxO0NurslPRqETXlb1se/vCCfWCRXAeYmgIAJ8r\nx7vvnGZgxE5GuJBAMpJj4OlRtt3fw/Q0PP20gKFRRDrDyxM5tnhP0PaW/cQyjgKZgJKyTyadOJ0a\nly9r7NtnsqQYgsOBzDPk0c9MFn6dTgmefjhRIJTyqCwQKLmrD4fRnnuObaYJDpBz3cimyp4ydXVw\nxx0mkYiSgVGZ2qL0Tvn1GQhMout12GyKmLZsMWlrkwvIp1x6x/q5mvROORG9UsZWy/kYrUYvK8cq\noSwD0zT53ve+x+/+7u8uuc7a+E3TXNY5sbwNeSV+8eWDjN13dJCczzA3mqSmyUn7a4ILjlmpE82C\n270wl223Vyli2u3IdesYOjrBmas1SJ+Piw8351NSyid9zRqTWEySCSehv5/NLdOIE37k7iom4zYb\n5gc/qFQDNY3ptl2ELuvU1EhqahTZRSNqw3dpyvJ3fjSNp/8svvMmMcMN3d04alyFzXR3Z4gLU+pO\nfz5j58bdBr/1GY01a5RL4vnzGh0dknQabrjBpL1dzS2Ypnq+vj7o7XWze7fGtm0UBiQDnhw71oYh\n5yEtDey2fASY3+ciEdCMHDKtXmc8rWM3lNUxnoWRx/i4Sns5naqmc/myakleCpGISs9Nx1xkkiZB\nbxYB1NQVN7iGBuWVMjIicLmgp8comHEJFUYUDzg6Cpurm5SNjir3RCmVUKU1VV8J27almZlRDpbt\n7Za8WZEgVoJK0U+p/W4mkyGdTnP58mWAArktpf/28/i/r0Yo1w+rhLIMstks73znO5d1e7SijlIR\nyKXWSikLbb2VCvcWrMgnGo0ukE/RHTq73rGhsM7S8spkMpimuaRfva4rw6lLl1RE0dMjK9XbAZBb\nt3L1aifSKTG9fsLHIRHJ0n/FRjYnePObndxzT47MM2dw1fXji8zAcRcyEKgoEHbsGIyNuejo2Etr\nK5w8YZ0nwZYtSql34waTq2fC+HUVxazb4UAfGeKWbgf9E17MRIqu195QSNH1TS0kZAMdkMzNCXw+\n8Htz1NcL6usFmiZobpYcPGgyNQXf+Y5GMqkRi7nIZjUaG6Fpxw546SW883GSmpuWG9sYPzNHV0MM\np1dn3UHVelxfD+s3agwMa8isSU9LnOa6LKbXi8+r0lyWbW9rqyISa69byZ738svK2/3et7h46J9S\npLMat+w3eP2vLgwlOzvVc01PQ2+vIBJx0NwMTrebBU+zxHWWyxXJBGB4WKkVLIpa83A6Tfbvl9hs\nP39brWUeV55+s5BKpZiZmaGjo6Oq8rVFPtbvrO43S9i0WvRTqny92uV1/bBKKMvA6XRy7733Mj4+\nvuQ6i1BKRSCXWgvFocPlLmbruK1L+OhaJFVtkLEcfj/ceOPKNoOM0894QsBskqGj87xwvhYDcAed\njIzUsmdPmu7EMNroxcJjtPPnMcsI5Zln4Ic/1MnlwO/XWbfOXOAxPz2tHA7Xf+sOvvsbTzE/6uTA\nXS469mswN4fLYbKtM4p0pJElxVyXniFulF7KyhzKYZeMvzwFcynC81lcOxsZDvhxOAQ9ParF1Er9\nmaayo00kIFfn4x8v7eObX8mBNOloSvPbfxCkoTaIv9WH3aM2wGwWnC5BcEsrXfIKr7slirlpT6F1\naccOSVdXPl15dZ6Hvj2P5nVTu8fLpk2SyGiU/qeU+VX37W0E2hd2IeQFrenocfMfP+tm40bJhg2V\nP7NoVLX9ptMa8/N2jh0THLx1DTISUeGRx1NoqDBN5eWeSql6V10dDB6b5W/+S5xEQmP9Brjnw51U\nmNMll1OHm5jQ6eh45XxKVqJ8Xf7Y8ujHmiMr/Z11Y2ez2QiFQhVJZyWq36tQWCWUFWA5rxMothb7\nfL4VXfRSyqpDkuXHzWQyi+Rbqj3/Us0A1vNeC6anYXJSMDICIydieG0ZDFMViG1mmv5LOT7y9hQ/\n+e8lOTNNQ+bPQTarIiJNgx/9SOOll1RnWSAATqdYQCgeT1GevuGB19AAhJ2SxrrzC7Xdywj7Yh+s\nXZ/DwAZIPv7OUW67rRXb/DQDw2PMmZLIWJxzgxFq3tTAP/2sge4NgpsOOKmtlczOivyGpQQb+/oE\nP/xONp8S1BifsfPodzN84g8XTpWfPCnweATd293AFk4T4q/eM8t8+Cr79uf4T1/qxu+H2b45znzv\nCnWJBNmYjbUyjUtv5cm/7SebVLv2zHA/h39jW4GsQLXBXrqk/m2zUbXZAVSWrbSeFQ4LTCnQtm+H\n7QvNxV5+WRRasIeGBAcOmPzVfxknGfOSzNg4dwa6nxrhta9V1f1M0iARzuKtc/L8CxrRqGBiwokQ\nGvv3V31JJJNKtNlmU80P15pV+kW6vErrM8thfHwcn8+H0+lcpHydyWSu2Vr7/2asEsoKUKrltRQM\nw1hRdGCtraurW/YLI4QgkUgU2kCXWpdMJgkEAtc1RD9+XHDpkjqeTZi01KcIerNkchrnLjsAJ9Nz\nXrxvuoexP5oi6M1CTQ2ypZUTJwTj42qj3rnTZGioaJgViSjTKTMSYy4k2HKjh82bVWPT4KCgpkbV\nHNJpQULzk735ZvRwWKVtyrrXGtocxFMAObJZuHq1FdMEQZawP0vD9ACzfifhuI3//id+ElJD1wUf\n/48Rtt8S4OpVCIWyvP71Gl6vTiq18ByYpvpvfl5t8MGgyuZlswvP8//67QgzM0qV4KEf2en44yHe\n+qlOJntDSFOiaRK3zWTyUpimTTUFMgHIJg0Ss0lqSghlwwZJICBJJhXRLdViW1NTmk4T+P2y6gY+\nNbVQPHJmBuYjOkFHGrcth0QQFFk0rY2+YyG+8xchMinw1dlZf3sHDrdOLCY4eVK1Ym/YsPg5Uik1\noW/V6aanJTfddG03M69k23A1/6FXqjHg1YJVQlkBdF1f9s7e8nVfKoqwYOWDa1Zgwm0ZUq1EPiWV\nSi1SFf5FMTFR7NzytfnJhqM8cOsI33y8CShGJTlsvPNrd/DIl6+Cz8dY7TbGTwswTYxUhjMvO+jp\ngVxOEg0ZBMx5Us/2cyLnBLcHc1wjEOji0iWNqSmVftq0SVJXJ5md1YnFArS1NdBRuRMaWCzt4dCa\ncXomyBgGHluG3rlmkhkb2CQS+O53NH79dySJhGR2NkZ9vYPpaaipkRy4w8lD38mQy0K9L81r7nXw\nl3+pFTSjDh0y6eiQjIyon10uWRAntDBwQbk8OgN2SKcR8QS43Xhq7HgbPdjdeoFUHG4NT/3iGkdz\nM0oqPxwHvcwGsgSBANx4o8mlSyYeT25JW12fb6G5l88H27aaHD+u49QMbHbJ/vvUjdGP/36eTJ5g\nZycNMkfDrLuxjoEBB3V1gt5egWFINm9e+HyzsxTIBFSUaxgqFVkJ1jVWyh+vFKGsanldP7zqCSWb\nzfLZz36W0dFRMpkMH/vYx7jzzjuv6RgrSXnNzc2tOL8bjUYLBcnlYA09LgcrLbbca4jH4/nBtYWd\nMr29Gj/9qXo9R46YhZmOjg61n0Ui0Nrj5fD+FjqCcTp2R/jAb5el61wu5IEDAAwfhUtnMjjGBllT\nE0Xz2DhyaBOa5sEcGKbeFmbmcgpdJJE2G0MDDhxPxwl2+Nm4EcbG1B12QwOcOePA7xdMT2uAuSDj\nNTenNq/2drUpLZD2MG3c+K7t0BnBFZvliZkk/XMS7OC0GWhCpeP8fnWM55+3kc2qc3D7a10cvF0j\n/vzL3Ng+xQvPNZKcsmH1+p44Ifit3zLx2xK8/P0hMFPUud2MpXwIAUJIbrlLEcS6LklCG2YklMSH\nnW13bsfusbP/Pd2FGkrPa9oXpLsKuHoV7exZpaYMyMOHK07fg9KvGh6WTEzonD0r2LVLVpT5uuEG\nyZkzKoro6JA0N8Nv/OUWvvLrpxkbkhw44mDTIRUR57LF695pN2mtz6juNg26uiQ2m2B8fHHzWHn9\n3+GoTiYDA3DxojrvW7eaBXuXV5JQlmrxX8XK8aonlB/84AcEg0H+6I/+iPn5ee6///5rJpSl3Bih\naAe8EoKwCudW1LPUBWsYBplMBn+VDaQU8Xh8WeIxTZOpqSnq6upIJpOFfPHcnMk3vlFbVAgeFnzo\nQxGCQRudnQ4iERemqdHQoLF9rwOHw827tgs++bspIln1nHaR5eEXVEvQ1BRMTGikR2eIzOnk0l7u\nu3GCjXW9bP21XaQfuYTfluBP+vPpwVyO3mGNz3/Xiwlsa5vmD/6ska1blflU6TmamBDMziqCC4Xg\n1CkN0wSvV/Irv2Jis6nCMahNL1Bnx/P222B0lM9uSdL7G5LZsInNJvnQh4spp8lJW37y2/pZcPeO\nWbS5KQA8zhxidhZZUwNTU7jFHOKJMWbO1+DMqE33gbvmOXYhh2lzcvg+F7e9XTVR6FcG2HXAR9c2\nReTu2AwffUuGnz7n59Yba/nrh6uLWYmBAVVYungRLZnEnJnhwel9nDvv4CO/305DZ3Eic2AAhoZU\n19rkpODsWdizZ/GNkNvNoghm6IUxOlsNOlvBjKa4/JMhNty1lgP3+Hj0n2JIE7w+uP99AaYiJplM\nmvp69blUSsXV1Sklg8FBNfRarQU5Hldt3RbOndNobjZxuV65CfZVccjrh1c9odx7773cc889gLo4\nVhpFlGK5lJdlBzw9Pb0sSVhDh5Ym11Jrw+Ew3qUS53mk02mEEMsSmiVAWS4sKSXU1Oj5f0ukNNF1\nGx6PckQ8cCBGMmmg61nGxoqtmc+dhP/9OZN4wsb//Es3v3K/g5NnvDjs8LZ3G2xcEyXhSODSs5x7\nbJzHvxtlzX6D1x5xY4+muH1vlKeO14DDyfdPbsYaNjw31szX/ucQ3322jXJjQSWVpdY9+qiy89Wy\naSLTcPy4nX37TM6dUzMmHR2ScBhA4OnooLUDfvi0ycnnE6xZ56Czp9Rxc+HzCMGC/Mu+DSEGpgJc\nikTwZed508FRRCJJ6lIEgipkcrh03vNBF1vesJ5UStWfkklon/LR7Svqqd9xv5+fDajHDP2khgub\npnn+YpXam80GExOIvPXh6z93A0/NbgUE//sf07x0NEznDpU6jceLGlnq58WHy+XgqaeU/fPWrSZb\nuxKI4WFCL06A6SmciLkh9eB9b2hizWYPockMXTv8zITt9PVphEI6QqhOwWpk0d2tZH2WQvlQqZTq\ndy7XL0/KaxUrx6ueUKwNORaL8YlPfIJPfvKT13wMK+VlmuaiC690lmQlF//c3ByNjY1MTU0tSVLW\nIGNDQwOJZcwd5ufn8fv9ZMu/nWXHC4fDBQHK/n7VgbN+vZqRqKmRhMMqGqir0+jqcleXMC855n/+\n0yl0Xeev/ljw7FE1qxMx4WtfEXz2007scoTLL0SIztiRPjvDPwmTzmocflM9u+832fDeFs6f0+HB\nhefumVO1jIxAW5tkYiLHyPkojqkBMhlB3ZZG9MZ6hIALL8XJRNVgoSuV4b77Grj9dsnUFBw/rqIX\nXYf9+9Vkusencetdi6c4W1sNEgmTWEyt37FDKr+QpibE1BSD5xJ018+x0zvHto55dLsi4PY1grw7\nLpouaNmu5I5PnhTMzan3FGYbnngSD2HMujpeurKwweLM1eqNHHLHDsTFfDt2IMDTs9uxiDdlOvm1\nt0/wUK8ilPp6yalTRUKpVE7753/WOHdOLTh7Gt6z/iQbG+cJZhJMjjmUHD5Q01ok29YNPlrzhffe\n57X8+crS2qoGIH8Rd4RAAGprJaGQek0NDcUh21dqo1/V8rp+eNUTCqi2wI9//OO8613v4g1veMM1\nP96avq1EAKUikFZqrFoUZKXGnE7nsnUZywd+uejI0vcKBAJkMpmq61KpFHa7HZvNxrFj8PDD6jU+\n+SS8/e0GH/ygyfPPKx/3W26p7plRitLhseFhW+FLadNBmjot3WsZs7czfeocWjCBz5lDIkmEBPat\nW8nlcthyObY2LB54aPDHeeyxGDabjteVwD10FbuuEwr7CD83Rtddbta1aZx9OouugdtpIGJRRi+4\naN/sY3BQEImou12/H65cEdTWVj+PmgY33WSgaWq4Ur1/gbzpJgZ/0s/FxCzYbMyGM4jpDDv2qQ23\n+/5teON+4jMp7M21DEcDTJynMK0OgM9HdONtaJ5RHD4fNk2SLsmg6poJqI3zn//sAt/4mpNf/WCa\nN//GZggGMd/xDsQzzyAqpF2NnMnpP36cK6MOXkztJO4KoOtOPvYxk+7uxe/zypWSH5IpBoYdbGyE\n7u1uTDPJXIONmrU1bDhSWemh/FL8RTNCmqaGbMfHlc1zS0uREH9ZIpRVslk5XvWEMjMzwwc+8AF+\n7/d+j1tuueXnPk4lQimf+1jK1x0W+qMstbbUh940zSWPael7LUdQ8/PzhX76U6eKX55UUvL9b6X5\nrd9zcs891747WO/jDW8W/OQpMPJ73ubuNFI6aG63M9lTQ++LdtbZQ9g0kw17axcpD7zr9n7+/qn1\nAHi1BH/01caCNMdwX5pWwJQmDd55+sZ89J2bZV1PnD1dGTJZDZczRy6rMT0jsU8l6Ovzcu6cIm63\nW9DWJpFSLLk5mKaay4jFlK9LZycgBJGQLDpOORzMB9dhbm9QgzONjbSg0kvPPKMVhgGnpoomT0JA\nfQPkcqot9at/Ns17PtaKgY6OyZf/1xjQwdv39vP9M2pm5NHfgtf/XT9/+M21dHf70O68ExkOc3v3\nEE/2dwECj57iQ3tPM3oyyY8vbyaamaT5gIZ027hyRVRMNzU2lqTC7Haaa9VNiNAEG/bWII/sWHJg\nZPNmk95e9fdgULLErO2KoWmWdMtCvJLikKukcX3wqieUv/iLvyASifDlL3+ZL3/5ywB85StfuWYX\nw0qF+XIRyKU2dUubyJJZWYpQrGjCbrcXai2VUKrvZU39VnvuUudGqyzTdybOj//FAdj5+l8k+Na3\nc+y6dXnnvUqv474HvKSSCf75O5LGRsln/oeXYy+pv+880ohz+gSN5hSb7+ti7we3LTrG13+8lq+j\nduPeXheDg8XJ6KY1TmyZGpy5HE+caeTiRA1+UcvAlGDfzUOcP5nDac+xe5/Oxhua8ucCNC1LOi0x\nTYNIJEx/v+p/tYbeSuU40uk0fX1Z5uay6LrO5KSGzSZpa4OUq4aBCQOPK0dzTZr6nloo85kJhVgw\nWd7QoCRtMhk1kFhbC1NT6vN58/tbiL9fAvnuATrI5eAHZxYOdDx0ah3b/04Rw3vf64DGRn5wrpFH\nvzrMxZdTvOPXGznxB6q2YkqBNCXp2TiOjiChWcndt6YYHbfRtSbHP3zfQSCo8cADJg89pBEKwebN\nOrs2rEVeVtOnctu2ZacP162DpiaTixcT7N5dfdblemB1o//3h1c9oXzuc5/jc5/73C98nHKyqCQC\nuVQ3mBUhlEpJVCOAlUYypfpeS60rf+6HvnSep881M5v24rGpFtpIws5nP5ni4WOLH2+a5FthF/+t\n9Hnf8l4Pb3lv8TE+nyQ2l0U89jj7a0McXHMFkRrGzO1cwmNWbcRKyl7g80m2bcuS3d1FciDKlRcb\n8Hd6QGhMTMCl4Bp2vz6Ny2nyuv/gLOh7tbQIamuV1L3dDps2+QvtxlLKRYKEqgMuSyIRKcz+nD6d\nob/foG/aS67JxeRslmCDncaba4hEItg0DfvQELZUCr+nFSHaizM7PsmmTSuL+KSEo89kkVS+yenv\nFwwPq2lzgHs/tIZ783/zN7mITqXYUj/FS5NrcNR5cbslX/likrMX1fFC53Te//Yk3/2xB58P3vGO\n0mu0g1htB6EQ1Nhh+X5CdUMSCJj/qmQCq4Ty7xGvekK5Xigni0oikNU29UruidXIJ5vNYhhGIZpY\niijC4XBhkHG557aIL+DXkVgRgiSZS+CyqfeQSC3+8p4+rdqIbTZlnlU+sC+EIBxWOlxOp6SrSxGP\npsGBA5Kh5yaweQdZ4w+jibzJ+fQ0S+VKHA649VaJaao74IkJSUODjm9LB3U/UFPaoFqH7XYINKpe\n3+HhYt1g1y7JyZMin5eXC1Iq1gCq3W4nnVatwslkkjVrfMzOFttwd+40mZxUaaK6ujoMw8AfyIFI\nkkwm0c6dQxsaUgQkT1DfcgNDyRZcLp2ODoPJSR1Ns5FI2HGZKfQLx9HtdlVoLzmR8TjMD0U50HSV\n56aKAx07O2cBNetTjX9v+vRhLv3tSzQnstyx20m8Wae+PsIj31nYyTcyVvkAs7Pwwx9qDA2pc/X6\n1xvs2VP1o1k5pFyZAuaSh/i3JZTVluFrxyqhrBClEYrVMVUuAlkt5RUOhxdJolRbazkyWqhGPMlk\nEl3XC5P51QglFost8KqXlMpLCEzcQBpdk3z81xcWxycnleosqCG4Bx/UuOEGSUdHMXcej2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dJKlgMha6D2x5svHItAbQX9HCJ5qJ6Z/bbzFEdKDhPosxTKrq9Mf3OR2A6NGYYYL+OAD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\nAAAAAElFTkSuQmCC\n"", + ""text/plain"": [ + """" + ] + }, + ""metadata"": {}, + ""output_type"": ""display_data"" + } + ], + ""source"": [ + ""import matplotlib.pyplot as plt\n"", + ""import seaborn as sns\n"", + ""from pandas.tools.plotting import scatter_matrix\n"", + ""# Plot the Figures Inline\n"", + ""%matplotlib inline\n"", + ""\n"", + ""\n"", + ""def PCA_plot3d(data):\n"", + "" # PCA's components graphed in 2D and 3D\n"", + "" # Apply Scaling \n"", + "" from sklearn.decomposition import PCA\n"", + "" from mpl_toolkits.mplot3d import Axes3D\n"", + "" from sklearn.preprocessing import StandardScaler\n"", + "" from matplotlib import pyplot as plt\n"", + "" data_pca = data\n"", + "" \n"", + "" # Apply Scaling \n"", + "" X = data_pca.drop('model', axis=1).as_matrix().astype(np.float)\n"", + "" y = data_pca['model'].values\n"", + "" \n"", + "" # Formatting\n"", + "" target_names = ['Train','Test']\n"", + "" colors = ['blue','red']\n"", + "" lw = 0\n"", + "" alpha = 0.3\n"", + ""\n"", + "" plt.style.use('seaborn-whitegrid')\n"", + "" plt.figure(2, figsize=(7, 7))\n"", + ""\n"", + "" # 3 Components PCA\n"", + "" ax = plt.subplot(1, 1, 1, projection='3d')\n"", + ""\n"", + "" pca = PCA(n_components=3)\n"", + "" X_std = StandardScaler().fit_transform(X)\n"", + "" X_r = pca.fit_transform(X_std)\n"", + "" X_reduced = pca.fit_transform(X_std)\n"", + "" for color, i, target_name in zip(colors, ['Train','Test'], target_names):\n"", + "" ax.scatter(X_reduced[y == i, 0], X_reduced[y == i, 1], X_reduced[y == i, 2], \n"", + "" color=color,\n"", + "" alpha=alpha,\n"", + "" lw=lw, \n"", + "" label=target_name)\n"", + "" plt.legend(loc='best', shadow=False, scatterpoints=1)\n"", + "" ax.set_xlabel(\""PC1\"", weight= 'bold')\n"", + "" ax.set_ylabel(\""PC2\"", weight= 'bold')\n"", + "" ax.set_zlabel(\""PC3\"", weight= 'bold')\n"", + ""\n"", + "" # rotate the axes\n"", + "" ax.view_init(30, 10)\n"", + "" plt.savefig('applicability_domain3D.pdf', dpi=300)\n"", + "" \n"", + ""PCA_plot3d(pca)"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": null, + ""metadata"": { + ""collapsed"": true + }, + ""outputs"": [], + ""source"": [] + }, + { + ""cell_type"": ""code"", + ""execution_count"": null, + ""metadata"": { + ""collapsed"": true + }, + ""outputs"": [], + ""source"": [] + }, + { + ""cell_type"": ""code"", + ""execution_count"": null, + ""metadata"": { + ""collapsed"": true + }, + ""outputs"": [], + ""source"": [] + } + ], + ""metadata"": { + ""kernelspec"": { + ""display_name"": ""Python 2"", + ""language"": ""python"", + ""name"": ""python2"" + }, + ""language_info"": { + ""codemirror_mode"": { + ""name"": ""ipython"", + ""version"": 2 + }, + ""file_extension"": "".py"", + ""mimetype"": ""text/x-python"", + ""name"": ""python"", + ""nbconvert_exporter"": ""python"", + ""pygments_lexer"": ""ipython2"", + ""version"": ""2.7.13"" + } + }, + ""nbformat"": 4, + ""nbformat_minor"": 2 +} +","Unknown" +"Inhibition","chaninlab/estrogen-receptor-alpha-qsar","07_External_test.ipynb",".ipynb","453868","3133","{ + ""cells"": [ + { + ""cell_type"": ""markdown"", + ""metadata"": {}, + ""source"": [ + ""# check best performing model"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 1, + ""metadata"": { + ""collapsed"": true + }, + ""outputs"": [], + ""source"": [ + ""# load the model from disk\n"", + ""\n"", + ""import pickle\n"", + ""\n"", + ""path = r'Result/corr cutoff 0.7 single/RF model/'\n"", + ""\n"", + ""AtomPairs2DFingerprintCount = pickle.load(open(path +'AtomPairs2DFingerprintCount.csv_model0.sav', 'rb'))\n"", + ""AtomPairs2DFingerprinter\t= pickle.load(open(path +'AtomPairs2DFingerprinter.csv_model0.sav', 'rb'))\n"", + ""EStateFingerprinter\t\t\t= pickle.load(open(path +'EStateFingerprinter.csv_model0.sav', 'rb'))\n"", + ""ExtendedFingerprinter\t\t= pickle.load(open(path +'ExtendedFingerprinter.csv_model0.sav', 'rb'))\n"", + ""Fingerprinter\t\t\t\t= pickle.load(open(path +'Fingerprinter.csv_model0.sav', 'rb'))\n"", + ""GraphOnlyFingerprinter\t\t= pickle.load(open(path +'GraphOnlyFingerprinter.csv_model0.sav', 'rb'))\n"", + ""KlekotaRothFingerprintCount = pickle.load(open(path +'KlekotaRothFingerprintCount.csv_model0.sav', 'rb'))\n"", + ""KlekotaRothFingerprinter\t= pickle.load(open(path +'KlekotaRothFingerprinter.csv_model0.sav', 'rb'))\n"", + ""MACCSFingerprinter\t\t\t= pickle.load(open(path +'MACCSFingerprinter.csv_model0.sav', 'rb'))\n"", + ""PubchemFingerprinter\t\t= pickle.load(open(path +'PubchemFingerprinter.csv_model0.sav', 'rb'))\n"", + ""SubstructureFingerprintCount= pickle.load(open(path +'SubstructureFingerprintCount.csv_model0.sav', 'rb'))\n"", + ""SubstructureFingerprinter\t= pickle.load(open(path +'SubstructureFingerprinter.csv_model0.sav', 'rb'))\n"" + ] + }, + { + ""cell_type"": ""markdown"", + ""metadata"": {}, + ""source"": [ + ""model 9 show best prefance"" + ] + }, + { + ""cell_type"": ""markdown"", + ""metadata"": {}, + ""source"": [ + ""# testing external"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 2, + ""metadata"": { + ""collapsed"": true + }, + ""outputs"": [], + ""source"": [ + ""import pandas as pd\n"", + ""import numpy as np\n"", + ""\n"", + ""df1 = pd.read_csv('model/Test_gra_ER_alpha.csv' , header = 0)\n"", + ""df2 = pd.read_csv('model/Test_les_ER_alpha.csv' , header = 0)"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 3, + ""metadata"": {}, + ""outputs"": [ + { + ""data"": { + ""text/plain"": [ + ""(283, 19)"" + ] + }, + ""execution_count"": 3, + ""metadata"": {}, + ""output_type"": ""execute_result"" + } + ], + ""source"": [ + ""len(df1),len(df2)"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 4, + ""metadata"": { + ""collapsed"": true + }, + ""outputs"": [], + ""source"": [ + ""# Convert IC50 to pIC 50\n"", + ""\n"", + ""from math import log10\n"", + ""\n"", + ""def pIC50(df0):\n"", + "" pIC50 = []\n"", + ""\n"", + "" for i in df0.value:\n"", + "" molar = i*(10**-9) # Converts nM to M\n"", + "" pIC50.append(-log10(molar))\n"", + "" #Y = pIC50\n"", + ""\n"", + "" df0['pIC50'] = pIC50\n"", + "" df0 = df0.drop('value', 1)\n"", + "" \n"", + "" return df0"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 5, + ""metadata"": { + ""collapsed"": true + }, + ""outputs"": [], + ""source"": [ + ""df1 = pIC50(df1)\n"", + ""df2 = pIC50(df2)"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 6, + ""metadata"": { + ""collapsed"": true + }, + ""outputs"": [], + ""source"": [ + ""path = r'Fingerprint/'\n"", + ""\n"", + ""Fp1 = pd.read_csv(path + 'Test_gra_ER_alpha-SubstructureFingerprintCount.csv' , header = 0)\n"", + ""Fp2 = pd.read_csv(path + 'Test_les_ER_alpha-SubstructureFingerprintCount.csv' , header = 0)"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 7, + ""metadata"": { + ""collapsed"": true + }, + ""outputs"": [], + ""source"": [ + ""Fp1 = Fp1.rename(columns = {'Name':'chemblId'})\n"", + ""Fp2 = Fp2.rename(columns = {'Name':'chemblId'})"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 8, + ""metadata"": { + ""collapsed"": true + }, + ""outputs"": [], + ""source"": [ + ""from sklearn import preprocessing\n"", + ""\n"", + ""def normalized (Fp):\n"", + "" \n"", + "" chemblId = Fp.chemblId\n"", + "" Fp_ix = Fp.ix[:,1:]\n"", + "" min_max_scaler = preprocessing.MinMaxScaler()\n"", + "" np_scaled = min_max_scaler.fit_transform(Fp_ix)\n"", + "" Fp_normalized = pd.DataFrame(np_scaled)\n"", + "" Fp_normalized\n"", + "" Fp_normalized = pd.DataFrame(np_scaled, columns=Fp_ix.columns)\n"", + "" Fp_normalized['chemblId'] = chemblId\n"", + "" \n"", + "" return Fp_normalized"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 9, + ""metadata"": {}, + ""outputs"": [ + { + ""name"": ""stderr"", + ""output_type"": ""stream"", + ""text"": [ + ""/Users/zeromtmu/anaconda2/envs/ER_alpha/lib/python2.7/site-packages/ipykernel_launcher.py:6: DeprecationWarning: \n"", + "".ix is deprecated. Please use\n"", + "".loc for label based indexing or\n"", + "".iloc for positional indexing\n"", + ""\n"", + ""See the documentation here:\n"", + ""http://pandas.pydata.org/pandas-docs/stable/indexing.html#deprecate_ix\n"", + "" \n"" + ] + } + ], + ""source"": [ + ""Fp1_n = normalized (Fp1 )\n"", + ""Fp2_n = normalized (Fp2 )"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 10, + ""metadata"": { + ""collapsed"": true + }, + ""outputs"": [], + ""source"": [ + ""raw1 = df1.merge(Fp1_n , on='chemblId', how='outer')\n"", + ""raw2 = df2.merge(Fp2_n , on='chemblId', how='outer')"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 11, + ""metadata"": { + ""collapsed"": true + }, + ""outputs"": [], + ""source"": [ + ""raw1 .to_csv('QSAR/Test_gra_ER_alpha.csv' , sep=',' ,index=False)\n"", + ""raw2 .to_csv('QSAR/Test_les_ER_alpha.csv' , sep=',' ,index=False)"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 12, + ""metadata"": { + ""collapsed"": true + }, + ""outputs"": [], + ""source"": [ + ""Ext1 = raw1 [['chemblId', 'pIC50'\n"", + "" ,'SubFPC169', 'SubFPC23', 'SubFPC274', 'SubFPC302', 'SubFPC88', 'SubFPC181', 'SubFPC2', 'SubFPC18', 'SubFPC287', 'SubFPC137', 'SubFPC1', 'SubFPC20', 'SubFPC5', 'SubFPC172', 'SubFPC135', 'SubFPC182', 'SubFPC100', 'SubFPC3', 'SubFPC183', 'SubFPC33']]\n"", + ""Ext2 = raw2 [['chemblId', 'pIC50'\n"", + "" ,'SubFPC169', 'SubFPC23', 'SubFPC274', 'SubFPC302', 'SubFPC88', 'SubFPC181', 'SubFPC2', 'SubFPC18', 'SubFPC287', 'SubFPC137', 'SubFPC1', 'SubFPC20', 'SubFPC5', 'SubFPC172', 'SubFPC135', 'SubFPC182', 'SubFPC100', 'SubFPC3', 'SubFPC183', 'SubFPC33']]"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 13, + ""metadata"": {}, + ""outputs"": [ + { + ""data"": { + ""text/html"": [ + ""
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chemblIdpIC50SubFPC169SubFPC23SubFPC274SubFPC302SubFPC88SubFPC181SubFPC2SubFPC18...SubFPC1SubFPC20SubFPC5SubFPC172SubFPC135SubFPC182SubFPC100SubFPC3SubFPC183SubFPC33
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"" + ], + ""text/plain"": [ + "" chemblId pIC50 SubFPC169 SubFPC23 SubFPC274 SubFPC302 \\\n"", + ""0 CHEMBL192325 5.30103 0.666667 0.0 0.461538 0.008772 \n"", + ""1 CHEMBL1331671 4.30103 0.000000 0.0 0.653846 0.035088 \n"", + ""\n"", + "" SubFPC88 SubFPC181 SubFPC2 SubFPC18 ... SubFPC1 SubFPC20 \\\n"", + ""0 0.00 0.0 0.0 0.0 ... 0.0 0.0 \n"", + ""1 0.05 0.4 0.0 0.0 ... 0.1 0.0 \n"", + ""\n"", + "" SubFPC5 SubFPC172 SubFPC135 SubFPC182 SubFPC100 SubFPC3 SubFPC183 \\\n"", + ""0 0.0 0.0 0.2 0.0 0.000000 0.0 0.0 \n"", + ""1 0.0 0.0 0.4 0.5 0.055556 0.0 0.0 \n"", + ""\n"", + "" SubFPC33 \n"", + ""0 0.0 \n"", + ""1 0.0 \n"", + ""\n"", + ""[2 rows x 22 columns]"" + ] + }, + ""execution_count"": 13, + ""metadata"": {}, + ""output_type"": ""execute_result"" + } + ], + ""source"": [ + ""Ext1.head(2)"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 14, + ""metadata"": {}, + ""outputs"": [ + { + ""name"": ""stderr"", + ""output_type"": ""stream"", + ""text"": [ + ""/Users/zeromtmu/anaconda2/envs/ER_alpha/lib/python2.7/site-packages/ipykernel_launcher.py:1: DeprecationWarning: \n"", + "".ix is deprecated. Please use\n"", + "".loc for label based indexing or\n"", + "".iloc for positional indexing\n"", + ""\n"", + ""See the documentation here:\n"", + ""http://pandas.pydata.org/pandas-docs/stable/indexing.html#deprecate_ix\n"", + "" \""\""\""Entry point for launching an IPython kernel.\n"" + ] + } + ], + ""source"": [ + ""X_pre = Ext1.ix[:,2:]\n"", + ""X_pre = X_pre.as_matrix()\n"", + ""X_pre = np.array(X_pre)\n"", + ""X_ext = X_pre\n"", + ""\n"", + ""Y_ext = Ext1[\""pIC50\""].tolist()"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 15, + ""metadata"": {}, + ""outputs"": [ + { + ""name"": ""stderr"", + ""output_type"": ""stream"", + ""text"": [ + ""/Users/zeromtmu/anaconda2/envs/ER_alpha/lib/python2.7/site-packages/sklearn/cross_validation.py:44: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.\n"", + "" \""This module will be removed in 0.20.\"", DeprecationWarning)\n"" + ] + } + ], + ""source"": [ + ""from sklearn.ensemble import RandomForestRegressor\n"", + ""from sklearn.cross_validation import StratifiedShuffleSplit\n"", + ""from sklearn.metrics import mean_squared_error\n"", + ""from sklearn.metrics import r2_score\n"", + ""from sklearn import cross_validation\n"", + ""from sklearn.metrics import roc_auc_score\n"", + ""\n"", + ""#Test_score = loaded_model.score(X_ext, Y_ext)\n"", + ""AtomPairs2DFingerprintCount.predict(X_ext)\n"", + ""AtomPairs2DFingerprinter.predict(X_ext)\n"", + ""EStateFingerprinter.predict(X_ext)\n"", + ""ExtendedFingerprinter.predict(X_ext)\n"", + ""Fingerprinter.predict(X_ext)\n"", + ""GraphOnlyFingerprinter.predict(X_ext)\n"", + ""KlekotaRothFingerprintCount.predict(X_ext)\n"", + ""KlekotaRothFingerprinter.predict(X_ext)\n"", + ""MACCSFingerprinter.predict(X_ext)\n"", + ""PubchemFingerprinter.predict(X_ext)\n"", + ""SubstructureFingerprintCount.predict(X_ext)\n"", + ""SubstructureFingerprinter.predict(X_ext)\n"", + ""\n"", + ""\n"", + ""prediction_ext1 = AtomPairs2DFingerprintCount.predict(X_ext)\n"", + ""prediction_ext2 = AtomPairs2DFingerprinter.predict(X_ext)\n"", + ""prediction_ext3 = EStateFingerprinter.predict(X_ext)\n"", + ""prediction_ext4 = ExtendedFingerprinter.predict(X_ext)\n"", + ""prediction_ext5 = Fingerprinter.predict(X_ext)\n"", + ""prediction_ext6 = GraphOnlyFingerprinter.predict(X_ext)\n"", + ""prediction_ext7 = KlekotaRothFingerprintCount.predict(X_ext)\n"", + ""prediction_ext8 = KlekotaRothFingerprinter.predict(X_ext)\n"", + ""prediction_ext9 = MACCSFingerprinter.predict(X_ext)\n"", + ""prediction_ext10 = PubchemFingerprinter.predict(X_ext)\n"", + ""prediction_ext11 = SubstructureFingerprintCount.predict(X_ext)\n"", + ""prediction_ext12 = SubstructureFingerprinter.predict(X_ext)\n"", + ""\n"", + ""\n"", + ""ext = pd.DataFrame(Y_ext, columns=['Y_ext'])\n"", + ""Pext1 = pd.DataFrame(prediction_ext1 , columns=['pred Model 1 '])\n"", + ""Pext2 = pd.DataFrame(prediction_ext2 , columns=['pred Model 2 '])\n"", + ""Pext3 = pd.DataFrame(prediction_ext3 , columns=['pred Model 3 '])\n"", + ""Pext4 = pd.DataFrame(prediction_ext4 , columns=['pred Model 4 '])\n"", + ""Pext5 = pd.DataFrame(prediction_ext5 , columns=['pred Model 5 '])\n"", + ""Pext6 = pd.DataFrame(prediction_ext6 , columns=['pred Model 6 '])\n"", + ""Pext7 = pd.DataFrame(prediction_ext7 , columns=['pred Model 7 '])\n"", + ""Pext8 = pd.DataFrame(prediction_ext8 , columns=['pred Model 8 '])\n"", + ""Pext9 = pd.DataFrame(prediction_ext9 , columns=['pred Model 9 '])\n"", + ""Pext10 = pd.DataFrame(prediction_ext10, columns=['pred Model 10'])\n"", + ""Pext11 = pd.DataFrame(prediction_ext11, columns=['pred Model 11'])\n"", + ""Pext12 = pd.DataFrame(prediction_ext12, columns=['pred Model 12'])\n"", + ""\n"", + ""result = pd.concat([ext,Pext1,Pext2,Pext3,Pext4,Pext5\n"", + "" ,Pext6,Pext7,Pext8,Pext9,Pext10,Pext11,Pext12],axis=1, \n"", + "" join='inner').to_csv(\""Result/External1_pred.csv\"", \n"", + "" header= ('Real','AtomPairs2DFingerprintCount'\n"", + "" ,'AtomPairs2DFingerprinter'\n"", + "" ,'EStateFingerprinter'\n"", + "" ,'ExtendedFingerprinter'\n"", + "" ,'Fingerprinter'\n"", + "" ,'GraphOnlyFingerprinter'\n"", + "" ,'KlekotaRothFingerprintCount'\n"", + "" ,'KlekotaRothFingerprinter'\n"", + "" ,'MACCSFingerprinter'\n"", + "" ,'PubchemFingerprinter'\n"", + "" ,'SubstructureFingerprintCount'\n"", + "" ,'SubstructureFingerprinter')\n"", + "" , index=False)\n"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 16, + ""metadata"": {}, + ""outputs"": [ + { + ""data"": { + ""text/html"": [ + ""
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RealAtomPairs2DFingerprintCountAtomPairs2DFingerprinterEStateFingerprinterExtendedFingerprinterFingerprinterGraphOnlyFingerprinterKlekotaRothFingerprintCountKlekotaRothFingerprinterMACCSFingerprinterPubchemFingerprinterSubstructureFingerprintCountSubstructureFingerprinter
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"" + ], + ""text/plain"": [ + "" Real AtomPairs2DFingerprintCount AtomPairs2DFingerprinter \\\n"", + ""0 5.30103 6.700713 5.152328 \n"", + ""1 4.30103 6.323529 6.069978 \n"", + ""\n"", + "" EStateFingerprinter ExtendedFingerprinter Fingerprinter \\\n"", + ""0 5.931076 5.451111 5.393469 \n"", + ""1 5.403990 4.832072 5.746504 \n"", + ""\n"", + "" GraphOnlyFingerprinter KlekotaRothFingerprintCount \\\n"", + ""0 5.750132 7.149428 \n"", + ""1 5.384854 5.779245 \n"", + ""\n"", + "" KlekotaRothFingerprinter MACCSFingerprinter PubchemFingerprinter \\\n"", + ""0 6.097815 5.181037 5.395692 \n"", + ""1 4.675582 4.962622 5.249253 \n"", + ""\n"", + "" SubstructureFingerprintCount SubstructureFingerprinter \n"", + ""0 6.222095 5.687484 \n"", + ""1 5.026321 4.848480 "" + ] + }, + ""execution_count"": 16, + ""metadata"": {}, + ""output_type"": ""execute_result"" + } + ], + ""source"": [ + ""Ext = pd.read_csv('Result/External1_pred.csv' , header = 0)\n"", + ""Ext.head(2)"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 17, + ""metadata"": {}, + ""outputs"": [ + { + ""name"": ""stderr"", + ""output_type"": ""stream"", + ""text"": [ + ""/Users/zeromtmu/anaconda2/envs/ER_alpha/lib/python2.7/site-packages/matplotlib/axes/_axes.py:545: UserWarning: No labelled objects found. Use label='...' kwarg on individual plots.\n"", + "" warnings.warn(\""No labelled objects found. \""\n"" + ] + }, + { + ""data"": { + ""image/png"": 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+ ""text/plain"": [ + """" + ] + }, + ""metadata"": {}, + ""output_type"": ""display_data"" + } + ], + ""source"": [ + ""import matplotlib.pyplot as plt\n"", + ""import pylab as py\n"", + ""\n"", + ""# m = SubstructureFingerprintCount\n"", + ""cm = plt.cm.RdBu\n"", + ""\n"", + ""x_test = np.array(Y_ext)\n"", + ""y_test = np.array(prediction_ext11)\n"", + ""py.scatter(x_test, y_test, s=50, marker='.', alpha=0.3,\n"", + "" c='r', cmap=cm ,edgecolors='r') \n"", + ""\n"", + ""# plot real values\n"", + ""#py.scatter(x_test, Y_ext, s=50, marker='.', alpha=0.3,\n"", + "" #c='b', cmap=cm ,edgecolors='b') \n"", + ""# calc the trendline\n"", + ""z = np.polyfit(x_test, Y_ext, 1)\n"", + ""p = np.poly1d(z)\n"", + ""py.plot(x_test,p(x_test),\""b\"", alpha=0.5)\n"", + ""\n"", + ""plt.legend(loc=2,prop={'size':6})\n"", + ""plt.xlabel(\""Experimental $pIC_{50}$ values\"", fontsize=10)\n"", + ""plt.ylabel(\""Predicted $pIC_{50}$ values\"", fontsize=10)\n"", + ""\n"", + ""plt.gca().set_aspect('equal', adjustable='box')\n"", + ""\n"", + ""min_axis = np.min(np.concatenate([x_test, y_test], axis=0))\n"", + ""max_axis = np.max(np.concatenate([x_test, y_test], axis=0))\n"", + ""\n"", + ""plt.xlim([(min_axis*0.9),(max_axis*1.05)])\n"", + ""plt.ylim([(min_axis*0.9),(max_axis*1.05)])\n"", + ""plt.tick_params(axis='both', which='major', labelsize=14)\n"", + ""\n"", + ""# Save plot to file\n"", + ""plt.savefig('Result/Test_gra_ER_alpha.pdf', dpi=300)\n"", + ""plt.show()"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 18, + ""metadata"": {}, + ""outputs"": [ + { + ""data"": { + ""image/png"": 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m3dqseGen+EGWK/lgal2H2O4kj3DypREp5e8ZnlUHcHsHKH2xHL30+UiNUbTgb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\nqh733kUc/wI0qeoVcWl7imVMrqjqkfhtEfka0MHxLxoXAm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+ ""text/plain"": [ + """" + ] + }, + ""metadata"": {}, + ""output_type"": ""display_data"" + } + ], + ""source"": [ + ""import pandas as pd\n"", + ""import matplotlib.pyplot as plt\n"", + ""import numpy as np\n"", + ""import matplotlib.cm as cm\n"", + ""\n"", + ""x = Ext.Real\n"", + ""\n"", + ""colors = iter(cm.rainbow(np.linspace(0, 1, 12)))\n"", + ""\n"", + ""ax = Ext.plot(x=\""Real\"", y='SubstructureFingerprintCount',kind=\""scatter\"", color=next(colors))\n"", + ""Ext.plot (x=\""Real\"", y='AtomPairs2DFingerprintCount', kind=\""scatter\"", ax=ax, color=next(colors))\n"", + ""Ext.plot (x=\""Real\"", y='AtomPairs2DFingerprinter', kind=\""scatter\"", ax=ax, color=next(colors))\n"", + ""Ext.plot (x=\""Real\"", y='EStateFingerprinter', kind=\""scatter\"", ax=ax, color=next(colors))\n"", + ""Ext.plot (x=\""Real\"", y='ExtendedFingerprinter', kind=\""scatter\"", ax=ax, color=next(colors))\n"", + ""Ext.plot (x=\""Real\"", y='Fingerprinter', kind=\""scatter\"", ax=ax, color=next(colors))\n"", + ""Ext.plot (x=\""Real\"", y='GraphOnlyFingerprinter', kind=\""scatter\"", ax=ax, color=next(colors))\n"", + ""Ext.plot (x=\""Real\"", y='KlekotaRothFingerprintCount', kind=\""scatter\"", ax=ax, color=next(colors))\n"", + ""Ext.plot (x=\""Real\"", y='KlekotaRothFingerprinter', kind=\""scatter\"", ax=ax, color=next(colors))\n"", + ""Ext.plot (x=\""Real\"", y='MACCSFingerprinter', kind=\""scatter\"", ax=ax, color=next(colors))\n"", + ""Ext.plot (x=\""Real\"", y='PubchemFingerprinter', kind=\""scatter\"", ax=ax, color=next(colors))\n"", + ""Ext.plot (x=\""Real\"", y='SubstructureFingerprinter', kind=\""scatter\"", ax=ax, color=next(colors))\n"", + ""\n"", + ""# calc the trendline\n"", + ""z = np.polyfit(x, x, 1)\n"", + ""p = np.poly1d(z)\n"", + ""py.plot(x,p(x),\""b--\"", alpha=0.5)\n"", + ""\n"", + ""plt.legend(loc=2,prop={'size':6})\n"", + ""plt.xlabel(\""Experimental $pIC_{50}$ values\"", fontsize=10)\n"", + ""plt.ylabel(\""Predicted $pIC_{50}$ values\"", fontsize=10)\n"", + ""\n"", + ""plt.gca().set_aspect('equal', adjustable='box')\n"", + ""\n"", + ""min_axis = np.min(np.concatenate([x_test, y_test], axis=0))\n"", + ""max_axis = np.max(np.concatenate([x_test, y_test], axis=0))\n"", + ""\n"", + ""plt.xlim([(min_axis*0.9),(max_axis*1.05)])\n"", + ""plt.ylim([(min_axis*0.9),(max_axis*1.05)])\n"", + ""plt.tick_params(axis='both', which='major', labelsize=14)\n"", + ""\n"", + ""plt.savefig('Result/Test_les_ER_alpha_all.pdf', dpi=300)\n"", + ""plt.show()"" + ] + }, + { + ""cell_type"": ""markdown"", + ""metadata"": {}, + ""source"": [ + ""## Test external less than values"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 19, + ""metadata"": {}, + ""outputs"": [ + { + ""name"": ""stderr"", + ""output_type"": ""stream"", + ""text"": [ + ""/Users/zeromtmu/anaconda2/envs/ER_alpha/lib/python2.7/site-packages/ipykernel_launcher.py:1: DeprecationWarning: \n"", + "".ix is deprecated. Please use\n"", + "".loc for label based indexing or\n"", + "".iloc for positional indexing\n"", + ""\n"", + ""See the documentation here:\n"", + ""http://pandas.pydata.org/pandas-docs/stable/indexing.html#deprecate_ix\n"", + "" \""\""\""Entry point for launching an IPython kernel.\n"" + ] + } + ], + ""source"": [ + ""X_pre = Ext2.ix[:,2:]\n"", + ""X_pre = X_pre.as_matrix()\n"", + ""X_pre = np.array(X_pre)\n"", + ""X_ext = X_pre\n"", + ""\n"", + ""Y_ext = Ext2[\""pIC50\""].tolist()"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 20, + ""metadata"": { + ""collapsed"": true + }, + ""outputs"": [], + ""source"": [ + ""from sklearn.ensemble import RandomForestRegressor\n"", + ""from sklearn.cross_validation import StratifiedShuffleSplit\n"", + ""from sklearn.metrics import mean_squared_error\n"", + ""from sklearn.metrics import r2_score\n"", + ""from sklearn import cross_validation\n"", + ""from sklearn.metrics import roc_auc_score\n"", + ""\n"", + ""#Test_score = loaded_model.score(X_ext, Y_ext)\n"", + ""AtomPairs2DFingerprintCount.predict(X_ext)\n"", + ""AtomPairs2DFingerprinter.predict(X_ext)\n"", + ""EStateFingerprinter.predict(X_ext)\n"", + ""ExtendedFingerprinter.predict(X_ext)\n"", + ""Fingerprinter.predict(X_ext)\n"", + ""GraphOnlyFingerprinter.predict(X_ext)\n"", + ""KlekotaRothFingerprintCount.predict(X_ext)\n"", + ""KlekotaRothFingerprinter.predict(X_ext)\n"", + ""MACCSFingerprinter.predict(X_ext)\n"", + ""PubchemFingerprinter.predict(X_ext)\n"", + ""SubstructureFingerprintCount.predict(X_ext)\n"", + ""SubstructureFingerprinter.predict(X_ext)\n"", + ""\n"", + ""\n"", + ""prediction_ext1 = AtomPairs2DFingerprintCount.predict(X_ext)\n"", + ""prediction_ext2 = AtomPairs2DFingerprinter.predict(X_ext)\n"", + ""prediction_ext3 = EStateFingerprinter.predict(X_ext)\n"", + ""prediction_ext4 = ExtendedFingerprinter.predict(X_ext)\n"", + ""prediction_ext5 = Fingerprinter.predict(X_ext)\n"", + ""prediction_ext6 = GraphOnlyFingerprinter.predict(X_ext)\n"", + ""prediction_ext7 = KlekotaRothFingerprintCount.predict(X_ext)\n"", + ""prediction_ext8 = KlekotaRothFingerprinter.predict(X_ext)\n"", + ""prediction_ext9 = MACCSFingerprinter.predict(X_ext)\n"", + ""prediction_ext10 = PubchemFingerprinter.predict(X_ext)\n"", + ""prediction_ext11 = SubstructureFingerprintCount.predict(X_ext)\n"", + ""prediction_ext12 = SubstructureFingerprinter.predict(X_ext)\n"", + ""\n"", + ""\n"", + ""ext = pd.DataFrame(Y_ext, columns=['Y_ext'])\n"", + ""Pext1 = pd.DataFrame(prediction_ext1 , columns=['pred Model 1 '])\n"", + ""Pext2 = pd.DataFrame(prediction_ext2 , columns=['pred Model 2 '])\n"", + ""Pext3 = pd.DataFrame(prediction_ext3 , columns=['pred Model 3 '])\n"", + ""Pext4 = pd.DataFrame(prediction_ext4 , columns=['pred Model 4 '])\n"", + ""Pext5 = pd.DataFrame(prediction_ext5 , columns=['pred Model 5 '])\n"", + ""Pext6 = pd.DataFrame(prediction_ext6 , columns=['pred Model 6 '])\n"", + ""Pext7 = pd.DataFrame(prediction_ext7 , columns=['pred Model 7 '])\n"", + ""Pext8 = pd.DataFrame(prediction_ext8 , columns=['pred Model 8 '])\n"", + ""Pext9 = pd.DataFrame(prediction_ext9 , columns=['pred Model 9 '])\n"", + ""Pext10 = pd.DataFrame(prediction_ext10, columns=['pred Model 10'])\n"", + ""Pext11 = pd.DataFrame(prediction_ext11, columns=['pred Model 11'])\n"", + ""Pext12 = pd.DataFrame(prediction_ext12, columns=['pred Model 12'])\n"", + ""\n"", + ""result = pd.concat([ext,Pext1,Pext2,Pext3,Pext4,Pext5\n"", + "" ,Pext6,Pext7,Pext8,Pext9,Pext10,Pext11,Pext12],axis=1, \n"", + "" join='inner').to_csv(\""Result/External2_pred.csv\"", \n"", + "" header= ('Real','AtomPairs2DFingerprintCount'\n"", + "" ,'AtomPairs2DFingerprinter'\n"", + "" ,'EStateFingerprinter'\n"", + "" ,'ExtendedFingerprinter'\n"", + "" ,'Fingerprinter'\n"", + "" ,'GraphOnlyFingerprinter'\n"", + "" ,'KlekotaRothFingerprintCount'\n"", + "" ,'KlekotaRothFingerprinter'\n"", + "" ,'MACCSFingerprinter'\n"", + "" ,'PubchemFingerprinter'\n"", + "" ,'SubstructureFingerprintCount'\n"", + "" ,'SubstructureFingerprinter')\n"", + "" , index=False)\n"", + ""\n"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 21, + ""metadata"": {}, + ""outputs"": [ + { + ""data"": { + ""text/html"": [ + ""
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RealAtomPairs2DFingerprintCountAtomPairs2DFingerprinterEStateFingerprinterExtendedFingerprinterFingerprinterGraphOnlyFingerprinterKlekotaRothFingerprintCountKlekotaRothFingerprinterMACCSFingerprinterPubchemFingerprinterSubstructureFingerprintCountSubstructureFingerprinter
05.806185.4267875.8375995.0983034.8702374.9042055.7484576.3455216.0486995.6423344.8598675.0312735.694228
15.806185.9726495.3318014.9518165.0404265.2923825.0994596.2791005.9675164.8738655.0333765.0750394.793754
24.000006.1117137.5790816.0596694.9154735.4357345.0185025.9304154.7852215.3763925.4586464.9403705.672814
34.000006.2633177.5790816.0596694.9154735.4357345.0185026.1374594.7852215.3763925.4586464.9867835.672814
48.494855.8454195.2817775.1826265.2031605.1447795.6894805.8616795.3307444.9108975.0960476.3362294.971919
54.000006.2803407.5790816.0596694.9154735.4357345.0185025.9655534.7852215.3763925.4586464.9390635.672814
64.000005.4990815.1948135.4947534.7085104.9729084.7894995.5839004.8684115.1916575.4666515.7506715.304336
74.000005.6226175.4378025.6956144.7454835.0904344.9424976.0789864.8034095.2914435.3479295.8985155.653088
84.000006.1027027.5790816.0596694.9154735.4357345.0185025.9304154.7852215.3763925.4586464.8930905.672814
94.000005.2867515.4867795.3066394.9029434.9110514.9722385.7376614.8177095.6015065.4418025.5458405.315491
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114.000005.5884285.4378025.6956144.7454835.0904344.9424976.0789864.8034095.2914435.3479295.8859405.653088
124.000006.3852116.6753515.6459404.8300245.3238664.9755915.2248614.7521665.0569875.4778954.9608805.258483
134.000005.6226175.4378025.6956144.7454835.0904344.9424976.0789864.8034095.2914435.3479295.9172035.653088
144.000005.2674425.6128025.4323374.8422554.8473514.9236305.5620154.9772565.3942495.4140975.5251315.397371
154.000005.2612635.6128025.4323374.8422554.8473514.9236305.5620154.9772565.3942495.4140975.5314755.397371
164.000005.4990815.1948135.4947534.7085104.9729084.7894995.5839004.8684115.1916575.4666515.7506715.304336
174.000006.1896095.7844575.3355864.7014615.7423435.0919355.9490575.4583214.8308855.0969145.0413815.015855
184.000006.1356867.5790816.0596694.9154735.4357345.0185025.9655534.7852215.3763925.4586464.9198955.672814
\n"", + ""
"" + ], + ""text/plain"": [ + "" Real AtomPairs2DFingerprintCount AtomPairs2DFingerprinter \\\n"", + ""0 5.80618 5.426787 5.837599 \n"", + ""1 5.80618 5.972649 5.331801 \n"", + ""2 4.00000 6.111713 7.579081 \n"", + ""3 4.00000 6.263317 7.579081 \n"", + ""4 8.49485 5.845419 5.281777 \n"", + ""5 4.00000 6.280340 7.579081 \n"", + ""6 4.00000 5.499081 5.194813 \n"", + ""7 4.00000 5.622617 5.437802 \n"", + ""8 4.00000 6.102702 7.579081 \n"", + ""9 4.00000 5.286751 5.486779 \n"", + ""10 4.00000 6.063293 5.540261 \n"", + ""11 4.00000 5.588428 5.437802 \n"", + ""12 4.00000 6.385211 6.675351 \n"", + ""13 4.00000 5.622617 5.437802 \n"", + ""14 4.00000 5.267442 5.612802 \n"", + ""15 4.00000 5.261263 5.612802 \n"", + ""16 4.00000 5.499081 5.194813 \n"", + ""17 4.00000 6.189609 5.784457 \n"", + ""18 4.00000 6.135686 7.579081 \n"", + ""\n"", + "" EStateFingerprinter ExtendedFingerprinter Fingerprinter \\\n"", + ""0 5.098303 4.870237 4.904205 \n"", + ""1 4.951816 5.040426 5.292382 \n"", + ""2 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\n"", + ""15 4.977256 5.394249 5.414097 \n"", + ""16 4.868411 5.191657 5.466651 \n"", + ""17 5.458321 4.830885 5.096914 \n"", + ""18 4.785221 5.376392 5.458646 \n"", + ""\n"", + "" SubstructureFingerprintCount SubstructureFingerprinter \n"", + ""0 5.031273 5.694228 \n"", + ""1 5.075039 4.793754 \n"", + ""2 4.940370 5.672814 \n"", + ""3 4.986783 5.672814 \n"", + ""4 6.336229 4.971919 \n"", + ""5 4.939063 5.672814 \n"", + ""6 5.750671 5.304336 \n"", + ""7 5.898515 5.653088 \n"", + ""8 4.893090 5.672814 \n"", + ""9 5.545840 5.315491 \n"", + ""10 5.024680 5.044253 \n"", + ""11 5.885940 5.653088 \n"", + ""12 4.960880 5.258483 \n"", + ""13 5.917203 5.653088 \n"", + ""14 5.525131 5.397371 \n"", + ""15 5.531475 5.397371 \n"", + ""16 5.750671 5.304336 \n"", + ""17 5.041381 5.015855 \n"", + ""18 4.919895 5.672814 "" + ] + }, + ""execution_count"": 21, + ""metadata"": {}, + ""output_type"": ""execute_result"" + } + ], + ""source"": [ + ""Ext2 = pd.read_csv('Result/External2_pred.csv' , header = 0)\n"", + ""Ext2"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 22, + ""metadata"": {}, + ""outputs"": [ + { + ""data"": { + ""text/plain"": [ + ""(19, 13)"" + ] + }, + ""execution_count"": 22, + ""metadata"": {}, + ""output_type"": ""execute_result"" + } + ], + ""source"": [ + ""Ext2.shape"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 23, + ""metadata"": {}, + ""outputs"": [ + { + ""data"": { + ""image/png"": 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+ ""text/plain"": [ + """" + ] + }, + ""metadata"": {}, + ""output_type"": ""display_data"" + } + ], + ""source"": [ + ""import matplotlib.pyplot as plt\n"", + ""import pylab as py\n"", + ""\n"", + ""import matplotlib.pyplot as plt\n"", + ""import pylab as py\n"", + ""\n"", + ""cm = plt.cm.RdBu\n"", + ""\n"", + ""x_test = np.array(Y_ext)\n"", + ""y_test = np.array(prediction_ext11)\n"", + ""py.scatter(x_test, y_test, s=50, marker='.', alpha=0.3,\n"", + "" c='r', cmap=cm ,edgecolors='r') \n"", + ""\n"", + ""# plot real values\n"", + ""#py.scatter(x_test, Y_ext, s=50, marker='.', alpha=0.3,\n"", + "" #c='b', cmap=cm ,edgecolors='b') \n"", + ""# calc the trendline\n"", + ""z = np.polyfit(x_test, Y_ext, 1)\n"", + ""p = np.poly1d(z)\n"", + ""py.plot(x_test,p(x_test),\""b\"", alpha=0.5)\n"", + ""\n"", + ""plt.legend(loc=2,prop={'size':6})\n"", + ""plt.xlabel(\""Experimental $pIC_{50}$ values\"", fontsize=10)\n"", + ""plt.ylabel(\""Predicted $pIC_{50}$ values\"", fontsize=10)\n"", + ""\n"", + ""plt.gca().set_aspect('equal', adjustable='box')\n"", + ""\n"", + ""min_axis = np.min(np.concatenate([x_test, y_test], axis=0))\n"", + ""max_axis = np.max(np.concatenate([x_test, y_test], axis=0))\n"", + ""\n"", + ""plt.xlim([(min_axis*0.9),(max_axis*1.05)])\n"", + ""plt.ylim([(min_axis*0.9),(max_axis*1.05)])\n"", + ""plt.tick_params(axis='both', which='major', labelsize=14)\n"", + ""\n"", + ""# Save plot to file\n"", + ""plt.savefig('Result/Test_les_ER_alpha.pdf', dpi=300)\n"", + ""plt.show()"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 33, + ""metadata"": {}, + ""outputs"": [ + { + ""data"": { + ""image/png"": 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""plt.gca().set_aspect('equal', adjustable='box')\n"", + ""\n"", + ""min_axis = np.min(np.concatenate([x_test, y_test], axis=0))\n"", + ""max_axis = np.max(np.concatenate([x_test, y_test], axis=0))\n"", + ""\n"", + ""plt.xlim([(min_axis*0.9),(max_axis*1.05)])\n"", + ""plt.ylim([(min_axis*0.9),(max_axis*1.05)])\n"", + ""plt.tick_params(axis='both', which='major', labelsize=14)\n"", + ""\n"", + ""plt.savefig('Result/Test_ER_alpha_all.pdf', dpi=300)\n"", + ""plt.show()"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 25, + ""metadata"": {}, + ""outputs"": [ + { + ""data"": { + ""text/html"": [ + ""
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"" + ], + ""text/plain"": [ + "" acdAcidicPka acdBasicPka acdLogd acdLogp alogp chemblId \\\n"", + ""0 2.57 NaN -2.24 1.25 2.33 CHEMBL192325 \n"", + ""1 12.76 4.54 3.13 3.13 2.68 CHEMBL1331671 \n"", + ""\n"", + "" knownDrug molecularFormula molecularWeight numRo5Violations ... \\\n"", + ""0 No C15H10O4 254.24 0.0 ... \n"", + ""1 No C17H14ClN3O2 327.76 0.0 ... \n"", + ""\n"", + "" SubFPC298 SubFPC299 SubFPC300 SubFPC301 SubFPC302 SubFPC303 SubFPC304 \\\n"", + ""0 0.0 0.0 0.021739 0.021739 0.008772 0.333333 0.0 \n"", + ""1 0.0 0.0 0.028986 0.028986 0.035088 0.000000 0.0 \n"", + ""\n"", + "" SubFPC305 SubFPC306 SubFPC307 \n"", + ""0 0.0 0.0 0.129870 \n"", + ""1 0.0 0.0 0.142857 \n"", + ""\n"", + ""[2 rows x 341 columns]"" + ] + }, + ""execution_count"": 25, + ""metadata"": {}, + ""output_type"": ""execute_result"" + } + ], + ""source"": [ + ""raw1.head(2)"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 26, + ""metadata"": {}, + ""outputs"": [ + { + ""data"": { + ""text/html"": [ + ""
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"" + ], + ""text/plain"": [ + "" acdAcidicPka acdBasicPka acdLogd acdLogp alogp chemblId \\\n"", + ""0 8.19 NaN 2.06 2.15 1.24 CHEMBL1532515 \n"", + ""1 10.70 1.94 0.63 0.63 1.93 CHEMBL1566820 \n"", + ""\n"", + "" knownDrug molecularFormula molecularWeight numRo5Violations ... \\\n"", + ""0 No C17H22N4O4S 378.45 0 ... \n"", + ""1 No C18H19N5O3S 385.44 0 ... \n"", + ""\n"", + "" SubFPC298 SubFPC299 SubFPC300 SubFPC301 SubFPC302 SubFPC303 SubFPC304 \\\n"", + ""0 0.0 0.0 0.578947 0.578947 0.666667 0.0 0.0 \n"", + ""1 0.0 0.0 0.315789 0.315789 1.000000 0.0 0.0 \n"", + ""\n"", + "" SubFPC305 SubFPC306 SubFPC307 \n"", + ""0 0.0 0.0 0.214286 \n"", + ""1 0.0 0.0 0.571429 \n"", + ""\n"", + ""[2 rows x 339 columns]"" + ] + }, + ""execution_count"": 26, + ""metadata"": {}, + ""output_type"": ""execute_result"" + } + ], + ""source"": [ + ""raw2.head(2)"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 27, + ""metadata"": { + ""collapsed"": true + }, + ""outputs"": [], + ""source"": [ + ""import matplotlib.pyplot as plt\n"", + ""import seaborn as sns\n"", + ""from pandas.tools.plotting import scatter_matrix\n"", + ""# Plot the Figures Inline\n"", + ""%matplotlib inline\n"", + ""\n"", + ""\n"", + ""def PCA_plot(data):\n"", + "" # PCA's components graphed in 2D and 3D\n"", + "" # Apply Scaling \n"", + "" from sklearn.decomposition import PCA\n"", + "" from mpl_toolkits.mplot3d import Axes3D\n"", + "" from sklearn.preprocessing import StandardScaler\n"", + "" from matplotlib import pyplot as plt\n"", + "" data_pca = data\n"", + "" \n"", + "" # Apply Scaling \n"", + "" X = data_pca.drop('values', axis=1).as_matrix().astype(np.float)\n"", + "" y = data_pca['values'].values\n"", + "" \n"", + "" # Formatting\n"", + "" target_names = [\"">\"",\""<\""]\n"", + "" colors = ['red','blue']\n"", + "" lw = 2\n"", + "" alpha = 0.3\n"", + ""\n"", + "" # 2 Components PCA\n"", + "" plt.style.use('seaborn-whitegrid')\n"", + "" plt.figure(2, figsize=(20, 8))\n"", + ""\n"", + "" plt.subplot(1, 2, 1)\n"", + "" pca = PCA(n_components=2)\n"", + "" X_std = StandardScaler().fit_transform(X)\n"", + "" X_r = pca.fit_transform(X_std)\n"", + "" #X_r = pca.fit(X).transform(X)\n"", + "" \n"", + "" for color, i, target_name in zip(colors, ['>','<'], target_names):\n"", + "" plt.scatter(X_r[y == i, 0], X_r[y == i, 1], \n"", + "" color=color, \n"", + "" alpha=alpha, \n"", + "" lw=lw,\n"", + "" label=target_name)\n"", + "" plt.legend(loc='best', shadow=False, scatterpoints=1)\n"", + "" plt.title('First two PCA directions');\n"", + ""\n"", + "" # 3 Components PCA\n"", + "" ax = plt.subplot(1, 2, 2, projection='3d')\n"", + ""\n"", + "" pca = PCA(n_components=3)\n"", + "" X_reduced = pca.fit_transform(X_std)\n"", + "" for color, i, target_name in zip(colors, ['>','<'], target_names):\n"", + "" ax.scatter(X_reduced[y == i, 0], X_reduced[y == i, 1], X_reduced[y == i, 2], \n"", + "" color=color,\n"", + "" alpha=alpha,\n"", + "" lw=lw, \n"", + "" label=target_name)\n"", + "" plt.legend(loc='best', shadow=False, scatterpoints=1)\n"", + "" ax.set_title(\""First three PCA directions\"")\n"", + "" ax.set_xlabel(\""1st eigenvector\"")\n"", + "" ax.set_ylabel(\""2nd eigenvector\"")\n"", + "" ax.set_zlabel(\""3rd eigenvector\"")\n"", + ""\n"", + "" # rotate the axes\n"", + "" ax.view_init(30, 10)\n"", + "" plt.savefig('Result/Testing PCA.pdf', dpi=300)"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 28, + ""metadata"": {}, + ""outputs"": [ + { + ""data"": { + ""text/plain"": [ + ""['acdAcidicPka',\n"", + "" 'acdBasicPka',\n"", + "" 'acdLogd',\n"", + "" 'acdLogp',\n"", + "" 'alogp',\n"", + "" 'chemblId',\n"", + "" 'knownDrug',\n"", + "" 'molecularFormula',\n"", + "" 'molecularWeight',\n"", + "" 'numRo5Violations',\n"", + "" 'passesRuleOfThree',\n"", + "" 'preferredCompoundName',\n"", + "" 'rotatableBonds',\n"", + "" 'smiles',\n"", + "" 'species',\n"", + "" 'stdInChiKey',\n"", + "" 'synonyms',\n"", + "" 'activity_comment',\n"", + "" 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DxWTzpcJg7HFuPERERERHZtW7dOlRWVg64//nnn0/rPH6/H++9917cx1paWnD48OGMxpcN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\nA8TUlBVJE5PLAeJyzv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sklearn.preprocessing import StandardScaler\n"", + "" from matplotlib import pyplot as plt\n"", + "" data_pca = data\n"", + "" \n"", + "" # Apply Scaling \n"", + "" X = data_pca.drop('values', axis=1).as_matrix().astype(np.float)\n"", + "" y = data_pca['values'].values\n"", + "" \n"", + "" # Formatting\n"", + "" target_names = [\"">\"",\""<\"",\""Training\""]\n"", + "" colors = ['red','blue',\""green\""]\n"", + "" lw = 2\n"", + "" alpha = 0.3\n"", + ""\n"", + "" # 2 Components PCA\n"", + "" plt.style.use('seaborn-whitegrid')\n"", + "" plt.figure(2, figsize=(20, 8))\n"", + ""\n"", + "" plt.subplot(1, 2, 1)\n"", + "" pca = PCA(n_components=2)\n"", + "" X_std = StandardScaler().fit_transform(X)\n"", + "" X_r = pca.fit_transform(X_std)\n"", + "" #X_r = pca.fit(X).transform(X)\n"", + "" \n"", + "" for color, i, target_name in zip(colors, [\"">\"",\""<\"",\""Training\""], target_names):\n"", + "" plt.scatter(X_r[y == i, 0], X_r[y == i, 1], \n"", + "" color=color, \n"", + "" alpha=alpha, \n"", + "" lw=lw,\n"", + "" label=target_name)\n"", + "" plt.xlabel(\""PC1\"", fontsize=12)\n"", + "" plt.ylabel(\""PC2\"", fontsize=12)\n"", + "" plt.legend(loc='best', shadow=False, scatterpoints=1)\n"", + "" plt.title('First two PCA directions');\n"", + ""\n"", + "" # 3 Components PCA\n"", + "" ax = plt.subplot(1, 2, 2, projection='3d')\n"", + ""\n"", + "" pca = PCA(n_components=3)\n"", + "" X_reduced = pca.fit_transform(X_std)\n"", + "" for color, i, target_name in zip(colors, [\"">\"",\""<\"",\""Training\""], target_names):\n"", + "" ax.scatter(X_reduced[y == i, 0], X_reduced[y == i, 1], X_reduced[y == i, 2], \n"", + "" color=color,\n"", + "" alpha=alpha,\n"", + "" lw=lw, \n"", + "" label=target_name)\n"", + "" plt.legend(loc='best', shadow=False, scatterpoints=1)\n"", + "" ax.set_title(\""First three PCA directions\"")\n"", + "" ax.set_xlabel(\""PC1\"", fontsize=12)\n"", + "" ax.set_ylabel(\""PC2\"", fontsize=12)\n"", + "" ax.set_zlabel(\""PC3\"", fontsize=12)\n"", + ""\n"", + "" # rotate the axes\n"", + "" ax.view_init(30, 10)\n"", + "" plt.savefig('Result/PCA.pdf', dpi=300)"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 38, + ""metadata"": {}, + ""outputs"": [ + { + ""data"": { + ""image/png"": 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ERUYydxPDZZ33aSSwqk6nnsTo7M3NcIiIiIqI0yQM9ACIiogEXYycxlJT0aScxIiIiIqLB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XDnkOA6u6zI2Njb0ub52u41t26TT6dDn9XXzJrGf/Ww9HHJdtBs30F57DbtYoJ2/gY1DyrLBgXTr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\n7KUg6vVQSuG67p4MFvd9H9u27xv5cjtl0mZY3+S2Xo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s79m8mlP7wD/8Q13VZXV1dywdS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O42NjQDceOONfPjDH56VdQkhTh8SDom5Z6wWnOZm+L//U2HFsmVw/vnqgrijY9yX8Tl9+Fw+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\nJkK38cIwr1fN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0)\n"", + ""df9 = pd.read_csv('QSAR/ER_alpha_KlekotaRothFingerprinter.csv' , header = 0)\n"", + ""df10 = pd.read_csv('QSAR/ER_alpha_KlekotaRothFingerprintCount.csv' , header = 0)\n"", + ""df11 = pd.read_csv('QSAR/ER_alpha_AtomPairs2DFingerprinter.csv' , header = 0)\n"", + ""df12 = pd.read_csv('QSAR/ER_alpha_AtomPairs2DFingerprintCount.csv' , header = 0)"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 2, + ""metadata"": {}, + ""outputs"": [ + { + ""data"": { + ""text/html"": [ + ""
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"" + ], + ""text/plain"": [ + "" chemblId pIC50 FP1 FP2 FP3 FP4 FP5 FP6 FP7 FP8 ... \\\n"", + ""0 CHEMBL370037 8.221849 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 ... \n"", + ""1 CHEMBL189073 5.762708 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... \n"", + ""\n"", + "" FP1015 FP1016 FP1017 FP1018 FP1019 FP1020 FP1021 FP1022 FP1023 \\\n"", + ""0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 \n"", + ""1 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 \n"", + ""\n"", + "" FP1024 \n"", + ""0 0.0 \n"", + ""1 1.0 \n"", + ""\n"", + ""[2 rows x 1026 columns]"" + ] + }, + ""execution_count"": 2, + ""metadata"": {}, + ""output_type"": ""execute_result"" + } + ], + ""source"": [ + ""df1.head(2)"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 3, + ""metadata"": { + ""collapsed"": true + }, + ""outputs"": [], + ""source"": [ + ""#select feature\n"", + ""\n"", + ""df1 = df1 [['chemblId', 'pIC50'\n"", + "" ,'FP135', 'FP97', 'FP933', 'FP639', 'FP75', 'FP28', 'FP239', 'FP571', 'FP404', 'FP373', 'FP137', 'FP243', 'FP362', 'FP676', 'FP343', 'FP133', 'FP237', 'FP78', 'FP206', 'FP385']]\n"", + ""df2 = df2 [['chemblId', 'pIC50'\n"", + "" ,'ExtFP286', 'ExtFP914', 'ExtFP315', 'ExtFP767', 'ExtFP338', 'ExtFP343', 'ExtFP106', 'ExtFP111', 'ExtFP726', 'ExtFP711', 'ExtFP868', 'ExtFP811', 'ExtFP783', 'ExtFP423', 'ExtFP212', 'ExtFP718', 'ExtFP840', 'ExtFP467', 'ExtFP31', 'ExtFP560']]\n"", + ""df3 = df3 [['chemblId', 'pIC50'\n"", + "" ,'EStateFP34', 'EStateFP30', 'EStateFP29', 'EStateFP35', 'EStateFP36', 'EStateFP37', 'EStateFP24', 'EStateFP50', 'EStateFP13', 'EStateFP16', 'EStateFP18', 'EStateFP51', 'EStateFP9', 'EStateFP32', 'EStateFP38', 'EStateFP7', 'EStateFP25', 'EStateFP19', 'EStateFP11', 'EStateFP54']]\n"", + ""df4 = df4 [['chemblId', 'pIC50'\n"", + "" ,'GraphFP60', 'GraphFP382', 'GraphFP83', 'GraphFP21', 'GraphFP342', 'GraphFP228', 'GraphFP101', 'GraphFP140', 'GraphFP16', 'GraphFP224', 'GraphFP394', 'GraphFP163', 'GraphFP329', 'GraphFP72', 'GraphFP3', 'GraphFP499', 'GraphFP59', 'GraphFP270', 'GraphFP462', 'GraphFP89']]\n"", + ""df5 = df5 [['chemblId', 'pIC50'\n"", + "" ,'MACCSFP86', 'MACCSFP139', 'MACCSFP92', 'MACCSFP97', 'MACCSFP77', 'MACCSFP36', 'MACCSFP131', 'MACCSFP75', 'MACCSFP154', 'MACCSFP118', 'MACCSFP113', 'MACCSFP109', 'MACCSFP80', 'MACCSFP133', 'MACCSFP95', 'MACCSFP136', 'MACCSFP62', 'MACCSFP98', 'MACCSFP127', 'MACCSFP72']]\n"", + ""df6 = df6 [['chemblId', 'pIC50'\n"", + "" ,'PubchemFP193', 'PubchemFP308', 'PubchemFP375', 'PubchemFP439', 'PubchemFP696', 'PubchemFP391', 'PubchemFP420', 'PubchemFP534', 'PubchemFP199', 'PubchemFP493', 'PubchemFP345', 'PubchemFP186', 'PubchemFP566', 'PubchemFP580', 'PubchemFP258', 'PubchemFP405', 'PubchemFP346', 'PubchemFP681', 'PubchemFP33', 'PubchemFP757']]\n"", + ""df7 = df7 [['chemblId', 'pIC50'\n"", + "" ,'SubFP23', 'SubFP169', 'SubFP88', 'SubFP18', 'SubFP181', 'SubFP137', 'SubFP20', 'SubFP2', 'SubFP184', 'SubFP5', 'SubFP183', 'SubFP1', 'SubFP135', 'SubFP182', 'SubFP172', 'SubFP49', 'SubFP3', 'SubFP275', 'SubFP100', 'SubFP33']]\n"", + ""df8 = df8 [['chemblId', 'pIC50'\n"", + "" ,'SubFPC169', 'SubFPC23', 'SubFPC274', 'SubFPC302', 'SubFPC88', 'SubFPC181', 'SubFPC2', 'SubFPC18', 'SubFPC287', 'SubFPC137', 'SubFPC1', 'SubFPC20', 'SubFPC5', 'SubFPC172', 'SubFPC135', 'SubFPC182', 'SubFPC100', 'SubFPC3', 'SubFPC183', 'SubFPC33']]\n"", + ""df9 = df9 [['chemblId', 'pIC50'\n"", + "" ,'KRFP1148', 'KRFP1642', 'KRFP1193', 'KRFP3560', 'KRFP433', 'KRFP4736', 'KRFP341', 'KRFP1645', 'KRFP2981', 'KRFP677', 'KRFP45', 'KRFP2242', 'KRFP2856', 'KRFP2', 'KRFP4820', 'KRFP2979', 'KRFP3788', 'KRFP2855', 'KRFP344', 'KRFP3681']]\n"", + ""df10 = df10[['chemblId', 'pIC50'\n"", + "" ,'KRFPC1148', 'KRFPC1642', 'KRFPC297', 'KRFPC2711', 'KRFPC1193', 'KRFPC45', 'KRFPC3560', 'KRFPC444', 'KRFPC1645', 'KRFPC433', 'KRFPC1', 'KRFPC2981', 'KRFPC2262', 'KRFPC677', 'KRFPC4331', 'KRFPC2547', 'KRFPC669', 'KRFPC2856', 'KRFPC2979', 'KRFPC2438']]\n"", + ""df11 = df11[['chemblId', 'pIC50'\n"", + "" ,'AD2D92', 'AD2D726', 'AD2D248', 'AD2D170', 'AD2D91', 'AD2D638', 'AD2D180', 'AD2D415', 'AD2D102', 'AD2D482', 'AD2D717', 'AD2D414', 'AD2D492', 'AD2D258', 'AD2D326', 'AD2D703', 'AD2D648', 'AD2D93', 'AD2D336', 'AD2D247']]\n"", + ""df12 = df12[['chemblId', 'pIC50'\n"", + "" ,'APC2D1_C_C', 'APC2D2_N_O', 'APC2D10_O_O', 'APC2D1_C_N', 'APC2D1_C_O', 'APC2D4_N_O', 'APC2D2_N_N', 'APC2D3_N_O', 'APC2D9_N_O', 'APC2D7_O_O', 'APC2D6_O_S', 'APC2D3_O_O', 'APC2D5_N_O', 'APC2D7_N_O', 'APC2D5_O_O', 'APC2D10_N_S', 'APC2D6_O_O', 'APC2D1_C_S', 'APC2D4_O_O', 'APC2D2_N_S']]"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 4, + ""metadata"": {}, + ""outputs"": [ + { + ""data"": { + ""text/html"": [ + ""
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chemblIdpIC50FP135FP97FP933FP639FP75FP28FP239FP571...FP137FP243FP362FP676FP343FP133FP237FP78FP206FP385
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"" + ], + ""text/plain"": [ + "" chemblId pIC50 FP135 FP97 FP933 FP639 FP75 FP28 FP239 \\\n"", + ""0 CHEMBL370037 8.221849 1.0 1.0 1.0 0.0 0.0 0.0 1.0 \n"", + ""1 CHEMBL189073 5.762708 1.0 1.0 1.0 0.0 0.0 0.0 1.0 \n"", + ""\n"", + "" FP571 ... FP137 FP243 FP362 FP676 FP343 FP133 FP237 FP78 FP206 \\\n"", + ""0 0.0 ... 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 1.0 \n"", + ""1 0.0 ... 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 1.0 \n"", + ""\n"", + "" FP385 \n"", + ""0 1.0 \n"", + ""1 1.0 \n"", + ""\n"", + ""[2 rows x 22 columns]"" + ] + }, + ""execution_count"": 4, + ""metadata"": {}, + ""output_type"": ""execute_result"" + } + ], + ""source"": [ + ""df1.head(2)"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 5, + ""metadata"": { + ""collapsed"": true + }, + ""outputs"": [], + ""source"": [ + ""df1 .to_csv('QSAR_select/Fingerprinter.csv' , sep=',' ,index=False)\n"", + ""df2 .to_csv('QSAR_select/ExtendedFingerprinter.csv' , sep=',' ,index=False)\n"", + ""df3 .to_csv('QSAR_select/EStateFingerprinter.csv' , sep=',' ,index=False)\n"", + ""df4 .to_csv('QSAR_select/GraphOnlyFingerprinter.csv' , sep=',' ,index=False)\n"", + ""df5 .to_csv('QSAR_select/MACCSFingerprinter.csv' , sep=',' ,index=False)\n"", + ""df6 .to_csv('QSAR_select/PubchemFingerprinter.csv' , sep=',' ,index=False)\n"", + ""df7 .to_csv('QSAR_select/SubstructureFingerprinter.csv' , sep=',' ,index=False)\n"", + ""df8 .to_csv('QSAR_select/SubstructureFingerprintCount.csv', sep=',' ,index=False)\n"", + ""df9 .to_csv('QSAR_select/KlekotaRothFingerprinter.csv' , sep=',' ,index=False)\n"", + ""df10.to_csv('QSAR_select/KlekotaRothFingerprintCount.csv' , sep=',' ,index=False)\n"", + ""df11.to_csv('QSAR_select/AtomPairs2DFingerprinter.csv' , sep=',' ,index=False)\n"", + ""df12.to_csv('QSAR_select/AtomPairs2DFingerprintCount.csv' , sep=',' ,index=False)"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 6, + ""metadata"": {}, + ""outputs"": [ + { + ""data"": { + ""text/plain"": [ + ""(1231, 1231, 1231, 1231, 1231, 1231, 1231, 1231, 1231, 1231, 1231, 1231)"" + ] + }, + ""execution_count"": 6, + ""metadata"": {}, + ""output_type"": ""execute_result"" + } + ], + ""source"": [ + ""len(df1 ),len(df2 ),len(df3 ),len(df4 ),len(df5 )\\\n"", + "",len(df6 ),len(df7 ),len(df8 ),len(df9 ),len(df10)\\\n"", + "",len(df11),len(df12)"" + ] + }, + { + ""cell_type"": ""markdown"", + ""metadata"": {}, + ""source"": [ + ""# Build function"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 7, + ""metadata"": {}, + ""outputs"": [ + { + ""name"": ""stderr"", + ""output_type"": ""stream"", + ""text"": [ + ""/Users/zeromtmu/anaconda2/envs/ER_alpha/lib/python2.7/site-packages/sklearn/cross_validation.py:44: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.\n"", + "" \""This module will be removed in 0.20.\"", DeprecationWarning)\n"" + ] + } + ], + ""source"": [ + ""from sklearn.cross_validation import train_test_split\n"", + ""from sklearn.ensemble import RandomForestRegressor\n"", + ""from sklearn.ensemble import RandomForestClassifier\n"", + ""from sklearn.cross_validation import StratifiedShuffleSplit\n"", + ""from sklearn.metrics import mean_squared_error\n"", + ""from sklearn.metrics import mean_absolute_error\n"", + ""from sklearn.metrics import r2_score\n"", + ""from sklearn import cross_validation\n"", + ""from sklearn.metrics import roc_auc_score\n"", + ""from collections import defaultdict\n"", + ""import os.path\n"", + ""import shutil\n"", + ""import pickle\n"", + ""\n"", + ""def build_model(X, Y, seed, hx, f):\n"", + "" \n"", + "" \n"", + "" #Data split using 70/30 ratio\n"", + "" X_internal, X_external, Y_internal, Y_external = train_test_split(X,Y,\n"", + "" test_size=0.3,\n"", + "" random_state=seed)\n"", + ""\n"", + "" # Training set\n"", + "" rf = RandomForestRegressor(n_estimators=300, \n"", + "" max_features='sqrt', \n"", + "" min_samples_leaf = 1,\n"", + "" random_state = 13,\n"", + "" n_jobs=-1)\n"", + "" \n"", + "" rf.fit(X_internal,Y_internal)\n"", + "" prediction = rf.predict(X_internal)\n"", + "" \n"", + "" # save the model to disk\n"", + "" filename = f + '_model' + str(seed) + '.sav'\n"", + "" pickle.dump(rf, open(filename, 'wb'))\n"", + "" \n"", + "" # Cross-validation\n"", + "" cv = cross_validation.cross_val_predict(rf, X_internal, Y_internal, cv=10, n_jobs=-1)\n"", + "" \n"", + "" # External set \n"", + "" prediction_external = rf.predict(X_external)\n"", + "" \n"", + "" #print result from each seed \n"", + "" R2_train.append(r2_score(Y_internal, prediction))\n"", + "" RMSE_train.append((mean_squared_error(Y_internal, prediction)))\n"", + "" Q2_CV.append(r2_score(Y_internal, cv))\n"", + "" RMSE_CV.append((mean_squared_error(Y_internal, cv)))\n"", + "" Q2_External.append(r2_score(Y_external, prediction_external))\n"", + "" RMSE_External.append((mean_squared_error(Y_external, prediction_external)))\n"", + "" \n"", + "" #Feature Importance\n"", + "" Feature = hx[:]\n"", + "" feature_importance = rf.feature_importances_\n"", + "" importances = 100.0 * (feature_importance / feature_importance.max()) #index\n"", + "" \n"", + "" for i, fx in enumerate(Feature):\n"", + "" importances_dict[fx].append(importances[i])\n"", + "" \n"", + "" return R2_train, RMSE_train, Q2_CV, RMSE_CV, Q2_External, RMSE_External, Feature, \\\n"", + "" X_internal, X_external, Y_internal, Y_external, rf, prediction, importances_dict"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 8, + ""metadata"": { + ""collapsed"": true + }, + ""outputs"": [], + ""source"": [ + ""from copy import deepcopy\n"", + ""\n"", + ""def Y_scrambling(X_internal, X_external, Y_internal, Y_external):\n"", + "" # Do the Y-scrambling. Loop over the actual learning for 100 times.\n"", + "" for randomseedcounter in range(1,101):\n"", + "" y_train_scrambled = deepcopy(Y_internal)\n"", + "" X_train_scrambled = deepcopy(X_internal)\n"", + "" np.random.shuffle(y_train_scrambled)\n"", + "" np.random.shuffle(X_train_scrambled)\n"", + ""\n"", + "" # training was done on \""scrambled\"" data - prediction on test set\n"", + "" RF_scrambled = RandomForestRegressor()\n"", + "" RF_scrambled = RF_scrambled.fit(X_internal,y_train_scrambled)\n"", + "" y_predict_scrambled = RF_scrambled.predict(X_external)\n"", + "" \n"", + "" acclist_predictionOnTest_scrambledtrain.append((RF_scrambled.score(X_external,Y_external))**2)\n"", + "" \n"", + "" # training was done on \""scrambled\"" data - prediction on train set\n"", + "" y_predict_scrambled_predictTrain = RF_scrambled.predict(X_internal)\n"", + "" \n"", + "" acclist_predictionOnTrain_scrambledtrain.append((RF_scrambled.score(X_internal,Y_internal))**2)\n"", + "" \n"", + "" r2 = pd.DataFrame(acclist_predictionOnTrain_scrambledtrain, columns=['R2'])\n"", + "" q2 = pd.DataFrame(acclist_predictionOnTest_scrambledtrain, columns=['Q2']) \n"", + "" \n"", + "" return acclist_predictionOnTest_scrambledtrain,acclist_predictionOnTrain_scrambledtrain"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 9, + ""metadata"": { + ""collapsed"": true + }, + ""outputs"": [], + ""source"": [ + ""def mean(R2_train, RMSE_train, Q2_CV, RMSE_CV, Q2_External, RMSE_External, importances_dict):\n"", + "" R2_train_mean = np.mean(R2_train)\n"", + "" RMSE_train_mean = np.mean(RMSE_train)\n"", + "" Q2_CV_mean = np.mean(Q2_CV)\n"", + "" RMSE_CV_mean = np.mean(RMSE_CV)\n"", + "" Q2_External_mean = np.mean(Q2_External)\n"", + "" RMSE_External_mean = np.mean(RMSE_External)\n"", + "" importances_mean0 = {}\n"", + "" for fx in importances_dict:\n"", + "" importances_mean0[fx] = np.mean(importances_dict[fx])\n"", + "" importances_mean = sorted([(k,v) for k,v in importances_mean0.iteritems()],\n"", + "" key=lambda x: x[1], reverse=True)\n"", + "" \n"", + "" return R2_train_mean, RMSE_train_mean, Q2_CV_mean, RMSE_CV_mean, Q2_External_mean, \\\n"", + "" RMSE_External_mean, importances_mean"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 10, + ""metadata"": { + ""collapsed"": true + }, + ""outputs"": [], + ""source"": [ + ""def std(R2_train, RMSE_train, Q2_CV, RMSE_CV, Q2_External, RMSE_External, importances_dict):\n"", + "" R2_train_std = np.std(R2_train)\n"", + "" RMSE_train_std = np.std(RMSE_train)\n"", + "" Q2_CV_std = np.std(Q2_CV)\n"", + "" RMSE_CV_std = np.std(RMSE_CV)\n"", + "" Q2_External_std = np.std(Q2_External)\n"", + "" RMSE_External_std = np.std(RMSE_External)\n"", + "" importances_std0 = {}\n"", + "" for fx in importances_dict:\n"", + "" importances_std0[fx] = np.std(importances_dict[fx])\n"", + "" importances_std = sorted([(k,v) for k,v in importances_std0.iteritems()],\n"", + "" key=lambda x: x[1], reverse=True)\n"", + "" \n"", + "" return R2_train_std, RMSE_train_std, Q2_CV_std, RMSE_CV_std, Q2_External_std,\\\n"", + "" RMSE_External_std, importances_std"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 11, + ""metadata"": { + ""collapsed"": true + }, + ""outputs"": [], + ""source"": [ + ""def print_output(R2_train_mean, RMSE_train_mean, Q2_CV_mean, RMSE_CV_mean, Q2_External_mean, \n"", + "" RMSE_External_mean, R2_train_std, RMSE_train_std, Q2_CV_std, RMSE_CV_std, Q2_External_std, \n"", + "" RMSE_External_std, X_internal, X_external, X, f ):\n"", + "" #outfile = open('all_output.csv', 'a')\n"", + "" print >> outfile, '%s,%d,%d,%0.4f,%0.4f,%0.4f,%0.4f,%d,%0.4f,%0.4f,%0.4f,%0.4f,%d,%0.4f,%0.4f,%0.4f,%0.4f' \\\n"", + "" % (f, len(X_internal),\n"", + "" len(X[0]),\n"", + "" R2_train_mean, R2_train_std,\n"", + "" RMSE_train_mean, RMSE_train_std,\n"", + "" len(X_internal),\n"", + "" Q2_CV_mean, Q2_CV_std,\n"", + "" RMSE_CV_mean, RMSE_CV_std,\n"", + "" len(X_external),\n"", + "" Q2_External_mean, Q2_External_std,\n"", + "" RMSE_External_mean, RMSE_External_std,\n"", + "" ) \n"", + "" \n"", + "" \n"", + "" \n"", + "" print '\\nTraining set\\n------------'\n"", + "" print 'N: ' + (str(len(X_internal)))\n"", + "" print 'R2: %0.4f'%(R2_train_mean)\n"", + "" print 'std_R2: %0.4f'%(R2_train_std)\n"", + "" print 'RMSE: %0.4f'%(RMSE_train_mean)\n"", + "" print 'std_RMSE: %0.4f'%(RMSE_train_std)\n"", + ""\n"", + "" print '\\nCross-validation set\\n------------'\n"", + "" print 'N: ' + (str(len(X_internal)))\n"", + "" print 'Q2: %0.4f'%(Q2_CV_mean)\n"", + "" print 'std_Q2: %0.4f'%(Q2_CV_std)\n"", + "" print 'RMSE: %0.4f'%(RMSE_CV_mean)\n"", + "" print 'std_RMSE: %0.4f'%(RMSE_CV_std)\n"", + ""\n"", + "" print '\\nExternal set\\n------------'\n"", + "" print 'N: ' + (str(len(X_external)))\n"", + "" print 'Q2_EXt: %0.4f'%(Q2_External_mean)\n"", + "" print 'std_Q2_EXt: %0.4f'%(Q2_External_std)\n"", + "" print 'RMSE: %0.4f'%(RMSE_External_mean)\n"", + "" print 'std_RMSE: %0.4f'%(RMSE_External_std)"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 12, + ""metadata"": {}, + ""outputs"": [ + { + ""name"": ""stderr"", + ""output_type"": ""stream"", + ""text"": [ + ""/Users/zeromtmu/anaconda2/envs/ER_alpha/lib/python2.7/site-packages/statsmodels/compat/pandas.py:56: FutureWarning: The pandas.core.datetools module is deprecated and will be removed in a future version. Please use the pandas.tseries module instead.\n"", + "" from pandas.core import datetools\n"" + ] + } + ], + ""source"": [ + ""import matplotlib.pyplot as plt\n"", + ""from matplotlib.ticker import MultipleLocator\n"", + ""import pylab as py \n"", + ""import statsmodels.api as sm\n"", + ""from statsmodels.stats.outliers_influence import summary_table\n"", + ""\n"", + ""def plot_model (f, X_internal, X_external, Y_internal, Y_external,\n"", + "" R2_train_mean, Q2_External_mean,\n"", + "" importances_mean, importances_std, Feature, prediction, \n"", + "" acclist_predictionOnTest_scrambledtrain, acclist_predictionOnTrain_scrambledtrain):\n"", + "" \n"", + "" # Prepare plot\n"", + "" m = rf.fit(X_internal,Y_internal)\n"", + "" cm = plt.cm.RdBu\n"", + "" cv = cross_validation.cross_val_predict(rf, X_internal, Y_internal, cv=10, n_jobs=-1)\n"", + ""\n"", + "" fig_size = plt.rcParams[\""figure.figsize\""]\n"", + "" fig_size[0]=5\n"", + "" fig_size[1]=5\n"", + ""\n"", + "" # Train Set\n"", + "" x_train = np.array(Y_internal)\n"", + "" y_train = m.predict(X_internal).flatten()\n"", + "" py.scatter(x_train, y_train, s=50, marker='.', alpha=0.3,\n"", + "" c='g', cmap=cm ,edgecolors='g')\n"", + "" #label=r\""$R^{2}_{Tr}$ = %.4f\"" % R2_train_mean)\n"", + "" \n"", + "" \n"", + "" # CV Set\n"", + "" np.array(cv)\n"", + "" x_test = np.array(Y_internal)\n"", + "" y_test = cv\n"", + "" py.scatter(x_test, y_test, s=50, marker='.', alpha=0.3,\n"", + "" c='b', cmap=cm ,edgecolors='b') \n"", + "" #label=r\""$Q^{2}_{Ext}$ = %.4f\"" % Q2_External_mean)\n"", + "" #2SD line\n"", + "" X = sm.add_constant(x_test)\n"", + "" res = sm.OLS(y_test, X).fit()\n"", + ""\n"", + "" st, data, ss2 = summary_table(res, alpha=0.05)\n"", + "" fittedvalues = data[:,2]\n"", + "" #predict_mean_se  = data[:,3]\n"", + "" predict_mean_ci_low, predict_mean_ci_upp = data[:,4:6].T\n"", + "" predict_ci_low, predict_ci_upp = data[:,6:8].T\n"", + "" #2SD line\n"", + "" plt.plot(X, predict_ci_low, '--b', linewidth=0.5, alpha=0.5)\n"", + "" plt.plot(X, predict_ci_upp, '--b', linewidth=0.5, alpha=0.5)\n"", + ""\n"", + "" \n"", + "" # External Set\n"", + "" x_test = np.array(Y_external)\n"", + "" y_test = m.predict(X_external).flatten()\n"", + "" py.scatter(x_test, y_test, s=50, marker='.', alpha=0.3,\n"", + "" c='r', cmap=cm ,edgecolors='r') \n"", + "" \n"", + "" plt.legend(loc=2,prop={'size':6})\n"", + "" plt.xlabel(\""Experimental $pIC_{50}$ values\"", fontsize=10)\n"", + "" plt.ylabel(\""Predicted $pIC_{50}$ values\"", fontsize=10)\n"", + "" \n"", + "" min_axis = np.min(np.concatenate([Y_internal, prediction], axis=0))\n"", + "" max_axis = np.max(np.concatenate([Y_internal, prediction], axis=0))\n"", + "" plt.xlim([(min_axis*0.9),(max_axis*1.05)])\n"", + "" plt.ylim([(min_axis*0.9),(max_axis*1.05)])\n"", + "" plt.tick_params(axis='both', which='major', labelsize=14)\n"", + ""\n"", + "" \n"", + "" # Save plot to file\n"", + "" plt.savefig(f+'_compair_Fp.pdf', dpi=300)\n"", + "" plt.show()\n"", + "" \n"", + "" # Y-scrambling plot\n"", + "" py.scatter(Q2_CV_mean, R2_train_mean, s=100, marker='.', alpha=0.3,\n"", + "" c='b', cmap=cm ,edgecolors='b') \n"", + "" \n"", + "" py.scatter(acclist_predictionOnTest_scrambledtrain,acclist_predictionOnTrain_scrambledtrain, \\\n"", + "" s=100, marker='.', alpha=0.3, c='r', cmap=cm ,edgecolors='r')\n"", + ""\n"", + "" plt.legend(loc=2,prop={'size':6})\n"", + "" plt.xlabel(\""$Q^{2}$\"", fontsize=10)\n"", + "" plt.ylabel(\""$R^{2}$\"", fontsize=10)\n"", + "" plt.tick_params(axis='both', which='major', labelsize=14)\n"", + "" \n"", + "" plt.ylim([0, 1])\n"", + "" plt.xlim([0, 1])\n"", + "" plt.axhline(0.5, color='gray',linestyle='--', dashes=(5, 10), linewidth=0.5)\n"", + "" plt.axvline(0.5, color='gray',linestyle='--', dashes=(5, 10), linewidth=0.5)\n"", + "" \n"", + "" plt.savefig(f+'_Y-scrambling.pdf', dpi=300)\n"", + "" plt.show()\n"", + ""\n"", + "" #Feature Importance \n"", + "" fig_size = plt.rcParams[\""figure.figsize\""]\n"", + "" fig_size[0]=5\n"", + "" fig_size[1]=10\n"", + ""\n"", + "" barlist = plt.barh(range(20), [x[1] for x in importances_mean[:20]], \n"", + "" color=\""g\"", align=\""center\"") # , xerr=[x[1] for x in importances_std[:20]], error_kw=dict(ecolor='gray', lw=2, capsize=5, capthick=2)\n"", + ""\n"", + "" \n"", + "" plt.yticks(range(20), [x[0] for x in importances_mean[:20]])\n"", + "" plt.ylim([-1, 20])\n"", + "" \n"", + "" plt.xlabel(r\""$\\bf{Gini}$\"" + \"" \""+ r\""$\\bf{index}$\"", fontsize=12)\n"", + "" ax = plt.gca()\n"", + "" ax.invert_yaxis()\n"", + "" plt.tight_layout(pad=2.0, w_pad=0.7, h_pad=2.0)\n"", + "" plt.savefig(f+'_Feature_importances.pdf', dpi=300)\n"", + "" plt.show()"" + ] + }, + { + ""cell_type"": ""markdown"", + ""metadata"": {}, + ""source"": [ + ""# Constructing the QSAR model\n"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": 13, + ""metadata"": { + ""scrolled"": true + }, + ""outputs"": [ + { + ""name"": ""stdout"", + ""output_type"": ""stream"", + ""text"": [ + ""running....QSAR_select/AtomPairs2DFingerprintCount.csv\n"" + ] + }, + { + ""name"": ""stderr"", + ""output_type"": ""stream"", + ""text"": [ + ""/Users/zeromtmu/anaconda2/envs/ER_alpha/lib/python2.7/site-packages/ipykernel_launcher.py:25: DeprecationWarning: \n"", + "".ix is deprecated. Please use\n"", + "".loc for label based indexing or\n"", + "".iloc for positional indexing\n"", + ""\n"", + ""See the documentation here:\n"", + ""http://pandas.pydata.org/pandas-docs/stable/indexing.html#deprecate_ix\n"" + ] + }, + { + ""name"": ""stdout"", + ""output_type"": ""stream"", + ""text"": [ + ""\n"", + ""Training set\n"", + ""------------\n"", + ""N: 861\n"", + ""R2: 0.9158\n"", + ""std_R2: 0.0000\n"", + ""RMSE: 0.1599\n"", + ""std_RMSE: 0.0000\n"", + ""\n"", + ""Cross-validation set\n"", + ""------------\n"", + ""N: 861\n"", + ""Q2: 0.7001\n"", + ""std_Q2: 0.0000\n"", + ""RMSE: 0.5696\n"", + ""std_RMSE: 0.0000\n"", + ""\n"", + ""External set\n"", + ""------------\n"", + ""N: 370\n"", + ""Q2_EXt: 0.7219\n"", + ""std_Q2_EXt: 0.0000\n"", + ""RMSE: 0.5326\n"", + ""std_RMSE: 0.0000\n"", + ""running....QSAR_select/SubstructureFingerprintCount.csv\n"", + ""\n"", + ""Training set\n"", + ""------------\n"", + ""N: 861\n"", + ""R2: 0.9340\n"", + ""std_R2: 0.0000\n"", + ""RMSE: 0.1254\n"", + ""std_RMSE: 0.0000\n"", + ""\n"", + ""Cross-validation set\n"", + ""------------\n"", + ""N: 861\n"", + ""Q2: 0.7011\n"", + ""std_Q2: 0.0000\n"", + ""RMSE: 0.5677\n"", + ""std_RMSE: 0.0000\n"", + ""\n"", + ""External set\n"", + ""------------\n"", + ""N: 370\n"", + ""Q2_EXt: 0.7374\n"", + ""std_Q2_EXt: 0.0000\n"", + ""RMSE: 0.5029\n"", + ""std_RMSE: 0.0000\n"", + ""running....QSAR_select/SubstructureFingerprinter.csv\n"", + ""\n"", + ""Training set\n"", + ""------------\n"", + ""N: 861\n"", + ""R2: 0.8501\n"", + ""std_R2: 0.0000\n"", + ""RMSE: 0.2846\n"", + ""std_RMSE: 0.0000\n"", + ""\n"", + ""Cross-validation set\n"", + ""------------\n"", + ""N: 861\n"", + ""Q2: 0.6585\n"", + ""std_Q2: 0.0000\n"", + ""RMSE: 0.6486\n"", + ""std_RMSE: 0.0000\n"", + ""\n"", + ""External set\n"", + ""------------\n"", + ""N: 370\n"", + ""Q2_EXt: 0.6853\n"", + ""std_Q2_EXt: 0.0000\n"", + ""RMSE: 0.6027\n"", + ""std_RMSE: 0.0000\n"", + ""running....QSAR_select/PubchemFingerprinter.csv\n"", + ""\n"", + ""Training set\n"", + ""------------\n"", + ""N: 861\n"", + ""R2: 0.8127\n"", + ""std_R2: 0.0000\n"", + ""RMSE: 0.3558\n"", + ""std_RMSE: 0.0000\n"", + ""\n"", + ""Cross-validation set\n"", + ""------------\n"", + ""N: 861\n"", + ""Q2: 0.6673\n"", + ""std_Q2: 0.0000\n"", + ""RMSE: 0.6318\n"", + ""std_RMSE: 0.0000\n"", + ""\n"", + ""External set\n"", + ""------------\n"", + ""N: 370\n"", + ""Q2_EXt: 0.7217\n"", + ""std_Q2_EXt: 0.0000\n"", + ""RMSE: 0.5329\n"", + ""std_RMSE: 0.0000\n"", + ""running....QSAR_select/GraphOnlyFingerprinter.csv\n"", + ""\n"", + ""Training set\n"", + ""------------\n"", + ""N: 861\n"", + ""R2: 0.7961\n"", + ""std_R2: 0.0000\n"", + ""RMSE: 0.3872\n"", + ""std_RMSE: 0.0000\n"", + ""\n"", + ""Cross-validation set\n"", + ""------------\n"", + ""N: 861\n"", + ""Q2: 0.6455\n"", + ""std_Q2: 0.0000\n"", + ""RMSE: 0.6733\n"", + ""std_RMSE: 0.0000\n"", + ""\n"", + ""External set\n"", + ""------------\n"", + ""N: 370\n"", + ""Q2_EXt: 0.6298\n"", + ""std_Q2_EXt: 0.0000\n"", + ""RMSE: 0.7091\n"", + ""std_RMSE: 0.0000\n"", + ""running....QSAR_select/KlekotaRothFingerprinter.csv\n"", + ""\n"", + ""Training set\n"", + ""------------\n"", + ""N: 861\n"", + ""R2: 0.8077\n"", + ""std_R2: 0.0000\n"", + ""RMSE: 0.3651\n"", + ""std_RMSE: 0.0000\n"", + ""\n"", + ""Cross-validation set\n"", + ""------------\n"", + ""N: 861\n"", + ""Q2: 0.6422\n"", + ""std_Q2: 0.0000\n"", + ""RMSE: 0.6796\n"", + ""std_RMSE: 0.0000\n"", + ""\n"", + ""External set\n"", + ""------------\n"", + ""N: 370\n"", + ""Q2_EXt: 0.6842\n"", + ""std_Q2_EXt: 0.0000\n"", + ""RMSE: 0.6049\n"", + ""std_RMSE: 0.0000\n"", + ""running....QSAR_select/ExtendedFingerprinter.csv\n"", + ""\n"", + ""Training set\n"", + ""------------\n"", + ""N: 861\n"", + ""R2: 0.8252\n"", + ""std_R2: 0.0000\n"", + ""RMSE: 0.3319\n"", + ""std_RMSE: 0.0000\n"", + ""\n"", + ""Cross-validation set\n"", + ""------------\n"", + ""N: 861\n"", + ""Q2: 0.6575\n"", + ""std_Q2: 0.0000\n"", + ""RMSE: 0.6505\n"", + ""std_RMSE: 0.0000\n"", + ""\n"", + ""External set\n"", + ""------------\n"", + ""N: 370\n"", + ""Q2_EXt: 0.6701\n"", + ""std_Q2_EXt: 0.0000\n"", + ""RMSE: 0.6318\n"", + ""std_RMSE: 0.0000\n"", + ""running....QSAR_select/AtomPairs2DFingerprinter.csv\n"", + ""\n"", + ""Training set\n"", + ""------------\n"", + ""N: 861\n"", + ""R2: 0.8398\n"", + ""std_R2: 0.0000\n"", + ""RMSE: 0.3042\n"", + ""std_RMSE: 0.0000\n"", + ""\n"", + ""Cross-validation set\n"", + ""------------\n"", + ""N: 861\n"", + ""Q2: 0.6539\n"", + ""std_Q2: 0.0000\n"", + ""RMSE: 0.6573\n"", + ""std_RMSE: 0.0000\n"", + ""\n"", + ""External set\n"", + ""------------\n"", + ""N: 370\n"", + ""Q2_EXt: 0.6735\n"", + ""std_Q2_EXt: 0.0000\n"", + ""RMSE: 0.6252\n"", + ""std_RMSE: 0.0000\n"", + ""running....QSAR_select/MACCSFingerprinter.csv\n"", + ""\n"", + ""Training set\n"", + ""------------\n"", + ""N: 861\n"", + ""R2: 0.8391\n"", + ""std_R2: 0.0000\n"", + ""RMSE: 0.3056\n"", + ""std_RMSE: 0.0000\n"", + ""\n"", + ""Cross-validation set\n"", + ""------------\n"", + ""N: 861\n"", + ""Q2: 0.7022\n"", + ""std_Q2: 0.0000\n"", + ""RMSE: 0.5655\n"", + ""std_RMSE: 0.0000\n"", + ""\n"", + ""External set\n"", + ""------------\n"", + ""N: 370\n"", + ""Q2_EXt: 0.7063\n"", + ""std_Q2_EXt: 0.0000\n"", + ""RMSE: 0.5624\n"", + ""std_RMSE: 0.0000\n"", + ""running....QSAR_select/KlekotaRothFingerprintCount.csv\n"", + ""\n"", + ""Training set\n"", + ""------------\n"", + ""N: 861\n"", + ""R2: 0.9017\n"", + ""std_R2: 0.0000\n"", + ""RMSE: 0.1866\n"", + ""std_RMSE: 0.0000\n"", + ""\n"", + ""Cross-validation set\n"", + ""------------\n"", + ""N: 861\n"", + ""Q2: 0.6780\n"", + ""std_Q2: 0.0000\n"", + ""RMSE: 0.6115\n"", + ""std_RMSE: 0.0000\n"", + ""\n"", + ""External set\n"", + ""------------\n"", + ""N: 370\n"", + ""Q2_EXt: 0.7125\n"", + ""std_Q2_EXt: 0.0000\n"", + ""RMSE: 0.5507\n"", + ""std_RMSE: 0.0000\n"", + ""running....QSAR_select/EStateFingerprinter.csv\n"", + ""\n"", + ""Training set\n"", + ""------------\n"", + ""N: 861\n"", + ""R2: 0.8500\n"", + ""std_R2: 0.0000\n"", + ""RMSE: 0.2849\n"", + ""std_RMSE: 0.0000\n"", + ""\n"", + ""Cross-validation set\n"", + ""------------\n"", + ""N: 861\n"", + ""Q2: 0.6460\n"", + ""std_Q2: 0.0000\n"", + ""RMSE: 0.6724\n"", + ""std_RMSE: 0.0000\n"", + ""\n"", + ""External set\n"", + ""------------\n"", + ""N: 370\n"", + ""Q2_EXt: 0.7138\n"", + ""std_Q2_EXt: 0.0000\n"", + ""RMSE: 0.5481\n"", + ""std_RMSE: 0.0000\n"", + ""running....QSAR_select/Fingerprinter.csv\n"", + ""\n"", + ""Training set\n"", + ""------------\n"", + ""N: 861\n"", + ""R2: 0.7924\n"", + ""std_R2: 0.0000\n"", + ""RMSE: 0.3943\n"", + ""std_RMSE: 0.0000\n"", + ""\n"", + ""Cross-validation set\n"", + ""------------\n"", + ""N: 861\n"", + ""Q2: 0.6281\n"", + ""std_Q2: 0.0000\n"", + ""RMSE: 0.7063\n"", + ""std_RMSE: 0.0000\n"", + ""\n"", + ""External set\n"", + ""------------\n"", + ""N: 370\n"", + ""Q2_EXt: 0.7001\n"", + ""std_Q2_EXt: 0.0000\n"", + ""RMSE: 0.5743\n"", + ""std_RMSE: 0.0000\n"" + ] + } + ], + ""source"": [ + ""import glob #to read multiple files \n"", + ""from rdkit import Chem, DataStructs\n"", + ""from rdkit.Chem import AllChem\n"", + ""\n"", + ""%config InlineBackend.figure_format = 'retina'\n"", + ""\n"", + ""\n"", + ""! rm Result/CHEMBL206_IC50_Revision2.csv\n"", + ""\n"", + ""outfile = open('Result/CHEMBL206_IC50_Revision2.csv', 'a')\n"", + ""\n"", + ""print >> outfile, 'Filename,N_train,Descriptors,R2_train,R2_train_std,' + \\\n"", + "" 'RMSE_train,RMSE_train_std,N_CV,Q2_CV,Q2_CV_std,RMSE_CV,' + \\\n"", + "" 'RMSE_CV_std,N_External,Q2_External,Q2_External_std,RMSE_External,RMSE_External_std'\n"", + ""\n"", + ""path = r'QSAR_select/'\n"", + ""\n"", + ""for f in glob.glob(path + '*.csv'):\n"", + "" print 'running....' + f\n"", + "" \n"", + "" df = pd.read_csv(f)\n"", + "" df = df.apply(lambda x: pd.to_numeric(x,errors='ignore'))\n"", + "" df = df.fillna(method='ffill')\n"", + "" \n"", + "" Y = df[\""pIC50\""].tolist()\n"", + "" \n"", + "" X = df.ix[:,2:]\n"", + "" hx = X.columns.tolist()\n"", + "" data = X.as_matrix().astype(np.float)\n"", + "" X = np.array(data) \n"", + "" \n"", + "" # Prepare empty lists to plot QSAR model\n"", + "" R2_train = []\n"", + "" RMSE_train = []\n"", + "" Q2_CV = []\n"", + "" RMSE_CV = []\n"", + "" Q2_External = []\n"", + "" RMSE_External = []\n"", + "" importances_dict = defaultdict(list)\n"", + "" \n"", + "" # Prepare empty lists to plot the performance of accuracy.\n"", + "" acclist_realRF = []\n"", + "" acclist_realRF_predictTrain = []\n"", + "" acclist_predictionOnTest_scrambledtrain = []\n"", + "" acclist_predictionOnTrain_scrambledtrain = []\n"", + "" \n"", + "" seed = 0\n"", + "" i = seed \n"", + "" R2_train, RMSE_train, Q2_CV, RMSE_CV, Q2_External, RMSE_External, Feature, \\\n"", + "" X_internal, X_external, Y_internal, Y_external, rf, prediction, importances_dict = build_model(X, Y, i, hx, f)\n"", + "" \n"", + "" acclist_predictionOnTest_scrambledtrain, acclist_predictionOnTrain_scrambledtrain = Y_scrambling( \\\n"", + "" X_internal, X_external, Y_internal, Y_external)\n"", + "" R2_train_mean, RMSE_train_mean, Q2_CV_mean, RMSE_CV_mean, Q2_External_mean, \\\n"", + "" RMSE_External_mean, importances_mean = mean(R2_train, RMSE_train, Q2_CV, RMSE_CV, \\\n"", + "" Q2_External, RMSE_External, importances_dict)\n"", + "" R2_train_std, RMSE_train_std, Q2_CV_std, RMSE_CV_std, Q2_External_std,\\\n"", + "" RMSE_External_std, importances_std = std(R2_train, RMSE_train, Q2_CV, RMSE_CV, \\\n"", + "" Q2_External, RMSE_External, importances_dict)\n"", + "" print_output(R2_train_mean, RMSE_train_mean, Q2_CV_mean, RMSE_CV_mean, Q2_External_mean, \n"", + "" RMSE_External_mean, R2_train_std, RMSE_train_std, Q2_CV_std, RMSE_CV_std, Q2_External_std, \n"", + "" RMSE_External_std, X_internal, X_external, X, f)\n"", + "" \n"", + ""outfile.close()\n"", + "" #END"" + ] + }, + { + ""cell_type"": ""code"", + ""execution_count"": null, + ""metadata"": { + ""collapsed"": true + }, + ""outputs"": [], + ""source"": [] + }, + { + ""cell_type"": ""code"", + ""execution_count"": null, + ""metadata"": { + ""collapsed"": true + }, + ""outputs"": [], + ""source"": [] + } + ], + ""metadata"": { + ""kernelspec"": { + ""display_name"": ""Python 2"", + ""language"": ""python"", + ""name"": ""python2"" + }, + ""language_info"": { + ""codemirror_mode"": { + ""name"": ""ipython"", + ""version"": 2 + }, + ""file_extension"": "".py"", + ""mimetype"": ""text/x-python"", + ""name"": ""python"", + ""nbconvert_exporter"": ""python"", + ""pygments_lexer"": ""ipython2"", + ""version"": ""2.7.13"" + } + }, + ""nbformat"": 4, + ""nbformat_minor"": 2 +} +","Unknown" +"Inhibition","CADD-SC/ADMET_Prediction_Models","predict.py",".py","2377","57","from sklearn.ensemble import RandomForestClassifier ###1. RandomForest +from sklearn import svm ###2. SVM +import xgboost as xgb ###3. XGB +from sklearn.ensemble import GradientBoostingClassifier ###4.GB +import time +import pandas as pd +import numpy as np +from numpy import array +import pickle +import os +from os import path +import sklearn +from sklearn.metrics import roc_auc_score, make_scorer, recall_score, accuracy_score, matthews_corrcoef, confusion_matrix +from sklearn.model_selection import StratifiedKFold, cross_validate +from hpsklearn import HyperoptEstimator, any_preprocessing, any_classifier +from hpsklearn import random_forest_classifier, gradient_boosting_classifier, svc, xgboost_classification, k_neighbors_classifier +from sklearn.svm import SVC +from hyperopt import tpe, hp +import argparse +from utils import data_preprocessing + +if __name__==""__main__"": + parser=argparse.ArgumentParser() + parser.add_argument('--file_name', type=str, default='example_train.csv', help='Path to the input file') + parser.add_argument('--model_name', type=str, default=None, help='User-defined model name') + args =parser.parse_args() + print(args) + features = eval(open(f'./features.txt', 'r').read()) + + print(f'Prediction model for {args.model_name}') + print(f'Loading the data set : {args.file_name}') + + # prprocessing data feature + df = pd.read_csv(args.file_name) + df['bioclass']=1 + scaled_data = data_preprocessing(df) + + result_df = pd.DataFrame({'SMILES':scaled_data['SMILES']}) + if path.exists(f'models/{args.model_name}.pkl'): + learner_model = pickle.load(open(f'models/{args.model_name}.pkl', 'rb')) + elif path.exists(f'{args.model_name}.pkl'): + learner_model = pickle.load(open(f'{args.model_name}.pkl','rb')) + else: + print('Check the model path (.pkl) !!') + exit(-1) + + # predict label (int) + predicted = learner_model.predict(scaled_data[features].values) + result_df[f'{args.model_name}']=learner_model.predict(scaled_data[features].values) + + # predict prediction prob (float) + predict_proba = learner_model.predict_proba(scaled_data[features].values) + max_proba = np.max(predict_proba, axis=1) + result_df['pred_prob']=max_proba + print(f'Output for prediction of {args.model_name} \n {result_df}') + result_df.to_csv(f'{args.model_name}_predict_results.csv', index=False) +","Python" +"Inhibition","CADD-SC/ADMET_Prediction_Models","data_split.py",".py","2711","58","import numpy as np +import pandas as pd +from rdkit import Chem +from rdkit.Chem import AllChem +from rdkit.ML.Descriptors import MoleculeDescriptors +from rdkit.Chem import Descriptors, Lipinski, rdMolDescriptors +import sklearn.metrics as metrics +from sklearn.preprocessing import StandardScaler +from sklearn.model_selection import train_test_split +import argparse +import pickle +import joblib +import os +from utils import data_preprocessing + +if __name__==""__main__"": + parser=argparse.ArgumentParser() + parser.add_argument('--file_name', type=str, default='smiles.csv', help='Path to the input file') + parser.add_argument('--split_mode', choices=['scaffold', 'stratify'], default='stratify', help='Seperate method of dataset for train/test set') + parser.add_argument('--test_frac', type=float, default=0.2, help='Test set fraction (default: 0.2)') + args =parser.parse_args() + + df = pd.read_csv(args.file_name) + # preprocess from smiles to features + print(f'Start data pre-processing for {args.file_name}') + df = data_preprocessing(df) + + if args.split_mode == 'stratify' : + print('stratified split') + from sklearn.model_selection import train_test_split + train_data, test_data = train_test_split( + df, + random_state=42, + test_size = args.test_frac, + stratify=df['bioclass'], + shuffle = True + ) + print(f'Size of train dataset: {len(train_data)} ') + print(f'Size of test dataset: {len(test_data)} ') + train_data.to_csv(f'train_{args.file_name}',index=False) + test_data.to_csv(f'test_{args.file_name}',index=False) + elif args.split_mode =='scaffold' : + print('scaffold-based split') + from dgllife.utils import ScaffoldSplitter + df = df.rename(columns={'SMILES': 'smiles'}) + train_set, val_set, test_set = ScaffoldSplitter.train_val_test_split( + df, + frac_train=1-args.test_frac, + frac_val=0, + frac_test=args.test_frac, + ) + train_data = df.iloc[train_set.indices] + test_data = df.iloc[test_set.indices] + print(f'Size of train dataset: {len(train_data)} ') + print(f'Size of test dataset: {len(test_data)} ') + train_data.to_csv(f'train_{args.file_name}',index=False) + test_data.to_csv(f'test_{args.file_name}',index=False) +","Python" +"Inhibition","CADD-SC/ADMET_Prediction_Models","model.py",".py","7230","151","from sklearn.ensemble import RandomForestClassifier ###1. RandomForest +from sklearn import svm ###2. SVM +import xgboost as xgb ###3. XGB +from sklearn.ensemble import GradientBoostingClassifier ###4.GB +import time +import pandas as pd +import numpy as np +from numpy import array +import pickle +import os +from os import path +import sklearn +from sklearn.metrics import roc_auc_score, make_scorer, recall_score, accuracy_score, matthews_corrcoef, confusion_matrix +from sklearn.model_selection import StratifiedKFold, cross_validate +from hpsklearn import HyperoptEstimator, any_preprocessing, any_classifier +from hpsklearn import random_forest_classifier, gradient_boosting_classifier, svc, xgboost_classification, k_neighbors_classifier +from sklearn.svm import SVC +from hyperopt import tpe, hp +import argparse +from utils import data_preprocessing +from torch.utils.data import Dataset +from dgllife.utils import ScaffoldSplitter + +class MoleculeDataset(Dataset): + def __init__(self, smiles, labels): + self.smiles = smiles + self.labels = labels + + def __len__(self): + return len(self.smiles) + + def __getitem__(self, idx): + return self.smiles[idx], self.labels[idx] + +if __name__==""__main__"": + parser=argparse.ArgumentParser() + parser.add_argument('--file_name', type=str, default='smiles.csv', help='Path to the input file') + parser.add_argument('--model_name', type=str, default=None, help='User-defined model name') + parser.add_argument('--max_eval', type=int, default=200, help='Maximum number of model evaluation') + parser.add_argument('--time_out', type=int, default=120, help='Maximum trial time out') + parser.add_argument('--cross_validation', action='store_true', default=False, help='Use this option in case of cross validation') + parser.add_argument('--training', action='store_true', default=False, help='Use this option in case of training model') + parser.add_argument('--split', choices=['scaffold', 'stratify'], default='stratify', help='Dataset split method in cross validation') + parser.add_argument('--k_fold', type=int, default=5, help='k-fold cross validtion') + + args =parser.parse_args() + print(args) + features = eval(open(f'./features.txt', 'r').read()) + if args.model_name is None: + args.model_name = args.file_name[:-4] + print(f'User-defined model name: {args.model_name}') + + if args.training: + train_data = pd.read_csv(f'train_{args.file_name}') + test_data = pd.read_csv(f'test_{args.file_name}') + print(f'Loading train/test set: {len(train_data)}, {len(test_data)}') + print(f'Hyperparameters for hpsklearn \n max_eval : {args.max_eval} \n time_out : {args.time_out}') + + clf_name = f'{args.max_eval}_{args.time_out}' + + print(f'Start training model \n') + + tt = time.time() + estimator = HyperoptEstimator( classifier=any_classifier('cla'), + max_evals = args.max_eval, + trial_timeout = args.time_out, + preprocessing=any_preprocessing('pre'), + algo=tpe.suggest) + + estimator.fit(train_data[features].values, np.array(train_data['bioclass'])) + et = time.time() + print(f'Time for model.fit: {et-tt}') + + confidence_score = estimator.score(test_data[features].values, np.array(test_data['bioclass'])) + print('Confidence score of trained model', confidence_score) + + best_model = estimator.best_model()['learner'] + print('Best performance model: ', best_model) + + pickle.dump(best_model, open(f'./{args.model_name}.pkl', 'wb')) + + predicted = best_model.predict(test_data[features].values) + try: + predicted_proba = best_model.predict_proba(test_data[features].values)[:,1] + except: + predicted_proba = best_model.decision_function(test_data[features].values) + + #confusion matrix print + tn, fp, fn, tp = confusion_matrix(test_data['bioclass'], predicted).ravel() + print('Confusion matrix (Se)', tp/(tp+fn)) + print('Confusion matrix (Sp)', tn/(tn+fp)) + print('Confusion matrix (acc)', (tp+tn)/(tp+fp+tn+fn)) + print('Confusion matrix (mcc)', ((tp*tn)-(fp*fn))/((tp+fp)*(tp+fn)*(tn+fp)*(tn+fn))**0.5 ) + print('AUC', roc_auc_score(test_data['bioclass'], predicted_proba) ) + print(f'========== End of traininig model for {args.model_name} using {args.file_name}') + + if args.cross_validation: + print(f'Start {args.k_fold}-fold cross validation using {args.file_name}') + train_data = pd.read_csv(f'train_{args.file_name}') + X = train_data[features] + y = train_data['bioclass'] + n_samples, n_features = X.shape + + tprs, aucs = [], [] + if args.split == 'stratify': + print(f'train/test data divided by stratify-based method \n') + cv = StratifiedKFold(n_splits=args.k_fold) + folds = cv.split(X, y) + elif args.split =='scaffold': + print(f'train/test data divided by scaffold-based method \n') + list_smiles = train_data['SMILES'].tolist() + list_labels = train_data['bioclass'].tolist() + dataset = MoleculeDataset(list_smiles, list_labels) + splitter = ScaffoldSplitter() + folds = splitter.k_fold_split(dataset=dataset, k=args.k_fold) + + for fold_idx, (train_subset, test_subset) in enumerate(folds): + + clf_name = f'{fold_idx}_{args.model_name}' + try: + train = train_subset.indices + test = test_subset.indices + except: + train = train_subset + test = test_subset + + print(f'[fold idx {fold_idx+1}] train: {len(train)}, test: {len(test)}') + + classifier = HyperoptEstimator( classifier = any_classifier('cla'), + max_evals = args.max_eval, + trial_timeout = args.time_out, + preprocessing= any_preprocessing('pre'), + algo=tpe.suggest) + + classifier.fit(X.iloc[train].values, y.iloc[train].values) + + classifier = classifier.best_model()['learner'] + print(classifier, type(classifier)) + + predicted = classifier.predict(X.iloc[test].values) + predicted_proba = classifier.predict_proba(X.iloc[test].values)[:,1] + + #confusion matrix print + tn, fp, fn, tp = confusion_matrix(y.iloc[test].values, predicted).ravel() + print('Confusion matrix (Se)', tp/(tp+fn)) + print('Confusion matrix (Sp)', tn/(tn+fp)) + print('Confusion matrix (acc)', (tp+tn)/(tp+fp+tn+fn)) + print('Confusion matrix (mcc)', ((tp*tn)-(fp*fn))/((tp+fp)*(tp+fn)*(tn+fp)*(tn+fn))**0.5 ) + print('AUC', roc_auc_score(y.iloc[test].values, predicted_proba) ) + print(f'========== End of {fold_idx+1}-fold cross validation model for {args.model_name} using {args.file_name}') +","Python" +"Inhibition","CADD-SC/ADMET_Prediction_Models","utils.py",".py","4091","97","import numpy as np +import pandas as pd +from rdkit import Chem +from rdkit.Chem import AllChem +from rdkit.ML.Descriptors import MoleculeDescriptors +from rdkit.Chem import Descriptors, Lipinski, rdMolDescriptors +import sklearn.metrics as metrics +from sklearn.preprocessing import StandardScaler +import argparse +import pickle +import joblib +import os +#from hpsklearn.components import classifier + +pd.set_option('display.max_columns', None) + +def data_preprocessing(tmp_df): + org_df = len(tmp_df) + tmp_df['mol'] = [Chem.MolFromSmiles(s) for s in tmp_df['SMILES']] #filtering invalid mol + data = tmp_df.dropna(subset=['mol']) + print('Remove invalid mol object: ', org_df - len(data)) + data.loc[:, 'SMILES'] = canonical_smiles(data['SMILES']) #canonicalize smi + data = data.drop_duplicates(subset=['SMILES'], keep='first') #remove duplicate + data = data[['SMILES','bioclass']] + all_data = calculate_allfeatures(data) #1234 featurize , smi, bioclass + all_data = standard_scaled(all_data) + return all_data + +def canonical_smiles(smiles_list): + clean_smiles=[] + for smiles in smiles_list: + try: + cpd=str(smiles).split('.') + cpd_longest=max(cpd,key=len) + can_smi = Chem.CanonSmiles(cpd_longest) + clean_smiles.append(can_smi) + except: + print('!!!!!!!!except smi:', smiles) + continue + return clean_smiles + +def calculate_allfeatures(df): + dump_list = [(smi, bioclass) for smi, bioclass in zip (df['SMILES'], df['bioclass'])] + descriptors_list = [] + ecfp6_list = [] + from multiprocessing import Pool + with Pool(processes=24) as p: + store_results = p.map(calculate_ecfp, dump_list) + # Create a pandas DataFrame of the calculated features + ecfp6_list = [store_results[idx][0] for idx in range(len(dump_list))] + descriptors_list = [store_results[idx][1] for idx in range(len(dump_list))] + bioclass_list = [store_results[idx][2] for idx in range(len(dump_list))] + smiles_list = [store_results[idx][3] for idx in range(len(dump_list))] + descriptors_df = pd.DataFrame(descriptors_list) + all_df = pd.DataFrame({'SMILES': smiles_list, 'bioclass': bioclass_list}) + ecfp6_df = pd.DataFrame(ecfp6_list, columns=['ECFP6_{}'.format(i+1) for i in range(ecfp6_list[0].size)]) + features_df = pd.concat([descriptors_df, ecfp6_df, all_df], axis=1) + return features_df + +def calculate_ecfp(tup_input): + smiles, bioclass= tup_input[0], tup_input[1] + mol = Chem.MolFromSmiles(smiles) + desc = {} + for desc_name, desc_func in Descriptors.descList: + desc[desc_name] = desc_func(mol) + ecfp = AllChem.GetMorganFingerprintAsBitVect(mol, 3, nBits=1024) + return np.array(ecfp), desc, bioclass, smiles + +def standard_scaled(all_data): + categorical_columns = all_data.select_dtypes(exclude=['int64', 'float64']).columns + all_data[categorical_columns] = all_data[categorical_columns].fillna('empty') + all_data.fillna(0, inplace=True) + features_df = all_data.drop(columns=['SMILES', 'bioclass', 'SPS', 'AvgIpc', 'NumAmideBonds', 'NumAtomStereoCenters', 'NumBridgeheadAtoms', 'NumHeterocycles', 'NumSpiroAtoms', 'NumUnspecifiedAtomStereoCenters', 'Phi']) + + ### load_scaler for all compounds + scaler_load = joblib.load('scaler/new_scaler.pkl') + results_df = scaler_load.transform(features_df) + results_df = pd.DataFrame(results_df, columns=features_df.columns) + results_df = results_df.map(lambda x: round(x, 10)) + + return pd.concat([results_df, all_data[['SMILES','bioclass']]], axis=1) + +if __name__==""__main__"": + parser=argparse.ArgumentParser() + parser.add_argument('--file_name', type=str, default='Papp_final_data') + parser.add_argument('--feature_name', type=str, default='Caco2_permeability') + args =parser.parse_args() + + features = eval(open(f'./features.txt', 'r').read())#CYP1A2_inhibition/ + print(len(features)) + tmp_df = pd.read_csv('test_example.csv') + + # preprocess the smiles + scaled_all_data = data_preprocessing(tmp_df) + print(scaled_all_data) + exit(-1) +","Python"