File size: 1,876 Bytes
ef7926d d3901c3 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 | # -*- coding: utf-8 -*-
"""
Created on Mon Sep 4 10:38:59 2023
@author: BM109X32G-10GPU-02
"""
from sklearn.metrics import confusion_matrix
import numpy as np
from rdkit.Chem import AllChem
from sklearn.datasets import make_blobs
import json
import numpy as np
import math
import pickle
from scipy import sparse
from sklearn.metrics import median_absolute_error,r2_score, mean_absolute_error,mean_squared_error
import joblib
from tqdm import tqdm
import pandas as pd
from rdkit import Chem
from sklearn.ensemble import RandomForestRegressor
def split_string(string):
result = []
for char in string:
result.append(char)
return result
def main(sm):
inchis = list([sm])
rts = list([0])
smiles, targets,features = [], [],[]
for i, inc in enumerate(tqdm(inchis)):
mol = Chem.MolFromSmiles(inc)
if mol is None:
continue
else:
smi =AllChem. GetMorganFingerprintAsBitVect(mol,3,2048)
smi = smi.ToBitString()
a = split_string(smi)
a = np.array(a)
#smi = Chem.MolToSmiles(mol)
features.append(a)
targets.append(rts[i])
features = np.asarray(features)
targets = np.asarray(targets)
X_test=features
Y_test=targets
n_features=10
model = RandomForestRegressor(n_estimators=100)
load_model = pickle.load(open(r"tool/deepdonor/pm.pkl", 'rb'))
# model = load_model('C:/Users/sunjinyu/Desktop/FingerID Reference/drug-likeness/CNN/single_model.h5')
Y_predict = load_model.predict(X_test)
#Y_predict = model.predict(X_test)
x = list(Y_test)
y = list(Y_predict)
return Y_predict
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