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# -*- 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 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((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=500)
load_model = pickle.load(open(r"tool/deepacceptor/deepacceptor.pkl","rb"))
Y_predict = load_model.predict(X_test)
return Y_predict |