| # -*- 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 |