111 lines
4.1 KiB
Python
111 lines
4.1 KiB
Python
#!/usr/bin/env python
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# -*- encoding: utf-8 -*-
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'''
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@file :qsar_1d.py
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@Description: : 1D-QSAR modeling with SMILES and MIC data
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@Date :2024/09/28
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@Author :lyzeng
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@Email :pylyzeng@gmail.com
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@version :1.0
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'''
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import pandas as pd
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import numpy as np
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from rdkit import Chem
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from rdkit.Chem import Descriptors
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from sklearn.model_selection import train_test_split
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from sklearn.linear_model import LinearRegression, SGDRegressor
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from sklearn.ensemble import RandomForestRegressor
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from sklearn.neighbors import KNeighborsRegressor
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from sklearn.tree import DecisionTreeRegressor
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from sklearn.neural_network import MLPRegressor
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from xgboost import XGBRegressor
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from sklearn.metrics import mean_squared_error, r2_score
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import matplotlib.pyplot as plt
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import joblib
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import os
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# Function to calculate 1D-QSAR descriptors
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def calculate_1dqsar_repr(smiles):
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mol = Chem.MolFromSmiles(smiles)
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if mol is None:
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return None
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descriptors = [
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Descriptors.MolWt(mol),
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Descriptors.MolLogP(mol),
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Descriptors.NumHDonors(mol),
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Descriptors.NumHAcceptors(mol),
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Descriptors.TPSA(mol),
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Descriptors.NumRotatableBonds(mol),
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Descriptors.NumAromaticRings(mol),
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Descriptors.NumAliphaticRings(mol),
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Descriptors.NumSaturatedRings(mol),
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Descriptors.NumHeteroatoms(mol),
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Descriptors.NumValenceElectrons(mol),
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Descriptors.NumRadicalElectrons(mol),
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Descriptors.qed(mol)
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]
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return descriptors
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if __name__ == '__main__':
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# Load your dataset
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df = pd.read_csv('/mnt/c/project/qsar/data/A_85.csv', sep=';')
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df_with_MIC = df[df['Standard Type'] == 'MIC']
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# Select relevant columns
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qsar_df = df_with_MIC[['Smiles', 'Standard Value']].copy()
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qsar_df.rename(columns={'Smiles': 'SMILES', 'Standard Value': 'TARGET'}, inplace=True)
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# Calculate 1D-QSAR descriptors for each molecule
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qsar_df['1dqsar_mr'] = qsar_df['SMILES'].apply(calculate_1dqsar_repr)
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# Remove rows where descriptor calculation failed
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qsar_df = qsar_df.dropna()
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# Split the data into training and testing sets
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train_df, test_df = train_test_split(qsar_df, test_size=0.2, random_state=42)
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# Convert the data to NumPy arrays
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train_x = np.array(train_df['1dqsar_mr'].tolist())
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train_y = np.array(train_df['TARGET'].tolist())
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test_x = np.array(test_df['1dqsar_mr'].tolist())
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test_y = np.array(test_df['TARGET'].tolist())
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# Define regressors to use
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regressors = [
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("Linear Regression", LinearRegression()),
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("Stochastic Gradient Descent", SGDRegressor(random_state=42)),
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("K-Nearest Neighbors", KNeighborsRegressor()),
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("Decision Tree", DecisionTreeRegressor(random_state=42)),
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("Random Forest", RandomForestRegressor(random_state=42)),
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("XGBoost", XGBRegressor(eval_metric="rmse", random_state=42)),
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("Multi-layer Perceptron", MLPRegressor(hidden_layer_sizes=(128, 64, 32), activation='relu', solver='adam', max_iter=10000, random_state=42)),
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]
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# Initialize results dictionary
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results = {}
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# Train and evaluate each regressor
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for name, regressor in regressors:
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regressor.fit(train_x, train_y)
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pred_y = regressor.predict(test_x)
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mse = mean_squared_error(test_y, pred_y)
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r2 = r2_score(test_y, pred_y)
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results[f"1D-QSAR-{name}"] = {"MSE": mse, "R2": r2}
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print(f"[1D-QSAR][{name}]\tMSE:{mse:.4f}\tR2:{r2:.4f}")
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# Save the trained model
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model_filename = f"1d_qsar_{name.replace(' ', '_').lower()}_model.pkl"
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joblib.dump(regressor, os.path.join(os.getcwd(), model_filename))
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print(f"Model saved to {model_filename}")
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# Sort results by R2 score
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sorted_results = sorted(results.items(), key=lambda x: x[1]["R2"], reverse=True)
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# Plot results (optional, for example purposes)
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plt.figure(figsize=(10, 6), dpi=300)
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plt.title("R2 Scores for Regressors")
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plt.barh([name for name, _ in sorted_results], [result['R2'] for _, result in sorted_results])
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plt.xlabel("R2 Score")
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plt.savefig('qsar_1D_r2_scores.png', dpi=300)
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