qsar

Getting started

chembl

DOI https://doi.org/10.1016/j.ejmech.2022.114495

Design, synthesis and activity against drug-resistant bacteria evaluation of C-20, C-23 modified 5-O-mycaminosyltylonolide derivatives

A类似物：22个活性数据 B类似物：7个活性数据 C类似物：47个活性数据

检索条件：Structure2D_A1.mol 85% 以上相似度

检索结果：A_85.csv

env

unimol install

python -m pip install --upgrade pip
pip config set global.index-url https://mirrors.tuna.tsinghua.edu.cn/pypi/web/simple
pip install unimol_tools
pip install huggingface_hub

result

(analyse) (base) root@DESK4090:/mnt/c/project/qsar/MIC# python qsar_1D.py 
[1D-QSAR][Linear Regression]    MSE:32.3949     R2:0.6525
Model saved to 1d_qsar_linear_regression_model.pkl
[1D-QSAR][Stochastic Gradient Descent]  MSE:230009980374197965960989638656.0000 R2:-2467672699617844819673481216.0000
Model saved to 1d_qsar_stochastic_gradient_descent_model.pkl
[1D-QSAR][K-Nearest Neighbors]  MSE:30.2081     R2:0.6759
Model saved to 1d_qsar_k-nearest_neighbors_model.pkl
[1D-QSAR][Decision Tree]        MSE:27.7150     R2:0.7027
Model saved to 1d_qsar_decision_tree_model.pkl
[1D-QSAR][Random Forest]        MSE:26.5204     R2:0.7155
Model saved to 1d_qsar_random_forest_model.pkl
[1D-QSAR][XGBoost]      MSE:27.7147     R2:0.7027
Model saved to 1d_qsar_xgboost_model.pkl
[1D-QSAR][Multi-layer Perceptron]       MSE:143.3505    R2:-0.5379
Model saved to 1d_qsar_multi-layer_perceptron_model.pkl
---
[2D-QSAR][Linear Regression]    MSE:30.1093     R2:0.6770
Model saved to 2d_qsar_linear_regression_model.pkl
[2D-QSAR][Stochastic Gradient Descent]  MSE:33.7336     R2:0.6381
Model saved to 2d_qsar_stochastic_gradient_descent_model.pkl
[2D-QSAR][K-Nearest Neighbors]  MSE:48.8179     R2:0.4763
Model saved to 2d_qsar_k-nearest_neighbors_model.pkl
[2D-QSAR][Decision Tree]        MSE:30.2360     R2:0.6756
Model saved to 2d_qsar_decision_tree_model.pkl
[2D-QSAR][Random Forest]        MSE:28.7916     R2:0.6911
Model saved to 2d_qsar_random_forest_model.pkl
[2D-QSAR][XGBoost]      MSE:30.2351     R2:0.6756
Model saved to 2d_qsar_xgboost_model.pkl
[2D-QSAR][Multi-layer Perceptron]       MSE:30.1715     R2:0.6763
Model saved to 2d_qsar_multi-layer_perceptron_model.pkl
---
[3D-QSAR][Stochastic Gradient Descent]  MSE:64.5768     R2:0.3072
Model saved to 3d_qsar_stochastic_gradient_descent_model.pkl
[3D-QSAR][K-Nearest Neighbors]  MSE:38.6921     R2:0.5849
Model saved to 3d_qsar_k-nearest_neighbors_model.pkl
[3D-QSAR][Decision Tree]        MSE:30.2360     R2:0.6756
Model saved to 3d_qsar_decision_tree_model.pkl
[3D-QSAR][Random Forest]        MSE:30.8310     R2:0.6692
Model saved to 3d_qsar_random_forest_model.pkl
[3D-QSAR][XGBoost]      MSE:30.2362     R2:0.6756
Model saved to 3d_qsar_xgboost_model.pkl
[3D-QSAR][Multi-layer Perceptron]       MSE:29.9844     R2:0.6783
Model saved to 3d_qsar_multi-layer_perceptron_model.pkl
---
unimol qsar
{'mse': 59.72037918598548, 'mae': 5.179289798987539, 'pearsonr': 0.638764928149331, 'spearmanr': 0.6006870492749102, 'r2': 0.35928715315601223}

deepMD-kit

notebookLM

pytorch code

3D-QSAR tutorial