用ChEMBL、RDKit、SHAP和BRICS手把手搭一个EGFR抑制剂预测模型，数据清理到模型解释全有，代码示例清晰。
本教程使用ChEMBL和UniProt获取EGFR C797S靶点信息，从IC50记录中提取pIC50数据集。利用RDKit标准化分子并计算Morgan指纹，训练基于scaffold-split的随机森林QSAR模型。通过SHAP解释效力驱动因素，并使用BRICS碎片重组生成并排名新候选分子。
Building a Scaffold-Split Random Forest QSAR Co-Scientist for EGFR Inhibitor Discovery Using ChEMBL, RDKit, SHAP, and BRICS
In this tutorial, we build an autonomous AI co-scientist for EGFR C797S inhibitor discovery. We resolve the target through ChEMBL and UniProt, then mine IC50 records into a clean pIC50 dataset. We use RDKit to standardize molecules, compute Morgan fingerprints, and train a scaffold-split Random Forest QSAR model. We interpret potency drivers with SHAP, then recombine BRICS fragments to generate and rank novel candidates. The post Building a Scaffold-Split Random Forest QSAR Co-Scientist for EGFR Inhibitor Discovery Using ChEMBL, RDKit, SHAP, and BRICS appeared first on MarkTechPost .