Modeling and Interpretability Study of the Structure–Activity Relationship for Multigeneration EGFR Inhibitors

The fourth-generation EGFR inhibitors targeting L858R/T790M/C797S mutations are in clinical trials mostly, and it is necessary to develop new inhibitors. In this study, an internal data set containing 2302 multitarget EGFR inhibitors targeting the wild type (83%) and the L858R (92%), L858R/T790M (96%), and L858R/T790M/C797S (60%) mutations was collected. We established a structure-activity relationship model for predicting the bioactivities of multigeneration EGFR inhibitors by a multitask deep neural network (MT-DNN). We also constructed four single-task models on 1384 L858R/T790M/C797S (60%) mutation inhibitors by support vector machine (SVM), random forest (RF), XGBoost (XGB), and single-target neural network (ST-DNN), respectively. The MT-DNN model significantly outperformed single-task models on the external data set of 304 fourth-generation EGFR inhibitors. Furthermore, the combined application of MT-DNN and SHAP/delta-SHAP value interpretability analysis offers rigorous structural information from a global perspective. With SHAP/delta-SHAP methods, the MT-DNN model can mine the core scaffold and important fragments of multigeneration EGFR inhibitors and provide valuable information from a structure-activity relationship perspective to address the resistant mutation problem.