Discovery of novel natural product-derived EGFR inhibitors using multiple linear regression, stacked ensemble...

This study developed and validated Quantitative Structure-Activity Relationship models to predict the inhibitory activity (pIC₅₀) of 225 EGFR inhibitors. A genetic algorithm selected eight molecular descriptors, which were used to construct two models: a multiple linear regression (MLR) and a stacked ensemble regression (SER). The SER model showed only marginally higher accuracy ([Formula: see text]) but exhibited greater predictive instability ([Formula: see text] vs. MLR's 0.0184) and reduced interpretability. Thus, MLR was retained as the primary model due to its OECD-compliant mechanistic transparency and superior generalizability. Rigorous applicability domain analysis confirmed the MLR model's reliability. Notably, molecular docking (PDB ID: 8A27) identified a top-ranked inhibitor (Compound 121) with high binding affinity ([Formula: see text] kcal/mol), forming critical hydrogen bonds and hydrophobic interactions with EGFR's active site. Virtual screening of 32 structural analogs of Compound 121 revealed additional promising candidates. This work provides a robust framework for EGFR inhibitor discovery, combining computational modeling with structural insights.