AI-driven design of Novel EGFR Inhibitors with Enhanced Potency and Drug-Like Properties

The Epidermal Growth Factor Receptor (EGFR) is a clinically validated target in oncology, with aberrant EGFR signaling implicated in several cancers, including non-small cell lung cancer and glioblastoma. While existing EGFR inhibitors have shown therapeutic success, challenges such as acquired resistance, suboptimal drug-likeness, and limited affinity for mutant receptor variants persist. In this study, we present an integrated deep learning-based pipeline for the de novo design of novel EGFR inhibitors with improved potency and drug-like characteristics. A curated dataset of 260 high-affinity EGFR inhibitors was used to train a Gated Recurrent Unit (GRU)-based Recurrent Neural Network (RNN), enabling the generation of novel SMILES structures. Of the 42 generated compounds, 40 were chemically valid, as confirmed by RDKit. Predicted inhibitory potency (IC₅₀) was estimated using a Random Forest regression model trained on molecular descriptors, and a multi-criteria scoring system was applied to prioritize compounds based on IC₅₀, QED, LogP, and TPSA. Molecular docking studies using the EGFR crystal structure (PDB ID: 1M17) validated the binding potential of top candidates, with the highest-ranked compound showing binding affinity comparable to a known reference inhibitor. This work highlights the potential of AI-driven methods in accelerating early-stage drug discovery and offers promising candidates for further experimental validation as next-generation EGFR inhibitors.