Discovery of Novel EGFR Kinase Inhibitors Using a Wild-Type EGFR: A Computational Approach

Since current tyrosine kinase inhibitors frequently fail, it is imperative to find novel inhibitors of the Epidermal Growth Factor Receptor (EGFR). In this study, we integrated computational approaches to find interesting compounds targeting wild-type EGFR. Starting with the 3D structure of EGFR (PDB ID: 4WKQ), we constructed a pharmacophore model, and tested it against a dataset containing both active and inert drugs. The accuracy in identifying active chemicals (enrichment factor = 56.8, ROC-AUC = 0.94) demonstrated excellent effectiveness. The model was then applied to screen the extensive ChEMBL library, yielding 25,000 top-ranking candidates that underwent molecular docking using Autodock Vina. Validation through redocking confirmed the reliability of our docking protocol with RMSD values below 2 Å, and four lead compounds exhibiting superior binding affinities relative to Gefitinib were identified. Subsequent ADME and toxicity assessments refined these leads, resulting in the exclusion of one candidate due to predicted irritant effects. The remaining hits were further evaluated via 500 ns molecular dynamics simulations, with analyses of RMSD, RMSF, hydrogen bonding, and solvent accessible surface area confirming stable binding interactions. Binding free energy calculations were done for two representative ligands. This comprehensive in silico approach not only identifies potential novel EGFR inhibitors but also establishes a robust computational pipeline applicable to the broader field of kinase-targeted drug discovery.