METHODS: This study employs a suite of computational techniques, including 3D-QSAR, ligand-based virtual screening, molecular docking, fingerprinting analysis, ADME, and DFT-based analyses (MESP, HOMO, LUMO), supplemented by molecular dynamics simulations and MMGB/PBSA free energy calculations, to explore the binding dynamics of quinazoline derivatives with EGFR. With strong q2 and r2 values from CoMFA and CoMSIA models, our 3D- QSAR models reliably predict EGFR inhibitors' efficacy.
RESULTS AND DISCUSSION: Utilizing a potent model compound as a reference, an E-pharmacophore model was developed to sift through the eMolecules database, identifying 19 virtual screening hits based on ShapeTanimoto, ColourTanimoto, and TanimotoCombo scores. These hits, assessed via 3D- QSAR, showed pIC50 predictions consistent with experimental data. Our analyses elucidate key features essential for EGFR inhibition, reinforced by ADME studies that reveal favorable pharmacokinetic profiles for most compounds. Among the primary phytochemicals examined, potential EGFR inhibitors were identified. Detailed MD simulation analyses on three select ligands-1Q1, 2Q17, and VS1-demonstrated their stability and consistent interaction over 200 ns, with MM/GBSA values corroborating their docking scores and highlighting 1Q1 and VS1's superior EGFR1 affinity. These results position VS1 as an especially promising lead in EGFR1 inhibitor development, contributing valuable insights towards crafting novel, effective EGFR1 inhibitors.