Computational docking of small molecules to protein targets for drug discovery

Abstract

Identifying potent and selective small molecule inhibitors remains a fundamental challenge in drug discovery, yet traditional screening approaches are prohibitively expensive for most targets. This research employed computational docking methodologies to screen a diverse compound library against six clinically relevant protein kinases implicated in cancer progression: CDK2, EGFR, VEGFR, JAK2, ABL1, and SRC. A curated library of 50,000 drug-like compounds was screened using Auto Dock Vina and Glide SP algorithms, with top-ranked candidates subjected to molecular dynamics simulations and binding free energy calculations. Cross-validation between docking algorithms revealed 78.4% concordance for top 100 compounds per target. The most promising candidates demonstrated predicted binding energies ranging from -8.6 to -9.3 kcal/mol, comparing favorably with known inhibitors. Molecular dynamics simulations over 100 nanoseconds confirmed binding pose stability for 23 of 30 selected compounds. Five novel scaffolds emerged showing predicted selectivity profiles suitable for further development. This integrated computational pipeline demonstrates cost-effective identification of lead compounds while highlighting the complementary strengths of different docking methodologies.