Drug Repurposing Against KRAS Mutant G12C: A Machine Learning, Molecular Docking, and Molecular Dynamics Study

This study aimed to repurpose FDA-approved drugs to inhibit the KRASG12C mutation in non-small-cell lung cancer using machine learning, molecular docking, and molecular dynamics. The XGBoost algorithm predicted inhibitors with 0.85 validation accuracy from 67 drugs, identifying afatinib, dacomitinib, acalabrutinib, neratinib, zanubrutinib, dutasteride, and finasteride as potential candidates. Afatinib, neratinib, and zanubrutinib showed stable covalent binding at the KRASG12C active site, similar to existing inhibitors. The study concludes that these drugs, especially afatinib, could be effective against KRASG12C-mutant tumors, warranting further investigation.