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

The Kirsten rat sarcoma viral G12C (KRAS G12C ) protein is one of the most common mutations in non-small-cell lung cancer (NSCLC). KRAS G12C inhibitors are promising for NSCLC treatment, but their weaker activity in resistant tumors is their drawback. This study aims to identify new KRAS G12C inhibitors from among the FDA-approved covalent drugs by taking advantage of artificial intelligence. The machine learning models were constructed using an extreme gradient boosting (XGBoost) algorithm. The models can predict KRAS G12C inhibitors well, with an accuracy score of validation = 0.85 and Q 2 Ext = 0.76. From 67 FDA-covalent drugs, afatinib, dacomitinib, acalabrutinib, neratinib, zanubrutinib, dutasteride, and finasteride were predicted to be active inhibitors. Afatinib obtained the highest predictive log-inhibitory concentration at 50% (pIC 50 ) value against KRAS G12C protein close to the KRAS G12C inhibitors. Only afatinib, neratinib, and zanubrutinib covalently bond at the active site like the KRAS G12C inhibitors in the KRAS G12C protein (PDB ID: 6OIM). Moreover, afatinib, neratinib, and zanubrutinib exhibited a distance deviation between the KRAS G2C protein-ligand complex similar to the KRAS G12C inhibitors. Therefore, afatinib, neratinib, and zanubrutinib could be used as drug candidates against the KRAS G12C protein. This finding unfolds the benefit of artificial intelligence in drug repurposing against KRAS G12C protein.