Abstract
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.