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

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 Q²_Ext = 0.76. From 67 FDA-covalent drugs, afatinib, dacomitinib, acalabrutinib, neratinib, zanubrutinib, dutasteride, and finasteride were predi

SUBMITTER: Srisongkram T 

PROVIDER: S-EPMC9821013 | biostudies-literature | 2022 Dec

REPOSITORIES: biostudies-literature