Virtual Screening, Molecular Docking, and Molecular Dynamic Simulation Methods for the In Silico Prediction of Novel Inhibitors against Kirsten Rat Sarcoma G12D Cancer Drug Target

Abstract

The vast majority of human cancers are caused by mutations in the Kirsten rat sarcoma (KRAS) viral proto-oncogene. A significant portion—approximately 30%—of lung, pancreas, and colon cancers in humans are triggered by oncogenic KRAS mutations. An appealing therapeutic target is one of the most common mutant KRAS G12D mutations, which causes pancreatic cancer. There are currently no medications that have been authorised for use by the FDA that target the KRAS G12D mutation. In light of this, research
towards a viable treatment for KRAS G12D must proceed. Discovering new medications is a laborious and costly procedure. Alternatively, in silico drug development approaches save time and money. In this study, we used ML methods including K-nearest neighbour (KNN), support vector machine (SVM), and random forest (RF) to find novel inhibitors for the KRAS G12D mutant. Based on the predictions, 82 hits were active against the KRAS G12D mutant. Docking the active hits into the KRAS G12D mutant's active site was the process. In addition, the stability of the compounds with strong docking scores was assessed by running 200 ns MD simulations on the top two complexes and the reference complex (MRTX-1133). As compared to the conventional compound, the top two hits demonstrated great stability. In comparison to the gold standard compound, the binding energies of the top two hits were respectable. Our discovered hits may aid in the fight against cancer by blocking the KRAS G12D mutation. We are unaware of any previous research that has used molecular docking, molecular dynamics simulation, virtual screening based on machine learning, and the KRAS G12D mutant to find potential novel inhibitors.