Computational investigation of novel farnesyltransferase inhibitors using 3D-QSAR pharmacophore modeling, virtual screening, molecular docking and molecular dynamics simulation studies: A new insight into cancer treatment

Abstract

Farnesyltransferase (FTase) is considered as an effective target in treating a variety of cancers. In this investigation, a 3D-QSAR pharmacophore search was performed to identify potential inhibitors against FTase. Correspondingly, 3D-QSAR pharmacophore models for FTase inhibitors were generated utilizing the training set compounds and validated through the test set, cost analysis and Fischer's randomization test. The created pharmacophore models were evaluated based on Debnath's analysis. Then, the pharmacophore model Hypo1 was selected to perform pharmacophore-based screening. Subsequently, the identified compounds were filtered considering Lipinski's Rule of Five (RoF) and Absorption, Distribution, Metabolism, Excretion and Toxicity (ADMET) properties. In the next step, the inter-molecular interactions of the retrieved compounds and the most active compound of the training set with FTase were analyzed utilizing the flexible docking technique. Eventually, the best two compounds, ZINC08952683 and ZINC08918035, along with the reference compound, ZINC13559176, were selected for Molecular Dynamics (MD) studies in order to conduct an in-depth study on their complexes with FTase. MD results confirmed docking studies and showed that these two compounds could make stable complexes with the FTase active site and may have great inhibitory effects on FTase.