Pharmacophore Identification, Molecular Docking, Virtual Screening, and In Silico ADME Studies of Non-Nucleoside Reverse Transcriptase Inhibitors

摘要

Abstract: Non-nucleoside reverse transcriptase inhibitors (NNRTIs) have gained a definitive place due to their unique antiviral potency, high specificity and low toxicity in antire-troviral combination therapies used to treat HIV. In this study, chemical feature based pharmacophore models of different classes of NNRT inhibitors of HIV-1 have been developed. The best HypoRefine pharmacophore model, Hypo 1, which has the best correlation coefficient (0.95) and the lowest RMS (0.97), contains two hydrogen bond acceptors, one hydrophobic and one ring aromatic feature, as well as four excluded volumes. Hypo 1 was further validated by test set and Fischer validation method. The best pharmacophore model was then utilized as a 3D search query to perform a virtual screening to retrieve potential inhibitors. The hit compounds were subsequently subjected to filtering by Lipinski's rule of five and docking studies by Libdock and Gold methods to refine the retrieved hits. Finally, 7 top ranked compounds based on Gold score fitness function were subjected to in silico ADME studies to investigate for compliance with the standard ranges.