Non-nucleosidase reverse transcriptase inhibitors (NNRTIs) are highly promising agents for use in highly effective antiretroviral therapy. We implemented a rational approach for the identification of promising NNRTIs based on the validated ligand- and structure-based approaches. In view of our state-of-the-art techniques in drug design and discovery utilizing multiple modeling approaches, we report here, for the first time, quantitative pharmacophore modeling (HypoGen), docking, and in-house database screening approaches in the identification of potential NNRTIs. The validated pharmacophore model with three hydrophobic groups, one aromatic ring group, and a hydrogen-bond acceptor explains the interactions at the active site by the inhibitors. The model was implemented in pharmacophore-based virtual screening (in-house and commercially available databases) and molecular docking for prioritizing the potential compounds as NNRTI. The identified leads are in good corroboration with binding affinities and interactions as compared to standard ligands. The model can be utilized for designing and identifying the potential leads in the area of NNRTIs.
Keywords: ligand mapping; pharmacophore; reverse transcriptase; virtual screening.