Data mining using template-based molecular docking on tetrahydroimidazo-[4,5,1-jk][1,4]-benzodiazepinone (TIBO) derivatives as HIV-1RT inhibitors

Abstract

TIBO (Tetrahydroimidazo-[4,5,1-jk][1,4]-benzodiazepinone) compounds are potent non-nucleoside reverse transcriptase inhibitors (NNRTIs) that show a great promise for the treatment of AIDS. A structure-based molecular modeling approach based on template-based flexible docking simulation followed by 'Tabu clustering' was performed on a series of 46 TIBO derivatives considered as training set of HIV-1 NNRTIs. Four different templates of the highest active ligand (pIC(50) = 8.52) of the series were used. The results were reasonably satisfactory. A good correlation was observed between the biological activity and binding affinity of the compounds, which suggest that identified binding conformations of these inhibitors are reliable. Statistical modeling yielded satisfactory results (r(2) = 0.878). Our studies suggest that template-based docking followed by 'Tabu clustering' enhances the docking efficiency. Also, cross-validation with a test-set containing 16 compounds gave satisfactory results (r(2) = 0.836). Data mining of PubChem database yielded a total of 31 hits (25 novel TIBO like compounds, as well as, 6 novel scaffolds) with enhanced binding efficacy as hits. These hits may, be targeted toward potent lead-optimization and, help in designing and synthesizing novel compounds with enhanced therapeutic efficacy.