Design of novel leads: ligand based computational modeling studies on non-nucleoside reverse transcriptase inhibitors (NNRTIs) of HIV-1.

Researchers are on the constant lookout for new antiviral agents for the treatment of AIDS. In the present work, ligand based modeling studies are performed on analogues of substituted phenyl-thio-thymines, which act as non-nucleoside reverse transcriptase inhibitors (NNRTIs) and novel leads are extracted. Using alignment-dependent descriptors, based on group center overlap (SALL, HDALL, HAALL and RALL), an alignment-independent descriptor (S log P), a topological descriptor (Balaban index (J)) and a 3D descriptor dipole moment (μ) and shape based descriptors (Kappa 2 index ((2)κ)), a correlation is derived with inhibitory activity.

A group center overlap based approach for "3D QSAR" studies on TIBO derivatives.

Current challenges in drug designing and lead optimization has reached a bottle neck where the main onus lies on rigorous validation to afford robust and predictive models. In the present study, we have suggested that predictive structure-activity relationship (SAR) models based on robust statistical analyses can serve as effective screening tools for large volume of compounds present either in chemical databases or in virtual libraries. 3D descriptors derived from the similarity-based alignment of molecules with respect to group center overlap from each individual template point and other "alignment averaged," but significant descriptors (ClogP, molar refractivity, connolly accessible area) were used to generate QSAR models.

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

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.

Computational modeling of tetrahydroimidazo-[4,5,1-jk][1,4]-benzodiazepinone derivatives: an atomistic drug design approach using Kier-Hall electrotopological state (E-state) indices.

Quantitative structure-activity relationships (QSAR), based on E-state indices have been developed for a series of tetrahydroimidazo-[4,5,1-jk]-benzodiazepinone derivatives against HIV-1 reverse transcriptase (HIV-1 RT). Statistical modeling using multiple linear regression technique in predicting the anti-HIV activity yielded a good correlation for the training set (R(2) = 0.913, R(2)(adj) = 0.

Molecular docking studies on tetrahydroimidazo-[4,5,1-jk][1,4]-benzodiazepinone (TIBO) derivatives as HIV-1 NNRT inhibitors.

At present, chemotherapy seems to be the main weapon in the arsenal of remedies for the ongoing crusade against AIDS. The mode of binding of the TIBO family of inhibitors has been of interest because these compounds do not fit the two-hinged-ring model as generally observed in the NNRTIs. Flexible docking simulations were performed with a series of 53 TIBO derivatives as NNRTIs.