Available online at www.scholarsresearchlibrary.com Scholars Research Library Der Pharmacia Lettre, 2012, 4 (6):1888-1900 (http://scholarsresearchlibrary.com/archive.html) ISSN 0975-5071 USA CODEN: DPLEB4 1888 Scholar Research Library Denovo insilico design of triazole analogs as reverse transcriptase inhibitors S Banerjee 1 , S. Ganguly 2 and K. K. Sen 1 1 Department of Pharmacy, Gupta College of Technological Sciences, Ashram more, G.T. road, Asansol 713301, West Bengal, India. 2 Department of Pharmaceutical Sciences, Birla Institute of Technology, Mesra, Ranchi 835215, Jharkhand, India _____________________________________________________________________________________________ ABSTRACT Non-nucleoside reverse transcriptase inhibitors (NNRTIs) have, in addition to the nucleoside reverse transcriptase inhibitors (NRTIs) and protease inhibitors (PIs), gained a definitive place in the treatment of HIV-1 infections. The present work deals with computational ligand docking methodology, AutoDock 4.0, based on Lamarckian genetic algorithm for virtual screens of a compound database of 36 entries (tri-substituted 1,2,4-triazoles) for novel and selective inhibitors of the enzyme Reverse transcriptase (PDB entry;1RT2), a potential anti-AIDs drug target. Considering free energy of binding and inhibition constant (KI) as a criterion of evaluation, a total of 34 compounds were predicted to be potential inhibitors of reverse transcriptase and 14 compounds displayed greater binding affinities than Nevirapine, a well-known reverse transcriptase inhibitor. Compound AM31, 2-{[4-amino-5- (2- hydroxyphenyl)-4H-1, 2, 4-triazol-3-yl]-thio}-N-(4-nitrophenyl)acetamide; compound AM33, 2-{[4-amino-5-(2- hydroxyphenyl)-4H-1,2,4-triazol-3-yl]-thio}-N-(4- methoxyphenyl) acetamide; and compound AM34, 2-{[4-amino- 5-(2-hydroxyphenyl)- 4H-1,2,4-triazol-3-yl]thio}-N-(4-ethoxyphenyl)acetamide were considered to be the most potent reverse transcriptase inhibitors. Putative interactions between reverse transcriptase and inhibitors were identified by inspection of docking-predicted poses. Most of the compounds under study have shown significant binding energy as well as interaction in nanomolar range, thus suggesting the effectiveness of Autodock as an effective desktop molecular modelling tool. Attempts at discovering broad spectrum antiviral agents are presented herein. Key words: AutoDock 4.0; Reverse transcriptase; Lamarckian genetic algorithm; Nevirapine. _____________________________________________________________________________________________ INTRODUCTION With the advent of high-performance and low-cost computing systems, exemplified by enterprise grid-based networks and large Linux farms, the past decade has been witness to a major change in the practice of molecular modeling in the pharmaceutical industry, particularly in the resources available to the computational chemist [1]. As a result, computational methods are being increasingly used in various stages of the drug-discovery process [2, 3]. Coupled with a rapidly rising number of protein structures, structure based drug design, driven by molecular docking and binding prediction has been undergoing somewhat of a renaissance. Molecular-docking methodologies ultimately seek to predict (or often retrospectively reproduce) the best mode by which a given compound will fit into a binding site of a macromolecular target. Docking, as a result, usually involves two independent steps: (1) the sampling of the ligand’s positional, conformational, and configurational space to predict the ligand’s pose within the