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.
