Mycobacterium tuberculosis (Mtb) infection remains the leading cause of death from a single infectious disease (World Health Organization, 2001), with two million deaths and over eight million new tuberculosis cases annually (Dye et al., 1999). The current WHO-recommended strategy for tuberculosis control, known as directly observed treatment, short-course (DOTS), requires patients to adhere to a drug regimen comprising isoniazid, rifampin, pyrazinamide and ethambutol for a minimum of six months. This prolonged and complicated therapy frequently causes severe side effects, which in turn often results in patient noncompliance. As a consequence, many patients relapse and multi-drug-resistant Mtb strains emerge. The only available human vaccine,Mycobacterium bovis-derived bacillus Calmette–Guérin (BCG), provides inconsistent efficacy, with reported variation between 0 and 80% (Fine, 1995) .Potential new and effective drugs should inhibit proteins that are essential for bacterial viability but are not present in humans.
Virtual Screening has become a very important step in Computational Drug discovery process. It involves screening thousands of compounds against a target to find probable hits for the drug discovery.The identification of small drug-like compounds that selectively inhibit the function of biological targets has historically been a major focus in the pharmaceutical industry, and in recent years, has generated much interest in academia as well. Drug-like compounds are valuable as chemical genetics tools to probe biological pathways in a reversible, dose- and time-dependent manner for drug target identification. In addition, small molecule compounds can be used to characterize the shape and charge preferences of macromolecular binding sites, for both structure-based and ligand-based drug design.