The increasing resistance of Mycobacterium tuberculosis to the existing drugs has alarmed the worldwide scientific community. In an attempt to overcome this problem computer-aided drug design has provide an extraordinary support to the different strategies in drug discovery. There are around 250 biological receptors such as enzymes that can be used in principle, for the design of antituberculosis compounds that act by a specific mechanism of action. Also, there more than 5000 compound available in the literature, and that constitute important information in order to search new molecular patterns for the design of new antituberculosis agents. The purpose of this paper is to explored the current state of drug discovery of antituberculosis agents and how the different strategies supported by computeraided drug design methods has influenced in a determinant way in the design of new molecular entities that can result the future antituberculosis drugs.
Keywords: Structure based-drug design, ligand based-drug design, enzymatic inhibitors, 3D-QSAR methodologies, graph-theoretical approaches, computer-aided drug design, antituberculosis, MDR-TB, Artificial Neural Networks (ANN), ANTI-TB AGENTS, CoMSIA methods, CoMFA methods, MIC, Heterogeneous Series, Mycobacterium tuberculosis, QSPR, QSTR, Multiple Linear Regression, Hydropathic Interactions, CP-MLR
Rights & PermissionsPrintExport