Computational Approaches as Rational Decision Support Systems for Discovering Next-Generation Antitubercular Agents: Mini-Review

Abstract

Tuberculosis, malaria, dengue, chikungunya, leishmaniasis etc. are a large group of neglected tropical diseases that prevail in tropical and subtropical countries, affecting one billion people every year. Minimal funding and grants for research on these scientific problems challenge many researchers to find a different way to reduce the extensive time and cost involved in the drug discovery cycle of these problems. Computer-aided drug design techniques have already been proved successful in the discovery of new molecules rationally by reducing the time and cost involved in the development of drugs. In the current minireview, we are highlighting on the molecular modeling studies published during 2010-2018 for target specific antitubercular agents. This review includes the studies of Structure-Based (SB) and Ligand-Based (LB) modeling and those involving Machine Learning (ML) techniques against different antitubercular targets such as dihydrofolate reductase (DHFR), enoyl Acyl Carrier Protein (ACP) reductase (InhA), catalase-peroxidase (KatG), enzyme antigen 85C, protein tyrosine phosphatases (PtpA and PtpB), dUTPase, thioredoxin reductase (MtTrxR), etc. The information presented in this review will help the researchers to get acquainted with the recent progress in the modeling studies of antitubercular agents.

Keywords: Neglected diseases; antitubercular targets; ligand-based; machine learning; structure-based; thioredoxin reductase..