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

摘要

Tuberculosis, malaria, dengue, chikungunya, leishmaniasis etc. are a large group of neglectedtropical diseases that prevail in tropical and subtropical countries, affecting one billion peopleevery year. Minimal funding and grants for research on these scientific problems challengemany researchers to find a different way to reduce the extensive time and cost involved in the drugdiscovery cycle of these problems. Computer-aided drug design techniques have already beenproved successful in the discovery of new molecules rationally by reducing the time and cost involvedin the development of drugs. In the current minireview, we are highlighting on the molecularmodeling studies published during 2010-2018 for target specific antitubercular agents. This review includesthe studies of Structure-Based (SB) and Ligand-Based (LB) modeling and those involving MachineLearning (ML) techniques against different antitubercular targets such as dihydrofolatereductase (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 acquaintedwith the recent progress in the modeling studies of antitubercular agents.