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.
Correction to: Synthesis and biological evaluation of some 4-aminoquinoline derivatives as potential antitubercular agents
