Computational databases, pathway and cheminformatics tools for tuberculosis drug discovery.
We are witnessing the growing menace of both increasing cases of drug-sensitive and drug-resistant Mycobacterium tuberculosis strains and the challenge to produce the first new tuberculosis (TB) drug in well over 40 years. The TB community, having invested in extensive high-throughput screening efforts, is faced with the question of how to optimally leverage these data to move from a hit to a lead to a clinical candidate and potentially, a new drug. Complementing this approach, yet conducted on a much smaller scale, cheminformatic techniques have been leveraged and are examined in this review. We suggest that these computational approaches should be optimally integrated within a workflow with experimental approaches to accelerate TB drug discovery.
Collaborations in Chemistry, 601 Runnymede Avenue, Jenkintown, PA 19046, USA. ekinssean@yahoo.com
Sean
S
Ekins
Joel S
JS
Freundlich
Inhee
I
Choi
Malabika
M
Sarker
Carolyn
C
Talcott
D000995:Antitubercular Agents / Q000737:chemistry* / Q000494:pharmacology*; D016208:Databases, Factual; D055808:Drug Discovery; D057166:High-Throughput Screening Assays; D006801:Humans; D009169:Mycobacterium tuberculosis / Q000187:drug effects
D000995:Antitubercular Agents