Computational databases, pathway and cheminformatics tools for tuberculosis drug discovery

被引:68
|
作者
Ekins, Sean [1 ,2 ,3 ,4 ]
Freundlich, Joel S. [5 ]
Choi, Inhee [6 ]
Sarker, Malabika [7 ]
Talcott, Carolyn [7 ]
机构
[1] Collaborat Chem, Jenkintown, PA 19046 USA
[2] Univ Maryland, Dept Pharmaceut Sci, Baltimore, MD 21201 USA
[3] Univ Med & Dent New Jersey, Robert Wood Johnson Med Sch, Dept Pharmacol, Piscataway, NJ 08854 USA
[4] Collaborat Drug Discovery, Burlingame, CA 94010 USA
[5] Texas A&M Univ, Dept Biochem & Biophys, Coll Stn, College Stn, TX 77843 USA
[6] NIAID, NIH, Lab Clin Infect Dis, TB Res Sect, Bethesda, MD 20892 USA
[7] SRI Int, Menlo Pk, CA 94025 USA
关键词
MONOPHOSPHATE KINASE INHIBITORS; STRUCTURAL GENOMICS CONSORTIUM; MYCOBACTERIUM-TUBERCULOSIS; ANTITUBERCULAR COMPOUNDS; METABOLIC PATHWAYS; PHYSICOCHEMICAL PROPERTIES; IN-VITRO; 3D-QSAR; DESIGN; AGENTS;
D O I
10.1016/j.tim.2010.10.005
中图分类号
Q5 [生物化学]; Q7 [分子生物学];
学科分类号
071010 ; 081704 ;
摘要
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.
引用
收藏
页码:65 / 74
页数:10
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