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Computational databases, pathway and cheminformatics tools for tuberculosis drug discovery |
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| Authors: | Ekins Sean Freundlich Joel S Choi Inhee Sarker Malabika Talcott Carolyn |
| Institution: | 1 Collaborations in Chemistry, 601 Runnymede Avenue, Jenkintown, PA 19046, USA 2 Department of Pharmaceutical Sciences, University of Maryland, Baltimore, MD, USA 3 Department of Pharmacology, Robert Wood Johnson Medical School, University of Medicine and Dentistry of New Jersey, Piscataway, NJ 08854, USA 4 Collaborative Drug Discovery, 1633 Bayshore Highway, Suite 342, Burlingame, CA 94010, USA 5 Department of Biochemistry and Biophysics, Texas A&M University, College Station, TX 77843, USA 6 Tuberculosis Research Section, Laboratory of Clinical Infectious Diseases, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD 20892, USA 7 SRI International, 333 Ravenswood Avenue, Menlo Park, CA 94025, USA |
| Abstract: | 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. |
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