Combining computational methods for hit to lead optimization in mycobacterium tuberculosis drug discovery

Pharmaceutical Research

Volumn 31, Issue 2, 2014, Pages 414-435

  Ekins, Sean ^a,b   Freundlich, Joel S ^c   Hobrath, Judith V ^d   Lucile White, E ^d   Reynolds, Robert C ^d,e  

^a Collaborative Drug Discovery ^*  (United States)

^b Collaborations in Chemistry ^*  (United States)

^c RUTGERS NEW JERSEY MEDICAL SCHOOL   (United States)

^d SOUTHERN RESEARCH INSTITUTE   (United States)

^e UNIVERSITY OF ALABAMA AT BIRMINGHAM   (United States)

Author keywords

bayesian models; clustering; Collaborative Drug Discovery Tuberculosis database; dual event models; function class fingerprints; lead optimization; Mycobacterium tuberculosis; tuberculosis

Indexed keywords

TUBERCULOSTATIC AGENT;

ANTIBACTERIAL ACTIVITY; ANTIBIOTIC RESISTANCE; ARTICLE; BACTERIAL STRAIN; BAYESIAN LEARNING; CELL LINE; CELL STRAIN HEPG2; COMPUTER MODEL; CYTOTOXICITY; DOSE RESPONSE; GROWTH INHIBITION; HUMAN; MAMMAL CELL; MYCOBACTERIUM TUBERCULOSIS; NONHUMAN; PREDICTION; PRIORITY JOURNAL; RETROSPECTIVE STUDY; SCREENING TEST; TUBERCULOSIS; VERO CELL;

ANTITUBERCULAR AGENTS; ARTIFICIAL INTELLIGENCE; BAYES THEOREM; CELL LINE, TUMOR; COMPUTER SIMULATION; DOSE-RESPONSE RELATIONSHIP, DRUG; DRUG DISCOVERY; HEP G2 CELLS; HUMANS; MYCOBACTERIUM TUBERCULOSIS; SMALL MOLECULE LIBRARIES; TUBERCULOSIS;

EID: 84896809914     PISSN: 07248741     EISSN: 1573904X     Source Type: Journal    
DOI: 10.1007/s11095-013-1172-7     Document Type: Article

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