Bayesian models for screening and TB Mobile for target inference with Mycobacterium tuberculosis

Tuberculosis

Volumn 94, Issue 2, 2014, Pages 162-169

  Ekins, Sean ^a,b   Casey, Allen C ^c   Roberts, David ^c   Parish, Tanya ^c   Bunin, Barry A ^a  

^a Collaborative Drug Discovery ^*  (United States)

^b Collaborations in Chemistry ^*  (United States)

^c INFECTIOUS DISEASE RESEARCH INSTITUTE   (United States)

Author keywords

Bayesian models; Collaborative drug discovery tuberculosis database; Function class fingerprints; Mycobacterium tuberculosis; Virtual screening

Indexed keywords

ACETAMIDE; ALIPHATIC COMPOUND; BENZOTHIAZOLE; CIPROFLOXACIN; DIPHENYL ETHER; IMIDAZOLE; LIGAND; PIPERAZINE; PYRROLE; SULFIDE; MOLECULAR LIBRARY; TUBERCULOSTATIC AGENT;

ANTIMICROBIAL ACTIVITY; ARTICLE; BAYESIAN LEARNING; HIGH THROUGHPUT SCREENING; IN VITRO STUDY; MOBILE APPLICATION; MOLECULAR LIBRARY; MOLECULE; MYCOBACTERIUM TUBERCULOSIS; NONHUMAN; PREDICTION; PRIORITY JOURNAL; PROSPECTIVE STUDY; BAYES THEOREM; CHEMISTRY; DRUG DEVELOPMENT; DRUG EFFECTS; FEMALE; HUMAN; MALE; PHARMACOLOGY; PROTEIN DATABASE; REPRODUCIBILITY;

ANTITUBERCULAR AGENTS; BAYES THEOREM; DATABASES, PROTEIN; DRUG DISCOVERY; FEMALE; HUMANS; MALE; MYCOBACTERIUM TUBERCULOSIS; REPRODUCIBILITY OF RESULTS; SMALL MOLECULE LIBRARIES;

EID: 84893914880     PISSN: 14729792     EISSN: 1873281X     Source Type: Journal    
DOI: 10.1016/j.tube.2013.12.001     Document Type: Article

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