Machine learning approaches for customized docking scores: Modeling of inhibition of Mycobacterium tuberculosis enoyl acyl carrier protein reductase

2010 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB 2010

Volumn , Issue , 2010, Pages 243-248

  Fogel, Gary B ^a   Tran, Jonathan ^b   Johnson, Stephen ^b   Hecht, David ^b  

^a NATURAL SELECTION INC   (United States)

^b Southwestern College   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ACYL CARRIER PROTEINS; AUTODOCK; DESCRIPTORS; DOCKING SCORE; DRUG COMPOUNDS; FEATURE SELECTION; HIGH-THROUGHPUT SCREENING; IN-SILICO; MACHINE LEARNING ALGORITHMS; MACHINE-LEARNING; MODEL GENERATION; MYCOBACTERIUM TUBERCULOSIS; QUANTITATIVE STRUCTURE ACTIVITY RELATIONSHIP; SMALL MOLECULES;

ARTIFICIAL INTELLIGENCE; BIOINFORMATICS; DOCKING; FEATURE EXTRACTION; LEARNING SYSTEMS; PROTEINS;

LEARNING ALGORITHMS;

EID: 77955599624     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CIBCB.2010.5510700     Document Type: Conference Paper

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