Deep neural nets as a method for quantitative structure-activity relationships

Journal of Chemical Information and Modeling

Volumn 55, Issue 2, 2015, Pages 263-274

  Ma, Junshui ^a   Sheridan, Robert P ^a   Liaw, Andy ^a   Dahl, George E ^b   Svetnik, Vladimir ^a  

^a MERCK RESEARCH LABORATORIES   (United States)

^b UNIVERSITY OF TORONTO   (Canada)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTATIONAL CHEMISTRY; COMPUTER HARDWARE; DECISION TREES; DEEP NEURAL NETWORKS; GRAPHICS PROCESSING UNIT; MOLECULAR GRAPHICS; NATURAL LANGUAGE PROCESSING SYSTEMS; PROGRAM PROCESSORS; SUPPORT VECTOR MACHINES;

ADJUSTABLE PARAMETERS; GRAPHICAL PROCESSING UNIT (GPUS); MACHINE LEARNING COMMUNITIES; NATURAL LANGUAGE PROCESSING; PRACTICAL ISSUES; QUANTITATIVE STRUCTURE ACTIVITY RELATIONSHIP; QUANTITATIVE STRUCTURE-ACTIVITY RELATIONSHIPS; TRAINING ALGORITHMS;

NEURAL NETWORKS;

ALGORITHM; ARTIFICIAL NEURAL NETWORK; DRUG DEVELOPMENT; MACHINE LEARNING; PROSPECTIVE STUDY; QUANTITATIVE STRUCTURE ACTIVITY RELATION; SUPPORT VECTOR MACHINE; WORKFLOW;

ALGORITHMS; DRUG DISCOVERY; MACHINE LEARNING; NEURAL NETWORKS (COMPUTER); PROSPECTIVE STUDIES; QUANTITATIVE STRUCTURE-ACTIVITY RELATIONSHIP; SUPPORT VECTOR MACHINE; WORKFLOW;

EID: 84923367417     PISSN: 15499596     EISSN: 1549960X     Source Type: Journal    
DOI: 10.1021/ci500747n     Document Type: Article

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