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Volumn 53, Issue 3, 2013, Pages 553-559
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Predicting potent compounds via model-based global optimization
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Author keywords
[No Author keywords available]
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Indexed keywords
CONSTRAINED OPTIMIZATION;
FUNCTION EVALUATION;
GLOBAL OPTIMIZATION;
LEARNING ALGORITHMS;
MACHINE LEARNING;
MOLECULAR GRAPHICS;
ACTIVE COMPOUNDS;
GLOBAL OPTIMIZATION PROBLEMS;
HIGH PROBABILITY;
MINIMIZING THE NUMBER OF;
NEAREST-NEIGHBOR APPROACHES;
OPTIMIZATION FUNCTION;
POTENT COMPOUNDS;
TRAINING DATA;
COMPUTATIONAL CHEMISTRY;
ALGORITHM;
ARTICLE;
ARTIFICIAL INTELLIGENCE;
CHEMICAL DATABASE;
CHEMICAL MODEL;
COMPUTER SIMULATION;
DRUG DEVELOPMENT;
HIGH THROUGHPUT SCREENING;
HUMAN;
METHODOLOGY;
NORMAL DISTRIBUTION;
QUANTITATIVE STRUCTURE ACTIVITY RELATION;
ALGORITHMS;
ARTIFICIAL INTELLIGENCE;
COMPUTER SIMULATION;
DATABASES, CHEMICAL;
DRUG DISCOVERY;
HIGH-THROUGHPUT SCREENING ASSAYS;
HUMANS;
MODELS, CHEMICAL;
NORMAL DISTRIBUTION;
QUANTITATIVE STRUCTURE-ACTIVITY RELATIONSHIP;
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EID: 84875468578
PISSN: 15499596
EISSN: 1549960X
Source Type: Journal
DOI: 10.1021/ci3004682 Document Type: Article |
Times cited : (24)
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References (11)
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