GA(M)E-QSAR: A novel, fully automatic genetic-algorithm-(meta)-ensembles approach for binary classification in ligand-based drug design

Journal of Chemical Information and Modeling

Volumn 52, Issue 9, 2012, Pages 2366-2386

  Pérez Castillo, Yunierkis ^a,b,c   Lazar, Cosmin ^a   Taminau, Jonatan ^a   Froeyen, Mathy ^c   Cabrera Pérez, Miguel Ángel ^b,d   Nowé, Ann ^a  

^a VRIJE UNIVERSITEIT BRUSSEL   (Belgium)

^b CENTRAL UNIVERSITY OF LAS VILLAS   (Cuba)

^c REGA INSTITUTE FOR MEDICAL RESEARCH   (Belgium)

^d MIGUEL HERNÁNDEZ UNIVERSITY   (Spain)

Author keywords

[No Author keywords available]

Indexed keywords

ADAPTIVE BOOSTING; COMPUTATIONAL CHEMISTRY; DRUG PRODUCTS; FEATURE EXTRACTION; GENETIC ALGORITHMS; LIGANDS;

BINARY CLASSIFICATION PROBLEMS; CLASSIFICATION ALGORITHM; CLASSIFICATION RESULTS; COMPUTER AIDED DRUG DESIGN; DRUG DISCOVERY PROCESS; FEATURE SELECTION AND CLASSIFICATION; GENETIC-ALGORITHM OPTIMIZATIONS; OPTIMIZATION CAPABILITIES;

CLASSIFICATION (OF INFORMATION);

LIGAND;

ALGORITHM; ARTICLE; AUTOMATION; DRUG DESIGN; QUANTITATIVE STRUCTURE ACTIVITY RELATION; THEORETICAL MODEL;

ALGORITHMS; AUTOMATION; DRUG DESIGN; LIGANDS; MODELS, THEORETICAL; QUANTITATIVE STRUCTURE-ACTIVITY RELATIONSHIP;

EID: 84866647773     PISSN: 15499596     EISSN: 1549960X     Source Type: Journal    
DOI: 10.1021/ci300146h     Document Type: Article

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