Prediction of P2Y12 antagonists using a novel genetic algorithm-support vector machine coupled approach

Analytica Chimica Acta

Volumn 690, Issue 1, 2011, Pages 53-63

  Hao, Ming ^a   Li, Yan ^a   Wang, Yonghua ^b   Zhang, Shuwei ^a  

^a DALIAN UNIVERSITY OF TECHNOLOGY   (China)

^b NORTHWEST A F UNIVERSITY   (China)

Author keywords

Descriptor selection; Genetic algorithm; P2Y12; Support vector machine

Indexed keywords

CHEMICAL SYNTHESIS; CORRELATION COEFFICIENT; CROSS VALIDATION; DATA SETS; DESCRIPTORS; DRUG DEVELOPMENT; EXTERNAL QUALITY; G PROTEIN COUPLED RECEPTORS; GAUSSIAN PROCESS; INHIBITION ACTIVITY; MOLECULAR DESCRIPTORS; NONLINEAR PROJECTIONS; NOVEL GENETIC ALGORITHM; P2Y12; PARTIAL LEAST SQUARES; QSAR STUDIES; RANDOM FORESTS; RIGOROUS EVALUATION; STATISTICAL PERFORMANCE; SVM MODEL; TEST SETS; TRAINING SETS;

COMPUTATIONAL CHEMISTRY; DECISION TREES; FORECASTING; LEAST SQUARES APPROXIMATIONS; OPTIMIZATION; SUPPORT VECTOR MACHINES; SYNTHESIS (CHEMICAL); VECTORS;

GENETIC ALGORITHMS;

PURINERGIC P2Y12 RECEPTOR ANTAGONIST; PURINERGIC RECEPTOR BLOCKING AGENT; UNCLASSIFIED DRUG; PURINERGIC P2Y RECEPTOR ANTAGONIST; PURINERGIC P2Y12 RECEPTOR;

ARTICLE; CONTROLLED STUDY; CORRELATION COEFFICIENT; DRUG RESEARCH; DRUG SCREENING; DRUG STRUCTURE; GENETIC ALGORITHM; INTERMETHOD COMPARISON; KERNEL METHOD; NONLINEAR SYSTEM; PARTIAL LEAST SQUARES REGRESSION; PREDICTION; PRIORITY JOURNAL; PROCESS OPTIMIZATION; QUANTITATIVE STRUCTURE ACTIVITY RELATION; RANDOM FOREST; SUPPORT VECTOR MACHINE; ALGORITHM; CHEMISTRY; METABOLISM; REGRESSION ANALYSIS; STATISTICAL MODEL;

ALGORITHMS; LEAST-SQUARES ANALYSIS; MODELS, STATISTICAL; PURINERGIC P2Y RECEPTOR ANTAGONISTS; QUANTITATIVE STRUCTURE-ACTIVITY RELATIONSHIP; RECEPTORS, PURINERGIC P2Y12;

EID: 79952623168     PISSN: 00032670     EISSN: 18734324     Source Type: Journal    
DOI: 10.1016/j.aca.2011.02.004     Document Type: Article

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