New hybrid genetic based support vector regression as QSAR approach for analyzing flavonoids-GABA(A) complexes

Journal of Chemical Information and Modeling

Volumn 49, Issue 6, 2009, Pages 1475-1485

  Goodarzi, Mohammad ^a   Duchowicz, Pablo R ^a   Wu, Chih H ^b   Fernández, Francisco M ^a   Castro, Eduardo A ^a  

^a INSTITUTO DE INVESTIGACIONES FISICOQUÍMICAS TEÓRICAS Y APLICADAS INIFTA   (Argentina)

^b NATIONAL TAICHUNG UNIVERSITY OF EDUCATION   (Taiwan)

Author keywords

[No Author keywords available]

Indexed keywords

BACKPROPAGATION; BINDING ENERGY; BINDING SITES; COMPLEX NETWORKS; COMPUTATIONAL CHEMISTRY; GENETIC ALGORITHMS; LEAST SQUARES APPROXIMATIONS; SUPPORT VECTOR MACHINES; VECTORS;

BACK PROPAGATION ARTIFICIAL NEURAL NETWORK (BPANN); BINDING AFFINITIES; CHEMOMETRIC TECHNIQUES; CROSS VALIDATION; EVOLUTIONARY PROCESS; PARTIAL LEAST SQUARE (PLS); PREDICTIVE CAPABILITIES; STATISTICAL QUALITY;

SUPPORT VECTOR REGRESSION;

4 AMINOBUTYRIC ACID A RECEPTOR; FLAVONOID;

ALGORITHM; ARTICLE; ARTIFICIAL INTELLIGENCE; CHEMISTRY; GENETICS; METABOLISM; NONLINEAR SYSTEM; PROTEIN BINDING; QUANTITATIVE STRUCTURE ACTIVITY RELATION; REGRESSION ANALYSIS;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; FLAVONOIDS; GENETICS; NONLINEAR DYNAMICS; PROTEIN BINDING; QUANTITATIVE STRUCTURE-ACTIVITY RELATIONSHIP; RECEPTORS, GABA-A; REGRESSION ANALYSIS;

EID: 67650075324     PISSN: 15499596     EISSN: 1549960X     Source Type: Journal    
DOI: 10.1021/ci900075f     Document Type: Article

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