Prediction of standard Gibbs energies of the transfer of peptide anions from aqueous solution to nitrobenzene based on support vector machine and the heuristic method

Journal of Computer-Aided Molecular Design

Volumn 20, Issue 1, 2006, Pages 1-11

  Feng, Luan ^a   Xiaoyun, Zhang ^a   Haixia, Zhang ^a   Ruisheng, Zhang ^a   Mancang, Liu ^a   Zhide, Hu ^a   Botao, Fan ^b  

^a LANZHOU UNIVERSITY   (China)

^b UNIVERSITÉ PARIS DESCARTES   (France)

Author keywords

HM; Peptide; QSPR; SVM; G

Indexed keywords

FORECASTING; GIBBS FREE ENERGY; HEURISTIC METHODS; MEAN SQUARE ERROR; NITROBENZENE; RADIAL BASIS FUNCTION NETWORKS; SOLUTIONS; SUPPORT VECTOR MACHINES;

DESCRIPTORS; MACHINE LEARNING TECHNIQUES; MOLECULAR DESCRIPTORS; PEPTIDE ANIONS; QUANTITATIVE STRUCTURE PROPERTY RELATIONSHIPS; RADIAL BASIS FUNCTION NEURAL NETWORKS (RBF); ROOT MEAN SQUARED ERRORS; STANDARD GIBBS ENERGY; SUPPORT VECTORS MACHINE; ΔGΘ;

PEPTIDES;

NITROBENZENE; PROTEIN;

AQUEOUS SOLUTION; ARTICLE; ARTIFICIAL NEURAL NETWORK; CHEMICAL STRUCTURE; COMPUTER AIDED DESIGN; COMPUTER PROGRAM; LIPOPHILICITY; MATHEMATICAL COMPUTING; PHYSICAL CHEMISTRY; PREDICTION; PRIORITY JOURNAL; PROTEIN TRANSPORT; QUANTITATIVE STRUCTURE ACTIVITY RELATION; QUANTITATIVE STRUCTURE PROPERTY RELATION; STATISTICAL ANALYSIS;

EID: 33745134668     PISSN: 0920654X     EISSN: 15734951     Source Type: Journal    
DOI: 10.1007/s10822-005-9031-1     Document Type: Article

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