SVM approach for predicting LogP

Molecular Diversity

Volumn 10, Issue 3, 2006, Pages 301-309

  Liao, Quan ^a   Yao, Jianhua ^a   Yuan, Shengang ^a  

^a SHANGHAI INSTITUTE OF ORGANIC CHEMISTRY   (China)

Author keywords

LogP prediction; Multiple linear regression (MLR); Partial least squares (PLS); Support vector machines (SVM)

Indexed keywords

AROMATIC COMPOUND; BROMINE; CHLORINE; FLUORINE; HYDROGEN; IODINE; NITROGEN; OCTANOL; PHOSPHORUS; SULFUR; WATER;

ARTICLE; ATOM; CHEMICAL STRUCTURE; MACHINE; MATHEMATICAL ANALYSIS; MULTIPLE LINEAR REGRESSION ANALYSIS; PARTITION COEFFICIENT; PREDICTION; PRIORITY JOURNAL; QUANTITATIVE STRUCTURE ACTIVITY RELATION; REGRESSION ANALYSIS; STATISTICAL MODEL; STRUCTURE ANALYSIS; SUPPORT VECTOR MACHINE;

ARTIFICIAL INTELLIGENCE; CHEMISTRY, PHYSICAL; DIAGNOSIS, COMPUTER-ASSISTED; LEAST-SQUARES ANALYSIS; LINEAR MODELS; MODELS, CHEMICAL; MODELS, THEORETICAL; OCTANOLS; PHARMACEUTICAL PREPARATIONS; PHYSICOCHEMICAL PHENOMENA; SOLUBILITY; WATER;

EID: 33749625953     PISSN: 13811991     EISSN: None     Source Type: Journal    
DOI: 10.1007/s11030-006-9036-2     Document Type: Article

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