Quantitative structure-activity relationship models for prediction of sensory irritants (log RD50) of volatile organic chemicals

Chemosphere

Volumn 63, Issue 7, 2006, Pages 1142-1153

  Luan, Feng ^a   Ma, Weiping ^a   Zhang, Xiaoyun ^a   Zhang, Haixia ^a   Liu, Mancan ^a   Hu, Zhide ^a   Fan, B T ^b  

^a LANZHOU UNIVERSITY   (China)

^b UNIVERSITÉ PARIS DESCARTES   (France)

Author keywords

Linear discriminant analysis (LDA); QSAR QSPR; Radial basis function neural networks (RNFNN); Sensory irritants; Support vector machine (SVM); The heuristic method (HM)

Indexed keywords

GEOMETRY; HEURISTIC METHODS; MATHEMATICAL MODELS; REGRESSION ANALYSIS; TOPOLOGY; TOXIC MATERIALS;

LINEAR DISCRIMINANT ANALYSIS (LDA); QSAR/QSPR; RADIAL BASIS FUNCTION NEURAL NETWORKS (RNFNN); SENSORY IRRITANTS; SUPPORT VECTOR MACHINE (SVM); THE HEURISTIC METHOD (HM);

VOLATILE ORGANIC COMPOUNDS;

POISON; VOLATILE ORGANIC COMPOUND;

GEOMETRY; HEURISTIC METHODS; MATHEMATICAL MODELS; REGRESSION ANALYSIS; TOPOLOGY; TOXIC MATERIALS; VOLATILE ORGANIC COMPOUNDS;

DATA SET; REGRESSION ANALYSIS; TOXIC MATERIAL; VOLATILE ORGANIC COMPOUND;

ACCURACY; ARTICLE; COMPARATIVE STUDY; DATA ANALYSIS; ELECTRICITY; GEOMETRY; MATHEMATICAL COMPUTING; METHODOLOGY; NERVE CELL NETWORK; NONLINEAR REGRESSION ANALYSIS; QUANTITATIVE STRUCTURE ACTIVITY RELATION; QUANTUM CHEMISTRY; SCREENING; SENSORY STIMULATION; STATISTICAL ANALYSIS;

EID: 33646155141     PISSN: 00456535     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.chemosphere.2005.09.053     Document Type: Article

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