Accurate prediction of aquatic toxicity of aromatic compounds based on Genetic Algorithm and Least Squares Support Vector Machines

QSAR and Combinatorial Science

Volumn 27, Issue 7, 2008, Pages 850-865

  Lei, Beilei ^a   Li, Jiazhong ^a   Liu, Huanxiang ^a   Yao, Xiaojun ^a  

^a LANZHOU UNIVERSITY   (China)

Author keywords

Applicability domain; Aquatic toxicity; Genetic algorithm; Kennard Stone algorithm; Least squares support vector machines; Quantitative structure toxicity relationships

Indexed keywords

AROMATIC COMPOUNDS; FEATURE SELECTION; FORECASTING; LINEAR REGRESSION; STATISTICAL TESTS; SUPPORT VECTOR MACHINES; TOXICITY;

ACCURATE PREDICTION; APPLICABILITY DOMAIN; AQUATIC TOXICITY; ECO-TOXICOLOGY; KENNARD-STONE ALGORITHM; LEAST SQUARE SUPPORT VECTOR MACHINES; POTENTIAL NEGATIVE EFFECTS; QUANTITATIVE STRUCTURE - TOXICITY RELATIONSHIP; QUANTITATIVE STRUCTURES; RELATIONSHIP MODEL;

GENETIC ALGORITHMS;

AROMATIC COMPOUND;

ACCURACY; ARTICLE; CHEMICAL STRUCTURE; COMPUTER PROGRAM; ECOTOXICOLOGY; FEASIBILITY STUDY; GENETIC ALGORITHM; MULTIPLE LINEAR REGRESSION ANALYSIS; PREDICTION; PRIORITY JOURNAL; QUANTITATIVE STRUCTURE ACTIVITY RELATION; RELIABILITY; SUPPORT VECTOR MACHINE; TOXICITY TESTING; VALIDATION PROCESS; WATER POLLUTION;

EID: 54949109779     PISSN: 1611020X     EISSN: 16110218     Source Type: Journal    
DOI: 10.1002/qsar.200760167     Document Type: Article

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