Use of artifcial neural networks in the qsar prediction of physicochemical properties and toxicities for reach legislation

Methods in Molecular Biology

Volumn 1260, Issue , 2015, Pages 65-88

  Dearden, John C ^a   Rowe, Philip H ^a  

^a LIVERPOOL JOHN MOORES UNIVERSITY   (United Kingdom)

Author keywords

Artifcial neural networks; Physicochemical properties; QSAR; REACH; Toxicity

Indexed keywords

ACTIVATED SLUDGE; ACUTE TOXICITY; AQUATIC SPECIES; ARTICLE; ARTIFICIAL NEURAL NETWORK; BIOACCUMULATION; BIODEGRADABILITY; CARCINOGENICITY; DISSOCIATION CONSTANT; EYE IRRITATION; GENETIC ALGORITHM; HYDROLYSIS; LAW; LIPID MEMBRANE; MELTING POINT; MUTAGENICITY; NONHUMAN; PARTITION COEFFICIENT; PHYSICAL CHEMISTRY; QUANTITATIVE STRUCTURE ACTIVITY RELATION; QUANTITATIVE STRUCTURE PROPERTY RELATION; RELATIVE DENSITY; REPRODUCTIVE TOXICITY; RESPIRATION DEPRESSION; SKIN IRRITATION; SKIN SENSITIZATION; SUPPORT VECTOR MACHINE; SURFACE TENSION; TEMPERATURE; TERRESTRIAL SPECIES; VALIDATION STUDY; VAPOR PRESSURE; ANIMAL; ENVIRONMENTAL PLANNING; EUROPEAN UNION; HUMAN; LEGISLATION AND JURISPRUDENCE; POLLUTANT; TOXICITY;

POLLUTANT;

ANIMALS; ENVIRONMENTAL POLICY; ENVIRONMENTAL POLLUTANTS; EUROPEAN UNION; HUMANS; NEURAL NETWORKS (COMPUTER); QUANTITATIVE STRUCTURE-ACTIVITY RELATIONSHIP;

EID: 84917690958     PISSN: 10643745     EISSN: None     Source Type: Book Series    
DOI: 10.1007/978-1-4939-2239-0_5     Document Type: Article

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