Estimation of the chemical-induced eye injury using a weight-of-evidence (WoE) battery of 21 artificial neural network (ANN) c-QSAR models (QSAR-21): Part I: Irritation potential

Regulatory Toxicology and Pharmacology

Volumn 71, Issue 2, 2015, Pages 318-330

  Verma, Rajeshwar P ^a   Matthews, Edwin J ^a  

^a CENTER FOR DRUG EVALUATION AND RESEARCH   (United States)

Author keywords

Artificial neural network; C QSAR; Eye irritation; QSAR 21; Weight of evidence

Indexed keywords

COSMETIC; IRRITANT AGENT;

ANIMAL TESTING ALTERNATIVE; ARTICLE; ARTIFICIAL NEURAL NETWORK; CHEMICAL INJURY; COMPUTER MODEL; CORROSION; DRAIZE TEST; EYE INJURY; EYE IRRITATION; HUMAN; IN VITRO STUDY; INTERNAL VALIDITY; LAW; MARKETING; NONHUMAN; PREDICTION; QUANTITATIVE ANALYSIS; QUANTITATIVE STRUCTURE ACTIVITY RELATION; RISK ASSESSMENT; SENSITIVITY AND SPECIFICITY; TOXICITY TESTING; WEIGHT OF EVIDENCE; ANIMAL; CHEMICALLY INDUCED; PROCEDURES; RABBIT;

ANIMALIA; ORYCTOLAGUS CUNICULUS;

ANIMAL TESTING ALTERNATIVES; ANIMALS; COSMETICS; EYE INJURIES; IRRITANTS; NEURAL NETWORKS (COMPUTER); QUANTITATIVE STRUCTURE-ACTIVITY RELATIONSHIP; RABBITS;

EID: 84923047525     PISSN: 02732300     EISSN: 10960295     Source Type: Journal    
DOI: 10.1016/j.yrtph.2014.11.011     Document Type: Article

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