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A comparative study of molecular similarity, statistical, and neural methods for predicting toxic modes of action
Author(s) -
Basak Subhash C.,
Grunwald Gregory D.,
Host George E.,
Niemi Gerald J.,
Bradbury Steven P.
Publication year - 1998
Publication title -
environmental toxicology and chemistry
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.1
H-Index - 171
eISSN - 1552-8618
pISSN - 0730-7268
DOI - 10.1002/etc.5620170611
Subject(s) - quantitative structure–activity relationship , toxicodynamics , molecular descriptor , similarity (geometry) , mode of action , artificial neural network , chemistry , artificial intelligence , computational biology , biological system , computer science , toxicology , biology , stereochemistry , toxicokinetics , toxicity , organic chemistry , image (mathematics)
Quantitative structure–activity relationship (QSAR) models are routinely used in predicting toxicologic and ecotoxicologic effects of untested chemicals. One critical factor in QSAR‐based risk assessment is the proper assignment of a chemical to a mode of action and associated QSAR. In this paper, we used molecular similarity, neural networks, and discriminant analysis methods to predict acute toxic modes of action for a set of 283 chemicals. The majority of these molecules had been previously determined through toxicodynamic studies in fish to be narcotics (two classes), electrophiles/proelectrophiles, uncouplers of oxidative phosphorylation, acetylcholinesterase inhibitors, and neurotoxicants. Nonempirical parameters, such as topological indices and atom pairs, were used as structural descriptors for the development of similarity‐based, statistical, and neural network models. Rates of correct classification ranged from 65 to 95% for these 283 chemicals.