Using probabilistic neural networks to model the toxicity of chemicals to the fathead minnow ( Pimephales promelas): A study based on 865 compounds

We investigate the use of probabilistic neural networks (PNN) to model the acute toxicity (96-hr LC50) to the fathead minnow ( Pimephales promelas) based on a 865 chemicals data set. In contrast to most other toxicological models, the octanol/water partition coefficient is not used as input paramete...

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Bibliographic Details
Published in:Chemosphere (Oxford) 1999-06, Vol.38 (14), p.3237-3245
Main Authors: Kaiser, Klaus L.E., Niculescu, Stefan P.
Format: Article
Language:English
Subjects:
acute toxicity
Agnatha. Pisces
Animal, plant and microbial ecology
Animals
Applied ecology
Biological and medical sciences
Cyprinidae
Ecotoxicology, biological effects of pollution
Effects of pollution and side effects of pesticides on vertebrates
Fathead minnow
Forecasting
Fundamental and applied biological sciences. Psychology
Lethal Dose 50
Models, Theoretical
neural network
neural networks
Neural Networks (Computer)
Pimephales promelas
prediction
Toxicity Tests - methods
Xenobiotics - toxicity
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Summary:We investigate the use of probabilistic neural networks (PNN) to model the acute toxicity (96-hr LC50) to the fathead minnow ( Pimephales promelas) based on a 865 chemicals data set. In contrast to most other toxicological models, the octanol/water partition coefficient is not used as input parameter. The information fed into the neural network is solely based on simple molecular descriptors as can be derived from the chemicals' structures and indicates the potential of this approach as general methodology for the estimation of toxicological effects of chemicals.
ISSN:0045-6535
1879-1298
DOI:10.1016/S0045-6535(99)00553-6