A General QSAR Model for Predicting the Acute Toxicity of Pesticides to Oncorhynchus mykiss

A Quantitative Structure-Activity Relationship (QSAR) model was derived for estimating the acute toxicity of pesticides against Oncorhynchus mykiss under varying experimental conditions. Chemicals were described by means of autocorrelation descriptors encoding lipophilicity (H 0 to H 5 ) and the H-b...

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Bibliographic Details
Published inSAR and QSAR in environmental research Vol. 11; no. 1; pp. 25 - 43
Main Authors Devillers, J., Flatin, J.
Format Journal Article
LanguageEnglish
Published England Taylor & Francis Group 01.01.2000
Subjects
acute toxicity
Algorithms
Animals
autocorrelation method
Body Weight
Chemical Phenomena
Chemistry, Physical
Hydrogen Bonding
Hydrogen-Ion Concentration
neural network
Neural Networks (Computer)
Oncorhynchus mykiss
pesticides
Pesticides - chemistry
Pesticides - toxicity
QSAR
Structure-Activity Relationship
Temperature
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Summary:A Quantitative Structure-Activity Relationship (QSAR) model was derived for estimating the acute toxicity of pesticides against Oncorhynchus mykiss under varying experimental conditions. Chemicals were described by means of autocorrelation descriptors encoding lipophilicity (H 0 to H 5 ) and the H-bonding acceptor ability (HBA 0 ) and H-bonding donor ability (HBD 0 ) of the pesticides. A three-layer feedforward neural network trained by the back-propagation algorithm was used as statistical engine for deriving a powerful QSAR model accounting for the weight of the fish, time of exposure, temperature, pH, and hardness.
ISSN:1062-936X
1029-046X
DOI:10.1080/10629360008033227