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Quantitative structure‐activity relationships for toxicity of phenols using regression analysis and computational neural networks
Author(s) -
Xu Lu,
Ball J.W.,
Dixon S.L.,
Jurs P.C.
Publication year - 1994
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.5620130520
Subject(s) - phenols , logarithm , artificial neural network , quantitative structure–activity relationship , regression analysis , set (abstract data type) , toxicity , regression , biological system , linear regression , mathematics , computer science , chemistry , statistics , artificial intelligence , stereochemistry , biology , organic chemistry , mathematical analysis , programming language
Quantitative structure‐toxicity models were developed that directly link the molecular structures of a set of 50 alkylated and/or halogenated phenols with their polar narcosis toxicity, expressed as the negative logarithm of the IGC50 (50% growth inhibitory concentration) value in millimoles per liter. Regression analysis and fully connected, feed‐forward neural networks were used to develop the models. Two neural network training algorithms (back‐propagation and a quasi‐Newton method) were employed. The best model was a quasi‐Newton neural network that had a root‐mean‐square error of 0.070 log units for the 45 training set phenols and 0.069 log units for the five cross‐validation set phenols.