Quantitative structure-activity relationships for toxicity of phenols using regression analysis and computational neural networks

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.