Prediction of n-octanol/water partition coefficients for polychlorinated dibenzo-p-dioxins using a general regression neural network.

A general regression neural network was used for the first time to study quantitative structure and property relationships of organic pollutants to correlate and predict n-octanol/water partition coefficients of polychlorinated dibenzo- p -dioxins from their topological molecular descriptors. In total, 42 polychlorinated dibenzo- p -dioxins and dibenzo- p -dioxins were available for this study-42 polychlorinated dibenzo- p -dioxins and dibenzo- p -dioxins in the training data set and 41 polychlorinated dibenzo- p -dioxins in the test data set. Partial least squares regression, back propagation network and general regression neural network models were trained using the training data set, and the accuracy of the models obtained were examined by the use of leave-one-out cross-validation. For prediction of the n-octanol/water partition coefficient, the best method is the general regression neural network. With the test data set, the correlation coefficient, root mean square error and mean absolute relative error for the general regression neural network model are 0.9276, 0.22 and 2.79%, respectively. For describing the structure of polychlorinated dibenzo- p -dioxins, the topological molecular descriptors outperform the mobile order and disorder thermodynamic method.