QSPR studies on soot-water partition coefficients of persistent organic pollutants by using artificial neural network

Two quantitative structure property relationship (QSPR) models for predicting soot-water partition coefficients (K-sc) of 25 persistent organic pollutants (POPS) were developed. One model was established with linear artificial neural network (L-ANN). the other model was developed by using back propagation artificial neural network (BP-ANN) Leave one out cross validation was adopted to assess the predictive ability of the developed models. For the L-ANN model, the square of correlation coefficient (R-2) between the predicted and experimental log K-sc is 0.8358 and the RMS%RE is 6.32 for all the compounds. For the BP-ANN model, R-2 is 0 9628 and the RMS%RE is 4.12 for all the compounds. The result of leave one out cross validation demonstrates that both L-ANN and BP-ANN are practicable for developing the QSPR model for K-sc of the investigated POPs. However, the model established with BP-ANN is better than the model established with L-ANN in prediction accuracy. It is shown that BP-ANN is a promising method for developing QSPR models for K-sc of POPs. (C) 2010 Elsevier Ltd All rights reserved.