Neural network studies : Part 3. Prediction of partition coefficients

In a previous paper (N. Bodor, A. Harget and M.-J. Huang, J. Am. Chem. Soc., 113 (1991) 9480) we demonstrated the utility of a neural network approach in the estimation of the aqueous solubility of organic compounds. This approach has now been extended to the prediction of partition coefficients. A training set of AM1 calculated properties and experimental values for 302 compounds was used and, after training, the neural network was tested for its ability to predict the partition coefficients of 21 compounds not included in the training set. We also tested six more compounds with molecular properties out of the training set property range. A comparison was made with the results obtained from a previous study which had used a regression analysis approach (N. Bodor and M.-J. Huang, J. Pharm. Sci., 81 (1992) 272). The neural network results compared favorably with those given by the regression analysis approach, both for the training set and for the new compounds.