Linear and nonlinear functions on modeling of aqueous solubility of organic compounds by two structure representation methods.

Abstract

Several quantitative models for the prediction of aqueous solubility of organic compounds were developed based on a diverse dataset with 2084 compounds by using multi-linear regression analysis and backpropagation neural networks. The compounds were described by two different structure representation methods: (1) with 18 topological descriptors; and (2) with 32 radial distribution function codes representing the 3D structure of a molecule and eight additional descriptors. The dataset was divided into a training and a test set based on Kohonen's self-organizing neural network. Good prediction results were obtained for backpropagation neural network models: with 18 topological descriptors, for the 936 compounds in the test set, a correlation coefficient of 0.92, and a standard deviation of 0.62 were achieved; with 3D descriptors, for the 866 compounds in the test set, a correlation coefficient of 0.90, and a standard deviation of 0.73 were achieved. The models were also tested by using another dataset, and the relationship of the two datasets was examined by Kohonen's self-organizing neural network.