Prediction of aqueous solubility for a diverse set of heteroatom-containing organic compounds using a quantitative structure-property relationship

The primary goal of a quantitative structure-property relationship (QSPR) is to identify a set of structurally based numerical descriptors that can be mathematically linked to a property of interest. The types of descriptors fall into three categories: topological, electronic, and geometric. In this study, 140 organic compounds with diverse structures were split into a training set, a cross-validation set, and a prediction set. The training set was used to build multiple linear regression and computational neural network models, the cross-validation set was used to prevent overtraining of the neural network, and the prediction set was used to validate the mathematical models. A set of nine descriptors was found that effectively linked the aqueous solubility to each structure. However, the polychlorinated biphenyls (PCBs) had a large root-mean-square (rms) error associated with them. Therefore models were also built using a training set that contained no PCBs. A set of nine descriptors was found with a significant improvement of the rms error of the training set as well as the prediction set.