Prediction of the aqueous solubility of benzylamine salts using QSPR model

Models predicting aqueous solubility of benzylamine salts were developed using multivariate partial least squares (PLS) and artificial neural network (ANN). Molecular descriptors, including binding energy (BE) and surface area of salts (SA), were calculated by the use of Hyperchem and ChemPlus QSAR programs for Windows. Other physicochemical properties, such as hydrogen acceptor for oxygen atoms, hydrogen acceptor for nitrogen atoms, hydrogen bond donors, hydrogen bond forming ability, molecular weight (MW), and calculated log partition coefficient (clog P) of p-substituted benzoic acids, were also used as descriptors. In this study, the predictive ability of ANN, especially multilayer perceptron (MLP) architecture networks, was founded to be superior to PLS models. The best ANN model derived, a 6-1-1 architecture, had an overall R2 of 0.850 and root mean square error (RMSE) for cross-verification and test set of 0.189 and 0.185 log units, respectively. Since all the utilized descriptors are readily obtained from calculation, these derived models offer the advantage of not requiring the experimental determination of some descriptors.