The General Solubility Equation (GSE) is a QSPR model based on the melting point and log P of a chemical substance. It is used to predict the aqueous solubility of nonionizable chemical compounds. However, its reliance on experimentally derived descriptors, particularly melting point, limits its applicability to virtual compounds. The studies presented show that the GSE is able to predict, to within 1 log unit, the experimental aqueous solubility (log S) for 81% of the compounds in a data set of 1265 diverse chemical structures (−8.48 < log S < 1.58). However, the predictive ability of the GSE is reduced to 75% when applied to a subset of the data (1160 compounds −6.00 < log S < 0.00), which discounts those compounds occupying the sparsely populated regions of data space. This highlights how sparsely populated extremities of data sets can significantly skew results for linear regression-based models. Replacing the melting point descriptor of the GSE with a descriptor which accounts for topographical polar...
['Jogoth Ali', 'Patrick Camilleri', 'Marc B. Brown', 'Andrew J. Hutt', 'Stewart B. Kirton']
Revisiting the General Solubility Equation : In Silico Prediction of Aqueous Solubility Incorporating the Effect of Topographical Polar Surface Area