Support Vector Machines for the Estimation of Aqueous Solubility

Abstract

Support Vector Machines (SVMs) are used to estimate aqueous solubility of organic compounds. A SVM equipped with a Tanimoto similarity kernel estimates solubility with accuracy comparable to results from other reported methods where the same data sets have been studied. Complete cross-validation on a diverse data set resulted in a root-mean-squared error = 0.62 and R(2) = 0.88. The data input to the machine is in the form of molecular fingerprints. No physical parameters are explicitly involved in calculations.