A model developed to predict aqueous solubility at different temperatures has been proposed based on quantitative structure-property relationships (QSPR) methodology. The prediction consists of two steps. The first one predicts the value of k parameter in the linear equation lgSw=kT+c, where Sw is the value of solubility and T is the value of temperature. The second step uses Random Forest technique to create high-efficiency QSPR model. The performance of the model is assessed using cross-validation and external test set prediction. Predictive capacity of developed model is compared with COSMO-RS approximation, which has quantum chemical and thermodynamic foundations. The comparison shows slightly better prediction ability for the QSPR model presented in this publication. © 2016 Wiley Periodicals, Inc.
Keywords: QSPR; aqueous solubility; feature net; temperature-dependent.
© 2016 Wiley Periodicals, Inc.