Novel enhanced applications of QSPR models: Temperature dependence of aqueous solubility |
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| Authors: | Kyrylo Klimenko Victor Kuz'min Liudmila Ognichenko Leonid Gorb Manoj Shukla Natalia Vinas Edward Perkins Pavel Polishchuk Anatoly Artemenko Jerzy Leszczynski |
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| Affiliation: | 1. Department of Molecular Structure and Chemoinformatics, A.V. Bogatsky Physical‐Chemical Institute National Academy of Sciences of Ukraine, Odessa, Ukraine;2. Laboratoire de Chemoinformatique, (UMR 7140 CNRS/UniStra) Université de Strasbourg, Strasbourg, France;3. HX5 LLC, Vichsburg, Mississippi;4. US Army Engineer Research and Development Center, Vicksburg, Mississippi;5. Institute of Molecular and Translational Medicine, Palacky University Olomouc, Olomouc, Czech Republic;6. Interdisciplinary Center for Nanotoxicity, Department of Chemistry, Jackson State University, Jackson, Mississippi |
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| Abstract: | ![]()
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  , 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. |
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| Keywords: | QSPR feature net temperature‐dependent aqueous solubility |
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