Novel enhanced applications of QSPR models: Temperature dependence of aqueous solubility
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- 作者：Kyrylo Klimenko ; Victor Kuz' ; min ; Liudmila Ognichenko ; Leonid Gorb ; Manoj Shukla ; Natalia Vinas ; Edward Perkins ; Pavel Polishchuk ; Anatoly Artemenko and Jerzy Leszczynski
- 刊名：Journal of Computational Chemistry
- 出版年：2016
- 出版时间：August 15, 2016
- 年：2016
- 卷：37
- 期：22
- 页码：2045-2051
- 全文大小：483K
- ISSN：1096-987X
文摘
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.