Novel enhanced applications of QSPR models: Temperature dependence of aqueous solubility

被引：14

作者：

Klimenko, Kyrylo ^{[1
,2
]}

Kuz'min, Victor ^{[1
]}

Ognichenko, Liudmila ^{[1
]}

Gorb, Leonid ^{[3
]}

Shukla, Manoj ^{[4
]}

Vinas, Natalia ^{[4
]}

Perkins, Edward ^{[4
]}

Polishchuk, Pavel ^{[5
]}

Artemenko, Anatoly ^{[1
]}

Leszczynski, Jerzy ^{[6
]}

机构：

[1] Natl Acad Sci Ukraine, Dept Mol Struct & Chemoinformat, AV Bogatsky Phys Chem Inst, Lustdorfskaya Doroga 86, UA-65080 Odessa, Ukraine

[2] Univ Strasbourg, CNRS, UniStra, Lab Chemoinformat,UMR 7140, 1 Rue B Pascal, F-67000 Strasbourg, France

[3] HX5 LLC, Vichsburg, MS 39180 USA

[4] US Army Engineer Res & Dev Ctr, Vicksburg, MS 39180 USA

[5] Palacky Univ, Inst Mol & Translat Med, Hnevotinska 1333-5, Olomouc 77900, Czech Republic

[6] Jackson State Univ, Dept Chem, Interdisciplinary Ctr Nanotox, Jackson, MS 39217 USA

来源：

JOURNAL OF COMPUTATIONAL CHEMISTRY | 2016年 / 37卷 / 22期

基金：

美国国家科学基金会;

关键词：

QSPR; feature net; temperature-dependent; aqueous solubility; RANDOM FOREST; SIMPLEX REPRESENTATION; PREDICTION; QSAR; ETHANOL; WATER; SYSTEM; ACID;

D O I：

10.1002/jcc.24424

中图分类号：

O6 [化学];

学科分类号：

0703 ;

摘要：

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 S-w 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. (c) 2016 Wiley Periodicals, Inc.

引用

页码：2045 / 2051

页数：7

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