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有机分子的升华热力学是否可预测?

Are the Sublimation Thermodynamics of Organic Molecules Predictable?

作者信息

McDonagh James L, Palmer David S, Mourik Tanja van, Mitchell John B O

机构信息

Manchester Institute of Biotechnology, The University of Manchester , 131 Princess Street, Manchester, M1 7DN, U.K.

School of Chemistry, University of St Andrews , North Haugh, St Andrews, Fife, Scotland, United Kingdom , KY16 9ST.

出版信息

J Chem Inf Model. 2016 Nov 28;56(11):2162-2179. doi: 10.1021/acs.jcim.6b00033. Epub 2016 Nov 2.

Abstract

We compare a range of computational methods for the prediction of sublimation thermodynamics (enthalpy, entropy, and free energy of sublimation). These include a model from theoretical chemistry that utilizes crystal lattice energy minimization (with the DMACRYS program) and quantitative structure property relationship (QSPR) models generated by both machine learning (random forest and support vector machines) and regression (partial least squares) methods. Using these methods we investigate the predictability of the enthalpy, entropy and free energy of sublimation, with consideration of whether such a method may be able to improve solubility prediction schemes. Previous work has suggested that the major source of error in solubility prediction schemes involving a thermodynamic cycle via the solid state is in the modeling of the free energy change away from the solid state. Yet contrary to this conclusion other work has found that the inclusion of terms such as the enthalpy of sublimation in QSPR methods does not improve the predictions of solubility. We suggest the use of theoretical chemistry terms, detailed explicitly in the Methods section, as descriptors for the prediction of the enthalpy and free energy of sublimation. A data set of 158 molecules with experimental sublimation thermodynamics values and some CSD refcodes has been collected from the literature and is provided with their original source references.

摘要

我们比较了一系列用于预测升华热力学(升华焓、升华熵和升华自由能)的计算方法。这些方法包括一种来自理论化学的模型,该模型利用晶格能量最小化(使用DMACRYS程序)以及通过机器学习(随机森林和支持向量机)和回归(偏最小二乘法)方法生成的定量结构性质关系(QSPR)模型。我们使用这些方法研究升华焓、升华熵和升华自由能的可预测性,并考虑这样的方法是否能够改进溶解度预测方案。先前的工作表明,在涉及通过固态的热力学循环的溶解度预测方案中,主要误差来源在于远离固态的自由能变化的建模。然而,与这一结论相反,其他工作发现,在QSPR方法中纳入升华焓等项并不能改善溶解度预测。我们建议使用在方法部分中明确详细说明的理论化学术语,作为预测升华焓和升华自由能的描述符。已从文献中收集了一个包含158个具有实验升华热力学值和一些CSD参考文献代码的分子的数据集,并提供了它们的原始来源参考文献。

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