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理解小分子中的构象熵。

Understanding Conformational Entropy in Small Molecules.

作者信息

Chan Lucian, Morris Garrett M, Hutchison Geoffrey R

机构信息

Department of Statistics, University of Oxford, 24-29 St Giles', Oxford OX1 3LB, U.K.

Department of Chemistry, University of Pittsburgh, 219 Parkman Avenue, Pittsburgh, Pennsylvania 15260, United States.

出版信息

J Chem Theory Comput. 2021 Apr 13;17(4):2099-2106. doi: 10.1021/acs.jctc.0c01213. Epub 2021 Mar 24.

DOI:10.1021/acs.jctc.0c01213
PMID:33759518
Abstract

The calculation of the entropy of flexible molecules can be challenging, since the number of possible conformers can grow exponentially with molecule size and many low-energy conformers may be thermally accessible. Different methods have been proposed to approximate the contribution of conformational entropy to the molecular standard entropy, including performing thermochemistry calculations with all possible stable conformations and developing empirical corrections from experimental data. We have performed conformer sampling on over 120,000 small molecules generating some 12 million conformers, to develop models to predict conformational entropy across a wide range of molecules. Using insight into the nature of conformational disorder, our cross-validated physically motivated statistical model gives a mean absolute error of ∼4.8 J/mol·K or under 0.4 kcal/mol at 300 K. Beyond predicting molecular entropies and free energies, the model implies a high degree of correlation between torsions in most molecules, often assumed to be independent. While individual dihedral rotations may have low energetic barriers, the shape and chemical functionality of most molecules necessarily correlate their torsional degrees of freedom and hence restrict the number of low-energy conformations immensely. Our simple models capture these correlations and advance our understanding of small molecule conformational entropy.

摘要

计算柔性分子的熵可能具有挑战性,因为可能的构象数会随着分子大小呈指数增长,并且许多低能量构象在热学上是可及的。已经提出了不同的方法来估算构象熵对分子标准熵的贡献,包括对所有可能的稳定构象进行热化学计算以及根据实验数据开发经验校正。我们对超过120,000个小分子进行了构象采样,生成了约1200万个构象,以开发能够预测各种分子构象熵的模型。通过深入了解构象无序的本质,我们经过交叉验证的基于物理原理的统计模型在300 K时给出的平均绝对误差约为4.8 J/mol·K或低于0.4 kcal/mol。除了预测分子熵和自由能外,该模型还表明大多数分子中的扭转之间存在高度相关性,而通常认为这些扭转是相互独立的。虽然单个二面角旋转可能具有较低的能量势垒,但大多数分子的形状和化学官能团必然会使其扭转自由度相关联,从而极大地限制了低能量构象的数量。我们的简单模型捕捉到了这些相关性,并增进了我们对小分子构象熵的理解。

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