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一种用于局部失真的高效灵活方法:通过碎片化实现的失真分布分析。

An efficient and flexible approach for local distortion: distortion distribution analysis enabled by fragmentation.

作者信息

Yan Zeyin, Liao Yunteng Sam, Li Xin, Chung Lung Wa

机构信息

Shenzhen Grubbs Institute, Department of Chemistry, Guangdong Provincial Key Laboratory of Catalysis, Southern University of Science and Technology Shenzhen 518055 China

出版信息

Chem Sci. 2024 Dec 23;16(5):2351-2362. doi: 10.1039/d4sc07226j. eCollection 2025 Jan 29.

Abstract

Distortion can play crucial roles in influencing structures and properties, as well as enhancing reactivity or selectivity in many chemical and biological systems. The distortion/interaction or activation-strain model is a popular and powerful method for deciphering the origins of activation energies, in which distortion and interaction energies dictate an activation energy. However, decomposition of local distortion energy at the atomic scale remains less clear and straightforward. Knowing such information should deepen our understanding of reaction processes and improve reaction design. Herein, an efficient, general and flexible fragmentation-based approach was proposed to evaluate local distortion energies for various chemical and biological molecules, which can be obtained computationally and/or experimentally. Moreover, our distortion analysis is readily applicable to multiple structures from molecular dynamics (or the minimum energy path) as well as can be evaluated by different computational chemistry methods. Our systematic analysis shows that our approach not only aids computational and experimental chemists in visualizing (relative) distortion distributions within molecules (distortion map) and identifies the key distorted pieces, but also offers deeper understanding and insights into structures, reaction mechanisms and dynamics in various chemical and biological systems. Furthermore, our analysis offers indices of local distortion energy, which can potentially serve as a new descriptor for multi-linear regression (MLR) or machine learning (ML) modelling.

摘要

畸变在影响结构和性质,以及增强许多化学和生物体系中的反应活性或选择性方面发挥着关键作用。畸变/相互作用或活化应变模型是一种用于解释活化能起源的流行且强大的方法,其中畸变能和相互作用能决定了活化能。然而,在原子尺度上局部畸变能的分解仍不太清晰和直接。了解此类信息应能加深我们对反应过程的理解并改进反应设计。在此,我们提出了一种高效、通用且灵活的基于片段的方法,用于评估各种化学和生物分子的局部畸变能,这些能量可通过计算和/或实验获得。此外,我们的畸变分析很容易应用于分子动力学(或最小能量路径)中的多个结构,并且可以通过不同的计算化学方法进行评估。我们的系统分析表明,我们的方法不仅有助于计算化学家和实验化学家可视化分子内的(相对)畸变分布(畸变图)并识别关键的畸变片段,还能对各种化学和生物体系中的结构、反应机理和动力学提供更深入的理解和见解。此外,我们的分析提供了局部畸变能的指标,这有可能作为多线性回归(MLR)或机器学习(ML)建模的新描述符。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9be5/11778128/d8266cb4da10/d4sc07226j-s1.jpg

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