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究竟怎么回事?——利用量子化学对钯催化的Heck反应进行区域选择性自动计算

What the Heck?-Automated Regioselectivity Calculations of Palladium-Catalyzed Heck Reactions Using Quantum Chemistry.

作者信息

Ree Nicolai, Göller Andreas H, Jensen Jan H

机构信息

Department of Chemistry, University of Copenhagen, Universitetsparken 5, 2100 Copenhagen Ø, Denmark.

Bayer AG, Pharmaceuticals, R&D, Computational Molecular Design, 42096 Wuppertal, Germany.

出版信息

ACS Omega. 2022 Dec 2;7(49):45617-45623. doi: 10.1021/acsomega.2c06378. eCollection 2022 Dec 13.

Abstract

We present a quantum chemistry (QM)-based method that computes the relative energies of intermediates in the Heck reaction that relate to the regioselective reaction outcome: branched (α), linear (β), or a mix of the two. The calculations are done for two different reaction pathways (neutral and cationic) and are based on SCAN-3c single-point calculations on GFN2-xTB geometries that, in turn, derive from a GFNFF-xTB conformational search. The method is completely automated and is sufficiently efficient to allow for the calculation of thousands of reaction outcomes. The method can mostly reproduce systematic experimental studies where the ratios of regioisomers are carefully determined. For a larger dataset extracted from Reaxys, the results are somewhat worse with accuracies of 63% for β-selectivity using the neutral pathway and 29% for α-selectivity using the cationic pathway. Our analysis of the dataset suggests that only the major or desired regioisomer is reported in the literature in many cases, which makes accurate comparisons difficult. The code is freely available on GitHub under the MIT open-source license: https://github.com/jensengroup/HeckQM.

摘要

我们提出了一种基于量子化学(QM)的方法,该方法可计算Heck反应中与区域选择性反应结果相关的中间体的相对能量:支链(α)、直链(β)或两者的混合。计算针对两种不同的反应途径(中性和阳离子)进行,基于对GFN2-xTB几何结构的SCAN-3c单点计算,而这些几何结构又源自GFNFF-xTB构象搜索。该方法完全自动化,并且效率足够高,能够计算数千种反应结果。该方法大多能够重现精心确定区域异构体比例的系统实验研究。对于从Reaxys中提取的更大数据集,结果稍差,使用中性途径时β选择性的准确率为63%,使用阳离子途径时α选择性的准确率为29%。我们对数据集的分析表明,在许多情况下文献中仅报道了主要的或所需的区域异构体,这使得准确比较变得困难。该代码可在GitHub上根据MIT开源许可免费获取:https://github.com/jensengroup/HeckQM

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/32ca/9753166/3fec55e95b79/ao2c06378_0002.jpg

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