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不对称氧化还原接力 Heck 反应的过渡态力场。

Transition State Force Field for the Asymmetric Redox-Relay Heck Reaction.

机构信息

Department of Chemistry and Biochemistry, University of Notre Dame, Notre Dame, Indiana 46556, United States.

Department of Chemistry, University of Utah, Salt Lake City, Utah 84112, United States.

出版信息

J Am Chem Soc. 2020 May 27;142(21):9700-9707. doi: 10.1021/jacs.0c01979. Epub 2020 May 14.

DOI:10.1021/jacs.0c01979
PMID:32249569
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7304536/
Abstract

A transition state force field (TSFF) was developed using the quantum-guided molecular mechanics (Q2MM) method to describe the stereodetermining migratory insertion step of the enantioselective redox-relay Heck reaction for a range of multisubstituted alkenes. We show that the TSFF is highly predictive through an external validation of the TSFF against 151 experimentally determined stereoselectivities resulting in an of 0.89 and MUE of 1.8 kJ/mol. In addition, limitations in the underlying force field were identified by comparison of the TSFF results to DFT level calculations. A novel application of the TSFF was demonstrated for 31 cases where the enantiomer predicted by the TSFF differed from the originally published values. Experimental determination of the absolute configuration demonstrated that the computational predictions were accurate, suggesting that TSFFs can be used for the rapid prediction of the absolute stereochemistry for a class of reactions. Finally, a virtual ligand screen was conducted utilizing both the TSFF and a simple molecular correlation method. Both methods were similarly predictive, but the TSFF was able to show greater utility through transferability, speed, and interpretability.

摘要

采用量子引导分子力学(Q2MM)方法开发了过渡态力场(TSFF),以描述对映选择性氧化还原接力 Heck 反应中多取代烯烃的立体决定迁移插入步骤。我们通过将 TSFF 与 151 个实验确定的立体选择性进行外部验证,表明 TSFF 具有高度的预测性,得到的相关系数为 0.89,均方根误差为 1.8 kJ/mol。此外,通过将 TSFF 结果与 DFT 水平计算进行比较,确定了基础力场的局限性。通过对 31 个 TSFF 预测结果与原始发表值不同的案例进行了新的应用,实验确定了绝对构型证明了计算预测的准确性,这表明 TSFF 可用于快速预测一类反应的绝对立体化学。最后,利用 TSFF 和简单的分子相关方法进行了虚拟配体筛选。两种方法都具有相似的预测能力,但 TSFF 通过可转移性、速度和可解释性显示出更大的实用性。

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