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一种基于底物描述符的方法,用于预测和理解笼状催化氢甲酰化反应中的区域选择性。

A substrate descriptor based approach for the prediction and understanding of the regioselectivity in caged catalyzed hydroformylation.

作者信息

Linnebank Pim R, Poole David A, Kluwer Alexander M, Reek Joost N H

机构信息

Homogeneous, Supramolecular and Bio-Inspired Catalysis, Van't Hoff Institute for Molecular Sciences, University of Amsterdam, Science Park 904, 1098 XH Amsterdam, The Netherlands.

InCatT B.V., Science Park 904, 1098 XH Amsterdam, The Netherlands.

出版信息

Faraday Discuss. 2023 Aug 11;244(0):169-185. doi: 10.1039/d3fd00023k.

Abstract

The use of data driven tools to predict the selectivity of homogeneous catalysts has received considerable attention in the past years. In these studies often the catalyst structure is varied, but the use of substrate descriptors to rationalize the catalytic outcome is relatively unexplored. To study whether this may be an effective tool, we investigated both an encapsulated and a non-encapsulated rhodium based catalyst in the hydroformylation reaction of 41 terminal alkenes. For the non-encapsulated catalyst, CAT2, the regioselectivity of the acquired substrate scope could be predicted with high accuracy using the ΔC NMR shift of the alkene carbon atoms as a descriptor ( = 0.74) and when combined with a computed intensity of the CC stretch vibration () the accuracy increased further ( = 0.86). In contrast, a substrate descriptor approach with an encapsulated catalyst, CAT1, appeared more challenging indicating a confined space effect. We investigated Sterimol parameters of the substrates as well as computer-aided drug design descriptors of the substrates, but these parameters did not result in a predictive formula. The most accurate substrate descriptor based prediction was made with the ΔC NMR shift and ( = 0.52), suggestive of the involvement of CH-π interactions. To further understand the confined space effect of CAT1, we focused on the subset of 21 allylbenzene derivatives to investigate predictive parameters unique for this subset. These results showed the inclusion of a charge parameter of the aryl ring improved the regioselectivity predictions, which is in agreement with our assessment that noncovalent interactions between the phenyl ring of the cage and the aryl ring of the substrate are relevant for the regioselectivity outcome. However, the correlation is still weak ( = 0.36) and as such we are investigating novel parameters that should improve the overall regioselectivity outcome.

摘要

在过去几年中,使用数据驱动工具预测均相催化剂的选择性受到了相当大的关注。在这些研究中,催化剂结构常常发生变化,但利用底物描述符来合理化催化结果相对较少被探索。为了研究这是否可能是一种有效的工具,我们研究了41种末端烯烃氢甲酰化反应中的一种封装铑基催化剂和一种非封装铑基催化剂。对于非封装催化剂CAT2,使用烯烃碳原子的ΔC NMR位移作为描述符( = 0.74)可以高精度预测所获得底物范围的区域选择性,并且当与计算得到的CC伸缩振动强度()相结合时,准确性进一步提高( = 0.86)。相比之下,对于封装催化剂CAT1的底物描述符方法似乎更具挑战性,这表明存在受限空间效应。我们研究了底物的Sterimol参数以及底物的计算机辅助药物设计描述符,但这些参数并未得出预测公式。基于底物描述符的最准确预测是使用ΔC NMR位移和( = 0.52)做出的,这表明CH-π相互作用的参与。为了进一步理解CAT1的受限空间效应,我们专注于21种烯丙基苯衍生物的子集,以研究该子集特有的预测参数。这些结果表明,包含芳环的电荷参数可改善区域选择性预测,这与我们的评估一致,即笼状结构的苯环与底物的芳环之间的非共价相互作用与区域选择性结果相关。然而,相关性仍然较弱( = 0.36),因此我们正在研究应能改善整体区域选择性结果的新参数。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1df2/10416704/166f867e420b/d3fd00023k-f1.jpg

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