Computational design of high-performance ligand for enantioselective Markovnikov hydroboration of aliphatic terminal alkenes.
Computational design of high-performance ligand for enantioselective Markovnikov hydroboration of aliphatic terminal alkenes.
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脂肪族末端烯烃对映选择性马尔科夫尼科夫硼氢化反应高性能配体的计算设计
DOI：
10.1038/s41467-018-04693-9
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发表时间：
2018-06-12
影响因子：
16.6
通讯作者：
Ito H
中科院分区：
文献类型：
--
作者：
Iwamoto H;Imamoto T;Ito H
使用试错法寻找过渡金属催化的不对称反应的最佳手性配体通常是耗时和昂贵的，即使反应机理的细节已经知道。虽然现代的计算分析可以预测立体选择性，但尝试用计算方法设计手性配体来提高立体选择性的例子很少。本文报道了一种基于计算和实验评价序列的手性配体设计方法，用于脂肪端烯烃的对映选择性马尔可夫尼科夫硼化。我们开发了一个三阻碍象限的p -含氮双膦配体，根据该方法得出的设计准则进行设计，这使得Markovnikov氢硼化反应具有高对端选择性（高达99% ee）。反复试验的方法来确定合适的配体过渡金属催化是费时和昂贵的。在这里，作者开发了一种结合实验和计算的方法来设计手性配体的对映选择性Markovnikov氢化脂肪末端烯烃。
Finding optimal chiral ligands for transition-metal-catalyzed asymmetric reactions using trial-and-error methods is often time-consuming and costly, even if the details of the reaction mechanism are already known. Although modern computational analyses allow the prediction of the stereoselectivity, there are only very few examples for the attempted design of chiral ligands using a computational approach for the improvement of the stereoselectivity. Herein, we report a systematic method for the design of chiral ligands for the enantioselective Markovnikov hydroboration of aliphatic terminal alkenes based on a computational and experimental evaluation sequence. We developed a three-hindered-quadrant P-chirogenic bisphosphine ligand that was designed in accordance with the design guidelines derived from this method, which allowed the Markovnikov hydroboration to proceed with high enantioselectivity (up to 99% ee). Trial-and-error methods to identify suitable ligands for transition metal catalysis are time-consuming and costly. Here, the authors developed a combined experimental and computational approach to design chiral ligands for the enantioselective Markovnikov hydroboration of aliphatic terminal alkenes.
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影响因子：
16.6
作者：
Herbert, Myles B.;Marx, Vanessa M.;Pederson, Richard L.;Grubbs, Robert H.
通讯作者：
Grubbs, Robert H.
影响因子：
64.8
作者：
Houk, K. N.;Cheong, Paul Ha-Yeon
通讯作者：
Cheong, Paul Ha-Yeon
影响因子：
16.6
作者：
Guan, Yanfei;Wheeler, Steven E.
通讯作者：
Wheeler, Steven E.
影响因子：
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作者：
Imamoto, T;Sugita, K;Yoshida, K
通讯作者：
Yoshida, K
影响因子：
1.6
作者：
Imamoto, Tsuneo;Kumada, Atsushi;Yoshida, Kazuhiro
通讯作者：
Yoshida, Kazuhiro