手性(物理)
部分
羧酸
催化作用
组合化学
烷基化
化学
立体化学
计算机科学
有机化学
物理
手征异常
粒子物理学
费米子
Nambu–Jona Lasinio模型
作者
Zijing Zhang,Shuwen Li,João C. A. Oliveira,Yanjun Li,Xinran Chen,Shuo‐Qing Zhang,Li‐Cheng Xu,Torben Rogge,Xin Hong,Lutz Ackermann
标识
DOI:10.1038/s41467-023-38872-0
摘要
Challenging enantio- and diastereoselective cobalt-catalyzed C-H alkylation has been realized by an innovative data-driven knowledge transfer strategy. Harnessing the statistics of a related transformation as the knowledge source, the designed machine learning (ML) model took advantage of delta learning and enabled accurate and extrapolative enantioselectivity predictions. Powered by the knowledge transfer model, the virtual screening of a broad scope of 360 chiral carboxylic acids led to the discovery of a new catalyst featuring an intriguing furyl moiety. Further experiments verified that the predicted chiral carboxylic acid can achieve excellent stereochemical control for the target C-H alkylation, which supported the expedient synthesis for a large library of substituted indoles with C-central and C-N axial chirality. The reported machine learning approach provides a powerful data engine to accelerate the discovery of molecular catalysis by harnessing the hidden value of the available structure-performance statistics.
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