High-Throughput Experimentation and Machine Learning-Assisted Optimization of Iridium-Catalyzed Cross-Dimerization of Sulfoxonium Ylides

被引:15
作者
Xu, Yougen [1 ,2 ]
Gao, Yadong [1 ]
Su, Lebin [1 ,2 ]
Wu, Haiting [1 ]
Tian, Hao [1 ]
Zeng, Majian [1 ]
Xu, Chunqiu [3 ]
Zhu, Xinwei [3 ]
Liao, Kuangbiao [1 ,2 ]
机构
[1] Guangzhou Natl Lab, Guangzhou 510005, Peoples R China
[2] Bioland Lab, Guangzhou 510005, Peoples R China
[3] AIChemEco Inc, Guangzhou 510005, Peoples R China
基金
中国国家自然科学基金;
关键词
Cross-Dimerization; High-Throughput Experimentation; Machine Learning; Sulfoxonium Ylides; Unsymmetrical Alkenes; H BOND; FUNCTIONALIZATION; PREDICTION; REACTIVITY; INSERTION;
D O I
10.1002/anie.202313638
中图分类号
O6 [化学];
学科分类号
0703 ;
摘要
A novel and convenient approach that combines high-throughput experimentation (HTE) with machine learning (ML) technologies to achieve the first selective cross-dimerization of sulfoxonium ylides via iridium catalysis is presented. A variety of valuable amide-, ketone-, ester-, and N-heterocycle-substituted unsymmetrical E-alkenes are synthesized in good yields with high stereoselectivities. This mild method avoids the use of diazo compounds and is characterized by simple operation, high step-economy, and excellent chemoselectivity and functional group compatibility. The combined experimental and computational studies identify an amide-sulfoxonium ylide as a carbene precursor. Furthermore, a comprehensive exploration of the reaction space is also performed (600 reactions) and a machine learning model for reaction yield prediction has been constructed.
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页数:8
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