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

被引：22

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

来源：

ANGEWANDTE CHEMIE-INTERNATIONAL EDITION | 2023年 / 62卷 / 48期

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.

引用

页数：8