AbstractA 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.
摘要 介绍了一种结合高通量实验(HTE)和机器学习(ML)技术的新颖而便捷的方法,该方法首次通过铱催化实现了磺鎓酰化物的选择性交叉二聚。该研究以良好的产率和较高的立体选择性合成了多种有价值的酰胺、酮、酯和 N-杂环取代的不对称 E-烯。这种温和的方法避免了重氮化合物的使用,具有操作简单、步骤经济性高、化学选择性和官能团兼容性好的特点。结合实验和计算研究,确定了一种酰胺基磺酰亚胺作为碳烯前体。此外,还对反应空间(600 个反应)进行了全面探索,并构建了一个用于预测反应产率的机器学习模型。