作者
Xin Li, Shuo‐Qing Zhang, Li‐Cheng Xu, Xin Hong
发表日期
2020/8/3
期刊
Angewandte Chemie International Edition
卷号
59
期号
32
页码范围
13253-13259
简介
Radical C−H bond functionalization provides a versatile approach for elaborating heterocyclic compounds. The synthetic design of this transformation relies heavily on the knowledge of regioselectivity, while a quantified and efficient regioselectivity prediction approach is still elusive. Herein, we report the feasibility of using a machine learning model to predict the transition state barrier from the computed properties of isolated reactants. This enables rapid and reliable regioselectivity prediction for radical C−H bond functionalization of heterocycles. The Random Forest model with physical organic features achieved 94.2 % site accuracy and 89.9 % selectivity accuracy in the out‐of‐sample test set. The prediction performance was further validated by comparing the machine learning results with additional substituents, heteroarene scaffolds and experimental observations. This work revealed that the combination of …
学术搜索中的文章
X Li, SQ Zhang, LC Xu, X Hong - Angewandte Chemie International Edition, 2020