Predicting Regioselectivity in Radical C−H Functionalization of Heterocycles through Machine Learning

• Published in: Angewandte Chemie International Edition Vol. 59; no. 32; pp. 13253 - 13259
• Main Authors: Li, Xin, Zhang, Shuo‐Qing, Xu, Li‐Cheng, Hong, Xin
• Format: Journal Article
• Language: English
• Published: Germany Wiley Subscription Services, Inc 03.08.2020
• Edition: International ed. in English
• Subjects: Accuracy; Computer applications; Heterocyclic compounds; Hydrogen bonds; Learning algorithms; Machine learning; mechanism-based computational statistics; Predictions; radical C−H functionalization; random forest model; Regioselectivity; regioselectivity prediction; Selectivity; machine learning; radical C−H functionalization; random forest model; mechanism-based computational statistics; regioselectivity prediction

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...