Department of Chemistry, Zhejiang University, 38 Zheda Road, Hangzhou, 310027, China.
Angew Chem Int Ed Engl. 2020 Aug 3;59(32):13253-13259. doi: 10.1002/anie.202000959. Epub 2020 May 26.
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 mechanism-based computational statistics and machine learning model can serve as a useful strategy for selectivity prediction of organic transformations.
自由基 C-H 键官能团化提供了一种通用的方法来合成杂环化合物。这种转化的合成设计很大程度上依赖于区域选择性的知识，而量化和有效的区域选择性预测方法仍然难以捉摸。在此，我们报告了使用机器学习模型从孤立反应物的计算性质预测过渡态势垒的可行性。这使得快速可靠地预测杂环自由基 C-H 键官能团化的区域选择性成为可能。具有物理有机特征的随机森林模型在样本外测试集中实现了 94.2%的位置准确率和 89.9%的选择性准确率。通过将机器学习结果与其他取代基、杂芳环支架和实验观察进行比较，进一步验证了预测性能。这项工作表明，基于机制的计算统计和机器学习模型的结合可以作为有机转化选择性预测的有用策略。
