Machine Learning Predicts Regioselectivity in Pd-Catalyzed Directing Group-Assisted C–H Activation

Regioselectivity in C-H activation is crucial for applications in pharmaceuticals and advanced materials. Machine learning (ML) techniques enhance the speed and accuracy of regioselectivity prediction in palladium-catalyzed directing group-assisted C-H activation in aryl substrates. Among the models tested, the standard support vector machine (SVM) model demonstrated better generalizability, achieving an F1 score of 0.92 and MCC of 0.93 on the test set. This approach aids the development of efficient catalytic strategies for C-H activation.