Decoding Regioselectivity in Cu-Catalyzed Borylation of Al-kynes: Insights from Machine Learning and Artificial Intelligence

CuI-catalyzed hydroboration of alkynes is a cost-effective route to trans-alkenyl boronates (valuable intermediates for C–C cross-coupling) but controlling regioselectivity in these reactions is challenging. Here we address this challenge by developing a machine learning model, using high-throughput computational-derived descriptors and a combined experi-mental/literature dataset, to predict regioselectivity (expressed as ΔΔG‡). The resulting Random Forest model achieved high accuracy (cross-validated R² > 0.8, RMSE ~0.4–0.6 kcal/mol) and revealed mechanistically relevant trends through feature importance analysis. For example, the model rationalizes why certain N-donor ligands that fail to promote Cu-catalyzed hydroboration can effectively catalyze alkyne hydrosilylation, linking this divergence to differences in geomet-ric ligand descriptors. Moreover, ML-guided screening identified promising ligands that improved hydroboration out-comes (increased yields and regioselectivity), as confirmed in experiments (e.g., enabling reduced catalyst loading with-out sacrificing selectivity). Overall, this integrated ML strategy offers a powerful tool for understanding and predicting regioselectivity in CuI-mediated reactions and, with appropriate calibration, could be extended to other organocopper systems.