Researchers at ChemLex have achieved a breakthrough in synthesis planning by combining chemical expertise with data-driven techniques for regioselectivity prediction. Their innovative message passing neural network increases the prediction accuracy, outperforming experienced chemists and existing computational tools. This network provides a powerful means for designing optimal synthesis routes, especially for metal-catalyzed cross-coupling reactions, which are frequently used in organic chemistry.