In their model, molecules were represented as graphs, where local steric and electronic information was added to each node (atom). Additionally, the used graph neural network contains a molecular interaction module that allows the model to learn synergistic effects between molecules, crucial for reactivity prediction tasks. While reaching state-of-the art performance in predicting ∆∆G ‡ on the data set from Denmark and coworkers, the designed neural network also enables to interpret the effects leading to the observed enantioselectivity by eliminating the atom features and observing the change in predictive performance. Using this method, the authors observed that the main contribution towards enantioinduction by CPAs is through steric effects, in line with previous literature.