Chemical and biochemical structures and processes are of great interest to machine learning with graph neural networks, as they are supposedly well-represented by undirected and directed graphs, respectively . Conversely, machine learning plays an ever-increasing role in the life sciences, where new methods have been adopted and adapted for a wide range of tasks such as the prediction of physico-and quantum-chemical properties in material science, the prediction of pharmacokinetic properties in drug development, the prediction of binding affinities between small-molecule ligands and proteins in drug discovery, or the prediction of chemical reaction yields in synthetic and process chemistry .