Publicly available molecular benchmarks span several areas of chemistry, and are often tailored to domainspecific needs. For example, MoleculeNet is a suite of 16 molecular datasets and their variations, with tasks from quantum mechanics, physical chemistry, biophysics, and physiology . Consequently, there is a large variability in terms of the supervised learning task (regression or classification), number of output predictions/classes (multi-task learning), dataset size (ranging from under 1,000 compounds to over 430,000), and ML-specific concerns such as the node featurisation strategy (atomic coordinates, atom type, etc.) and the random split strategy. A similarly diverse effort is Atom3D, a collection of 8 tasks formulated specifically for learning based on three-dimensional molecular structures, for example protein-interface prediction, or predicting ligand binding affinity and small molecule properties .