were selected for classification tasks. Both MoleculeNet and OGB provide a diverse set of benchmark datasets that have been widely used to compare the performance of various machine learning models. They also host public leaderboards that allow us to directly compare our results to other public models. The HIV dataset contains results from an assay designed to detect HIV inhibition for 41,127 compounds. It has been observed that many of the species in the HIV dataset are at risk for assay result artifacts , so in the narrowest sense dataset performance should be viewed as a test for the assay result rather than strictly as a predictor for HIV inhibition. The PCBA dataset includes the 128 biological activities selected from PubChem BioAssay 13 for 437,929 compounds. The datasets were evaluated using the random and scaffold splits that were provided by MoleculeNet and OGB. We adopted the training, validation, and test sets of the scaffold-split HIV data and the random-split PCBA data from MoleculeNet. The scaffold-split PCBA data were adopted from OGB as MoleculeNet did not evaluate the PCBA model on the scaffold split. In all splits, the datasets were split into 80% training, 10% validation, and 10% test sets. For the random-split PCBA, MoleculeNet sets all missing targets to zero (in contrast to OGB), so we report performances for either case, i.e. filled-in zeros for comparability to the MoleculeNet leaderboard, and without filled-in values, to showcase how the observed performance drops when adopting a scaffold split versus a random split.