As the base 3D GNN architecture, we used DimeNet++, 63 which consistently performs on-par with or better than significantly more expensive equivariant 3D GNNs, especially for property prediction in relatively low-data tasks. To make use of atom features, we replaced DimeNet++'s default atom featurizer with the same one-hot encoded features employed in our 2D GNNs, but bonds were not featurized. Furthermore, when applying each 3D GNN, we averaged the model's predictions over <20 MMFF94 conformers to further reduce any noise relating to the choice of input conformer at test-time. Otherwise, the output of the 3D GNNs and the associated training protocols were analogous to those of the 2D GNNs.