The requirement of input conformers when using 3D GNNs introduces additional questions with regards to how those conformers should be generated. Applying DFT-level conformers, such as those used to compute the target acid/amine descriptors, would offset the desired cost savings of using a ML surrogate, so we instead employed cheap-to-acquire conformers that are separately embedded with the ETKDG algorithm 60 and optimized with MMFF94, 61 all via RDKit. For the relatively low molecular weight acids and amines studied here, this conformer generation was fast and accessible for thousands of molecules with limited computational resources (ca. 2.5 CPU seconds per molecule). Our use of MMFF94 conformers assumes that these structures provide an adequate representation of the ground-truth DFT-optimized conformer ensemble, which we later show to be empirically defensible, albeit imperfect. For instance, the cheap surrogate conformers may not exhibit the same stereoelectronic structural features present in the DFT-optimized ensembles, which would reduce the capability of the 3D GNNs to capture such effects. While this risk could potentially be mitigated by exploring the use of higher-cost optimization strategies, here our priority was providing rapid and facile access to predictions of DFT-level descriptors. In cases where there were multiple MMFF94 conformers for a given molecule, we used up to 20 conformers as a form of data augmentation during model training, mapping each individual MMFF94 structure to the same descriptor of the DFT-optimized ensemble. This data augmentation strategy has been shown to modestly improve the modeling of conformer ensembles with little additional computation cost apart from conformer generation. Further details on our conformer generation workflow can be found in the SI section 5.2.