Moreover, with the exception of IR frequencies, the 3D GNNs always achieved lower MAE, even if the R 2 did not improve (or was worsened), suggesting that encoding 3D molecular geometries improves the accuracy of descriptor prediction on average but may lead to greater sensitivity to outliers. Interestingly, we found that two of the six outliers (i.e., amine N 1 lone pair energy and amine H 4 NMR) identified for the 2D GNN in Figure were predicted with substantially higher accuracy by the corresponding 3D GNN, but the remaining four cases saw little to no improvement by 3D modeling (see SI section 5.3). It is worth noting that DFT-derived descriptors have some inherent noise associated with them due to various sources of stochasticity during structure generation, geometry optimization, and conformer clustering. For geometrysensitive properties like Sterimol values, error from the descriptors will propagate into the model, negatively impacting predictive accuracy. This noise may especially impact the descriptors of the lowest-energy conformers, which generally saw significantly reduced predictive accuracy for both the 2D and 3D models. The imperfect ability of the 3D GNNs to account for instances of stereoelectronic effects may also be due to the use of surrogate MMFF94 conformers as model inputs, or simply due to the rarity of these interactions in the training data.