Table . Accuracy of models trained to predict coordinating atom identities as evaluated on test set (6,616 ligands). Overall accuracy considers predictions made on all atoms for all ligands in the dataset. Positive accuracy considers only predictions made on true coordinating atoms, while negative accuracy considers only predictions made on true non-coordinating atoms. Balanced accuracy is the average of positive and negative accuracy. To determine if we could improve upon the models trained on a learned representation by incorporating domain-specific chemical information, we considered alternative descriptors. First, since canonical SMILES strings do not explicitly account for hydrogens, we considered whether adding explicit hydrogen atoms would aid model predictions. We anticipated that this additional information could enhance model predictions derived from greater information about the local environment of each potential coordinating atom. We also considered whether adding quantum mechanical information could aid our models. Specifically, we used reactivity descriptors (i.e., Hirshfeld partial charges, Fukui indices, NMR shielding constants, bond length and order) predicted from literature-reported machine learning models to mitigate computational cost of featurization. These features were generated and added to the models to predict the number and identity of coordinating atoms (see Methods and SI Appendix, Tables ). Finally, to attempt to account for the class imbalance in coordinating atoms, we also trained models with an increased weight applied to positive predictions. Despite the expectation that quantum mechanical information or explicit hydrogens should inform the models, we observed comparable performance of these new models (SI Appendix, Tables ). Specifically, adding explicit hydrogens to denticity prediction lowered model accuracy slightly (88.5% to 87.6%), with a similar reduction in accuracy (to 87.8%) observed after adding QM descriptors (SI Appendix, Table ). Adding both explicit hydrogens and QM descriptors together resulted in a model with only marginally increased accuracy (89.0%), possibly due to the synergistic benefit of these two models to resolve subtle differences in ligands with similar heavy atoms but distinct saturation (SI Appendix, Table ). For coordinating atom prediction, positive weighting worsened molecular accuracy substantially (to 70.7% versus 84.8%, SI Appendix, Table ). For addition of explicit hydrogen atoms and QM descriptors, either individually or in combination, we observed essentially unchanged (i.e., less than 1% difference) overall/positive/negative/balanced accuracy as well as molecular accuracy (SI Appendix, Table ). These observations are consistent with previous work suggesting the addition of QM descriptors to D-MPNN architectures has little benefit for datasets as large as the one considered here. Given the additional complexity and cost of these alternative models and the desire to avoid using an overparameterized model less likely to generalize well, we retained our original models and used the learned molecular representations for further analysis.