In addition, we set aside a final holdout set (5%) also selected based on Tanimoto similarity for analysis of model generalization. Using Tanimoto similarity as a metric, we observe that many ligands are dissimilar from others in the dataset, suggesting that our separate train/test sets are sufficiently distinct but indicating potential challenges in training models accurate across the entire dataset. To predict the number of coordinating atoms in a ligand, we treat this as a graph-level multiclass classification problem (i.e., to select one of six possible classes). To identify individual coordinating atoms, we treat this as a node-level binary classification task, with the target represented as a length N vector where N is the number of atoms in the molecular graph and each atom indicated as being either coordinating (1) or non-coordinating (0) (see Methods). Given the distinct nature of the way the models are trained, the models for predicting the count and identity of coordinating atoms, we will later show that they provide synergistic information that can also be used to assess each model's reliability.