We then trained individual graph neural network models to predict the total number and identity of coordinating atoms of ligands from their SMILES representations with high accuracy (88.5% for denticity, 98.8% for coordinating atoms) and precision (0.82 for denticity, 0.97 for coordinating atoms). The trained model for coordinating atoms identification also performed well even in terms of the strictly defined molecular accuracy metric (84.8%). We developed a strategy to use the two trained models synergistically to further improve predictions by leveraging information from the more confident model in cases of conflicting predictions, increasing accuracy in test set predictions of coordinating atom count from 88.5% to 90.0% and molecular accuracy in coordinating atom identity from 84.8% to 86.9%. Analyzing the learned representations of the trained model to predict coordinating atom count revealed distinct regions of latent space corresponding to each class of total numbers of coordinating atoms, while no such trends were apparent in the latent space of the coordinating atoms identification model. These findings revealed that denticity and hapticity alone are not sufficient to identify coordinating atoms, and that 3p coordinating atoms are highly under-sampled for ligands with higher total numbers of coordinating atoms.