To train low-cost models that generalize well across unique ligands, we opted to start from a string-based (i.e., SMILES ) representation to train message-passing neural networks. Specifically, we independently trained both models using a combined directed message-passing neural network (D-MPNN) and feed-forward neural network (FFN) architecture. We used the validation data for hyperparameter tuning and model training (see Methods and SI Appendix, Table ). First, we trained the multi-class model to predict the total number of coordinating atoms in each ligand. This SMILES-based model achieves 88.5% accuracy and its high receiver operating characteristic area under the curve (ROC-AUC) score of 0.98 indicates effective distinguishment between the multiple classes (Figure ). Nearly identical precision and recall scores (0.82) suggest the model is balanced and has no bias toward false positives nor false negatives (Figure ). While evaluating accuracy over each individual class is misleading due to class imbalance, comparing precision and recall reveals better model performance among the ligands well-represented in our data set (i.e., one coordinating atom -0.89 precision and 0.92 recall, and two coordinating atoms (i.e., five coordinating atoms -0.57 precision and 0.60 recall, and six coordinating atoms -0.80 precision and 0.72 recall, Figure and SI Appendix, Table ). The poor performance on higherdenticity ligands is expected due to the rarity of samples in these classes in the overall dataset.