Analysis of the confusion matrix demonstrates that incorrect predictions are typically off by only one class (Figure ). Overall, the good accuracy of the model combined with the low cost and complexity for generation of the SMILES string suggests this model should provide a good approach to identification of likely ligand coordination number. While the overall accuracy of the model trained to predict the number of coordinating atoms is high, identifying the specific metal-coordinating atoms is essential for automating the generation of transition metal complex structures. For the model to predict coordinating atom identities, we again trained a D-MPNN and FNN combined model, using a similar training and hyperparameter tuning strategy to that used in the model trained to predict the total number of coordinating atoms (see Methods and SI Appendix, Table ). In the case of coordinating atoms, the model predictions are made individually on each atom within a ligand. For select cases where resonance applies, two or more atoms may be equivalently correct but one might be identified as the sole "true" correct coordinating atom, and we automate the identification of these cases to ensure they are identified as correct (SI Appendix, Text S1 and Figure ). The trained model reaches a very high overall accuracy of 98.8% and ROC-AUC score of 1.00, with similar precision and recall scores of approximately 0.97 (Figure ). Nevertheless, these metrics alone are misleading due to the inherent class imbalance between coordinating and non-coordinating atoms, and alternative measures are needed, even beyond the balanced accuracy (96.9%) that is still very high. Ideally, to accurately predict how a ligand will coordinate to a transition metal, we require that all predictions of coordinating atoms within that ligand be correct. We thus define our most stringent measure, molecular accuracy, in terms of the percentage of predictions where every individual atom-level prediction in a given ligand is correct. The trained model reaches a molecular accuracy of 84.8% when evaluated on the test set (Table ). Grouping coordinating atom predictions by the known total number of coordinating atoms in each ligand, we observe the highest molecular accuracies on two-coordinate (87.6%) and three-coordinate (86.2%) ligands (SI Appendix, Table ). Interestingly, one-coordinate, monodentate ligands have lower molecular accuracy (82.8%) in comparison to the relatively small four-coordinate set (87.8%), which also corresponds to lower balanced accuracy (SI Appendix, Table ). One reason for this lower molecular accuracy on monodentate ligands could be that the chemical structure is more constrained, and thus learnable, in the higher-denticity ligands. For example, an imidazole, triazole, or tetrazole-derived ligand with multiple nitrogen atoms in a monodentate structure could lead to only a single correct answer from a range of options, whereas two-coordinate or threecoordinate ligands with two or more nitrogen atoms are easier to predict. Finally, predictions made on five-coordinate (67.8%) and six-coordinate (67.3%) ligands again have lower molecular accuracy due to their smaller numbers in the curated dataset. The disparity between the balanced and molecular accuracy across the entire dataset highlights difficulties in accurately representing the chemical interactions across an entire ligand.