In this work, we leverage graph neural networks to answer the question of ligand coordination to transition metals with high accuracy and precision. We curate mononuclear TMCs from the CSD and extract their coordinated ligands to curate a large dataset of unique ligands with known connectivity to transition metals. This dataset provides valuable insight into chemical trends in ligands of particular coordination modes and reveals under-sampled regions of chemical space in ligand design. We train graph neural networks on the curated dataset to accurately predict coordinating atoms of unseen ligands directly from their SMILES representations. We interpret the trained models through analysis of the learned molecular representations and uncertainty quantification, demonstrating strategies for synergistic use of models in cases of high prediction uncertainty. Finally, we leverage the trained models by integrating them with the high-throughput structure generation code molSimplify. We illustrate this functionality through a case study in TMCs exhibiting novel metal-ligand combinations, validating the generated structures with DFT calculations to demonstrate our strategy to generate TMCs with de novo combinations of transition metals and ligands in physically realistic coordination.