We demonstrated the value of our models by integrating them into a structure generation workflow for transition metal complexes. We developed a Python-based package, pydentate, that takes as input a SMILES string of a ligand, predicts coordinating atom counts and identities, and adds bonds between the predicted coordinating atoms and a user-specified transition metal. We ). The metal atom was then converted to a dummy atom, and the structures and molecular graphs of each ligand were stored in both the coordinated (i.e., with the dummy atom present) and uncoordinated (i.e., without) states. Weisfeiler-Lehman graph hashing, filter was applied to all ligands used in any learning task. Only ligands with metal-coordinating atoms corresponding to C, N, O, Si, P, and S were retained (SI Appendix, Table ). For the remainder of the ligand, an element filter was applied that allowed these elements (i.e., C, N, O, Si, P, S) as well as other well-represented elements (i.e., H, B, F, Cl, Br, and I), removing ligands containing any other elements (SI Appendix, Table and Figure ). Additionally, ligands with more than six coordinating atoms or consisting of only a single atom (e.g., Cl) were also removed (SI Appendix, Table ). We employ Chemprop for model training (see next) on SMILES strings.