Next, we demonstrate the utility of our trained models for predicting metal-ligand coordination in high-throughput screening campaigns of novel TMCs. This involves both the evaluation of our models on a final held-out set of ligands as well as integrating our models with structure generation tools. Towards this end, we develop a Python-based package called pydentate to first read ligand SMILES strings and use the trained models to generate predictions of coordinating atoms (Figure ). The ligand SMILES and its predicted coordinating atoms from pydentate are passed to molSimplify, and bonds are added to connect every coordinating atom to a user-specified transition metal center. The code supports addition of multiple unique ligands of different denticities or hapticities to a single complex in geometries determined by the user. The resulting complex is optimized by the Universal force field (UFF) , and the atomic coordinates are provided to the user for use in property evaluation (e.g., with semiempirical methods or DFT). To determine the suitability of the machine learning models in a structure generation workflow, we use the held-out set of ligands so far unseen by the machine learning models.