Evaluated on this set with the synergistic model use strategy outlined previously, the trained model for predicting the number of coordinating atoms reaches 89.6% accuracy, while the coordinating atoms identification model reaches 87.4% molecular accuracy (97.1% balanced accuracy), both of which are comparable to the test set accuracy (SI Appendix, Table ). This comparable performance is indicative of the ability of our trained models to generalize well across chemical space to unseen ligands. Furthermore, given ligands with correctly predicted coordinating atoms, we validate the capability of pydentate to accurately recreate 3D structures of metal-ligand combinations observed in the CSD, with an average root mean squared deviation in atomic coordinates of 1.50 Å between structures observed in the CSD and recreated with pydentate (SI Appendix, Table , Figure ). By integrating good predictive coordinating atom assignment in pydentate with existing structure generation tools, this approach is expected to be suitable for constructing combinations of ligands needed for high-throughput virtual screening campaigns of novel TMCs.