The assignment of ligand charges is nontrivial because each complex in the tmQM and tmQMG databases does not have an assigned metal center oxidation state. To assign a charge to each ligand, which is necessary for each ligand to be easily integrated with software that automates building metal-ligand structures and performing quantum-chemical calculations, we assumed ligands are closed shell and only range in charge from 3-to 1+. Based on these assumptions we then carried out GFN2-xTB single-point energy calculations for each ligand at each of these five charge states (3-, 2-, 1-, 0, and 1+). Based on these xTB calculations, for each charge state, we extracted the HOMO-LUMO gap, internal force, and SCF iteration number, which were used as features to build a machine learning model to assign ligand charges. These features were selected based on the hypothesis that the correct charge assignment will correlated with the largest HOMO-LUMO gap, small internal forces, and small number of SCF iterations. To ensure diversity in the training ligand set we used RDkit 44 to calculate the circular morgan fingerprints of all available ligands and then calculate the dice similarity score. Model training was done by manual assignment of charge for 838 ligands that were a mixture of monodentate, bidentate, and tridentate ligands. In the training ligand dataset, there were four ligands with 3-charge, 112 ligands with 2-charge, 328 ligands with 1-charge, 391 ligands with 0 charge, and 3 ligands with 1+ charge, which represents a statistically relevant relative number of ligands for each type. Ligands were separated by charge parity of odd charge and even charge. Several classification models were generated using scikitlearn, and their performance is plotted in Figure (top). The random forest classification model performed best. A separate random forest model with cross validation was created for each set charge parity ligands. The average accuracy across the cross-validation trials was 90% for even charge ligands and 97% for odd charge ligands. We also characterized the accuracy of the random forest classification models in different subsets of the data. This was done by using the random forest vote percentage as a representation of the assigned charge confidence. Figure (bottom) shows that for even parity charges large intervals have accuracy that exceeds the general model accuracy. We then applied these trained random forest classification models to charge assignment for the ~30,000 extracted ligands. This was done by calculating the xTB HOMO-LUMO gap, internal force, and SCF iteration number for all ligands and then applying the models to determine charge assignment based on the feature values. While the majority of ligands have a very high confidence for charge assignment a few ligands have low confidence, and these ligands have a flag in the file that enables users to exclude these ligands if desired. All ligand files have xyz coordinates, total charge, labeled metal connectivity, and the original CSD code. All ligand files are in folders sorted by coordination denticity and the atom type for metal connection.