Machine learning models: Three different models were trained per maximum ligand denticity (i.e., bidentate, tridentate, and tetradentate), where the assignment of negative labels was different in each case. For each model, we used the 80/20 stratified random train/test split to ensure conservation of the same ratios of positive and negative classes in the training and test set. For random and semi-supervised sets, that contain identical positive labels, the identities of the positive train/test groups were preserved. However, since negative ligands are distinct for each set, in each case, negative classes were split randomly into 80/20 train/test split. We trained classification models using XGBoost v1.5.0, a gradient boosting ensemble model, to classify ligands as either hemilabile or non-hemilabile. Hyperparameters were optimized using Hyperopt v.0.2.7 (Supporting Information Table ). Hyperparameters were selected on the basis of stratified three-fold cross validation of the training set with binary cross-entropy as the figure of merit.