We further analyzed the predictions by the model to gain insight into the confidence we should have in its predictions and to identify where it could be used in ligand design. For example, a bidentate, bisphosphine ligand with a short, rigid linker between two coordinating atoms is confidently classified as non-hemilabile. However, by changing the bisphosphine ligand's electronic character to bisphosphinite, which has a more flexible ethane diol linker between two coordinating atoms, the resulting ligand is classified as hemilabile (Figure , inset-top). Similarly, we find that the structure of a tridentate N,N,N-coordinating ligand consisting of rigid sp 2 hybridized linkages between coordinating atoms and bulky substituents that constrains movement results in the classification of non-hemilabile, whereas a ligand possessing a freely rotatable ethylene linker in place of a rigid linker is confidently classified as hemilabile (Figure , insetmiddle). Finally, we find that certain classes of macrocyclic tetradentate ligands, such as porphyrin-derived ligands, are confidently classified as non-hemilabile. On the other hand, when one of the coordinating nitrogen atoms in such a structure is alkylated, leading to a significant reduction in its donor ability, the classifier confidently assigns this ligand as hemilabile, highlighting how the model is sensitive to small alterations in the overall structure, including those two bonds or more from the metal center (Figure , inset-bottom). Thus, the ML models, especially those trained on bidentate and tetradentate ligands, should provide new pathways to discovering novel hemilabile ligands. We propose that tridentate model predictions could be paired with highthroughput DFT, i.e., to rule out any model-predicted hemilabile ligands that remain strongly bound to a representative metal center, to provide additional support to model predictions given limitations in tridentate dataset quality from labels obtained purely from the CSD.