As for all structure-based methods, the imbalanced dataset of kinase structures is a challenge. Some kinases are structurally well represented (e.g., EGFR or CDK2), while others have only few structures available. And unfortunately still roughly half of the humane kinome has no structural information available at all. The recent breakthrough of AlphaFold2 75 could help here; predicted structures for almost all human kinases are available now on the AlphaFold DB. Modi and Dunbrack 77 have already classified the structures' conformations and found most structures in the DFG-in conformation. An AlphaFold-enhanced KiSSim tree may further increase the usefulness of the KiSSim methodology for kinome-wide similarity studies. Furthermore, the KiSSim fingerprint can be applied in machine learning, e.g. to extract the most important features in the kinase pocket.