Models trained with random splits performed best, providing a cautionary example of potential overoptimism in literature reports. Such splitting strategy is known to be prone to overestimate model generalization . In contrast, kinase-based splits showed adequate performance, hinting that predictions within kinase families are more feasible than across families, despite the integration of MSA-based kinase relationships. Adding to the evidence on the importance of data integrity, Li et al. developed a 'leak-proof' version of the PDBbind dataset, explicitly excluding proteins and ligands with high similarity to the training set from the test set. This approach led to the retraining of several established scoring functions, including AutoDock Vina 50 , RFScore, Deep-DTA and InteractionGraphNet 51 , which then performed substantially better than initially reported.