A second significant source of bias arises from data splitting strategies, which critically influence how models are trained, tested, and their ability to generalize to unseen data. Li and Yang (2017) utilized TMAlign and Needleman-Wunsch 17 algorithms to cluster proteins within PDBbind dataset based on structural and sequence similarity, respectively. They found that ML-based SFs did not outperform traditional SFs when similar examples were removed from the training data . Despite these findings, many stud-ies continue to employ random splitting of datasets into training and testing sets, or into cross-validation folds . Such approaches do not adequately address the challenge of generalizing models to out-of-distribution (OOD) data or to targets not seen during training, underscoring a persistent gap in model evaluation practices.