Interestingly, the PFP model trained with random splitting consistently outperformed the baseline by PCC metric but had a similar RMSE. This discrepancy suggests that the baseline better fits the data distribution, while PFP produced a stronger linear correlation. When considering the protein splitting performance, PFP significantly outperformed the baseline, suggesting that the graph model has better generalization performance. In addition, when we applied our best-performing model to the CASF-2016 benchmark, the PFP encoder outperformed all methods aside from deltaVinaRF20 (Table ).