We further considered investigating the relationship between model performance on AC data and overall data to expose the AC-induced discrepancies in CPI prediction. Firstly, we defined the metric ∆RMSE=RMSE clif f -RMSE as the difference between AC performance and overall performance. Then, the ∆RMSE values for each protein subsets were calculated, and the density distributions of ∆RMSE for GGAP-CPI and selected baselines (ECFP-ESM-RF, PerceiverCPI, KANO, and GBM) on four datasets were shown in Fig. . The results indicate that GGAP-CPI and other CPI baselines achieve lower ∆RMSE compared to target-specific baselines, suggesting that the integration of protein information can mitigate AC-induced performance discrepancies. Considering the performance results from the previous section, it remains essential to address these discrepancies, as there is still room for improvement in the ∆RMSE and RMSE clif f values for CPI methods.