Protein assay distributions. We further analysed the distribution of the number of CPIs for each target protein within the CPI2M dataset to illustrate the overall sparsity of assay data. As depicted in Fig. ), with results for the four datasets in CPI2M are shown in Fig. , Fig. and Fig. for an overall performance evaluation. Task-specific performance across all protein subsets is detailed in Fig. and Fig. . These results demonstrate that GGAP-CPI consistently surpasses both target-specific and general CPI baselines across most bioactivity-specific datasets, maintaining its superiority in activity cliff-specific metrics across all datasets. Specifically, GGAP-CPI ranks first in most metrics and datasets, except for ranking second in RMSE and R-squared on CPI2M-main-pK d . Moreover, the relative improvements (Table ) achieved by GGAP-CPI compared to the best-performing baseline results highlight its exceptional performance on AC data. Notably, previously proposed porting the superior performance of GGAP-CPI, especially with AC molecules.