Comparison on transfer learning validation dataset. In our study, we employed a widely used activity cliff estimation benchmark, MoleculeACE, as the validation dataset for transfer learning. We finetuned GGAP-CPI and GGAP-CPI-IntEns on MoleculeACE-pK i and -pEC 50 datasets. To ensure the integrity of the training process, any overlapping data in CPI2M were excluded to prevent data leakage. We evaluated the performance of GGAP-CPI, GGAP-CPI-IntEns, other selected target-specific and CPI baselines on MoleculeACE-pK i and MoleculeACE-pEC 50 datasets. As shown in Table and Table , with full-set overall and average ranking results shown in Fig. and Fig. , GGAP-CPI-IntEns significantly enhanced the performance over GGAP-CPI and outperformed other baselines on all datasets, with notable relative improvements achieved compared to suboptimal baseline methods (e.g., pEC 50 : 15.05% for RMSE and 9.04% for RMSE clif f ; pK i : 17.09% for RMSE and 7.40%