Data splitting and benchmark dataset construction. For the construction of benchmark datasets, an AC-specific data splitting approach was employed as described in MoleculeACE. This method involved spectral clustering based on molecular fingerprints for each target-specific subset to ensure an even distribution of AC and non-AC molecules across training and testing sets. This was crucial to avoid overestimation or underestimation of model performance. Each cluster then underwent a stratified data splitting at a ratio of 8:2, using the AC labels to appropriately allocate molecules between training and testing sets. This approach ensured that similar molecules were preserved in the training set, while