We pre-trained the structure module using PDBbind2020 33 , excluding 285 data points from CASF-2016. For testing, we utilized the FEP1 set from Merck and FEP2 set from Schrödinger. PDBbind2020 contains 19,443 curated co-crystal structures from diverse protein families. However, due to the diversity of sources and assay types, the data inherently contains notable noise. For instance, PDB entry 2w9h reports a dissociation constant (K d ) of 430 nM in PDBbind, whereas BindingDB lists 7 inhibition constant (K i ) values ranging from 1.2 nM to 5.5 μM . In cases with abundant data, noise may enhance the model's generalization ability. In 2021, Takashi et al. investigated the impact of noise and batch size on prediction accuracy through noise augmentation on real datasets. They found that, within acceptable noise levels, augmentation can effectively bolster a model's generalization. Nevertheless, insufficient data for comprehensive training may introduce unavoidable biases, posing challenges to the model's performance even in similar systems. Therefore, depending solely on the PDBbind dataset to achieve robust generalization in predicting ABFE poses substantial challenges.