First, we tested our pre-trained model on CASF-2016 to validate its screening power. We found that, through the efficient information extraction of the Subgraph Transformer and the sufficient fitting of the MDN, the enrichment factor for the top 1% of ligands reached a noteworthy value of 26.5, suggesting that the structure module has proficiently learned the structural information of protein-ligand interactions. Next, we fine-tuned our pre-trained model using two distinct loss functions and individually assessed their efficacy in predicting ABFE. Unfortunately, both approaches exhibited significant limitations, rendering them insufficient for achieving highprecision predictions of ABFE. In the encoding module for small molecules, we used the Subgraph Transformer to enable the model to understand ligand information more deeply. This is logic given that small molecules collectively possess extensive structural diversity, and the model tends to use them to distinguish overall structural states. However, this approach may introduce bias as the model heavily depends on ligands for ABFE prediction. To mitigate this, we effectively convert the structural information of protein-ligand interactions into complex distance distributions, encapsulating the overall structural context despite differing labels. The Subgraph Extractor explores each central atom and its adjacent atoms to construct new subgraphs, which are then aggregated via the GT module. This process surpasses the limitations of the first-order Weisfeiler-Leman (1-WL) isomorphism test , which can capture higher-order information and integrate structural information at different levels. resulting in superior expressive ability.