Ultimately, we found a 5Å pocket to offer the best trade-off between computational efficiency and predictive accuracy. For protein encoding, Graph Transformer is employed, whereas ligand encoding utilizes Subgraph Transformer (SubGT). Their respective coordinates are separately fed into the network. In the SubGT block, ligands are decomposed into substructures and subgraphs, encoded individually, and then aggregated via centroid, subgraph, and context block after 6 iterations to derive the feature representation of each atom. These representations are then concatenated with protein-ligand features to fully capture structural information, which the MDN module then uses to fit the distance. The second module incorporates an interaction block and four energy terms for physical scoring, serving to fine-tune the pre-trained model. The information extracted by the pretrain model, combined with real distance data, is inputted into the bidirectional EGCL module, functioning as an interaction block for additional structural refinement to improve the fit of d'ij. This block determines interaction edges based on protein-ligand atom distances, enabling authentic information exchange. Unlike the pretraining phase that inferred atom pairs information in a resultdriven manner, this method directly aggregates information across atom pairs. By integrating these two approaches, the objective of capturing structural information smoothly shifts from dij to d'ij. The resultant information is then evaluated by four energy terms and further integrated with the mdn score to compute the final score. A more detailed description of the model's architecture is provided in the Methods section.