The LumiNet model was developed based on physical energy terms, inherently providing interpretability. The transparency of individual energy terms, their weights, and the core parameter d' ij allows for direct quantitative verification of the effectiveness of a system. Adhering to the interpretability principle, each module in the model is designed accordingly. Although the pretrained model adeptly captures structural information, the BiEGCL layer, a crucial interaction block component of the model, remains indispensable. We aim to refine d' ij using this method because the pretraining focuses on learning atom pairs distance in the current protein-ligand configuration.