To enhance the model's generalization capability and scoring accuracy, we integrated DL with physical scoring and parameterized structural information derived from the pre-trained model. To asssess the integration's effectiveness and its ability to fulfill previous tests, we trained the model using 19,158 data points from PDBbind2020. During this process, we kept the weights of the pretrained model fixed and fine-tuned only the physical scoring component. This step culminated in the mapping of structure and distance to ABFE was completed. For validation, we benchmarked our model on the FEP1 and FEP2 datasets against Schrödinger's FEP+, Schrödinger's Glide SP , MM/GBSA 7 , and two DL-based models, PIGNet2 21 , and PBCNET , which predict ABFE either directly or indirectly. For PIGNet2, we used the author's original prediction data and calculated the corresponding metrics, while for the other baselines, we referred to their original publications for performance metrics. As detailed in Tables and the LumiNet method exhibited significant advantages over all baselines, achieving RMSEs of 1.13 kcal•mol -1 and 1.32 kcal•mol -1 on the respective datasets, compared to 1.08 kcal•mol -1 and 1.67 kcal•mol -1 for the FEP+ method, with a slight advantage on the FEP2 dataset. Morever, the correlation coefficients for all targets in FEP1 exceeded 0.4, indicating stable performance. Despite the lack of intricate data preprocessing or extensive augmentation, the model still effectively learned the patterns and information governing protein-ligand interactions. This success can be attributed to the fusion of physical scoring and mixed density scoring. We acknowledge that energy-based scoring methods inherently rely on multidimensional approximations, especially for non-bonded interactions where energy terms overlap without clear boundaries. Consequently, we incorporated linear fitting when combining energy terms, enabling the model to independently learn their weight relationships. Furthermore, to bridge the gap between the four energy terms and ABFE, we transformed the mdn score scoring into a bias energy term, thereby enhancing the rationality of each term (as detailed in the method section). For feedback propagation, we carefully designed multiple loss functions to ensure the model's interpretability. Although the actual values of individual energy terms were unknown during training, the predicted values proved to be significant.