With the advancement of AI technology, a number of models for predicting ABFE have been developed. Nevertheless, there is still room for improvement in terms of both generalization capability and precision. LumiNet stands out by innovatively merging the structure and physics modules, utilizing distance distribution to achieve exceptional performance. Using only 19,000 data points from PDBbind, it outperformed baseline models on the FEP dataset and achieved an 18.5% improvement over state-of-the-art results on the PDE10A dataset, demonstrating impressive generalization across multiple key targets. By adopting a semi-supervised approach, further improvement was achieved. With a modest amount of target-specific data, LumiNet can match or even exceed the predictive power of FEP+, holding considerable potential in optimizing lead compounds with notable structural differences. However, it should be noted that such superiority has its limitations. Moreover, we applied LumiNet in the scaffold hopping process, which accurately guided the discovery of the optimal ligands. Although LumiNet demonstrated resilience to data volume dependency and promoted the integration of AI with fundamental physics, it still failed to fully capture the underlying patterns of protein-ligand interactions based on the current dataset, thereby not breaking through the existing bottleneck. LumiNet provides intuitive displays of proteinligand interactions, allowing us to understand the contribution of different atom pairs to the overall energy and the importance of certain functional groups. Moreover, we aim to expand beyond just protein-ligand interactions in the future. We will augment the training dataset to gain a deeper understanding of non-covalent interactions and target a wider spectrum of dimer types, such as peptide-peptide, and peptide-ligand. This expansion will further enhance the versatility of the models, and potentially result in a qualitative leap in predictive accuracy for various interactions as the data volume increases.