However, in practical ABFE prediction, estimating binding free energies through RBFE calculations limit its applicability. Some studies emphasize learning structural information to infer ABFE. For example, DSMbind introduced an interesting hypothesis: assuming that the default crystal structure represents the lowest protein-ligand energy state, it employs SE(3) denoising score matching (DSM) to estimate the likelihood of complexes, thereby inferring binding free energy. GenScore , on the other hand, initially utilizes a mixture density network (MDN) to fit the distance distribution of residue-atom pairs in protein-ligand complexes, learning more structural information. The model is then fine-tuned with actual binding free energy labels, delivering remarkable results on the FEP2 dataset developed by Schrödinger . While both GenScore and DSMbind exhibit notable statistical correlation between predicted and experimental energies, their actual errors remain substantial for practical applications. For instance, when dealing with specific systems, direct application is challenging and requires a certain number of known ABFE values for correction. In summary, the reliance of current DL-based models on data may introduce biases, thereby limiting their generalizability and practical applications. Additionally, the inherent opacity of machine learning (ML) algorithms contributes to a lack of clear and intuitive physical explanations in existing models.