Naturally, if these energy values were derived through high-throughput calculations and integrated into our training model, its performance would undoubtedly be further improved. However, it is evident that the model's predictive performance has markedly declined across various targets in the FEP2 dataset. In contrast to datasets like FEP1, molecules in FEP2 undergo more transformations, such as changes in net charge and charge distribution, ring openings and core hopping . These transformations lead to substantial alterations in solvent interactions. Additionally, the ligand sets display a slightly broader range of structural diversity compared to previous benchmarks. For our model, the influence of solvent interactions is significant. For instance, in the SYK target, a ligand with two aromatic rings extending into the solvent results in a notable discrepancy between predicted and actual values. Similar situations can be observed with small molecules in the PFKFB3 target. Since solvent molecules were not included in the modeling process, the model is less sensitive to changes in this aspect of the energy. Regarding net charge changes, the model can partially distinguish them. For ligands in the c-Met target, which involve six perturbations with a change in net charge, the predictive performance aligns with the average level in the FEP1 dataset. This is attributed to the inclusion of nuclear charge and covalent bonds in the input features.