Notably, using the Pearson correlation coefficient as the loss minimizes trade-offs between fitting distance and scoring tasks due to their inherent correlation. Conversely, with MSE as the loss, balancing both objectives becomes more challenging, resulting in slightly inferior performance. For ranking tasks, the Pearson-based method is preferable for training, whereas the MSE-based method is more suitable for precise ABFE calculations. To meet both requirements, we employ transfer learning on the pre-trained model to fine-tune the parameters of physical energy terms, thereby achieving ABFE prediction results with exceptional generalization and accuracy.