The recent trend of utilizing deep learning (DL) to predict molecular properties has sparked innovative data-driven approaches , which may be adapted to estimate ABFE efficiently and accurately. Notable examples of such approaches include IGN , OnionNet , and GIGN . These training samples containing ligand pairs and proteins, PBCNET achieved comparable results to the FEP+ method on both the FEP1 and FEP2 tests through active learning. This method focuses on structural variations between ligand pairs, facilitating data-driven learning of possible RBFE values rather than directly modeling protein-ligand interaction patterns. Therefore, in the current scenario with limited labeled data, RBFE methods have a significant advantage in predicting accuracy.