This model consists of two main components: a structure information extraction module (structure module) built on Mixture Density Networks (MDN) and a physics-based scoring module (physics module), as shown in Figure . Distance plays a pivotal role in this integration. When predicting protein-ligand interaction patterns, it is typical to input distances between residue atoms or atom pairs of protein-ligand into the model for learning . Models that leverage this information tend to show more robust predictive capabilities, as absolute free energy is closely linked to geometric conformations, similar to how bond energy is sensitive to bond length. However, the study of Mastropietro et al. suggests that bond information contributes relatively little to the model's prediction process, partly due to challenges in distinguishing between bonds within protein-ligand complexes and internal bonds. Models trained without explicit distance information may rely more on inherent information within protein-ligand complexes. By omitting distance information from the model input and focusing solely on structural data of proteins and ligands as prediction targets, the model is more prone to learn information relevant to the interaction. This involves inferring the distance distribution of each atom pair between protein and ligand based on given atomic 2D and 3D information, thus enabling the model to learn the underlying patterns of protein-ligand interactions. In this process, the 3D information is used only to calculate the distance features within the protein and ligand separately and does not directly participate in the inference process. For ABFE prediction, the structure module is employed to infer the distance when the potential energy of interaction between two bodies is the lowest (d' ij ), with the bidirectional EGCL modules finetuning this information. The first module in LumiNet incorporates encoding methods for proteins and ligands, accompanied by an MDN designed to fit the distance (dij). This MDN functions as a pre-training model, learning the structural-to-distance mapping. Extracting protein pockets at 5Å yielded less comprehensive global information than those at 10Å, but atomic-level features provided more refined insights. We explored using larger fully atomic pockets, but the computational complexity escalates quickly. With a 10Å pocket, the training time increased by roughly 11-fold. While there was an improvement in prediction accuracy, it didn't justify the steep rise in computational cost.