To evaluate the effectiveness of predicting the correct binding pose in DBX2 and other docking programs, we compute the success rate on the hold-out test set as described in the Material and Methods section (Figure ). As expected, rescoring ensembles of docking poses with different scoring functions led to significantly improved performances for all the scoring functions likely due to enhanced pose sampling, as observed in previous studies . Noticeably, the node-level pose classification method implemented in DBX2 significantly outperformed all docking and rescoring schemes while considering the same pool of poses. These findings suggest that by leveraging neighbor information via the GNN framework, DBX2 offers a significant advantage in accurately identifying native near-to-native ligand binding poses compared with docking methods that score each pose individually. Figure illustrates an example of successful application of DBX2 for identifying the native pose of the potent TER-117 inhibitor bound to its target, the human Glutathione S-Transferase P1-1 (PDB id: 10gs) . Next, we evaluated the ability of the scoring functions to reproduce experimentally determined binding constants in the hold-out test set (Table ). DBX2 directly computes the binding affinity from an ensemble of poses, so it does not require selecting a specific docking pose as input, unlike other scoring functions. For traditional scoring functions, since DOCK showed the best success rate among classical docking programs, we focused only on poses with the best DOCK scores (after rescoring) in order to compute binding affinities with docking scoring functions, similarly to our previous work . Linear regression was performed to compare experimental binding affinities from the hold-out dataset with the scores of the best poses from DOCK using different scoring functions and their linear combinations . For DBX2, the affinity values for each protein-ligand complex in the hold-out dataset were predicted as graph-level tasks, hence as readouts of ensembles of poses generated for a system rather than relying on a single pose. the hold-out test set, respectively) as reported in the previous studied . The comparison of Rp and RMSE on all models is summarized in Table .