Additionally, GNNs have been applied in generative settings to replace physics-based sampling and generate and score potential ligand-protein poses, such as in DiffDock and MedusaGraph . Although these architectures have shown promising results, an increasing number of studies suggest that GNNs tend to memorize ligand and protein patterns instead of learning the true interactions between them , . Moreover, single pose graphs are generally mapped to binding affinities, potentially missing the opportunity to capture the full thermodynamic profile and dynamics of ligand-protein interactions that depends on multiple conformations .