mechanics with estimates of solvation energies. These methods were introduced in the late 1990s by the Kollman and Case labs, aiming to estimate the binding free energies, or relative binding free energies, of related compounds and protein-ligand binding modes. Despite their potential, MM-PBSA/GBSA have several limitations which include the need for prior knowledge of a protein-ligand bound complex to serve as a starting point, although starting conformations can be taken from prior docked poses. Their accuracy for binding poses prediction and ranking the order of compounds according to their affinities is very system dependent. Recent developments in machine and deep learning approaches have led to a possible paradigm shift in the way protein ligand docking solutions are generated. Programs such as LiGANN, DiffDock, or Pocket2Mol generate both molecule and pose simultaneously. However, tools such as PoseBusters or PoseCheck which were developed to assess the geometric and interaction quality of generated ligands have highlighted there is significant room for improvement compared to solutions derived from physics-based approaches. To overcome the issues outlined above, we introduce a novel protocol (Figure ), which combines Supervised Molecular Dynamics (SuMD) with Dynamical Averaging of Quantum Mechanics Fragment Molecular Orbital (DA-QM-FMO). SuMD allows a dynamical sampling of a protein-ligand system in a fully solvated environment with protein flexibility and ligand conformational sampling. DA-QM-FMO estimates the average potential binding interaction energies from dynamically generated protein-ligands binding modes. In this study DA-QM-FMO was able to outperform simple docking or MM-PBSA/GBSA at predicting the experimental binding pose, using PL-pro with published crystal structures of the enzyme with inhibitors bound.