In Fig. , we show the predictive performance of DockThor for predicting the experimental binding mode on redocking simulations as the top-scored and the lowest-RMSD solutions in the entire PDBbind refined set v.2020. The DockThor program ran successfully for 5,300 protein-ligand complexes, and failed for only 16 structures that are not considered in the following discussion. In this dataset, we provided 52,788 docked poses consisting of up to ten distinct binding poses for each protein-ligand complex to be used further in methodological developments, including machine learning-based models. When evaluating the top-energy binding pose, DockThor generated the co-crystallographic pose (i.e., with RMSD below 2.0 Å) for 68% of the entire dataset. We also investigated the success rates when considering the top-2 binding poses and the lowest RMSD pose generated. In such cases, we achieved higher success rates (76% and 90%, respectively), indicating that a rescoring strategy with more sophisticated scoring functions could recover the experimentally observed binding modes. Noteworthy, the generated poses could also be used in the development of new ML scoring functions for pose prediction.