. Both networks incorporate physical knowledge as an inductive bias in order to enhance pose discrimination power while ensuring tolerance to small interfacial structural noises. Combined with Rosetta GALigandDock sampling, DENOISer outperformed existing docking tools on the PoseBusters model-docking benchmark set, as well as on a broad cross-docking benchmark set. Further analyses reveal that the physics-based components and the consensus ranking approach are the two most crucial factors contributing to its ranking success. We expect that DENOISer may assist future drug discovery endeavors by providing more accurate structural models for protein-ligand complexes.