UniPoseScore: A Unified Graphormer3D-Based Framework for Protein-Ligand Binding Pose Scoring and Refinement

Accurate prediction of protein-ligand binding poses is essential for structure-based drug discovery. In recent years, computational approaches, particularly molecular docking, have generated large numbers of candidate ligand poses. However, existing scoring functions remain limited in their ability to distinguish near-native poses from incorrect docking decoys. In this work, we propose UniPoseScore, a Graphormer3D-based framework for ligand pose scoring and refinement. The model simultaneously predicts pose RMSD and atomic displacement vectors to guide ligand pose refinement. Across four benchmark data sets, UniPoseScore showed consistently strong performance and better generalization, with a particularly clear advantage in RMSD correlation. Visualization of the learned embeddings further suggests that the model captures structural features correlated with pose accuracy. On the CASF-2016 benchmark, UniPoseScore achieves a refinement success rate of 74.3%, highlighting the effectiveness of UniPoseScore in improving ligand binding poses and its potential applications in drug discovery. The UniPoseScore is available at https://github.com/April-Zhangjq/UniPoseScore.