Compass: A Comprehensive Tool for Accurate and Efficient Molecular Docking in Inference and Fine-Tuning

While there has been discussion about noise levels in molecular docking datasets such as PDBBind, a thorough analysis of their physical/chemical and bioactivity noise characteristics is still lacking. PoseCheck addresses this issue by examining molecular strain energy, molecular-protein clashes, and interactions, but it is primarily created for $de$ $novo$ drug design. Another important metric in molecular docking, Binding Affinity Energy, is better assessed by the new empirical score function, AA-Score, which has demonstrated improved performance over existing methods. To tackle these challenges, we propose the COMPASS method, which integrates the PoseCheck and AA-Score modules. This approach evaluates dataset noise levels and the physical/chemical and bioactivity feasibility of docked molecules. Our analysis of the PDBBind dataset using COMPASS reveals significant noise in the ground truth data. Additionally, we incorporate COMPASS with the state-of-the-art molecular docking method, DiffDock, in inference mode to achieve efficient and accurate assessments of docked ligands. Finally, we propose a new paradigm to enhance model performance for molecular docking through fine-tuning and discuss the potential benefits of this approach. The source code is available publicly at https://github.com/BIMSBbioinfo/Compass.