Improving the accuracy of pose prediction in molecular docking via structural filtering and conformational clustering

Structure-based virtual screening (molecular docking) is now one of the most pragmatic techniques to leverage target structure for ligand discovery. Accurate binding pose prediction is critical to molecular docking. Here, we describe a general strategy to improve the accuracy of docking pose prediction by implementing the structural descriptor-based filtering and KGS-penalty function-based conformational clustering in an unbiased manner. We assessed our method against 150 high-quality protein–ligand complex structures. Surprisingly, such simple components are sufficient to improve the accuracy of docking pose prediction. The success rate of predicting near-native docking pose increased from 53% of the targets to 78%. We expect that our strategy may have general usage in improving currently available molecular docking programs.

Accurate binding pose prediction is critical to molecular docking. Here we describe a general strategy to improve the accuracy of pose prediction by implementing the structural descriptor-based filtering and KGS-penalty function-based conformational clustering.Figure optionsDownload as PowerPoint slide