Structure-Based Virtual Screening with Supervised Consensus Scoring: Evaluation of Pose Prediction and Enrichment Factors

Since the evaluation of ligand conformations is a crucial aspect of structure-based virtual screening, scoring functions play significant roles in it. However, it is known that a scoring function does not always work well for all target proteins. When one cannot know which scoring function works best against a target protein a priori, there is no standard scoring method to know it even if 3D structure of a target protein–ligand complex is available. Therefore, development of the method to achieve high enrichments from given scoring functions and 3D structure of protein–ligand complex is a crucial and challenging task. To address this problem, we applied SCS (supervised consensus scoring), which employs a rough linear correlation between the binding free energy and the root-mean-square deviation (rmsd) of a native ligand conformations and incorporates protein–ligand binding process with docked ligand conformations using supervised learning, to virtual screening. We evaluated both the docking poses and enri...