10.1021/acs.jcim.5b00186.s002 Cen Gao Cen Gao Nels Thorsteinson Nels Thorsteinson Ian Watson Ian Watson Jibo Wang Jibo Wang Michal Vieth Michal Vieth Knowledge-Based Strategy to Improve Ligand Pose Prediction Accuracy for Lead Optimization American Chemical Society 2015 prediction drug discovery efforts docking ligand program accuracy molecule compound SBDD Ligand Pose Prediction Accuracy target protein MCS 2015-07-27 00:00:00 Dataset https://acs.figshare.com/articles/dataset/Knowledge_Based_Strategy_to_Improve_Ligand_Pose_Prediction_Accuracy_for_Lead_Optimization/2146651 Accurately predicting how a small molecule binds to its target protein is an essential requirement for structure-based drug design (SBDD) efforts. In structurally enabled medicinal chemistry programs, binding pose prediction is often applied to ligands after a related compound’s crystal structure bound to the target protein has been solved. In this article, we present an automated pose prediction protocol that makes extensive use of existing X-ray ligand information. It uses spatial restraints during docking based on maximum common substructure (MCS) overlap between candidate molecule and existing X-ray coordinates of the related compound. For a validation data set of 8784 docking runs, our protocol’s pose prediction accuracy (80–82%) is almost two times higher than that of one unbiased docking method software (43%). To demonstrate the utility of this protocol in a project setting, we show its application in a chronological manner for a number of internal drug discovery efforts. The accuracy and applicability of this algorithm (>70% of cases) to medicinal chemistry efforts make this the approach of choice for pose prediction in lead optimization programs.