%0 Generic %A Gao, Cen %A Thorsteinson, Nels %A Watson, Ian %A Wang, Jibo %A Vieth, Michal %D 2015 %T Knowledge-Based Strategy to Improve Ligand Pose Prediction Accuracy for Lead Optimization %U https://acs.figshare.com/articles/dataset/Knowledge_Based_Strategy_to_Improve_Ligand_Pose_Prediction_Accuracy_for_Lead_Optimization/2146651 %R 10.1021/acs.jcim.5b00186.s002 %2 https://ndownloader.figshare.com/files/3780499 %K prediction %K drug discovery efforts %K docking %K ligand %K program %K accuracy %K molecule %K compound %K SBDD %K Ligand Pose Prediction Accuracy %K target protein %K MCS %X 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. %I ACS Publications