title: Knowledge-Based Strategy to Improve Ligand Pose Prediction Accuracy for Lead Optimization.
abstract: 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.
authors.forename: Cen
authors.initials: C
authors.lastname: Gao
authors.affiliation: §Chemical Computing Group Inc., 1010 Sherbrooke St. W, Suite 910, Montreal, Quebec H3A 2R7, Canada.
authors.forename: Nels
authors.initials: N
authors.lastname: Thorsteinson
authors.forename: Ian
authors.initials: I
authors.lastname: Watson
authors.forename: Jibo
authors.initials: J
authors.lastname: Wang
authors.forename: Michal
authors.initials: M
authors.lastname: Vieth
mesh_terms: D017868:Cyclic AMP-Dependent Protein Kinases / Q000737:chemistry / Q000378:metabolism; D030562:Databases, Protein; D015195:Drug Design; D008024:Ligands; D000069550:Machine Learning; D062105:Molecular Docking Simulation / Q000379:methods*; D011487:Protein Conformation
chemical_list: D008024:Ligands; D017868:Cyclic AMP-Dependent Protein Kinases