Knowledge-Based Strategy to Improve Ligand Pose Prediction Accuracy for Lead Optimization

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• 作者：Cen Gao ; Nels Thorsteinson ; Ian Watson ; Jibo Wang ; Michal Vieth
• 刊名：Journal of Chemical Information and Modeling
• 出版年：2015
• 出版时间：July 27, 2015
• 年：2015
• 卷：55
• 期：7
• 页码：1460-1468
• 全文大小：503K
• ISSN：1549-960X

文摘

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鈥檚 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鈥檚 pose prediction accuracy (80鈥?2%) 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.