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BEAR,一种用于药物发现的新型虚拟筛选方法。

BEAR, a novel virtual screening methodology for drug discovery.

作者信息

Degliesposti Gianluca, Portioli Corinne, Parenti Marco Daniele, Rastelli Giulio

机构信息

Dipartimento di Scienze Farmaceutiche, Università di Modena e Reggio Emilia, Modena, Italy.

出版信息

J Biomol Screen. 2011 Jan;16(1):129-33. doi: 10.1177/1087057110388276. Epub 2010 Nov 17.

DOI:10.1177/1087057110388276
PMID:21084717
Abstract

BEAR (binding estimation after refinement) is a new virtual screening technology based on the conformational refinement of docking poses through molecular dynamics and prediction of binding free energies using accurate scoring functions. Here, the authors report the results of an extensive benchmark of the BEAR performance in identifying a smaller subset of known inhibitors seeded in a large (1.5 million) database of compounds. BEAR performance proved strikingly better if compared with standard docking screening methods. The validations performed so far showed that BEAR is a reliable tool for drug discovery. It is fast, modular, and automated, and it can be applied to virtual screenings against any biological target with known structure and any database of compounds.

摘要

BEAR(优化后结合能估算)是一种新的虚拟筛选技术,它基于通过分子动力学对对接构象进行优化以及使用精确评分函数预测结合自由能。在此,作者报告了BEAR在从一个庞大的(150万种)化合物数据库中识别出一小部分已知抑制剂方面的广泛性能基准测试结果。与标准对接筛选方法相比,BEAR的性能表现出显著优势。到目前为止所进行的验证表明,BEAR是药物发现的可靠工具。它快速、模块化且自动化,可应用于针对任何具有已知结构的生物靶点和任何化合物数据库的虚拟筛选。

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