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A teach-discover-treat application of ZincPharmer: An online interactive pharmacophore modeling and virtual screening tool

机译:ZincPharmer的教学-发现-治疗应用程序:在线交互式药效团建模和虚拟筛选工具

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摘要

© 2015 Koes et al. The 2012 Teach-Discover-Treat (TDT) community-wide experiment provided a unique opportunity to test prospective virtual screening protocols targeting the anti-malarial target dihydroorotate dehydrogenase (DHODH). Facilitated by ZincPharmer, an open access online interactive pharmacophore search of the ZINC database, the experience resulted in the development of a novel classification scheme that successfully predicted the bound structure of a non-triazolopyrimidine inhibitor, as well as an overall hit rate of 27% of tested active compounds from multiple novel chemical scaffolds. The general approach entailed exhaustively building and screening sparse pharmacophore models comprising of a minimum of three features for each bound ligand in all available DHODH co-crystals and iteratively adding features that increased the number of known binders returned by the query. Collectively, the TDT experiment provided a unique opportunity to teach computational methods of drug discovery, develop innovative methodologies and prospectively discover new compounds active against DHODH. Copyright:
机译:©2015 Koes等。 2012年的Teach-Discover-Treat(TDT)社区范围的实验提供了独特的机会来测试针对抗疟疾目标二氢乳清酸脱氢酶(DHODH)的前瞻性虚拟筛查方案。在ZINC数据库的开放访问在线交互式药效团搜索ZincPharmer的帮助下,该经验导致开发了一种新型分类方案,该分类方案成功预测了非三唑并嘧啶抑制剂的结合结构,总命中率为27%来自多种新型化学支架的测试活性化合物一般方法需要穷举建立和筛选稀疏药效团模型,该模型至少应包含所有可用DHODH共晶体中每个结合配体的三个特征,并反复添加增加查询返回的已知结合物数量的特征。总的来说,TDT实验提供了独特的机会来教授药物发现的计算方法,开发创新的方法并有前瞻性地发现对DHODH具有活性的新化合物。版权:

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