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Abstract：
\"Hit lists\" generated by high-throughput screening (HTS) typically contain a large percentage of false positives, making follow-up assays necessary to distinguish active from inactive substances. Here we present a method for improving the accuracy of HTS hit lists by computationally based virtual screening (VS) of the corresponding chemical libraries and selecting hits by HTS/VS consensus. This approach was applied in a case study on the target-enzyme angiogenin, a potent inducer of angiogenesis. In conjunction with HTS of the National Cancer Institute Diversity Set and ChemBridge DIVERSet E (approximately 18,000 compounds total), VS was performed with two flexible library docking/scoring methods, DockVision/Ludi and GOLD. Analysis of the results reveals that dramatic enrichment of the HTS hit rate can be achieved by selecting compounds in consensus with one or both of the VS functions. For example, HTS hits ranked in the top 2% by GOLD included 42% of the true hits, but only 8% of the false positives; this represents a sixfold enrichment over the HTS hit rate. Notably, the HTS/VS method was effective in selecting out inhibitors with midmicromolar dissociation constants typical of leads commonly obtained in primary screens.
摘要：
由高通量筛选（HTS）生成的“命中列表”通常包含很大比例的假阳性，进行必要的后续检测以区分活性物质和非活性物质。在这里，我们提出了一种方法，通过基于计算的虚拟筛选（VS）相应的化学文库和选择命中HTS/VS共识来提高HTS命中列表的准确性。这种方法被应用于靶酶血管生成素的案例研究中，一种有效的血管生成诱导剂。结合国家癌症研究所多样性集和ChemBridgeDIVERSetE的HTS（总计约18,000种化合物），VS采用两种灵活的库对接/评分方法进行，DockVision/Ludi和黄金。对结果的分析表明，通过选择具有一种或两种VS功能的化合物，可以实现HTS命中率的显着富集。例如,按GOLD排名前2%的HTS点击率包括42%的真实点击率，但只有8%的假阳性；这代表了HTS命中率的六倍富集。值得注意的是,HTS/VS方法可以有效地选择具有中微摩尔解离常数的抑制剂，这是通常在初级筛选中获得的引线的典型特征。
