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首页> 外文期刊>Journal of chemical information and modeling >Boosting virtual screening enrichments with data fusion: Coalescing hits from two-dimensional fingerprints, shape, and docking
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Boosting virtual screening enrichments with data fusion: Coalescing hits from two-dimensional fingerprints, shape, and docking

机译：通过数据融合促进虚拟筛选的充实：二维指纹，形状和对接的融合

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摘要

Virtual screening is an effective way to find hits in drug discovery, with approaches ranging from fast information-based similarity methods to more computationally intensive physics-based docking methods. However, the best approach to use for a given project is not clear in advance of the screen. In this work, we show that combining results from multiple methods using a standard score (Z-score) can significantly improve virtual screening enrichments over any of the single screening methods. We show that an augmented Z-score, which considers the best two out of three scores for a given compound, outperforms previously published data fusion algorithms. We use three different virtual screening methods (two-dimensional (2D) fingerprint similarity, shape-based similarity, and docking) and study two different databases (DUD and MDDR). The average enrichment in the top 1% was improved by 9% for DUD and 25% for the MDDR, compared with the top individual method. Improvements of 22% for DUD and 43% for MDDR are seen over the average of the three individual methods. Statistics are presented that show a high significance associated with the findings in this work.
机译：虚拟筛选是在药物发现中发现命中物的有效方法，其方法范围从基于信息的快速相似方法到基于物理的更多计算密集型对接方法。但是，在屏幕之前，尚不清楚用于给定项目的最佳方法。在这项工作中，我们表明，结合使用标准评分（Z评分）的多种方法的结果，可以显着改善任何单一筛选方法的虚拟筛选富集度。我们显示，对于给定的化合物，考虑了三个分数中最好的两个分数的增强Z分数要优于以前发布的数据融合算法。我们使用三种不同的虚拟筛选方法（二维（2D）指纹相似度，基于形状的相似度和对接），并研究了两个不同的数据库（DUD和MDDR）。与排名靠前的单个方法相比，排名前1％的平均富集DUD和MDDR的平均富集分别提高了9％和25％。与这三种方法的平均值相比，DUD的改进为22％，MDDR的改进为43％。统计数据显示出与这项工作中的发现相关的高度重要性。

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