Virtual Screening Data Fusion Using Both Structure- and Ligand-Based Methods
Virtual Screening Data Fusion Using Both Structure- and Ligand-Based Methods
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DOI：
10.1021/ci2004835
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发表时间：
2012-01
影响因子：
5.6
通讯作者：
F. Svensson;A. Karlén;Christian Sköld
中科院分区：
文献类型：
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作者：
F. Svensson;A. Karlén;Christian Sköld
虚拟筛选在药物发现中得到了广泛的应用，并且已经投入了大量的努力来改进现有的方法。在这项研究中，我们已经评估了性能的复合排名在虚拟筛选使用五种不同的数据融合算法，共16个数据集。通过对接，药效团搜索，形状相似性和静电相似性，跨越基于结构和配体的方法产生的数据。用于数据融合的算法有和秩、秩投票、和得分、Pareto排序和并行选择。除了来自单一方法的结果之外，融合方法都不需要任何先验知识或输入，因此是容易适用的。结果表明，与单一方法相比，利用数据融合的复合排序提高了虚拟筛选的性能和一致性。性能最好的数据融合算法是并行选择，但等级投票和Pareto排序也有很好的性能。
Virtual screening is widely applied in drug discovery, and significant effort has been put into improving current methods. In this study, we have evaluated the performance of compound ranking in virtual screening using five different data fusion algorithms on a total of 16 data sets. The data were generated by docking, pharmacophore search, shape similarity, and electrostatic similarity, spanning both structure- and ligand-based methods. The algorithms used for data fusion were sum rank, rank vote, sum score, Pareto ranking, and parallel selection. None of the fusion methods require any prior knowledge or input other than the results from the single methods and, thus, are readily applicable. The results show that compound ranking using data fusion improves the performance and consistency of virtual screening compared to the single methods alone. The best performing data fusion algorithm was parallel selection, but both rank voting and Pareto ranking also have good performance.