LOCAL KERNEL CANONICAL CORRELATION ANALYSIS WITH APPLICATION TO VIRTUAL DRUG SCREENING.
LOCAL KERNEL CANONICAL CORRELATION ANALYSIS WITH APPLICATION TO VIRTUAL DRUG SCREENING.
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DOI：
10.1214/11-aoas472
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发表时间：
2011-09-01
期刊：
影响因子：
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通讯作者：
Tropsha A
中科院分区：
文献类型：
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作者：
Samarov D;Marron JS;Liu Y;Grulke C;Tropsha A
药物发现是鉴定具有潜在生物活性的化合物的过程。出现的一个主要挑战是，要搜索的化合物数量可能相当大，有时多达数百万，这使得实验测试变得棘手。因此，采用计算方法来过滤掉那些不表现出强生物活性的化合物。这种过滤步骤，也称为虚拟筛选，减少了搜索空间，允许剩余的化合物进行实验测试。本文提出了几种基于典型相关分析（CCA）和基于核的扩展的虚拟筛选问题的新方法。频谱学习的思想激发了我们提出的新的方法，称为无限期核CCA （IKCCA）。我们展示了这种方法在玩具问题和使用真实世界数据方面的强大性能，与现有方法相比，虚拟筛选的预测准确性有了显着提高。
Drug discovery is the process of identifying compounds which have potentially meaningful biological activity. A major challenge that arises is that the number of compounds to search over can be quite large, sometimes numbering in the millions, making experimental testing intractable. For this reason computational methods are employed to filter out those compounds which do not exhibit strong biological activity. This filtering step, also called virtual screening reduces the search space, allowing for the remaining compounds to be experimentally tested. In this paper we propose several novel approaches to the problem of virtual screening based on Canonical Correlation Analysis (CCA) and on a kernel-based extension. Spectral learning ideas motivate our proposed new method called Indefinite Kernel CCA (IKCCA). We show the strong performance of this approach both for a toy problem as well as using real world data with dramatic improvements in predictive accuracy of virtual screening over an existing methodology.