局部核典型相关分析在虚拟药物筛选中的应用
Local kernel canonical correlation analysis with application to virtual drug screening
摘要：
药物发现是鉴定具有潜在意义的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，有时数以百万计，使得实验测试变得棘手。为此，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。这个过滤步骤，也称为虚拟筛选，减少了搜索空间，允许对剩余的化合物进行实验测试。在本文中，我们提出了几种新的方法，虚拟筛选的问题的基础上典型相关分析（CCA）和基于内核的扩展。谱学习思想激发了我们提出的称为不定核CCA（IKCCA）的新方法。我们展示了这种方法对于玩具问题以及使用现实世界数据的强大性能，虚拟筛选的预测准确性比现有方法有了显着的提高。
英文摘要：
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.
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