In the age of combinatorial chemistry and high throughput screening, large-scale data of bioactive chemicals oriented to drug development are being accumulated. Due to the difficulties inherent in understanding such large quantities of data, information visualization techniques are increasingly attractive. Authors apply "HeiankyoView", which is the technique for the representation of large-scale hierarchical data, for the visualization of multi-dimensional data of bioactive chemicals. In the present study, we investigated applicability of the visualization technique to the structure-activity relationship (SAR) analyses. The study first classifies chemicals according to similarity in their biological actions through self-organizing map analysis. It then applies a recursive partitioning method to find the relationship between biologically based categories and chemical structure, and finally it stores the drugs as hierarchical data. HeiankyoView is suitable for the visualization of such hierarchical data. This paper first describes the algorithmic overview of HeiankyoView, and then provides some example of visualization of multi-dimensional data of bioactive chemicals..
在组合化学和高通量筛选时代,针对药物开发的生物活性化学品的大规模数据正在积累。由于理解如此大量的数据固有的困难,信息可视化技术变得越来越有吸引力。作者将“ HeiankyoView”(一种用于表示大规模分层数据的技术)用于生物活性化学物质的多维数据的可视化。在本研究中,我们调查了可视化技术对结构-活性关系(SAR)分析的适用性。该研究首先通过自组织图分析根据化学物质在生物学行为上的相似性对其进行分类。然后应用递归分区方法查找基于生物学的类别与化学结构之间的关系,最后将药物存储为分层数据。 HeiankyoView适用于此类分层数据的可视化。本文首先介绍了HeiankyoView的算法概述,然后提供了一些可视化生物活性化学物质多维数据的示例。 P>
Ochanomizu University;
Kyoto University;
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