A similarity-based data-fusion approach to the visual characterization and comparison of compound databases
A similarity-based data-fusion approach to the visual characterization and comparison of compound databases
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DOI：
10.1111/j.1747-0285.2007.00579.x
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发表时间：
2007-11-01
影响因子：
3
通讯作者：
Houghten, Richard A.
中科院分区：
文献类型：
--
作者：
Medina-Franco, Jose L.;Maggiora, Gerald M.;Houghten, Richard A.
一个低维的方法，基于使用多个基于融合的相似性措施，描述了图形化描绘和表征化合物数据库中的分子之间的关系。该措施被用来构建多融合相似性映射，其特征在于一组“测试”分子与一组“参考”分子的关系。参考组是非常一般的，并且可以由来自例如测试分子本身的组（自参考情况）、来自小文库或大化合物集合、或来自给定测定或测定组中的活性物的分子组成。测试集是相对于指定的参考集要分析的化合物的任何集合。多个融合相似性度量倾向于提供比单个基于融合的度量更多的信息，包括关于参考集分子周围的化学空间邻域的性质的信息。一般讨论如何解释多融合相似性地图，并给出了几个例子，说明这些地图可以用来比较化合物库或集合，选择化合物进行筛选或收购，并确定新的活性分子使用基于配体的虚拟筛选。
A low-dimensional method, based on the use of multiple fusion-based similarity measures, is described for graphically depicting and characterizing relationships among molecules in compound databases. The measures are used to construct multi-fusion similarity maps that characterize the relationship of a set of 'test' molecules to a set of 'reference' molecules. The reference set is very general and can be made of molecules from, for example, the set of test molecules itself (the self-referencing case), from a small library or large compound collection, or from actives in a given assay or group of assays. The test set is any collection of compounds to be analyzed with respect to the specified reference set. Multiple fusion similarity measures tend to provide more information than single fusion-based measures, including information on the nature of the chemical-space neighborhoods surrounding reference-set molecules. A general discussion is presented on how to interpret multi-fusion similarity maps, and several examples are given that illustrate how these maps can be used to compare compound libraries or collections, to select compounds for screening or acquisition, and to identify new active molecules using ligand-based virtual screening.