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化学文库网络中的小世界现象:在基于片段的药物发现中的应用。

Small-world phenomena in chemical library networks: application to fragment-based drug discovery.

机构信息

Chemistry Research Laboratories, Drug Discovery Research, Astellas Pharma Inc., 21 Miyukigaoka, Tsukuba-shi, Ibaraki 305-8585, Japan.

出版信息

J Chem Inf Model. 2009 Dec;49(12):2677-86. doi: 10.1021/ci900123v.

Abstract

A wide variety of networks in various fields have been characterized as small-world networks. In scale-free networks, a representative class of small-world networks, numbers of contacts (degree distributions) of nodes follow power laws. Although several examples of power-law distributions have been found in the field of chemoinformatics, the network structures of chemical libraries have not been analyzed. Here, we show that small-world phenomena are observed not only in existing chemical libraries but also in virtual libraries generated from structurally diverse fragments when represented as networks. On the basis of this observation, we propose that an efficient compound-prioritization method of fragment-based drug discovery (FBDD) would be to select those fragments as a starting point such that the linked compounds become hubs in the library and therefore allow identification of many similar compounds when all-to-all fragment linkings are performed. Moreover, our analyses indicated that the variety of linkers had a marked influence on the network structure and thus on the diversity of the compounds synthesized by linking fragment hits.

摘要

各种领域的大量网络已被描述为小世界网络。在无标度网络中,节点的接触数量(度分布)遵循幂律。尽管在化学生物信息学领域已经发现了几个幂律分布的例子,但化学库的网络结构尚未被分析。在这里,我们表明,小世界现象不仅存在于现有的化学库中,而且当以网络形式表示时,也存在于从结构多样的片段生成的虚拟库中。基于这一观察结果,我们提出了一种基于片段的药物发现(FBDD)的有效化合物优先排序方法,即选择那些片段作为起点,以便连接的化合物成为库中的枢纽,从而在进行所有片段连接时可以识别出许多类似的化合物。此外,我们的分析表明,连接子的多样性对网络结构有显著影响,从而对通过连接片段命中合成的化合物的多样性有影响。

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