Kunimoto Ryo, Vogt Martin, Bajorath Jürgen
Department of Life Science Informatics , B-IT , LIMES Program Unit Chemical Biology and Medicinal Chemistry , Rheinische Friedrich-Wilhelms-Universität , Dahlmannstr. 2 , D-53113 Bonn , Germany . Email:
Medchemcomm. 2016 Dec 23;8(2):376-384. doi: 10.1039/c6md00628k. eCollection 2017 Feb 1.
Similarity-based compound networks are used as coordinate-free representations of chemical space. In so-called chemical space networks (CSNs), nodes represent compounds and edges pairwise similarity relationships. Nodes can be annotated with activity information, which enables visualization of structure-activity relationship (SAR) patterns. A major determinant of CSN structure and topology is the way in which similarity relationships are determined. Using different similarity measures, a number of CSN variants have been generated previously. Herein, we report a new type of CSN with an asymmetric similarity metric based upon the maximum common substructure of compound pairs. While CSNs have thus far mostly been used for SAR visualization, the new CSN variant was designed for another medicinal chemistry application, the identification of compound pathways in data sets. In this CSN, pathways consisting of structurally related compounds with increasing size can be systematically traced, which represent models of compound optimization paths. Compound series forming such paths can be extracted from the CSN. The network-based identification of hit-to-lead or lead optimization series in compound data sets is intuitive and thought to provide valuable information for medicinal chemistry.
基于相似性的化合物网络被用作化学空间的无坐标表示。在所谓的化学空间网络（CSN）中，节点代表化合物，边代表成对的相似性关系。节点可以用活性信息进行注释，这使得结构-活性关系（SAR）模式的可视化成为可能。CSN结构和拓扑的一个主要决定因素是确定相似性关系的方式。使用不同的相似性度量，先前已经生成了许多CSN变体。在此，我们报告一种新型的CSN，它基于化合物对的最大公共子结构具有不对称相似性度量。虽然CSN迄今为止大多用于SAR可视化，但新的CSN变体是为另一个药物化学应用而设计的，即识别数据集中的化合物途径。在这个CSN中，可以系统地追踪由结构相关且规模不断增大的化合物组成的途径，这些途径代表化合物优化路径的模型。形成此类路径的化合物系列可以从CSN中提取出来。在化合物数据集中基于网络识别从活性化合物到先导化合物或先导化合物优化系列是直观的，并且被认为可为药物化学提供有价值的信息。
