Representing Clusters Using a Maximum Common Edge Substructure Algorithm Applied to Reduced Graphs and Molecular Graphs

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• 作者：Eleanor J. Gardiner ; Valerie J. Gillet ; Peter Willett ; David A. Cosgrove
• 刊名：Journal of Chemical Information and Modeling
• 出版年：2007
• 出版时间：March 2007
• 年：2007
• 卷：47
• 期：2
• 页码：354 - 366
• 全文大小：423K
• 年卷期：v.47,no.2(March 2007)
• ISSN：1549-960X

文摘

Chemical databases are routinely clustered, with the aim of grouping molecules which share similar structuralfeatures. Ideally, medicinal chemists are then able to browse a few representatives of the cluster in order tointerpret the shared activity of the cluster members. However, when molecules are clustered using fingerprints,it may be difficult to decipher the structural commonalities which are present. Here, we seek to representa cluster by means of a maximum common substructure based on the shared functionality of the clustermembers. Previously, we have used reduced graphs, where each node corresponds to a generalized functionalgroup, as topological molecular descriptors for virtual screening. In this work, we precluster a databaseusing any clustering method. We then represent the molecules in a cluster as reduced graphs. By repeatedapplication of a maximum common edge substructure (MCES) algorithm, we obtain one or more reducedgraph cluster representatives. The sparsity of the reduced graphs means that the MCES calculations can beperformed in real time. The reduced graph cluster representatives are readily interpretable in terms of functionalactivity and can be mapped directly back to the molecules to which they correspond, giving the chemist arapid means of assessing potential activities contained within the cluster. Clusters of interest are then subjectto a detailed R-group analysis using the same iterated MCES algorithm applied to the molecular graphs.