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

@article{citeulike:1117879, abstract = {Chemical databases are routinely clustered, with the aim of grouping molecules which share similar structural features. Ideally, medicinal chemists are then able to browse a few representatives of the cluster in order to interpret 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 represent a cluster by means of a maximum common substructure based on the shared functionality of the cluster members. Previously, we have used reduced graphs, where each node corresponds to a generalized functional group, as topological molecular descriptors for virtual screening. In this work, we precluster a database using any clustering method. We then represent the molecules in a cluster as reduced graphs. By repeated application of a maximum common edge substructure (MCES) algorithm, we obtain one or more reduced graph cluster representatives. The sparsity of the reduced graphs means that the MCES calculations can be performed in real time. The reduced graph cluster representatives are readily interpretable in terms of functional activity and can be mapped directly back to the molecules to which they correspond, giving the chemist a rapid means of assessing potential activities contained within the cluster. Clusters of interest are then subject to a detailed R-group analysis using the same iterated MCES algorithm applied to the molecular graphs.}, address = {Department of Information Studies, University of Sheffield, 211 Portobello St., Regent Court, Sheffield, United Kingdom, and AstraZeneca, Mereside, Alderley Park, Macclesfield, Cheshire, United Kingdom.}, author = {Gardiner, Eleanor J. and Gillet, Valerie J. and Willett, Peter and Cosgrove, David A.}, citeulike-article-id = {1117879}, citeulike-linkout-0 = {http://dx.doi.org/10.1021/ci600444g}, citeulike-linkout-1 = {http://pubs.acs.org/doi/abs/10.1021/ci600444g}, citeulike-linkout-2 = {http://view.ncbi.nlm.nih.gov/pubmed/17309248}, citeulike-linkout-3 = {http://www.hubmed.org/display.cgi?uids=17309248}, day = {20}, doi = {10.1021/ci600444g}, issn = {1549-9596}, journal = {J Chem Inf Model}, keywords = {clustering, graph, reduced, similarity}, month = {February}, posted-at = {2008-01-03 13:27:48}, priority = {2}, title = {Representing Clusters Using a Maximum Common Edge Substructure Algorithm Applied to Reduced Graphs and Molecular Graphs.}, url = {http://dx.doi.org/10.1021/ci600444g}, year = {2007} }

TY - JOUR ID - citeulike:1117879 L3 - citeulike-article-id:1117879 N2 - Chemical databases are routinely clustered, with the aim of grouping molecules which share similar structural features. Ideally, medicinal chemists are then able to browse a few representatives of the cluster in order to interpret 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 represent a cluster by means of a maximum common substructure based on the shared functionality of the cluster members. Previously, we have used reduced graphs, where each node corresponds to a generalized functional group, as topological molecular descriptors for virtual screening. In this work, we precluster a database using any clustering method. We then represent the molecules in a cluster as reduced graphs. By repeated application of a maximum common edge substructure (MCES) algorithm, we obtain one or more reduced graph cluster representatives. The sparsity of the reduced graphs means that the MCES calculations can be performed in real time. The reduced graph cluster representatives are readily interpretable in terms of functional activity and can be mapped directly back to the molecules to which they correspond, giving the chemist a rapid means of assessing potential activities contained within the cluster. Clusters of interest are then subject to a detailed R-group analysis using the same iterated MCES algorithm applied to the molecular graphs. JF - J Chem Inf Model SN - 1549-9596 AD - Department of Information Studies, University of Sheffield, 211 Portobello St., Regent Court, Sheffield, United Kingdom, and AstraZeneca, Mereside, Alderley Park, Macclesfield, Cheshire, United Kingdom. TI - Representing Clusters Using a Maximum Common Edge Substructure Algorithm Applied to Reduced Graphs and Molecular Graphs. KW - clustering KW - graph KW - reduced KW - similarity AU - Gardiner, Eleanor AU - Gillet, Valerie AU - Willett, Peter AU - Cosgrove, David PY - 2007/02/20/ UR - http://dx.doi.org/10.1021/ci600444g ER -