Finding Key Members in Compound Libraries by Analyzing Networks of Molecules Assembled by Structural Similarity Export

@article{citeulike:6064492, abstract = {PMID: 19877594 Characterization of chemical libraries is an essential task in everyday chemoinformatics practice. This study describes some potential uses of network visualization and analysis methods to identify distinguished members of compound libraries. Molecules were ordered into networks by their structural similarity defined by molecular fingerprints. Various properties of such networks were examined. It was shown, that the correlation methods used to calculate the similarity between two structures radically determined the topology of networks. From the same set of molecules, the Russel−Rao and the Baroni−Urbani methods created sparser and denser networks, respectively, than using the Tanimoto method. Central nodes, corresponding to central compounds in the libraries, were determined for some example data sets. It was shown by the case of adenosine A1, A2, and dual antagonists that the methods used to identify central nodes could be divided into two groups: (1) centrality methods, exemplified by the centroid centrality, which could pick up structures that were the most similar to the largest number of other molecules and (2) group, exemplified by betweenness centrality, that could identify molecules that had intermediate structures between some homogeneous subsets of the library. The latter method gave significantly higher ranks to dual adenosine antagonists, hinting the suitability of this measure to identify molecules with multiple activities. Some practical applications of the method for clustering of and sample selection from chemical libraries are presented. In the frame of the study, a Jchem plug-in has been developed to the Cytoscape network visualization software, which makes the visual observation of molecular networks more convenient. The plug-in is included in the Supporting Information of the article for free usage.}, author = {Lepp, Zsolt and Huang, Chunfei and Okada, Takashi}, citeulike-article-id = {6064492}, citeulike-linkout-0 = {http://dx.doi.org/10.1021/ci9001102}, citeulike-linkout-1 = {http://pubs.acs.org/doi/abs/10.1021/ci9001102}, day = {30}, doi = {10.1021/ci9001102}, issn = {1549-9596}, journal = {Journal of Chemical Information and Modeling}, keywords = {chemical\_biology, chemical\_libraries, chemical\_networks}, month = {October}, number = {0}, pages = {091030094710018+}, posted-at = {2009-11-05 10:53:52}, priority = {2}, title = {Finding Key Members in Compound Libraries by Analyzing Networks of Molecules Assembled by Structural Similarity}, url = {http://dx.doi.org/10.1021/ci9001102}, volume = {0}, year = {0000} }

TY - JOUR ID - citeulike:6064492 L3 - citeulike-article-id:6064492 N2 - PMID: 19877594 Characterization of chemical libraries is an essential task in everyday chemoinformatics practice. This study describes some potential uses of network visualization and analysis methods to identify distinguished members of compound libraries. Molecules were ordered into networks by their structural similarity defined by molecular fingerprints. Various properties of such networks were examined. It was shown, that the correlation methods used to calculate the similarity between two structures radically determined the topology of networks. From the same set of molecules, the Russel−Rao and the Baroni−Urbani methods created sparser and denser networks, respectively, than using the Tanimoto method. Central nodes, corresponding to central compounds in the libraries, were determined for some example data sets. It was shown by the case of adenosine A1, A2, and dual antagonists that the methods used to identify central nodes could be divided into two groups: (1) centrality methods, exemplified by the centroid centrality, which could pick up structures that were the most similar to the largest number of other molecules and (2) group, exemplified by betweenness centrality, that could identify molecules that had intermediate structures between some homogeneous subsets of the library. The latter method gave significantly higher ranks to dual adenosine antagonists, hinting the suitability of this measure to identify molecules with multiple activities. Some practical applications of the method for clustering of and sample selection from chemical libraries are presented. In the frame of the study, a Jchem plug-in has been developed to the Cytoscape network visualization software, which makes the visual observation of molecular networks more convenient. The plug-in is included in the Supporting Information of the article for free usage. IS - 0 JF - Journal of Chemical Information and Modeling SN - 1549-9596 TI - Finding Key Members in Compound Libraries by Analyzing Networks of Molecules Assembled by Structural Similarity VL - 0 SP - 091030094710018 KW - chemical_biology KW - chemical_libraries KW - chemical_networks AU - Lepp, Zsolt AU - Huang, Chunfei AU - Okada, Takashi PY - 2000/10/30/ UR - http://dx.doi.org/10.1021/ci9001102 ER -