Quantifying the Relationships among Drug Classes

@article{citeulike:2525144, abstract = {Abstract: The similarity of drug targets is typically measured using sequence or structural information. Here, we consider chemo-centric approaches that measure target similarity on the basis of their ligands, asking how chemoinformatics similarities differ from those derived bioinformatically, how stable the ligand networks are to changes in chemoinformatics metrics, and which network is the most reliable for prediction of pharmacology. We calculated the similarities between hundreds of drug targets and their ligands and mapped the relationship between them in a formal network. Bioinformatics networks were based on the BLAST similarity between sequences, while chemoinformatics networks were based on the ligand-set similarities calculated with either the Similarity Ensemble Approach (SEA) or a method derived from Bayesian statistics. By multiple criteria, bioinformatics and chemoinformatics networks differed substantially, and only occasionally did a high sequence similarity correspond to a high ligand-set similarity. In contrast, the chemoinformatics networks were stable to the method used to calculate the ligand-set similarities and to the chemical representation of the ligands. Also, the chemoinformatics networks were more natural and more organized, by network theory, than their bioinformatics counterparts: ligand-based networks were found to be small-world and broad-scale.}, address = {Department of Pharmaceutical Chemistry, University of CaliforniaSan Francisco, 1700 4th St., San Francisco, California 94143-2550, and Division of Biocomputing, MSC11 6145, University of New Mexico School of Medicine, 2703 Frontier NE, Albuquerque, New Mexico 87131}, author = {Hert, Jerome and Keiser, Michael J. and Irwin, John J. and Oprea, Tudor I. and Shoichet, Brian K. }, citeulike-article-id = {2525144}, doi = {http://dx.doi.org/10.1021/ci8000259}, journal = {J. Chem. Inf. Model.}, month = {March}, pages = {755--765}, posted-at = {2008-06-03 14:57:51}, priority = {2}, title = {Quantifying the Relationships among Drug Classes}, url = {http://dx.doi.org/10.1021/ci8000259}, volume = {48}, year = {2008} }

TY - JOUR ID - citeulike:2525144 L3 - citeulike-article-id:2525144 TI - Quantifying the Relationships among Drug Classes JF - J. Chem. Inf. Model. VL - 48 SP - 755 EP - 765 AD - Department of Pharmaceutical Chemistry, University of CaliforniaSan Francisco, 1700 4th St., San Francisco, California 94143-2550, and Division of Biocomputing, MSC11 6145, University of New Mexico School of Medicine, 2703 Frontier NE, Albuquerque, New Mexico 87131 N2 - Abstract: The similarity of drug targets is typically measured using sequence or structural information. Here, we consider chemo-centric approaches that measure target similarity on the basis of their ligands, asking how chemoinformatics similarities differ from those derived bioinformatically, how stable the ligand networks are to changes in chemoinformatics metrics, and which network is the most reliable for prediction of pharmacology. We calculated the similarities between hundreds of drug targets and their ligands and mapped the relationship between them in a formal network. Bioinformatics networks were based on the BLAST similarity between sequences, while chemoinformatics networks were based on the ligand-set similarities calculated with either the Similarity Ensemble Approach (SEA) or a method derived from Bayesian statistics. By multiple criteria, bioinformatics and chemoinformatics networks differed substantially, and only occasionally did a high sequence similarity correspond to a high ligand-set similarity. In contrast, the chemoinformatics networks were stable to the method used to calculate the ligand-set similarities and to the chemical representation of the ligands. Also, the chemoinformatics networks were more natural and more organized, by network theory, than their bioinformatics counterparts: ligand-based networks were found to be small-world and broad-scale. AU - Hert, Jerome AU - Keiser, Michael AU - Irwin, John AU - Oprea, Tudor AU - Shoichet, Brian PY - 2008/03/13/ UR - http://dx.doi.org/10.1021/ci8000259 ER -