Predicting Compound Selectivity by Self-Organizing Maps: Cross-Activities of Metabotropic Glutamate Receptor Antagonists

A topological pharmacophore descriptor (CATS) and a self‐organizing map (SOM) were used for prediction of multiple receptor interaction of known mGluR antagonists. For a predicted target panel, the tested mGluR ligands exhibited the calculated binding pattern. This virtual screening concept might pr...

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Bibliographic Details
Published in:ChemMedChem 2006-10, Vol.1 (10), p.1066-1068
Main Authors: Noeske, Tobias, Sasse, Britta C., Stark, Holger, Parsons, Christopher G., Weil, Tanja, Schneider, Gisbert
Format: Article
Language:English
Subjects:
Algorithms
Aza Compounds - chemistry
Aza Compounds - pharmacology
Binding Sites
bioinformatics
Cluster Analysis
Databases, Factual
Drug Design
Excitatory Amino Acid Antagonists - chemistry
Excitatory Amino Acid Antagonists - pharmacology
Kohonen network
Ligands
Neural Networks (Computer)
Nonlinear Dynamics
Pattern Recognition, Automated
Pyridines - chemistry
Pyridines - pharmacology
Quinolines - chemistry
Quinolines - pharmacology
Receptors, Metabotropic Glutamate - antagonists & inhibitors
Structure-Activity Relationship
topological pharmacophore
virtual screening
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Summary:A topological pharmacophore descriptor (CATS) and a self‐organizing map (SOM) were used for prediction of multiple receptor interaction of known mGluR antagonists. For a predicted target panel, the tested mGluR ligands exhibited the calculated binding pattern. This virtual screening concept might provide a basis for early recognition of potential side‐effects in lead discovery.
ISSN:1860-7179
1860-7187
DOI:10.1002/cmdc.200600147