Predicting Compound Selectivity by Self‐Organizing Maps: Cross‐Activities of Metabotropic Glutamate Receptor Antagonists | Zendy

Noeske Tobias | Zendy; Sasse Britta C. | Zendy; Stark Holger | Zendy; Parsons Christopher G. | Zendy; Weil Tanja | Zendy; Schneider Gisbert | Zendy

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

Author(s) -

Noeske Tobias,

Sasse Britta C.,

Stark Holger,

Parsons Christopher G.,

Weil Tanja,

Schneider Gisbert

Publication year - 2006

Publication title -

chemmedchem

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.817

H-Index - 100

eISSN - 1860-7187

pISSN - 1860-7179

DOI - 10.1002/cmdc.200600147

Subject(s) - pharmacophore , metabotropic glutamate receptor , virtual screening , metabotropic glutamate receptor 5 , metabotropic receptor , metabotropic glutamate receptor 4 , metabotropic glutamate receptor 2 , metabotropic glutamate receptor 1 , chemistry , artificial intelligence , computer science , computational biology , neuroscience , receptor , glutamate receptor , biology , stereochemistry , biochemistry

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.

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