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Virtual Screening for PPAR Modulators Using a Probabilistic Neural Network
Author(s) -
Derksen Swetlana,
Rau Oliver,
Schneider Petra,
SchubertZsilavecz Manfred,
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.200600166
Subject(s) - probabilistic logic , peroxisome proliferator activated receptor , artificial neural network , virtual screening , receptor , computational biology , ligand (biochemistry) , docking (animal) , peroxisome proliferator , computer science , chemistry , superposition principle , biochemistry , artificial intelligence , biology , physics , medicine , drug discovery , nursing , quantum mechanics
Probabilistic Neural Networks (PNNs) were used for the retrieval of peroxisome proliferator‐activated receptor (PPAR) modulators from a large compound collection. Four out of nine compounds tested in cell‐based assays exhibited an agonistic effect toward PPARγ, one toward PPARα. The experimental binding mode of a potent ligand (red) of PPARγ is compared with the predicted orientation of another ligand shown by the superposition of several high‐ranking docking solutions.