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Substructure‐Based Virtual Screening for Adenosine A 2A Receptor Ligands
Author(s) -
van der Horst Eelke,
van der Pijl Rianne,
MulderKrieger Thea,
Bender Andreas,
IJzerman Adriaan P.
Publication year - 2011
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.201100369
Subject(s) - virtual screening , computer science , drug discovery , computational biology , yield (engineering) , substructure , data mining , selection (genetic algorithm) , false discovery rate , ligand (biochemistry) , adenosine receptor , chemistry , receptor , biology , machine learning , engineering , biochemistry , materials science , structural engineering , metallurgy , agonist , gene
A virtual ligand‐based screening approach was designed and evaluated for the discovery of new A 2A adenosine receptor (AR) ligands. For comparison and evaluation, the procedures from a recently published virtual screening study that used the A 2A AR X‐ray crystal structure for the target‐based discovery of new A 2A ligands were largely followed. Several screening models were constructed by deriving the distinguishing structural features from selected sets of A 2A AR antagonists, so‐called frequent substructure mining. The best model in statistical terms was subsequently applied to large‐scale virtual screens of a commercial vendor library. This resulted in the selection of 36 candidates for acquisition and testing. Of the selected candidates, eight compounds significantly inhibited radioligand binding at A 2A AR (>30 %) at 10 μ M , corresponding to a “hit rate” of 22 %. This hit rate is quite similar to that of the referenced target‐based virtual screening study, while both approaches yield new, non‐overlapping sets of ligands.