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@ARTICLE{Goen:1009674,
      author       = {Goßen, Jonas and Ribeiro, Rui Pedro and Bier, Dirk and
                      Neumaier, Bernd and Carloni, Paolo and Giorgetti, Alejandro
                      and Rossetti, Giulia},
      title        = {{AI}-based identification of therapeutic agents targeting
                      {GPCR}s: introducing ligand type classifiers and systems
                      biology},
      journal      = {Chemical science},
      volume       = {14},
      number       = {32},
      issn         = {2041-6520},
      address      = {Cambridge},
      publisher    = {RSC},
      reportid     = {FZJ-2023-02927},
      pages        = {8651-8661},
      year         = {2023},
      abstract     = {Identifying ligands targeting G protein coupled receptors
                      (GPCRs) with novel chemotypes other than the physiological
                      ligands is a challenge for in silico screening campaigns.
                      Here we present an approach that identifies novel chemotype
                      ligands by combining structural data with a random forest
                      agonist/antagonist classifier and a signal-transduction
                      kinetic model. As a test case, we apply this approach to
                      identify novel antagonists of the human adenosine
                      transmembrane receptor type 2A, an attractive target against
                      Parkinson's disease and cancer. The identified antagonists
                      were tested here in a radio ligand binding assay. Among
                      those, we found a promising ligand whose chemotype differs
                      significantly from all so-far reported antagonists, with a
                      binding affinity of 310 ± 23.4 nM. Thus, our protocol
                      emerges as a powerful approach to identify promising ligand
                      candidates with novel chemotypes while preserving
                      antagonistic potential and affinity in the nanomolar range},
      cin          = {IAS-5 / INM-9 / JSC / INM-5},
      ddc          = {540},
      cid          = {I:(DE-Juel1)IAS-5-20120330 / I:(DE-Juel1)INM-9-20140121 /
                      I:(DE-Juel1)JSC-20090406 / I:(DE-Juel1)INM-5-20090406},
      pnm          = {5254 - Neuroscientific Data Analytics and AI (POF4-525) /
                      5252 - Brain Dysfunction and Plasticity (POF4-525)},
      pid          = {G:(DE-HGF)POF4-5254 / G:(DE-HGF)POF4-5252},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {37592985},
      UT           = {WOS:001039483600001},
      doi          = {10.1039/D3SC02352D},
      url          = {https://juser.fz-juelich.de/record/1009674},
}