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@ARTICLE{Dittrich:858302,
      author       = {Dittrich, Jonas and Schmidt, Denis and Pfleger, Christopher
                      and Gohlke, Holger},
      title        = {{C}onverging a {K}nowledge-{B}ased {S}coring {F}unction:
                      {D}rug{S}core2018},
      journal      = {Journal of chemical information and modeling},
      volume       = {59},
      number       = {1},
      issn         = {1549-960X},
      address      = {Washington, DC},
      publisher    = {American Chemical Society64160},
      reportid     = {FZJ-2018-07190},
      pages        = {509–521},
      year         = {2019},
      abstract     = {We present DrugScore2018, a new version of the
                      knowledge-based scoring functionDrugScore, which builds upon
                      the same formalism used to derive DrugScore, but exploits
                      atraining data set of nearly 40,000 X-ray complex
                      structures, a highly diverse and the by farlargest dataset
                      ever used for such an endeavour. About 2.5 times as many
                      pair potentials thanbefore now have a data basis required to
                      yield smooth potentials, and pair potentials could nowbe
                      derived for eight more atom types, including interactions
                      involving halogen atoms and metalions highly relevant for
                      medicinal chemistry. Probing for dependence on training data
                      set sizeand quality, we show that DrugScore2018 potentials
                      are converged. We evaluated DrugScore2018in comprehensive
                      scoring, ranking, docking, and screening tests on the
                      CASF-2013 dataset,allowing for a comparison with >30 other
                      scoring functions. There, DrugScore2018 showedsimilar or
                      improved performance in all aspects when compared to either
                      DrugScore,DrugScoreCSD, or DSX and was, overall, the scoring
                      function showing a most consistently goodperformance in
                      scoring, ranking, and docking tests. Applying DrugScore2018
                      as objectivefunction in AutoDock3 in a large-scale docking
                      trial, using 4,056 protein-ligand complexesfrom PDBbind
                      2016, reproduced a docked pose to within 2 Å RMSD to the
                      crystal structure $in>75\%$ of all dockings. These results
                      are remarkable as the DrugScore2018 potentials werederived
                      from crystallographic information only, without any further
                      adaptation using bindingaffinity or docking decoy data.
                      DrugScore2018 should thus be a competitive scoring
                      andobjective function for structure-based ligand design
                      purposes.},
      cin          = {NIC / JSC / ICS-6},
      ddc          = {540},
      cid          = {I:(DE-Juel1)NIC-20090406 / I:(DE-Juel1)JSC-20090406 /
                      I:(DE-Juel1)ICS-6-20110106},
      pnm          = {511 - Computational Science and Mathematical Methods
                      (POF3-511) / 553 - Physical Basis of Diseases (POF3-553) /
                      Forschergruppe Gohlke $(hkf7_20170501)$},
      pid          = {G:(DE-HGF)POF3-511 / G:(DE-HGF)POF3-553 /
                      $G:(DE-Juel1)hkf7_20170501$},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:30513206},
      UT           = {WOS:000457206200047},
      doi          = {10.1021/acs.jcim.8b00582},
      url          = {https://juser.fz-juelich.de/record/858302},
}