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@ARTICLE{Manelfi:890453,
      author       = {Manelfi, Candida and Gossen, Jonas and Gervasoni, Silvia
                      and Talarico, Carmine and Albani, Simone and Philipp,
                      Benjamin Joseph and Musiani, Francesco and Vistoli, Giulio
                      and Rossetti, Giulia and Beccari, Andrea Rosario and
                      Pedretti, Alessandro},
      title        = {{C}ombining {D}ifferent {D}ocking {E}ngines and {C}onsensus
                      {S}trategies to {D}esign and {V}alidate {O}ptimized
                      {V}irtual {S}creening {P}rotocols for the {SARS}-{C}o{V}-2
                      3{CL} {P}rotease},
      journal      = {Molecules},
      volume       = {26},
      number       = {4},
      issn         = {1420-3049},
      address      = {Basel},
      publisher    = {MDPI70206},
      reportid     = {FZJ-2021-00969},
      pages        = {797 -},
      year         = {2021},
      abstract     = {The 3CL-Protease appears to be a very promising medicinal
                      target to develop anti-SARS-CoV-2 agents. The availability
                      of resolved structures allows structure-based computational
                      approaches to be carried out even though the lack of known
                      inhibitors prevents a proper validation of the performed
                      simulations. The innovative idea of the study is to exploit
                      known inhibitors of SARS-CoV 3CL-Pro as a training set to
                      perform and validate multiple virtual screening campaigns.
                      Docking simulations using four different programs (Fred,
                      Glide, LiGen, and PLANTS) were performed investigating the
                      role of both multiple binding modes (by binding space) and
                      multiple isomers/states (by developing the corresponding
                      isomeric space). The computed docking scores were used to
                      develop consensus models, which allow an in-depth comparison
                      of the resulting performances. On average, the reached
                      performances revealed the different sensitivity to isomeric
                      differences and multiple binding modes between the four
                      docking engines. In detail, Glide and LiGen are the tools
                      that best benefit from isomeric and binding space,
                      respectively, while Fred is the most insensitive program.
                      The obtained results emphasize the fruitful role of
                      combining various docking tools to optimize the predictive
                      performances. Taken together, the performed simulations
                      allowed the rational development of highly performing
                      virtual screening workflows, which could be further
                      optimized by considering different 3CL-Pro structures and,
                      more importantly, by including true SARS-CoV-2 3CL-Pro
                      inhibitors (as learning set) when available.},
      cin          = {INM-9 / JSC / IAS-5},
      ddc          = {540},
      cid          = {I:(DE-Juel1)INM-9-20140121 / I:(DE-Juel1)JSC-20090406 /
                      I:(DE-Juel1)IAS-5-20120330},
      pnm          = {525 - Decoding Brain Organization and Dysfunction
                      (POF4-525) / 5111 - Domain-Specific Simulation $\&$ Data
                      Life Cycle Labs (SDLs) and Research Groups (POF4-511)},
      pid          = {G:(DE-HGF)POF4-525 / G:(DE-HGF)POF4-5111},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {33557115},
      UT           = {WOS:000624153100001},
      doi          = {10.3390/molecules26040797},
      url          = {https://juser.fz-juelich.de/record/890453},
}