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@ARTICLE{Ahmad:909641,
      author       = {Ahmad, Katya and Rizzi, Andrea and Capelli, Riccardo and
                      Mandelli, Davide and Lyu, Wenping and Carloni, Paolo},
      title        = {{E}nhanced-{S}ampling {S}imulations for the {E}stimation of
                      {L}igand {B}inding {K}inetics: {C}urrent {S}tatus and
                      {P}erspective},
      journal      = {Frontiers in molecular biosciences},
      volume       = {9},
      issn         = {2296-889X},
      address      = {Lausanne},
      publisher    = {Frontiers},
      reportid     = {FZJ-2022-03312},
      pages        = {899805},
      year         = {2022},
      abstract     = {The dissociation rate (koff) associated with ligand
                      unbinding events from proteins is a parameter of fundamental
                      importance in drug design. Here we review recent major
                      advancements in molecular simulation methodologies for the
                      prediction of koff. Next, we discuss the impact of the
                      potential energy function models on the accuracy of
                      calculated koff values. Finally, we provide a perspective
                      from high-performance computing and machine learning which
                      might help improve such predictions.},
      cin          = {IAS-5 / INM-9},
      ddc          = {570},
      cid          = {I:(DE-Juel1)IAS-5-20120330 / I:(DE-Juel1)INM-9-20140121},
      pnm          = {1215 - Simulations, Theory, Optics, and Analytics (STOA)
                      (POF4-121) / HBP SGA3 - Human Brain Project Specific Grant
                      Agreement 3 (945539)},
      pid          = {G:(DE-HGF)POF4-1215 / G:(EU-Grant)945539},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {35755817},
      UT           = {WOS:000814363000001},
      doi          = {10.3389/fmolb.2022.899805},
      url          = {https://juser.fz-juelich.de/record/909641},
}