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@ARTICLE{Rossetti:866070,
      author       = {Rossetti, Giulia and Kless, Achim and Lai, Luhua and
                      Outeiro, Tiago F. and Carloni, Paolo},
      title        = {{I}nvestigating targets for neuropharmacological
                      intervention by molecular dynamics simulations},
      journal      = {Biochemical Society transactions},
      volume       = {47},
      number       = {3},
      issn         = {1470-8752},
      address      = {London},
      publisher    = {Biochemical Society},
      reportid     = {FZJ-2019-05309},
      pages        = {909 - 918},
      year         = {2019},
      abstract     = {Medical research has identified over 500 brain disorders.
                      Among these, there are still only very few neuropathologies
                      whose causes are fully understood and, consequently, very
                      few drugs whose mechanism of action is known. No FDA drug
                      has been identified for major neurodegenerative diseases,
                      such as Alzheimer's and Parkinson's. We still lack effective
                      treatments and strategies for modulating progression or even
                      early neurodegenerative disease onset diagnostic tools. A
                      great support toward the highly needed identification of
                      neuroactive drugs comes from computer simulation methods
                      and, in particular, from molecular dynamics (MD). This
                      provides insight into structure–function relationship of a
                      target and predicts structure, dynamics and energetics of
                      ligand/target complexes under biologically relevant
                      conditions like temperature and physiological saline
                      concentration. Here, we present examples of the predictive
                      power of MD for neuroactive ligands/target complexes. This
                      brief survey from our own research shows the usefulness of
                      partnerships between academia and industry, and from joint
                      efforts between experimental and theoretical groups.},
      cin          = {IAS-5 / INM-9 / JSC},
      ddc          = {540},
      cid          = {I:(DE-Juel1)IAS-5-20120330 / I:(DE-Juel1)INM-9-20140121 /
                      I:(DE-Juel1)JSC-20090406},
      pnm          = {572 - (Dys-)function and Plasticity (POF3-572) / 511 -
                      Computational Science and Mathematical Methods (POF3-511)},
      pid          = {G:(DE-HGF)POF3-572 / G:(DE-HGF)POF3-511},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:31085614},
      UT           = {WOS:000473225600013},
      doi          = {10.1042/BST20190048},
      url          = {https://juser.fz-juelich.de/record/866070},
}