% IMPORTANT: The following is UTF-8 encoded.  This means that in the presence
% of non-ASCII characters, it will not work with BibTeX 0.99 or older.
% Instead, you should use an up-to-date BibTeX implementation like “bibtex8” or
% “biber”.

@ARTICLE{Raghavan:1009524,
      author       = {Raghavan, Bharath and Paulikat, Mirko and Ahmad, Katya and
                      Callea, Lara and Rizzi, Andrea and Ippoliti, Emiliano and
                      Mandelli, Davide and Bonati, Laura and De Vivo, Marco and
                      Carloni, Paolo},
      title        = {{D}rug {D}esign in the {E}xascale {E}ra: {A} {P}erspective
                      from {M}assively {P}arallel {QM}/{MM} {S}imulations},
      journal      = {Journal of chemical information and modeling},
      volume       = {63},
      number       = {12},
      issn         = {0095-2338},
      address      = {Washington, DC},
      publisher    = {American Chemical Society},
      reportid     = {FZJ-2023-02854},
      pages        = {3647 - 3658},
      year         = {2023},
      note         = {Grant name: DBA01838 Innovative high-performance computing
                      applied to neurodegenerative diseasesOpen access
                      publication},
      abstract     = {The initial phases of drug discovery – in silico drug
                      design – could benefit from first principle Quantum
                      Mechanics/Molecular Mechanics (QM/MM) molecular dynamics
                      (MD) simulations in explicit solvent, yet many applications
                      are currently limited by the short time scales that this
                      approach can cover. Developing scalable first principle
                      QM/MM MD interfaces fully exploiting current exascale
                      machines – so far an unmet and crucial goal – will help
                      overcome this problem, opening the way to the study of the
                      thermodynamics and kinetics of ligand binding to protein
                      with first principle accuracy. Here, taking two relevant
                      case studies involving the interactions of ligands with
                      rather large enzymes, we showcase the use of our recently
                      developed massively scalable Multiscale Modeling in
                      Computational Chemistry (MiMiC) QM/MM framework (currently
                      using DFT to describe the QM region) to investigate
                      reactions and ligand binding in enzymes of pharmacological
                      relevance. We also demonstrate for the first time strong
                      scaling of MiMiC-QM/MM MD simulations with parallel
                      efficiency of $∼70\%$ up to >80,000 cores. Thus, among
                      many others, the MiMiC interface represents a promising
                      candidate toward exascale applications by combining machine
                      learning with statistical mechanics based algorithms
                      tailored for exascale supercomputers.},
      cin          = {IAS-5 / INM-9},
      ddc          = {540},
      cid          = {I:(DE-Juel1)IAS-5-20120330 / I:(DE-Juel1)INM-9-20140121},
      pnm          = {5241 - Molecular Information Processing in Cellular Systems
                      (POF4-524)},
      pid          = {G:(DE-HGF)POF4-5241},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {37319347},
      UT           = {WOS:001020993900001},
      doi          = {10.1021/acs.jcim.3c00557},
      url          = {https://juser.fz-juelich.de/record/1009524},
}