The Feasibility of an Efficient Drug Design Method with High-Performance Computers

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  • Yamashita Takefumi
    Laboratory for Systems Biology and Medicine, Research Center for Advanced Science and Technology, The University of Tokyo
  • Ueda Akihiko
    Bio-IT R&D Office, Next-Generation Healthcare Innovation Center, Fujitsu Limited
  • Mitsui Takashi
    Laboratory for Systems Biology and Medicine, Research Center for Advanced Science and Technology, The University of Tokyo Bio-IT R&D Office, Next-Generation Healthcare Innovation Center, Fujitsu Limited
  • Tomonaga Atsushi
    Bio-IT R&D Office, Next-Generation Healthcare Innovation Center, Fujitsu Limited
  • Matsumoto Shunji
    Bio-IT R&D Office, Next-Generation Healthcare Innovation Center, Fujitsu Limited
  • Kodama Tatsuhiko
    Laboratory for Systems Biology and Medicine, Research Center for Advanced Science and Technology, The University of Tokyo
  • Fujitani Hideaki
    Laboratory for Systems Biology and Medicine, Research Center for Advanced Science and Technology, The University of Tokyo

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In this study, we propose a supercomputer-assisted drug design approach involving all-atom molecular dynamics (MD)-based binding free energy prediction after the traditional design/selection step. Because this prediction is more accurate than the empirical binding affinity scoring of the traditional approach, the compounds selected by the MD-based prediction should be better drug candidates. In this study, we discuss the applicability of the new approach using two examples. Although the MD-based binding free energy prediction has a huge computational cost, it is feasible with the latest 10 petaflop-scale computer. The supercomputer-assisted drug design approach also involves two important feedback procedures: The first feedback is generated from the MD-based binding free energy prediction step to the drug design step. While the experimental feedback usually provides binding affinities of tens of compounds at one time, the supercomputer allows us to simultaneously obtain the binding free energies of hundreds of compounds. Because the number of calculated binding free energies is sufficiently large, the compounds can be classified into different categories whose properties will aid in the design of the next generation of drug candidates. The second feedback, which occurs from the experiments to the MD simulations, is important to validate the simulation parameters. To demonstrate this, we compare the binding free energies calculated with various force fields to the experimental ones. The results indicate that the prediction will not be very successful, if we use an inaccurate force field. By improving/validating such simulation parameters, the next prediction can be made more accurate.

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