DeepDriveMD: Deep-Learning Driven Adaptive Molecular Simulations for Protein Folding

@article{Lee2019DeepDriveMDDD,
  title={DeepDriveMD: Deep-Learning Driven Adaptive Molecular Simulations for Protein Folding},
  author={Hyungro Lee and H. Ma and Matteo Turilli and D. Bhowmik and S. Jha and A. Ramanathan},
  journal={2019 IEEE/ACM Third Workshop on Deep Learning on Supercomputers (DLS)},
  year={2019},
  pages={12-19}
}
  • Hyungro Lee, H. Ma, +3 authors A. Ramanathan
  • Published 2019
  • Computer Science
  • 2019 IEEE/ACM Third Workshop on Deep Learning on Supercomputers (DLS)
  • Simulations of biological macromolecules are important in understanding the physical basis of complex processes such as protein folding. However, even with increasing computational capacity and specialized architectures, the ability to simulate protein folding at atomistic scales still remains challenging. This stems from the dual aspects of high dimensionality of protein conformational landscapes, and the inability of atomistic molecular dynamics (MD) simulations to sufficiently sample these… CONTINUE READING
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    References

    SHOWING 1-10 OF 46 REFERENCES
    Deep Generative Model Driven Protein Folding Simulation
    • 7
    • PDF
    Deep clustering of protein folding simulations
    • 29
    VAMPnets for deep learning of molecular kinetics
    • 151
    • PDF
    Transferable Neural Networks for Enhanced Sampling of Protein Dynamics.
    • 40
    • PDF
    On-the-Fly Identification of Conformational Substates from Molecular Dynamics Simulations.
    • 18
    Large-scale conformational sampling of proteins using temperature-accelerated molecular dynamics
    • 137
    • PDF
    Note: Variational Encoding of Protein Dynamics Benefits from Maximizing Latent Autocorrelation
    • 4
    • PDF
    Markov State Models Provide Insights into Dynamic Modulation of Protein Function
    • 141
    • PDF
    Variational encoding of complex dynamics.
    • 65
    • PDF