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Constructing and Visualizing Models using Mime-based Machine-learning Framework
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The confluence of machine learning and multiscale simulations.

Harsh Bhatia1, Fikret Aydin2, Timothy S Carpenter2

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Machine learning, particularly deep learning, is enhancing multiscale modeling in structural biology. This approach aids in overcoming simulation limits and exploring molecular conformations for new discoveries.

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Area of Science:

  • Structural Biology
  • Computational Biology
  • Machine Learning

Background:

  • Multiscale modeling is crucial in structural biology to address the limitations of atomistic molecular dynamics.
  • Advances in machine learning, especially deep learning, are significantly impacting scientific and engineering fields.

Purpose of the Study:

  • To explore the revitalizing impact of deep learning on traditional multiscale modeling techniques.
  • To highlight the novel applications of machine learning in enhancing computational biology and structural biology simulations.

Main Methods:

  • Utilizing deep learning for distilling information from fine-scale models.
  • Developing surrogate models and coarse-grained potentials guided by machine learning.
  • Defining latent spaces using machine learning for efficient conformational space exploration.

Main Results:

  • Deep learning successfully aids in creating surrogate models and guiding coarse-grained potentials.
  • Machine learning enables the definition of latent spaces for efficient conformational exploration.
  • The integration of ML and multiscale simulations shows promise for structural biology.

Conclusions:

  • The synergy of machine learning and multiscale modeling, powered by high-performance computing, heralds a new era in structural biology.
  • Deep learning offers powerful tools for advancing computational simulations and uncovering biological insights.