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Advances and challenges in multiscale biomolecular simulations: artificial intelligence-driven paradigm shift.

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|February 12, 2026
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Biomolecular simulations, enhanced by machine learning, now quantitatively characterize complex molecular events like protein folding. This perspective reviews advancements, trends, and challenges in molecular dynamics simulations.

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artificial intelligencebiomoleculesmolecular dynamicsprotein dynamics

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Area of Science:

  • Computational Biology
  • Molecular Dynamics
  • Biophysics

Background:

  • Molecular simulation techniques are crucial for understanding life at the molecular level.
  • Biomolecular simulations have advanced over five decades, enabling quantitative analysis of complex events.
  • These simulations impact both fundamental and applied scientific research.

Purpose of the Study:

  • To discuss recent advancements in biomolecular simulation techniques.
  • To explore emerging applications and development trends in the field.
  • To identify major challenges in biomolecular dynamics simulations.

Main Methods:

  • Leveraging computational power for molecular dynamics.
  • Integrating machine learning, especially deep learning, into simulation workflows.
  • Analyzing complex biomolecular events such as protein folding and dynamics.

Main Results:

  • Biomolecular simulations now offer quantitative characterization of intricate processes.
  • Machine learning integration is driving significant innovation in simulation capabilities.
  • The field is rapidly evolving with new applications and methodologies.

Conclusions:

  • Biomolecular simulations are indispensable tools in modern biological research.
  • Continued integration of AI promises further breakthroughs in understanding molecular behavior.
  • Addressing current challenges is key to unlocking the full potential of these simulations.