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Probabilistic Forecasting for Coarse-Grained Molecular Dynamics.

Luc F Christians1, Anna Wojnar1, Alexander J Pak1,2,3

  • 1Department of Chemical and Biological Engineering, Colorado School of Mines, Golden, Colorado 80401, United States.

Journal of Chemical Theory and Computation
|April 21, 2026

View abstract on PubMed

Summary
This summary is machine-generated.

Probabilistic Forecasting for Coarse-Graining (PFCG) uses machine learning to improve biomolecular simulations. This new framework accurately captures complex molecular dynamics, enhancing coarse-grained models for better scientific discovery.

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Area of Science:

  • Computational Chemistry
  • Biophysics
  • Machine Learning

Background:

  • Coarse-grained molecular dynamics (CGMD) offers computational efficiency for large systems.
  • However, CGMD often struggles with atomistic kinetics, especially with memory effects and slow dynamics.
  • Existing methods may not fully capture non-Markovian dynamics crucial for accurate biomolecular simulations.

Purpose of the Study:

  • To introduce a novel machine learning framework, Probabilistic Forecasting for Coarse-Graining (PFCG).
  • To enable learning stochastic coarse-grained equations of motion directly from atomistic data.
  • To improve the accuracy of coarse-grained simulations by incorporating non-Markovian effects.

Main Methods:

  • Developed PFCG, a machine learning framework for coarse-grained simulations.
  • Formulated coarse-grained simulation as a probabilistic time-series forecasting problem.
  • Incorporated non-Markovian effects using finite trajectory history without explicit memory kernels.

    • Main Results:

    • PFCG successfully applied to miniproteins and polyalanine peptides.
    • Non-Markovian PFCG models significantly improved dynamical agreement with atomistic simulations compared to Markovian baselines.
    • Models maintained excellent agreement with stationary distributions and showed robustness under sparse sampling.

    Conclusions:

    • PFCG effectively learns stochastic coarse-grained equations of motion from atomistic trajectories.
    • The framework enhances dynamical fidelity in coarse-grained simulations by capturing non-Markovian contributions.
    • PFCG represents a valuable complementary approach to existing machine learning-based coarse-graining methods for biomolecular modeling.