ArticleScience advances2026
Transferable generative models bridge femtosecond to nanosecond time-step molecular dynamics.
Article in Science advances, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- AI-Physics-Experiment Trinity for Integrated Protein Dynamics Modeling.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- Minimum-Excess-Work Guidance: Score-Based Sampling with Experimental Data or Sparse Restraints.Journal of chemical theory and computation · 2026Article
- Enhanced Sampling for Efficient Learning of Coarse-Grained Machine Learning Potentials.Journal of chemical theory and computation · 2026Article
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Authors and funding
3 authors.
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Abstract
Understanding the molecular structure, dynamics, and reactivity requires bridging processes that occur across widely separated timescales. Conventional molecular dynamics simulations provide an atomistic resolution, but their femtosecond time steps limit access to the slow conformational changes and relaxation processes that govern chemical function. Here, we introduce a deep generative modeling framework that accelerates sampling of molecular dynamics by four orders of magnitude while retaining physical realism. Applied to small organic molecules and peptides, the approach enables quantitative characterization of equilibrium ensembles and dynamical relaxation processes that were previously only accessible by costly brute-force simulation. The method generalizes across chemical composition and system size, extrapolating to peptides larger than those used for training, and captures chemically meaningful transitions on extended timescales. By expanding the accessible range of molecular motions without sacrificing the atomistic detail, this approach opens opportunities for probing conformational landscapes, thermodynamics, and kinetics in systems central to chemistry and biophysics.
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Registered trials
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