Generative molecular dynamics

Understanding biomolecular function depends on bridging experimental observables with models that capture structural, stationary, and dynamical properties. Molecular dynamics (MD) simulations, in principle provide a bridge, but the sampling problem remains a fundamental roadblock toward this goal. In this mini-review, I outline recent progress in the area of Generative MD (GenMD)-an approach where generative AI (GenAI) is used to mimic the statistical distributions resulting from MD simulations, which are inaccessible using current numerical algorithms. Here, I highlight a few exemplars of GenMD and then outline open problems and current limitations.

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Publication Details

Journal
Current Opinion in Structural Biology
Published
2026-01-15
DOI
https://doi.org/10.1016/j.sbi.2025.103213
Citations
1
Primary Topic
Protein Structure and Dynamics
Type
article
Field-Weighted Citation Impact
22.24
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article

Generative molecular dynamics

Simon Olsson
1 citations
Current Opinion in Structural Biology
Protein Structure and Dynamics
22.24
article

Generative molecular dynamics

Simon Olsson
article en
1 citations

Abstract

Understanding biomolecular function depends on bridging experimental observables with models that capture structural, stationary, and dynamical properties. Molecular dynamics (MD) simulations, in principle provide a bridge, but the sampling problem remains a fundamental roadblock toward this goal. In this mini-review, I outline recent progress in the area of Generative MD (GenMD)-an approach where generative AI (GenAI) is used to mimic the statistical distributions resulting from MD simulations, which are inaccessible using current numerical algorithms. Here, I highlight a few exemplars of GenMD and then outline open problems and current limitations.

Current Opinion in Structural BiologyVol. 96
Chalmers University of Technology (SE)
Openalex Percentile: Top 3%
Protein Structure and Dynamics
22.24
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