Generative molecular dynamics
Journal article, 2026

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.

Author

Simon Olsson

Chalmers, Computer Science and Engineering (Chalmers), Data Science and AI

University of Gothenburg

Current Opinion in Structural Biology

0959-440X (ISSN) 1879033x (eISSN)

Vol. 96 103213

Subject Categories (SSIF 2025)

Theoretical Chemistry

Bioinformatics and Computational Biology

Artificial Intelligence

DOI

10.1016/j.sbi.2025.103213

More information

Latest update

1/23/2026