ReviewCurrent opinion in structural biology2025
Generation of protein dynamics by machine learning.
Review in Current opinion in structural biology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
4 citing papers in PubMed.
- ModelCIF Update: Supporting Emerging Classes of Computational Macromolecular Models.Journal of molecular biology · 2026Article
- AI-Physics-Experiment Trinity for Integrated Protein Dynamics Modeling.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- How far can you go? Extrapolating values of catalytic activity from known protein landscapes in natural and directed evolution.Chemical Society reviews · 2026Review
- Structure-Based Experimental Datasets for Benchmarking Protein Simulation Force Fields [Article v1.0].Living journal of computational molecular science · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
Abstract
tMachine learning has advanced protein structure prediction to deliver accurate but mostly static models. Capturing protein dynamics as conformational ensembles remains a significant challenge. Recent developments, especially generative models, are enabling the prediction of structural ensembles beyond traditional simulations. This review examines emerging machine learning approaches for modeling protein dynamics, in terms of generating PDB-like ensembles, accelerating molecular simulations, modeling non-globular protein ensembles, and integrating experimental data. General-purpose and system-specific models are discussed, particularly in terms of conformational coverage, transferability, and responsiveness to environmental conditions. Hybrid models, which combine experimental and simulation data, represent a promising direction. Nonetheless, key challenges remain, including generating states with correct probabilities, modeling unseen conformations, and integrating experimental constraints rigorously.
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Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.