ReviewBiophysical journal2026
Recent advances in machine learning and coarse-grained potentials for biomolecular simulations.
Review in Biophysical journal, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
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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.
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Who cites it
9 citing papers in PubMed.
- Building RNA coarse-grained force fields: Design principles and training strategies.Biophysical journal · 2026Review
- Exploring Conformational Transitions of Adenine RNA Dimer via Machine Learning Potentials.Journal of chemical theory and computation · 2026Article
- AI-Physics-Experiment Trinity for Integrated Protein Dynamics Modeling.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- An optimized contact map for GōMartini 3 enabling conformational changes in protein assemblies.Biophysical journal · 2026Article
- Advances and challenges in multiscale biomolecular simulations: artificial intelligence-driven paradigm shift.Quantitative biology (Beijing, China) · 2026Article
- Multiscale modeling of graphene and carbon nanostructures: advances in atomistic, coarse-grained, and machine learning approaches.RSC advances · 2026Review
- Computational methods in physical virology: a critical perspective across lengths and timescales.FEMS microbiology reviews · 2026Review
- Equivariant Neural Networks Reveal How Host-Guest Interactions ShapeThe journal of physical chemistry letters · 2025Article
- Machine learning interatomic potentials in biomolecular modeling: principles, architectures, and applications.Biophysical reviews · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Biomolecular simulations played a crucial role in advancing our understanding of the complex dynamics in biological systems with applications ranging from drug discovery to the molecular characterization of virus-host interactions. Despite their success, biomolecular simulations face inherent challenges due to the multiscale nature of biological processes, which involve intricate interactions across a wide range of length scales and timescales. All-atom (AA) molecular dynamics provides detailed insights at atomistic resolution, yet it remains limited by computational constraints, capturing only short timescales and small conformational changes. In contrast, coarse-grained (CG) models extend simulations to biologically relevant time and length scales by reducing molecular complexity. However, CG models sacrifice atomic-level accuracy, making the parameterization of reliable and transferable potentials a persistent challenge. This review discusses recent advancements in machine learning (ML)-driven biomolecular simulations, including the development of ML potentials with quantum-mechanical accuracy, ML-assisted backmapping strategies from CG to AA resolutions, and widely used CG potentials. By integrating ML and CG approaches, researchers can enhance simulation accuracy while extending time and length scales, overcoming key limitations in the study of biomolecular systems.
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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.