ReviewCurrent opinion in structural biology2025
Modeling Boltzmann-weighted structural ensembles of proteins using artificial intelligence-based methods.
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 16 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
16 citing papers in PubMed.
- Enhanced Sampling of Protein Conformations in AlphaFold3 with Repulsive Bias in the Diffusion Generative Model.JACS Au · 2026Article
- Navigating the uncharted: AI-driven advances in protein structure, dynamics, interactions and ligand interactions for understudied families.BioData mining · 2026Review
- Rectifying AI-generated protein structure ensembles for equilibrium using physics-based computations.bioRxiv : the preprint server for biology · 2026Article
- Assessing the Performance of BioEmu in Understanding Protein Dynamics.International journal of molecular sciences · 2026Article
- Article
- Enhanced Sampling in the Age of Machine Learning: Algorithms and Applications.Chemical reviews · 2026Review
- AlphaFold-RandomWalk and AlphaFold-Ensemble: Sampling Alternative Protein Conformations with Perturbed Versions of AlphaFold.Journal of chemical information and modeling · 2026Article
- Integrated framework for targeting dynamic penicillin-binding protein 2a via ensemble structural biology and deep generative modeling.Frontiers in microbiology · 2026Article
- Unlocking the undruggable spliceosome: generative AI and structural dynamics in cancer therapy.Frontiers in cell and developmental biology · 2026Review
- Deep generative modeling of temperature-dependent structural ensembles of proteins.Communications chemistry · 2025Article
- Quantified Dynamics-Property Relationships: Data-Efficient Protein Engineering with Machine Learning of Protein Dynamics.Journal of chemical information and modeling · 2025Article
- Deep learning-assisted tools to understand the structural biology of the synapse.Biomedical engineering letters · 2025Review
- Hierarchical AF2RAVE for Multiconformation Virtual Screening Targeting S100 CaJournal of chemical theory and computation · 2025Article
- af2rave: protein ensemble generation with physics-based sampling.Digital discovery · 2025Article
- Generation of protein dynamics by machine learning.Current opinion in structural biology · 2025Review
- Use of AI-methods over MD simulations in the sampling of conformational ensembles in IDPs.Frontiers in molecular biosciences · 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
5 authors.
Funding
Abstract
This review highlights recent advances in AI-driven methods for generating Boltzmann-weighted structural ensembles, which are crucial for understanding biomolecular dynamics and drug discovery. With the rise of deep learning models such as AlphaFold2, there has been a shift toward more accurate and efficient sampling of structural ensembles. The review discusses the integration of AI with traditional molecular dynamics techniques as well as experiments, the challenges of conformational sampling, and future directions for AI-driven research in structural biology, particularly in drug discovery and protein dynamics.
Indexed as
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.