ReviewCurrent opinion in structural biology2025
AI-based methods for biomolecular structure modeling for Cryo-EM.
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 12 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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
12 citing papers in PubMed.
- DAQplugin: Interactive Deep Learning-Based Validation of Cryo-EM Protein Models in ChimeraX.bioRxiv : the preprint server for biology · 2026Article
- Computational Approaches for Protein-DNA/RNA Complex Modeling for Cryo-EM Maps.Current protocols · 2026Article
- Emergence and evolution of protein-coding de novo genes.Nature reviews. Genetics · 2026Review
- Direct Detection and Atomic Modeling of Ligands in Cryo-EM Maps Using Deep Learning.bioRxiv : the preprint server for biology · 2026Article
- Accurate Macromolecular Complex Modeling for Cryo-EM with CryoZeta.bioRxiv : the preprint server for biology · 2026Article
- New York City's structural biology mosaic.Nature structural & molecular biology · 2025Article
- CryoDataBot: a pipeline to curate cryoEM datasets for AI-driven structural biology.bioRxiv : the preprint server for biology · 2025Article
- AI-based quality assessment methods for protein structure models from cryo-EM.Current research in structural biology · 2025Review
- Applications of visualization technology in the structural sciences.Structural dynamics (Melville, N.Y.) · 2025Article
- Advancing structure modeling from cryo-EM maps with deep learning.Biochemical Society transactions · 2025Review
- CryoDataBot: a pipeline to curate cryoEM datasets for AI-driven structural biology.GigaScience · 2025Article
- Connecting the dots: deep learning-based automated model building methods in cryo-EM.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
4 authors.
Funding
Abstract
Cryo-electron microscopy (Cryo-EM) has revolutionized structural biology by enabling the determination of macromolecular structures that were challenging to study with conventional methods. Processing cryo-EM data involves several computational steps to derive three-dimensional structures from raw projections. Recent advancements in artificial intelligence (AI) including deep learning have significantly improved the performance of these processes. In this review, we discuss state-of-the-art AI-based techniques used in key steps of cryo-EM data processing, including macromolecular structure modeling and heterogeneity analysis.
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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.