ReviewBiochemical Society transactions2025
Advancing structure modeling from cryo-EM maps with deep learning.
Review in Biochemical Society transactions, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 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
8 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
- AI-Predicted Model-Guided Rebuilding of the Experimental Structure of Mouse δ-Aminolevulinic Acid Dehydratase.International journal of molecular sciences · 2026Article
- 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
- Magellon - an extensible platform for cryo-EM data visualization, management and processing.IUCrJ · 2025Article
- DEMO-EMol: modeling protein-nucleic acid complex structures from cryo-EM maps by coupling chain assembly with map segmentation.Nucleic acids research · 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 biomolecular structures that are challenging to resolve using conventional methods. Interpreting a cryo-EM map requires accurate modeling of the structures of underlying biomolecules. Here, we concisely discuss the evolution and current state of automatic structure modeling from cryo-EM density maps. We classify modeling methods into two categories: de novo modeling methods from high-resolution maps (better than 5 Å) and methods that model by fitting individual structures of component proteins to maps at lower resolution (worse than 5 Å). Special attention is given to the role of deep learning in the modeling process, highlighting how AI-driven approaches are transformative in cryo-EM structure modeling. We conclude by discussing future directions in the field.
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.