ReviewCurrent opinion in structural biology2023
Deep learning for reconstructing protein structures from cryo-EM density maps: Recent advances and future directions.
Review in Current opinion in structural biology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 40 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
40 citing papers in PubMed.
- Deep learning architectures achieve state-of-the-art (SOTA) accuracy in protein secondary structure prediction.Molecular diversity · 2026Review
- Geometric Deep Learning-Based Drug Design Models for Small-Molecule Drug Discovery.Molecular informatics · 2026Review
- A Chromatin Biology Assessment of AlphaFold3.bioRxiv : the preprint server for biology · 2026Article
- Estimating Protein Conformational States from High-Speed AFM Images with Molecular Dynamics and Deep Learning.Journal of chemical information and modeling · 2026Article
- Deep Learning-Based Event Classification of Mass Photometry Data for Optimal Mass Measurement at the Single-Molecule Level.ACS nano · 2026Article
- Subspace Method of Moments forSIAM journal on imaging sciences · 2026Article
- Nanoscale Imaging of Biological Tissues: Techniques, Challenges and Emerging Frontiers.Nanomaterials (Basel, Switzerland) · 2025Review
- BioEmu: AI-Powered Revolution in Scalable Protein Dynamics Simulation.Journal of cellular and molecular medicine · 2025Article
- Multimodal deep learning integration of cryo-EM and AlphaFold3 for high-accuracy protein structure determination.Communications chemistry · 2025Article
- Modeling cryo-EM structures in alternative states with AlphaFold2-based models and density-guided simulations.Communications chemistry · 2025Article
- CryoEMNet driven symmetry-aware molecular reconstruction through deep learning enhanced electron microscopy.Scientific reports · 2025Article
- Recognizing amino acid sidechains in a medium-resolution cryo-electron density map.Protein science : a publication of the Protein Society · 2025Article
- A comprehensive survey and benchmark of deep learning-based methods for atomic model building from cryo-electron microscopy density maps.Briefings in bioinformatics · 2025Article
- Automated model-free analysis of cryo-EM volume ensembles with SIREn.Structure (London, England : 1993) · 2025Article
- The physics-AI dialogue in drug design.RSC medicinal chemistry · 2025Review
- A Labeled Dataset for AI-based Cryo-EM Map Enhancement.bioRxiv : the preprint server for biology · 2025Article
- Advancing structure modeling from cryo-EM maps with deep learning.Biochemical Society transactions · 2025Review
- A labeled dataset for AI-based cryo-EM map enhancement.Computational and structural biotechnology journal · 2025Article
- Connecting the dots: deep learning-based automated model building methods in cryo-EM.Frontiers in molecular biosciences · 2025Review
- Use of AI-methods over MD simulations in the sampling of conformational ensembles in IDPs.Frontiers in molecular biosciences · 2025Review
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Cryo-Electron Microscopy (cryo-EM) has emerged as a key technology to determine the structure of proteins, particularly large protein complexes and assemblies in recent years. A key challenge in cryo-EM data analysis is to automatically reconstruct accurate protein structures from cryo-EM density maps. In this review, we briefly overview various deep learning methods for building protein structures from cryo-EM density maps, analyze their impact, and discuss the challenges of preparing high-quality data sets for training deep learning models. Looking into the future, more advanced deep learning models of effectively integrating cryo-EM data with other sources of complementary data such as protein sequences and AlphaFold-predicted structures need to be developed to further advance the field.
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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.