# Deep learning for reconstructing protein structures from cryo-EM density maps: recent advances and future directions

arXiv: https://arxiv.org/abs/2209.08171 · HTML: https://mlsanity.com/paper/2209.08171/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 63 references that are in the map.
- First posted on arXiv 16 Sep 2022.

## Builds on (most cited first, 11 of 11)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [UNet++: A Nested U-Net Architecture for Medical Image Segmentation](https://mlsanity.com/paper/1807.10165/) (2018)
- [Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges](https://mlsanity.com/paper/2104.13478/) (2021)
- [SE(3)-Transformers: 3D Roto-Translation Equivariant Attention Networks](https://mlsanity.com/paper/2006.10503/) (2020)
- [Fundamentals of Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) Network](https://mlsanity.com/paper/1808.03314/) (2018)
- [Attention Mechanisms in Computer Vision: A Survey](https://mlsanity.com/paper/2111.07624/) (2021)
- [Deep learning based mixed-dimensional GMM for characterizing variability in CryoEM](https://mlsanity.com/paper/2101.10356/) (2021)
- [Sequence-guided protein structure determination using graph convolutional and recurrent networks](https://mlsanity.com/paper/2007.06847/) (2020)
- [Artificial Intelligence Advances for De Novo Molecular Structure Modeling in Cryo-EM](https://mlsanity.com/paper/2102.06125/) (2021)

## Built on by (most cited first, 4 of 4)

- [Equivariant amortized inference of poses for cryo-EM](https://mlsanity.com/paper/2406.01630/) (2024)
- [Beyond Current Boundaries: Integrating Deep Learning and AlphaFold for Enhanced Protein Structure Prediction from Low-Resolution Cryo-EM Maps](https://mlsanity.com/paper/2410.23321/) (2024)
- [Yeti: A compact protein structure tokenizer for reconstruction and multi-modal generation](https://mlsanity.com/paper/2605.09981/) (2026)
- [Application of Deep Learning in Biological Data Compression](https://mlsanity.com/paper/2512.12975/) (2025)

## Abstract (arXiv)

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.