# Artificial Intelligence Advances for De Novo Molecular Structure Modeling in Cryo-EM

arXiv: https://arxiv.org/abs/2102.06125 · HTML: https://mlsanity.com/paper/2102.06125/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 167 references that are in the map.
- First posted on arXiv 11 Feb 2021.

## Builds on (most cited first, 14 of 14)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [ProGen: Language Modeling for Protein Generation](https://mlsanity.com/paper/2004.03497/) (2020)
- [ProteinNet: a standardized data set for machine learning of protein structure](https://mlsanity.com/paper/1902.00249/) (2019)
- [Deep Learning in Protein Structural Modeling and Design](https://mlsanity.com/paper/2007.08383/) (2020)
- [Positive-unlabeled convolutional neural networks for particle picking in cryo-electron micrographs](https://mlsanity.com/paper/1803.08207/) (2018)
- [DeepPicker: a Deep Learning Approach for Fully Automated Particle Picking in Cryo-EM](https://mlsanity.com/paper/1605.01838/) (2016)
- [Estimation of Orientation and Camera Parameters from Cryo-Electron Microscopy Images with Variational Autoencoders and Generative Adversarial Networks](https://mlsanity.com/paper/1911.08121/) (2019)
- [Denoising and Covariance Estimation of Single Particle Cryo-EM Images](https://mlsanity.com/paper/1602.06632/) (2016)
- [A deep convolutional neural network approach to single-particle recognition in cryo-electron microscopy](https://mlsanity.com/paper/1605.05543/) (2016)
- [APPLE Picker: Automatic Particle Picking, a Low-Effort Cryo-EM Framework](https://mlsanity.com/paper/1802.00469/) (2018)
- [A^2-Net: Molecular Structure Estimation from Cryo-EM Density Volumes](https://mlsanity.com/paper/1901.00785/) (2019)
- [DeepQA: Improving the estimation of single protein model quality with deep belief networks](https://mlsanity.com/paper/1607.04379/) (2016)
- [Bias and variance reduction and denoising for CTF Estimation](https://mlsanity.com/paper/1908.03454/) (2019)
- [Sequence-guided protein structure determination using graph convolutional and recurrent networks](https://mlsanity.com/paper/2007.06847/) (2020)

## Built on by (most cited first, 2 of 2)

- [Deep Generative Modeling for Volume Reconstruction in Cryo-Electron Microscopy](https://mlsanity.com/paper/2201.02867/) (2022)
- [Deep learning for reconstructing protein structures from cryo-EM density maps: recent advances and future directions](https://mlsanity.com/paper/2209.08171/) (2022)

## Abstract (arXiv)

Cryo-electron microscopy (cryo-EM) has become a major experimental technique to determine the structures of large protein complexes and molecular assemblies, as evidenced by the 2017 Nobel Prize. Although cryo-EM has been drastically improved to generate high-resolution three-dimensional (3D) maps that contain detailed structural information about macromolecules, the computational methods for using the data to automatically build structure models are lagging far behind. The traditional cryo-EM model building approach is template-based homology modeling. Manual de novo modeling is very time-consuming when no template model is found in the database. In recent years, de novo cryo-EM modeling using machine learning (ML) and deep learning (DL) has ranked among the top-performing methods in macromolecular structure modeling. Deep-learning-based de novo cryo-EM modeling is an important application of artificial intelligence, with impressive results and great potential for the next generation of molecular biomedicine. Accordingly, we systematically review the representative ML/DL-based de novo cryo-EM modeling methods. And their significances are discussed from both practical and methodological viewpoints. We also briefly describe the background of cryo-EM data processing workflow. Overall, this review provides an introductory guide to modern research on artificial intelligence (AI) for de novo molecular structure modeling and future directions in this emerging field.