Introduction
Welcome to the documentation for cryoDRGN-AI!
CryoDRGN-AI is an open-source software package for ab initio heterogeneous reconstruction in cryo-EM and cryo-ET.
Latest News!
Note: We are currently merging the standalone cryoDRGN-AI github repository into the cryodrgn software package. CryoDRGN-AI will be available in the cryodrgn software's next major release.
June 2025: CryoDRGN-AI is now published in Nature Methods!
June 2025: Version 0.3.2-beta release with usability improvements. See the github for more updates.
April 2025: Updated name from DRGN-AI to cryoDRGN-AI in our preprint.
Dec 2024: Ab initio reconstruction of cryo-ET subtomograms is now described in our preprint.
Inputs/Outputs
CryoDRGN-AI takes a set of particle images with estimated CTF parameters and reconstructs a distribution of 3D density maps, modeling the conformational space of the biomolecular complex. The output includes a set of low-dimensional latent embeddings (one for each particle) and a neural network that associates this latent representation to 3D density maps.
Method Overview
CryoDRGN-AI aims to jointly infer the pose of each particle in the dataset while reconstructing the structural variability of the molecule. It combines the flexibility of implicit neural representations with a fast and scalable pose estimation strategy. Poses are optimized using a hierarchical search strategy and subsequently refined through stochastic gradient descent. Conformations are independently optimized in an autodecoding fashion. The whole model (neural field + conformations + poses) is optimized by gradient descent in order to minimize the difference between reconstructed images and real observations.
Tutorial Outline
The user guide is split into 3 parts:
Installation: details on how to install cryoDRGN-AI.
Running a job: tutorial explaining how to run cryoDRGN-AI, monitor your job and analyze the outputs.
Full API documentation: details on all the cryoDRGN-AI parameters.
Citing this Work
@article{cryodrgnai,
title = "CryoDRGN-AI: neural ab initio reconstruction of challenging cryo-EM and cryo-ET datasets",
author = "Levy, Axel and Raghu, Rishwanth and Feathers, Ryan and Grzadkowski, Michal and Poitevin, Frederic and
Johnston, Jake D, Vallese, Francesca and Clarke, Oliver B and Wetzstein, Gordon and Zhong, Ellen D",
journal = "Nature Methods",
doi = 10.1038/s41592-025-02720-4,
url = nature.com/articles/s41592-025-02720-4,
month = jun,
year = 2025,
}Last updated