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CryoFM2: Overview

A Generative Foundation Model for Cryo-EM Densities
under review, 2025.

What is CryoFM2?

CryoFM2 overview.

CryoFM2 overview.

Single-particle cryo-EM density reconstruction is a severely ill-posed inverse problem, commonly degraded by noise, preferred orientations, and reconstruction artifacts. Existing approaches either rely on strong hand-crafted assumptions or supervised post-processing models.

CryoFM2 addresses this challenge by introducing a generative foundation model for cryo-EM densities. The model is trained unsupervised on thousands of high-quality cryo-EM maps using flow matching, learning a reusable prior over macromolecular density distributions that generalizes across molecular systems. The model employs a UNet architecture and is pretrained on curated EMDB half maps.

At inference time, this learned prior is combined with explicit likelihood models that describe experimental degradations, forming a principled Bayesian inference framework. Within this framework, CryoFM2 performs flow-based posterior sampling (FPS), an inference-only procedure that simultaneously enables denoising, restoration, and refinement while remaining explicitly constrained by dataset-derived statistics. This approach supports applications such as:

  • Anisotropy-aware refinement
  • Non-uniform reconstruction
  • Controlled density modification
  • Density map enhancement

Model Variants

CryoFM2 is available in three variants:

  1. cryofm2-pretrain: Unconditional pretrained model for general density map generation and modification tasks.
  2. cryofm2-emhancer: Fine-tuned model for density map enhancement (EMhancer style).
  3. cryofm2-emready: Fine-tuned model for density map enhancement (EMReady style).

Get Started

For End Users

  • Quick Start: Learn how to use CryoFM2 for common tasks like denoising and style enhancement using simple command-line tools.

Understanding Operators

  • Operators: Explore the different forward operators available in CryoFM2, including denoising, inpainting, and non-uniform refinement.

For Developers

  • Unconditional Sampling: Learn how to use the Python API to generate unconditional samples from CryoFM2 models.
  • Likelihood Control: Understand how to use scripts for likelihood control (FPS) and fine-tune parameters for different tasks.
  • GUI Demo: Try CryoFM2 through an interactive web-based graphical interface.

Resources

  • Model Weights: Available on Hugging Face.
  • Source Code: Available on GitHub.
  • Dataset (EMDB ID Lists): Available on Zenodo.

Citation

If you use CryoFM2 in your research, please cite:

@article{
Li2025.12.29.696802,
author={Li, Yilai and Yuan, Jing and Zhou, Yi and Wang, Zhenghua and Chen, Suyi and Yang, Fengyu and Ling, Haibin and Kovalsky, Shahar Z and Zheng, Xiaoqing and Gu, Quanquan},
title={A Generative Foundation Model for Cryo-EM Densities},
elocation-id={2025.12.29.696802},
year={2025},
doi={10.64898/2025.12.29.696802},
publisher={Cold Spring Harbor Laboratory},
URL={https://www.biorxiv.org/content/early/2025/12/29/2025.12.29.696802},
eprint={https://www.biorxiv.org/content/early/2025/12/29/2025.12.29.696802.full.pdf},
journal={bioRxiv}
}