# CryoBench: Diverse and challenging datasets for the heterogeneity problem in cryo-EM

arXiv: https://arxiv.org/abs/2408.05526 · HTML: https://mlsanity.com/paper/2408.05526/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 65 references that are in the map.
- First posted on arXiv 10 Aug 2024.

## Builds on (most cited first, 10 of 10)

- [Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representations](https://mlsanity.com/paper/1811.12359/) (2018)
- [Reconstructing continuous distributions of 3D protein structure from cryo-EM images](https://mlsanity.com/paper/1909.05215/) (2019)
- [Embedding Comparator: Visualizing Differences in Global Structure and Local Neighborhoods via Small Multiples](https://mlsanity.com/paper/1912.04853/) (2019)
- [Amortized Inference for Heterogeneous Reconstruction in Cryo-EM](https://mlsanity.com/paper/2210.07387/) (2022)
- [Ranking the information content of distance measures](https://mlsanity.com/paper/2104.15079/) (2021)
- [DADApy: Distance-based Analysis of DAta-manifolds in Python](https://mlsanity.com/paper/2205.03373/) (2022)
- [Deep Generative Modeling for Volume Reconstruction in Cryo-Electron Microscopy](https://mlsanity.com/paper/2201.02867/) (2022)
- [Cryo-EM reconstruction of continuous heterogeneity by Laplacian spectral volumes](https://mlsanity.com/paper/1907.01898/) (2019)
- [Hyper-Molecules: on the Representation and Recovery of Dynamical Structures, with Application to Flexible Macro-Molecular Structures in Cryo-EM](https://mlsanity.com/paper/1907.01589/) (2019)
- [Collective variable discovery in the age of machine learning: reality, hype and everything in between](https://mlsanity.com/paper/2112.03202/) (2021)

## Built on by (most cited first, 15 of 15)

- [CryoFastAR: Fast Cryo-EM Ab Initio Reconstruction Made Easy](https://mlsanity.com/paper/2506.05864/) (2025)
- [Cryo-em images are intrinsically low dimensional](https://mlsanity.com/paper/2504.11249/) (2025)
- [CryoCCD: Conditional Cycle-consistent Diffusion with Biophysical Modeling for Cryo-EM Synthesis](https://mlsanity.com/paper/2505.23444/) (2025)
- [Reconstructing Heterogeneous Biomolecules via Hierarchical Gaussian Mixtures and Part Discovery](https://mlsanity.com/paper/2506.09063/) (2025)
- [CryoHype: Reconstructing a thousand cryo-EM structures with transformer-based hypernetworks](https://mlsanity.com/paper/2512.06332/) (2025)
- [Resolving compositional and conformational heterogeneity in cryo-EM with deformable 3D Gaussian representations](https://mlsanity.com/paper/2512.21599/) (2025)
- [Unsupervised SE(3) Disentanglement for in situ Macromolecular Morphology Identification from Cryo-Electron Tomography](https://mlsanity.com/paper/2601.01364/) (2026)
- [SOLVAR: Fast covariance-based heterogeneity analysis with pose refinement for cryo-EM](https://mlsanity.com/paper/2602.17603/) (2026)
- [Protein Graph Neural Networks for Heterogeneous Cryo-EM Reconstruction](https://mlsanity.com/paper/2602.21915/) (2026)
- [STEMGym: Benchmarking Sequential Decision-Making under Dose Budgets in Autonomous Electron Microscopy](https://mlsanity.com/paper/2606.29592/) (2026)
- [Noise-Robust Conditional Flow Matching: Generating Clean Samples from Noisy Datasets](https://mlsanity.com/paper/2608.00064/) (2026)
- [ARCHER: Amortized cross-specimen pose estimation for cryo-electron microscopy](https://mlsanity.com/paper/2608.22029/) (2026)
- [A meta-algorithm for ab initio reconstruction of complex mixtures in cryo-EM](https://mlsanity.com/paper/2608.25388/) (2026)
- [Atelier: Learning Local Self-Supervised Features for CryoEM Volumes via Hypernetworks](https://mlsanity.com/paper/2609.30569/) (2026)
- [Fold'EM: Direct atomic structure inference from Cryo-EM particles](https://mlsanity.com/paper/2610.01358/) (2026)

## Abstract (arXiv)

Cryo-electron microscopy (cryo-EM) is a powerful technique for determining high-resolution 3D biomolecular structures from imaging data. Its unique ability to capture structural variability has spurred the development of heterogeneous reconstruction algorithms that can infer distributions of 3D structures from noisy, unlabeled imaging data. Despite the growing number of advanced methods, progress in the field is hindered by the lack of standardized benchmarks with ground truth information and reliable validation metrics. Here, we introduce CryoBench, a suite of datasets, metrics, and benchmarks for heterogeneous reconstruction in cryo-EM. CryoBench includes five datasets representing different sources of heterogeneity and degrees of difficulty. These include conformational heterogeneity generated from designed motions of antibody complexes or sampled from a molecular dynamics simulation, as well as compositional heterogeneity from mixtures of ribosome assembly states or 100 common complexes present in cells. We then analyze state-of-the-art heterogeneous reconstruction tools, including neural and non-neural methods, assess their sensitivity to noise, and propose new metrics for quantitative evaluation. We hope that CryoBench will be a foundational resource for accelerating algorithmic development and evaluation in the cryo-EM and machine learning communities. Project page: https://cryobench.cs.princeton.edu.