mlsanityData to 14 Oct 2026

EigenFold: Generative Protein Structure Prediction with Diffusion Models

arXiv 2304.02198 ↗First posted 5 Apr 2023q-bio.BM cs.LG physics.bio-phcited by 63

On the map: Diffusion models → Flow matching, time series and science

Abstract (arXiv)

Protein structure prediction has reached revolutionary levels of accuracy on single structures, yet distributional modeling paradigms are needed to capture the conformational ensembles and flexibility that underlie biological function. Towards this goal, we develop EigenFold, a diffusion generative modeling framework for sampling a distribution of structures from a given protein sequence. We define a diffusion process that models the structure as a system of harmonic oscillators and which naturally induces a cascading-resolution generative process along the eigenmodes of the system. On recent CAMEO targets, EigenFold achieves a median TMScore of 0.84, while providing a more comprehensive picture of model uncertainty via the ensemble of sampled structures relative to existing methods. We then assess EigenFold's ability to model and predict conformational heterogeneity for fold-switching proteins and ligand-induced conformational change. Code is available at https://github.com/bjing2016/EigenFold.

Key facts. Cited by 63 papers in the map (data through 14 Oct 2026). Placed in Flow matching, time series and science, one of 14 directions in Diffusion models. Builds on 13 of its 32 references that are in the map. First posted on arXiv 5 Apr 2023.

Builds on

13 of 32 references.

Closest ancestors · 5

Same direction, newest first

  1. 2022Structure-based Drug Design with Equivariant Diffusion Models24 Oct
  2. 2022Protein structure generation via folding diffusion30 Sep
  3. 2022Diffusion probabilistic modeling of protein backbones in 3D for the motif-scaffolding problem8 Jun
  4. 2022Protein Structure and Sequence Generation with Equivariant Denoising Diffusion Probabilistic Models26 May
  5. 2022GeoDiff: a Geometric Diffusion Model for Molecular Conformation Generation6 Mar

Other references · 8

  1. 2020Denoising Diffusion Probabilistic Modelscited by 21,076
  2. 2020Score-Based Generative Modeling through Stochastic Differential Equationscited by 9,122
  3. 2015Deep Unsupervised Learning using Nonequilibrium Thermodynamicscited by 7,058
  4. 2021Cascaded Diffusion Models for High Fidelity Image Generationcited by 1,163
  5. 2018Tensor field networks: Rotation- and translation-equivariant neural networks for 3D point cloudscited by 746
  6. 2022DiffDock: Diffusion Steps, Twists, and Turns for Molecular Dockingcited by 313
  7. 2022Torsional Diffusion for Molecular Conformer Generationcited by 263
  8. 2022e3nn: Euclidean Neural Networkscited by 174

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 7; 2024: 22; 2025: 24; 2026: 10.

63 papers.

Sort:

By area:

  1. 2024AlphaFold Meets Flow Matching for Generating Protein Ensemblescited by 110
  2. 2024Transferable Boltzmann Generatorscited by 46
  3. 2024Protein Conformation Generation via Force-Guided SE(3) Diffusion Modelscited by 45
  4. 2024ET-Flow: Equivariant Flow-Matching for Molecular Conformer Generationcited by 43
  5. 2024Sequence-Augmented SE(3)-Flow Matching For Conditional Protein Backbone Generationcited by 36
  6. 2023AbDiffuser: Full-Atom Generation of in vitro Functioning Antibodiescited by 35
  7. 2023Harmonic Self-Conditioned Flow Matching for Multi-Ligand Docking and Binding Site Designcited by 30
  8. 2023Str2Str: A Score-based Framework for Zero-shot Protein Conformation Samplingcited by 29
  9. 2024A Survey of Generative AI for de novo Drug Design: New Frontiers in Molecule and Protein Generationcited by 29
  10. 2024A Survey of Geometric Graph Neural Networks: Data Structures, Models and Applicationscited by 28
  11. 2024ProteinBench: A Holistic Evaluation of Protein Foundation Modelscited by 25
  12. 2025Adjoint Schr"odinger Bridge Samplercited by 21
38 more
  1. 2023Extracting Reward Functions from Diffusion Modelscited by 18
  2. 2024Structure Language Models for Protein Conformation Generationcited by 17
  3. 2025SimpleFold: Folding Proteins is Simpler than You Thinkcited by 16
  4. 2024Geometric Trajectory Diffusion Modelscited by 15
  5. 2024Solving Inverse Problems in Protein Space Using Diffusion-Based Priorscited by 13
  6. 2025Equivariant Neural Diffusion for Molecule Generationcited by 13
  7. 2025Generative diffusion model with inverse renormalization group flowscited by 10
  8. 2024Flow Matching for Accelerated Simulation of Atomic Transport in Crystalline Materialscited by 9
  9. 2025ConfRover: Simultaneous Modeling of Protein Conformation and Dynamics via Autoregressioncited by 8
  10. 2024Energy based diffusion generator for efficient sampling of Boltzmann distributionscited by 7
  11. 2025Compositional Flows for 3D Molecule and Synthesis Pathway Co-designcited by 6
  12. 2025MarS-FM: Generative Modeling of Molecular Dynamics via Markov State Modelscited by 6
  13. 2023Generative artificial intelligence for de novo protein designcited by 5
  14. 2024Boltzmann-Aligned Inverse Folding Model as a Predictor of Mutational Effects on Protein-Protein Interactionscited by 5
  15. 2025Rao-Blackwell Gradient Estimators for Equivariant Denoising Diffusioncited by 5
  16. 2025Aligning Protein Conformation Ensemble Generation with Physical Feedbackcited by 5
  17. 2025Generative AI in Depth: A Survey of Recent Advances, Model Variants, and Real-World Applicationscited by 5
  18. 2026Flow Sampling: Learning to Sample from Unnormalized Densities via Denoising Conditional Processescited by 5
  19. 2025Leveraging Diffusion Models for Parameterized Quantum Circuit Generationcited by 4
  20. 2025Efficient Molecular Conformer Generation with SO(3)-Averaged Flow Matching and Reflowcited by 4
  21. 2024AlphaFolding: 4D Diffusion for Dynamic Protein Structure Prediction with Reference and Motion Guidancecited by 3
  22. 2025Large Language Models for Bioinformaticscited by 3
  23. 2025PolyConf: Unlocking Polymer Conformation Generation through Hierarchical Generative Modelscited by 3
  24. 2025TEMPO: Temporal Multi-scale Autoregressive Generation of Protein Conformational Ensemblescited by 3
  25. 2026Fast Organic Crystal Structure Prediction with Unit Cell Flow Matchingcited by 3
  26. 2024Diffusion-based Graph Generative Methodscited by 2
  27. 2024Improving AlphaFlow for Efficient Protein Ensembles Generationcited by 2
  28. 2025PETIMOT: A Novel Framework for Inferring Protein Motions from Sparse Data Using SE(3)-Equivariant Graph Neural Networkscited by 2
  29. 2025Designing Cyclic Peptides via Harmonic SDE with Atom-Bond Modelingcited by 2
  30. 2025Template-Guided 3D Molecular Pose Generation via Flow Matching and Differentiable Optimizationcited by 2
  31. 2025Monte Carlo Tree Diffusion with Multiple Experts for Protein Designcited by 2
  32. 2023Geometric Constraints in Probabilistic Manifolds: A Bridge from Molecular Dynamics to Structured Diffusion Processescited by 1
  33. 2023DockGame: Cooperative Games for Multimeric Rigid Protein Dockingcited by 1
  34. 2024Fusing Neural and Physical: Augment Protein Conformation Sampling with Tractable Simulationscited by 1
  35. 2024Sifting through the Noise: A Survey of Diffusion Probabilistic Models and Their Applications to Biomoleculescited by 1
  36. 2024Grand canonical generative diffusion model for crystalline phases and grain boundariescited by 1
  37. 2025Topology Guidance: Controlling the Outputs of Generative Models via Vector Field Topologycited by 1
  38. 2025EPO: Diverse and Realistic Protein Ensemble Generation via Energy Preference Optimizationcited by 1

50 most cited of 63 All 63 (JSON)

Similar, not cited

Close in wording, no citation either way

  1. 2023SE(3) diffusion model with application to protein backbone generation
  2. 2023A Latent Diffusion Model for Protein Structure Generation
  3. 2024Equivariant Blurring Diffusion for Hierarchical Molecular Conformer Generation
  4. 2023Generating Novel, Designable, and Diverse Protein Structures by Equivariantly Diffusing Oriented Residue Clouds
  5. 2026Generative Modeling in Protein Design: Neural Representations, Conditional Generation, and Evaluation Standards
  6. 2023Graph Denoising Diffusion for Inverse Protein Folding
  7. 2025Unlocking hidden biomolecular conformational landscapes in diffusion models at inference time
  8. 2025From thermodynamics to protein design: Diffusion models for biomolecule generation towards autonomous protein engineering