mlsanityData to 7 Oct 2026

Protein Structure and Sequence Generation with Equivariant Denoising Diffusion Probabilistic Models

arXiv 2205.15019 ↗First posted 26 May 2022q-bio.QM cs.AIcited by 97

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

Abstract (arXiv)

Proteins are macromolecules that mediate a significant fraction of the cellular processes that underlie life. An important task in bioengineering is designing proteins with specific 3D structures and chemical properties which enable targeted functions. To this end, we introduce a generative model of both protein structure and sequence that can operate at significantly larger scales than previous molecular generative modeling approaches. The model is learned entirely from experimental data and conditions its generation on a compact specification of protein topology to produce a full-atom backbone configuration as well as sequence and side-chain predictions. We demonstrate the quality of the model via qualitative and quantitative analysis of its samples. Videos of sampling trajectories are available at https://nanand2.github.io/proteins .

Key facts. Cited by 97 papers in the map (data through 7 Oct 2026). Placed in Flow matching, time series and science, one of 14 directions in Diffusion models. Builds on 15 of its 37 references that are in the map. First posted on arXiv 26 May 2022.

Builds on

15 of 37 references.

Closest ancestors · 1

Same direction, newest first

  1. 2022GeoDiff: a Geometric Diffusion Model for Molecular Conformation Generation6 Mar

Other references · 14

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  2. 2014Adam: A Method for Stochastic Optimizationcited by 36,960
  3. 2020Language Models are Few-Shot Learnerscited by 32,199
  4. 2020Denoising Diffusion Probabilistic Modelscited by 20,851
  5. 2021Diffusion Models Beat GANs on Image Synthesiscited by 8,185
  6. 2015Deep Unsupervised Learning using Nonequilibrium Thermodynamicscited by 7,005
  7. 2016SGDR: Stochastic Gradient Descent with Warm Restartscited by 4,993
  8. 2021Structured Denoising Diffusion Models in Discrete State-Spacescited by 1,665
  9. 2022Riemannian Score-Based Generative Modellingcited by 216
  10. 2020ProGen: Language Modeling for Protein Generationcited by 127
  11. 2022AlphaDesign: A graph protein design method and benchmark on AlphaFoldDBcited by 39
  12. 2020Energy-based models for atomic-resolution protein conformationscited by 20
2 more
  1. 2021Deep Generative Modeling for Protein Designcited by 17
  2. 2017Prediction of amino acid side chain conformation using a deep neural networkcited by 4

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 14; 2023: 41; 2024: 24; 2025: 14; 2026: 4.

97 papers.

Sort:

By area:

  1. 2022Diffusion Models: A Comprehensive Survey of Methods and Applicationscited by 1,107
  2. 2022DiffusionDet: Diffusion Model for Object Detectioncited by 354
  3. 2024Retrieval-Augmented Generation for AI-Generated Content: A Surveycited by 352
  4. 2024Generative Flows on Discrete State-Spaces: Enabling Multimodal Flows with Applications to Protein Co-Designcited by 294
  5. 2022Diffusion Models for Medical Image Analysis: A Comprehensive Surveycited by 269
  6. 2022A Survey on Generative Diffusion Modelcited by 238
  7. 2022Diffusion probabilistic modeling of protein backbones in 3D for the motif-scaffolding problemcited by 229
  8. 2023SE(3) diffusion model with application to protein backbone generationcited by 214
  9. 2022Structure-based Drug Design with Equivariant Diffusion Modelscited by 213
  10. 2023Geometric Latent Diffusion Models for 3D Molecule Generationcited by 185
  11. 2023Protein Design with Guided Discrete Diffusioncited by 154
  12. 2023DDP: Diffusion Model for Dense Visual Predictioncited by 148
38 more
  1. 2023SE(3)-Stochastic Flow Matching for Protein Backbone Generationcited by 147
  2. 2022Protein structure generation via folding diffusioncited by 113
  3. 2023Predict, Refine, Synthesize: Self-Guiding Diffusion Models for Probabilistic Time Series Forecastingcited by 86
  4. 2023A Complete Survey on Generative AI (AIGC): Is ChatGPT from GPT-4 to GPT-5 All You Need?cited by 84
  5. 2024Constrained Synthesis with Projected Diffusion Modelscited by 83
  6. 2023Generative Diffusion Models on Graphs: Methods and Applicationscited by 79
  7. 2023Restoration-Degradation Beyond Linear Diffusions: A Non-Asymptotic Analysis For DDIM-Type Samplerscited by 73
  8. 2023Dirichlet Diffusion Score Model for Biological Sequence Generationcited by 73
  9. 2024An Overview of Diffusion Models: Applications, Guided Generation, Statistical Rates and Optimizationcited by 72
  10. 2023EigenFold: Generative Protein Structure Prediction with Diffusion Modelscited by 63
  11. 2024ProtAgents: Protein discovery via large language model multi-agent collaborations combining physics and machine learningcited by 63
  12. 2023A Text-guided Protein Design Frameworkcited by 61
  13. 2023Equivariant Flow Matching with Hybrid Probability Transportcited by 59
  14. 2023Consistent Diffusion Models: Mitigating Sampling Drift by Learning to be Consistentcited by 58
  15. 2024Unveil Conditional Diffusion Models with Classifier-free Guidance: A Sharp Statistical Theorycited by 58
  16. 2023Generating Novel, Designable, and Diverse Protein Structures by Equivariantly Diffusing Oriented Residue Cloudscited by 53
  17. 2023Diffusion-based Data Augmentation for Skin Disease Classification: Impact Across Original Medical Datasets to Fully Synthetic Imagescited by 45
  18. 2023Graph Denoising Diffusion for Inverse Protein Foldingcited by 44
  19. 2024Protein Conformation Generation via Force-Guided SE(3) Diffusion Modelscited by 44
  20. 2022Modeling Temporal Data as Continuous Functions with Stochastic Process Diffusioncited by 39
  21. 2023Geometry-Complete Diffusion for 3D Molecule Generation and Optimizationcited by 36
  22. 2022State-specific protein-ligand complex structure prediction with a multi-scale deep generative modelcited by 35
  23. 2024Stochastic Runge-Kutta Methods: Provable Acceleration of Diffusion Modelscited by 35
  24. 2023A Survey on Graph Diffusion Models: Generative AI in Science for Molecule, Protein and Materialcited by 34
  25. 2024Full-Atom Peptide Design based on Multi-modal Flow Matchingcited by 34
  26. 2024Out of Many, One: Designing and Scaffolding Proteins at the Scale of the Structural Universe with Genie 2cited by 33
  27. 2022Protein Sequence and Structure Co-Design with Equivariant Translationcited by 31
  28. 2024A Survey of Generative AI for de novo Drug Design: New Frontiers in Molecule and Protein Generationcited by 29
  29. 2023A Latent Diffusion Model for Protein Structure Generationcited by 28
  30. 2023Learning Joint 2D & 3D Diffusion Models for Complete Molecule Generationcited by 27
  31. 2024Evaluating the design space of diffusion-based generative modelscited by 25
  32. 2023DiffPack: A Torsional Diffusion Model for Autoregressive Protein Side-Chain Packingcited by 24
  33. 2023DiffI2I: Efficient Diffusion Model for Image-to-Image Translationcited by 24
  34. 2022Graph Neural Networks for Moleculescited by 21
  35. 2024Warped Diffusion: Solving Video Inverse Problems with Image Diffusion Modelscited by 21
  36. 2022Reprogramming Pretrained Language Models for Antibody Sequence Infillingcited by 20
  37. 2025Training-Free Constrained Generation With Stable Diffusion Modelscited by 20
  38. 2023DiffTAD: Temporal Action Detection with Proposal Denoising Diffusioncited by 19

50 most cited of 97 All 97 (JSON)

Similar, not cited

Close in wording, no citation either way

  1. 2025From thermodynamics to protein design: Diffusion models for biomolecule generation towards autonomous protein engineering
  2. 2026Generative Modeling in Protein Design: Neural Representations, Conditional Generation, and Evaluation Standards
  3. 2025ProteinAE: Protein Diffusion Autoencoders for Structure Encoding
  4. 2024DPLM-2: A Multimodal Diffusion Protein Language Model
  5. 2022DiffBP: Generative Diffusion of 3D Molecules for Target Protein Binding
  6. 2024Diffusion Language Models Are Versatile Protein Learners
  7. 2024Sifting through the Noise: A Survey of Diffusion Probabilistic Models and Their Applications to Biomolecules
  8. 2025Let Physics Guide Your Protein Flows: Topology-aware Unfolding and Generation