mlsanityData to 7 Oct 2026

Diffusion probabilistic modeling of protein backbones in 3D for the motif-scaffolding problem

arXiv 2206.04119 ↗First posted 8 Jun 2022q-bio.BM cs.LG stat.MLcited by 229

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

Abstract (arXiv)

Construction of a scaffold structure that supports a desired motif, conferring protein function, shows promise for the design of vaccines and enzymes. But a general solution to this motif-scaffolding problem remains open. Current machine-learning techniques for scaffold design are either limited to unrealistically small scaffolds (up to length 20) or struggle to produce multiple diverse scaffolds. We propose to learn a distribution over diverse and longer protein backbone structures via an E(3)-equivariant graph neural network. We develop SMCDiff to efficiently sample scaffolds from this distribution conditioned on a given motif; our algorithm is the first to theoretically guarantee conditional samples from a diffusion model in the large-compute limit. We evaluate our designed backbones by how well they align with AlphaFold2-predicted structures. We show that our method can (1) sample scaffolds up to 80 residues and (2) achieve structurally diverse scaffolds for a fixed motif.

Key facts. Cited by 229 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 14 of its 53 references that are in the map. First posted on arXiv 8 Jun 2022.

Builds on

14 of 53 references.

Closest ancestors · 3

Same direction, newest first

  1. 2022Protein Structure and Sequence Generation with Equivariant Denoising Diffusion Probabilistic Models26 May
  2. 2022Equivariant Diffusion for Molecule Generation in 3D31 Mar
  3. 2022GeoDiff: a Geometric Diffusion Model for Molecular Conformation Generation6 Mar

Other references · 11

  1. 2017Attention Is All You Needcited by 59,932
  2. 2020Denoising Diffusion Probabilistic Modelscited by 20,851
  3. 2020Score-Based Generative Modeling through Stochastic Differential Equationscited by 8,964
  4. 2015Deep Unsupervised Learning using Nonequilibrium Thermodynamicscited by 7,005
  5. 2022Video Diffusion Modelscited by 2,280
  6. 2021Palette: Image-to-Image Diffusion Modelscited by 1,296
  7. 2021Variational Diffusion Modelscited by 1,145
  8. 2021E(n) Equivariant Graph Neural Networkscited by 951
  9. 2021E(3)-Equivariant Graph Neural Networks for Data-Efficient and Accurate Interatomic Potentialscited by 701
  10. 20213D Shape Generation and Completion through Point-Voxel Diffusioncited by 516
  11. 2021EBM-Fold: Fully-Differentiable Protein Folding Powered by Energy-based Modelscited by 3

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 16; 2023: 67; 2024: 54; 2025: 61; 2026: 31.

229 papers.

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  1. 2022Diffusion Models: A Comprehensive Survey of Methods and Applicationscited by 1,107
  2. 2022DiGress: Discrete Denoising diffusion for graph generationcited by 503
  3. 2022DiffusionDet: Diffusion Model for Object Detectioncited by 354
  4. 2022DiffDock: Diffusion Steps, Twists, and Turns for Molecular Dockingcited by 307
  5. 2024Generative Flows on Discrete State-Spaces: Enabling Multimodal Flows with Applications to Protein Co-Designcited by 294
  6. 2022Diffusion Models for Medical Image Analysis: A Comprehensive Surveycited by 269
  7. 2022A Survey on Generative Diffusion Modelcited by 238
  8. 2023Nearly d-Linear Convergence Bounds for Diffusion Models via Stochastic Localizationcited by 230
  9. 2023Practical and Asymptotically Exact Conditional Sampling in Diffusion Modelscited by 218
  10. 2023SE(3) diffusion model with application to protein backbone generationcited by 214
  11. 2022Structure-based Drug Design with Equivariant Diffusion Modelscited by 213
  12. 2022DiffRF: Rendering-Guided 3D Radiance Field Diffusioncited by 204
38 more
  1. 2025A General Framework for Inference-time Scaling and Steering of Diffusion Modelscited by 201
  2. 2022SSD-LM: Semi-autoregressive Simplex-based Diffusion Language Model for Text Generation and Modular Controlcited by 191
  3. 2023Geometric Latent Diffusion Models for 3D Molecule Generationcited by 185
  4. 2024A Survey on Diffusion Models for Inverse Problemscited by 173
  5. 2023Protein Design with Guided Discrete Diffusioncited by 154
  6. 2023DDP: Diffusion Model for Dense Visual Predictioncited by 148
  7. 2023Crystal Structure Prediction by Joint Equivariant Diffusioncited by 142
  8. 2024Derivative-Free Guidance in Continuous and Discrete Diffusion Models with Soft Value-Based Decodingcited by 127
  9. 2022Equivariant 3D-Conditional Diffusion Models for Molecular Linker Designcited by 121
  10. 2022Protein structure generation via folding diffusioncited by 113
  11. 2023Stochastic interpolants with data-dependent couplingscited by 94
  12. 2024Diffusion Language Models Are Versatile Protein Learnerscited by 93
  13. 2025Test-time Alignment of Diffusion Models without Reward Over-optimizationcited by 89
  14. 2025Inference-Time Alignment in Diffusion Models with Reward-Guided Generation: Tutorial and Reviewcited by 82
  15. 2023DiffusionDepth: Diffusion Denoising Approach for Monocular Depth Estimationcited by 81
  16. 2023Generative Diffusion Models on Graphs: Methods and Applicationscited by 79
  17. 2024Variational Flow Matching for Graph Generationcited by 78
  18. 2023Restoration-Degradation Beyond Linear Diffusions: A Non-Asymptotic Analysis For DDIM-Type Samplerscited by 73
  19. 2023Dirichlet Diffusion Score Model for Biological Sequence Generationcited by 73
  20. 2024Provably Robust Score-Based Diffusion Posterior Sampling for Plug-and-Play Image Reconstructioncited by 73
  21. 2023Two for One: Diffusion Models and Force Fields for Coarse-Grained Molecular Dynamicscited by 72
  22. 2024Fine-Tuning Discrete Diffusion Models via Reward Optimization with Applications to DNA and Protein Designcited by 72
  23. 2025InverseBench: Benchmarking Plug-and-Play Diffusion Priors for Inverse Problems in Physical Sciencescited by 67
  24. 2023Monte Carlo guided Diffusion for Bayesian linear inverse problemscited by 65
  25. 2023EigenFold: Generative Protein Structure Prediction with Diffusion Modelscited by 63
  26. 2025Proteina: Scaling Flow-based Protein Structure Generative Modelscited by 63
  27. 2023Towards Coherent Image Inpainting Using Denoising Diffusion Implicit Modelscited by 61
  28. 2023Diffusion Models for Constrained Domainscited by 60
  29. 2023Equivariant Flow Matching with Hybrid Probability Transportcited by 59
  30. 2024DPLM-2: A Multimodal Diffusion Protein Language Modelcited by 59
  31. 2023Consistent Diffusion Models: Mitigating Sampling Drift by Learning to be Consistentcited by 58
  32. 2024On conditional diffusion models for PDE simulationscited by 56
  33. 2023Generating Novel, Designable, and Diverse Protein Structures by Equivariantly Diffusing Oriented Residue Cloudscited by 53
  34. 2023Graph Denoising Diffusion for Inverse Protein Foldingcited by 44
  35. 2024Protein Conformation Generation via Force-Guided SE(3) Diffusion Modelscited by 44
  36. 2025On the Guidance of Flow Matchingcited by 44
  37. 2024Improved motif-scaffolding with SE(3) flow matchingcited by 43
  38. 2024Neural Flow Diffusion Models: Learnable Forward Process for Improved Diffusion Modellingcited by 43

50 most cited of 229 All 229 (JSON)

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  1. 2024Mask prior-guided denoising diffusion improves inverse protein folding
  2. 2023Accelerating Inference in Molecular Diffusion Models with Latent Representations of Protein Structure
  3. 2026Ligand-Conditioned Discrete Diffusion for Protein Sequence-Structure Co-Design
  4. 2024AUTODIFF: Autoregressive Diffusion Modeling for Structure-based Drug Design
  5. 2025Monte Carlo Tree Diffusion with Multiple Experts for Protein Design
  6. 2025ProteinAE: Protein Diffusion Autoencoders for Structure Encoding
  7. 2025Learning conformational ensembles of proteins based on backbone geometry
  8. 2026Generative Modeling in Protein Design: Neural Representations, Conditional Generation, and Evaluation Standards