mlsanityData to 14 Oct 2026

De novo antibody design with SE(3) diffusion

arXiv 2405.07622 ↗First posted 13 May 2024q-bio.BM cs.LGcited by 5

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Abstract (arXiv)

We introduce IgDiff, an antibody variable domain diffusion model based on a general protein backbone diffusion framework which was extended to handle multiple chains. Assessing the designability and novelty of the structures generated with our model, we find that IgDiff produces highly designable antibodies that can contain novel binding regions. The backbone dihedral angles of sampled structures show good agreement with a reference antibody distribution. We verify these designed antibodies experimentally and find that all express with high yield. Finally, we compare our model with a state-of-the-art generative backbone diffusion model on a range of antibody design tasks, such as the design of the complementarity determining regions or the pairing of a light chain to an existing heavy chain, and show improved properties and designability.

Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). Builds on 15 of its 64 references that are in the map. First posted on arXiv 13 May 2024.

Builds on

15 of 64 references.

References · 15

  1. 2017Attention Is All You Needcited by 60,361
  2. 2020Denoising Diffusion Probabilistic Modelscited by 21,076
  3. 2020Score-Based Generative Modeling through Stochastic Differential Equationscited by 9,122
  4. 2015Deep Unsupervised Learning using Nonequilibrium Thermodynamicscited by 7,058
  5. 2020SE(3)-Transformers: 3D Roto-Translation Equivariant Attention Networkscited by 607
  6. 2022Diffusion probabilistic modeling of protein backbones in 3D for the motif-scaffolding problemcited by 236
  7. 2023Practical and Asymptotically Exact Conditional Sampling in Diffusion Modelscited by 232
  8. 2023SE(3) diffusion model with application to protein backbone generationcited by 219
  9. 2023Protein Design with Guided Discrete Diffusioncited by 157
  10. 2021Iterative Refinement Graph Neural Network for Antibody Sequence-Structure Co-designcited by 86
  11. 2022Conditional Antibody Design as 3D Equivariant Graph Translationcited by 75
  12. 2023Generating Novel, Designable, and Diverse Protein Structures by Equivariantly Diffusing Oriented Residue Cloudscited by 54
3 more
  1. 2021Protein sequence design with deep generative modelscited by 35
  2. 2023DiffPack: A Torsional Diffusion Model for Autoregressive Protein Side-Chain Packingcited by 24
  3. 2023Inverse folding for antibody sequence design using deep learningcited by 13

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Citing papers per year: 2024: 1; 2025: 4.

5 papers.

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  1. 2023AbDiffuser: Full-Atom Generation of in vitro Functioning Antibodies
  2. 2024Retrieval Augmented Diffusion Model for Structure-informed Antibody Design and Optimization
  3. 2025Guided Sequence-Structure Generative Modeling for Iterative Antibody Optimization
  4. 2023AntiBARTy Diffusion for Property Guided Antibody Design
  5. 2024Improving Antibody Design with Force-Guided Sampling in Diffusion Models
  6. 2025Guided Generation for Developable Antibodies
  7. 2025Antibody Design and Optimization with Multi-scale Equivariant Graph Diffusion Models for Accurate Complex Antigen Binding
  8. 2026Conditional generation of antibody sequences with classifier-guided germline-absorbing discrete diffusion