mlsanityData to 14 Oct 2026

Iterative Refinement Graph Neural Network for Antibody Sequence-Structure Co-design

arXiv 2110.04624 ↗First posted 9 Oct 2021q-bio.BM cs.LGcited by 86

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Abstract (arXiv)

Antibodies are versatile proteins that bind to pathogens like viruses and stimulate the adaptive immune system. The specificity of antibody binding is determined by complementarity-determining regions (CDRs) at the tips of these Y-shaped proteins. In this paper, we propose a generative model to automatically design the CDRs of antibodies with enhanced binding specificity or neutralization capabilities. Previous generative approaches formulate protein design as a structure-conditioned sequence generation task, assuming the desired 3D structure is given a priori. In contrast, we propose to co-design the sequence and 3D structure of CDRs as graphs. Our model unravels a sequence autoregressively while iteratively refining its predicted global structure. The inferred structure in turn guides subsequent residue choices. For efficiency, we model the conditional dependence between residues inside and outside of a CDR in a coarse-grained manner. Our method achieves superior log-likelihood on the test set and outperforms previous baselines in designing antibodies capable of neutralizing the SARS-CoV-2 virus.

Key facts. Cited by 86 papers in the map (data through 14 Oct 2026). Builds on 12 of its 44 references that are in the map. First posted on arXiv 9 Oct 2021.

Builds on

12 of 44 references.

References · 12

  1. 2014Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translationcited by 4,057
  2. 2021Diffusion Probabilistic Models for 3D Point Cloud Generationcited by 735
  3. 2019Efficient Graph Generation with Graph Recurrent Attention Networkscited by 234
  4. 2018Learning Deep Generative Models of Graphscited by 229
  5. 2020Hierarchical Generation of Molecular Graphs using Structural Motifscited by 208
  6. 2018Constrained Graph Variational Autoencoders for Molecule Designcited by 185
  7. 2019Symmetry-adapted generation of 3d point sets for the targeted discovery of moleculescited by 172
  8. 2021Learning Gradient Fields for Molecular Conformation Generationcited by 163
  9. 2018Graphite: Iterative Generative Modeling of Graphscited by 112
  10. 2021When Attention Meets Fast Recurrence: Training Language Models with Reduced Computecited by 34
  11. 2021Fold2Seq: A Joint Sequence(1D)-Fold(3D) Embedding-based Generative Model for Protein Designcited by 23
  12. 2020Improving Molecular Design by Stochastic Iterative Target Augmentationcited by 18

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 2; 2022: 13; 2023: 16; 2024: 24; 2025: 17; 2026: 14.

86 papers.

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  1. 2022A Survey on Generative Diffusion Modelcited by 240
  2. 2022A 3D Generative Model for Structure-Based Drug Designcited by 149
  3. 2022Accelerating Bayesian Optimization for Biological Sequence Design with Denoising Autoencoderscited by 102
  4. 2021Independent SE(3)-Equivariant Models for End-to-End Rigid Protein Dockingcited by 82
  5. 2022Conditional Antibody Design as 3D Equivariant Graph Translationcited by 75
  6. 2022A Survey on Deep Graph Generation: Methods and Applicationscited by 69
  7. 2023Structure-informed Language Models Are Protein Designerscited by 68
  8. 2024DPLM-2: A Multimodal Diffusion Protein Language Modelcited by 64
  9. 2023Language models can generate molecules, materials, and protein binding sites directly in three dimensions as XYZ, CIF, and PDB filescited by 59
  10. 2023End-to-End Full-Atom Antibody Designcited by 49
  11. 2023Protein Discovery with Discrete Walk-Jump Samplingcited by 38
  12. 2023AbDiffuser: Full-Atom Generation of in vitro Functioning Antibodiescited by 35
38 more
  1. 2024Full-Atom Peptide Design based on Multi-modal Flow Matchingcited by 34
  2. 2022Protein Sequence and Structure Co-Design with Equivariant Translationcited by 31
  3. 2023IGB: Addressing The Gaps In Labeling, Features, Heterogeneity, and Size of Public Graph Datasets for Deep Learning Researchcited by 30
  4. 2023Harmonic Self-Conditioned Flow Matching for Multi-Ligand Docking and Binding Site Designcited by 30
  5. 2024Antigen-Specific Antibody Design via Direct Energy-based Preference Optimizationcited by 30
  6. 2024A Survey of Generative AI for de novo Drug Design: New Frontiers in Molecule and Protein Generationcited by 29
  7. 2024A Survey of Geometric Graph Neural Networks: Data Structures, Models and Applicationscited by 28
  8. 2024Deep Confident Steps to New Pockets: Strategies for Docking Generalizationcited by 27
  9. 2024FoldToken: Learning Protein Language via Vector Quantization and Beyondcited by 23
  10. 2024ProteinGPT: Multimodal LLM for Protein Property Prediction and Structure Understandingcited by 23
  11. 2022Reprogramming Pretrained Language Models for Antibody Sequence Infillingcited by 20
  12. 2023Curvature Filtrations for Graph Generative Model Evaluationcited by 19
  13. 2024ProtChatGPT: Towards Understanding Proteins with Large Language Modelscited by 18
  14. 2024Topological Neural Networks go Persistent, Equivariant, and Continuouscited by 17
  15. 2022AntBO: Towards Real-World Automated Antibody Design with Combinatorial Bayesian Optimisationcited by 16
  16. 2024Evaluating representation learning on the protein structure universecited by 16
  17. 2022Antibody-Antigen Docking and Design via Hierarchical Equivariant Refinementcited by 15
  18. 2023AbODE: Ab Initio Antibody Design using Conjoined ODEscited by 15
  19. 2023A Hierarchical Training Paradigm for Antibody Structure-sequence Co-designcited by 13
  20. 2024RNAFlow: RNA Structure & Sequence Design via Inverse Folding-Based Flow Matchingcited by 12
  21. 2025UniMoMo: Unified Generative Modeling of 3D Molecules for De Novo Binder Designcited by 12
  22. 2024Bayesian Optimization of Antibodies Informed by a Generative Model of Evolving Sequencescited by 11
  23. 2023DiffSDS: A language diffusion model for protein backbone inpainting under geometric conditions and constraintscited by 10
  24. 2025Large Language-Geometry Model: When LLM meets Equivariancecited by 10
  25. 2025dyAb: Flow Matching for Flexible Antibody Design with AlphaFold-driven Pre-binding Antigencited by 10
  26. 2021Benchmarking deep generative models for diverse antibody sequence designcited by 9
  27. 2024Retrieval Augmented Diffusion Model for Structure-informed Antibody Design and Optimizationcited by 9
  28. 2024Hotspot-Driven Peptide Design via Multi-Fragment Autoregressive Extensioncited by 9
  29. 2025Antibody Design and Optimization with Multi-scale Equivariant Graph Diffusion Models for Accurate Complex Antigen Bindingcited by 9
  30. 2023Cross-Gate MLP with Protein Complex Invariant Embedding is A One-Shot Antibody Designercited by 8
  31. 2024Relation-Aware Equivariant Graph Networks for Epitope-Unknown Antibody Design and Specificity Optimizationcited by 8
  32. 2024Pareto-Optimal Energy Alignment for Designing Nature-Like Antibodiescited by 7
  33. 2025Compositional Flows for 3D Molecule and Synthesis Pathway Co-designcited by 6
  34. 2024De novo antibody design with SE(3) diffusioncited by 5
  35. 2025TFG-Flow: Training-free Guidance in Multimodal Generative Flowcited by 5
  36. 2025Protein Design with Dynamic Protein Vocabularycited by 5
  37. 2024MAMMAL -- Molecular Aligned Multi-Modal Architecture and Languagecited by 4
  38. 2026CHIMERA-Bench: A Benchmark Dataset for Epitope-Specific Antibody Designcited by 4

50 most cited of 86 All 86 (JSON)