Diffusion-based generative model for antibody CDR design and antigen conditioning
DiffAb is an SE(3)-equivariant diffusion model that jointly generates the 3D structure and amino acid sequence of antibody CDR loops conditioned on target antigen epitopes.
Joint structure-sequence diffusion on antibody CDR-H3 and light chain loopsAntigen-conditioned generation with physical clash penaltiesBenchmark evaluations showing higher affinity and diversity than RosettaAntibodyOpen source codebase with PyTorch and PyG implementations
DiffAb is an SE(3)-equivariant diffusion model that jointly generates the 3D structure and amino acid sequence of antibody CDR loops conditioned on target antigen epitopes.
Information checked against an official source; not a hands-on test. Source · Last reviewed: 20/09/2026, 11:10:15 UTC
Key Features
Joint structure-sequence diffusion on antibody CDR-H3 and light chain loops
Antigen-conditioned generation with physical clash penalties
Benchmark evaluations showing higher affinity and diversity than RosettaAntibody
Open source codebase with PyTorch and PyG implementations
Interactive 3D Illustration
Biological Architecture (Stylized Ribbon)
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Illustrative Confidence Gradient:
>90 High / Low B 70-90 Confident 50-70 Low <50 Very low / High B
Academic Context & Research Evidence
Biological & Workflow Fit
Biological Application
Antibody CDR design, therapeutic optimization, and paratope engineering
Research Workflow
Target antigen PDB -> generate complementary CDR conformations and sequences
Use this citation format when referencing DiffAb in scientific publications and benchmark papers.
@software{diffab_2026,
title = {{DiffAb}},
author = {{Tsinghua University (AIR)}},
year = {2026},
url = {https://github.com/luost26/diffab},
note = {Indexed on aibioatlas - AI for Biology and Drug Discovery}
}
Peer-Reviewed Literature & Preprints
Live scientific citations queried from Europe PMC and PubMed for DiffAb.
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Technical / Product Information
Missing values mean the catalog has no recorded information. They do not mean a feature is absent.
Entry typeAI Model
Access modeOpen Source
AI roleEquivariant Generative Diffusion
Input dataNot recorded
Output dataNot recorded
Licence conditionsMIT License
Commercial eligibilityOpen source release
Compute requirements1x GPU (>= 16GB VRAM)
ValidationNot recorded
TypeEquivariant diffusion model
Intended useNot recorded
CompatibilityNot recorded
ManufacturerTsinghua University
Biological applicationAntibody CDR design, therapeutic optimization, and paratope engineering
Research workflowTarget antigen PDB -> generate complementary CDR conformations and sequences