Antibody Design Comparison
DiffAb vs RFantibody vs AntiFold: Antibody Design Compared (2026)
Last updated: 2026-04-16
AI antibody design reached a milestone in 2025 when RFantibody demonstrated atomically accurate de novo antibody design confirmed by cryo-EM (Nature 2025). Together with DiffAb (the first antibody-specific diffusion model) and AntiFold (antibody inverse folding), these tools cover the full spectrum from de novo generation to sequence optimization. They serve different purposes in an antibody engineering pipeline.
DiffAb
Luo et al. (NeurIPS 2022)
RFantibody
Baker Lab / IPD (University of Washington)
AntiFold
OPIG (Oxford)
Head-to-Head
Structured comparison across key dimensions.
| Dimension | DiffAb | RFantibody | AntiFold |
|---|---|---|---|
| Design approach | Diffusion — joint CDR sequence + structure co-generation | Diffusion — full antibody backbone generation (fine-tuned RFdiffusion) | Inverse folding — redesign sequence for a given antibody backbone |
| What it generates | CDR loops (sequence + 3D structure) conditioned on antigen | Complete antibody/nanobody/scFv structures + sequences (via ProteinMPNN) | Optimized CDR sequences for an existing antibody backbone |
| Input required | Antigen 3D structure | Target protein structure + epitope residues | Antibody backbone structure (PDB) |
| De novo vs optimization | De novo CDR generation | De novo full antibody generation | Optimization of existing antibody |
| Experimental validation | Limited wet-lab validation published | Cryo-EM confirmed atomic accuracy (Nature 2025); validated on influenza HA and C. difficile TcdB | In silico validation; broader wet-lab validation accumulating |
| Formats supported | CDR loops only (not full antibody backbone) | VHH (nanobody), scFv, and full IgG-like | Any antibody/nanobody backbone (structure-conditioned) |
| License | Open source | Open source (Baker Lab) | BSD 3-Clause |
| On Platform | Yes | Yes | Yes |
| Key limitation | CDR-H3 amino acid recovery lags newer methods; no full backbone generation | Requires screening 100s-1000s of designs; computationally intensive | Backbone must be provided; CDR-H3 diversity limited; not de novo |
When to Use Each
DiffAb
You have an antigen structure and want to generate diverse CDR sequences + structures from scratch. You're exploring the sequence space for a known target. This is your starting point for de novo CDR design.
RFantibody
You want to design entirely new antibodies or nanobodies against a specific epitope. You need the full antibody backbone, not just CDR loops. You want the most experimentally validated generative approach.
AntiFold
You already have an antibody and want to optimize it. You want to redesign CDR sequences while maintaining the overall fold. You're doing affinity maturation or humanization in silico.
Practitioner Verdict
Use RFantibody for de novo antibody/nanobody generation against a defined epitope — it's the most experimentally validated generative antibody design tool. Use DiffAb for CDR sequence+structure co-design when you have an antigen structure and want diverse CDR candidates. Use AntiFold for optimizing existing antibodies — it's the best tool for CDR sequence refinement on a known scaffold. All three are available on the platform.
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