Run IgGM online

Pick IgGM to design or humanize an antibody / nanobody against your antigen, or to predict the antibody-antigen complex, all in one model. For VHH backbones use RFantibody; for paired scFv CDRs use ESMFold2 design; to validate a designed binder's fold use Boltz-2.

IgGM (Wang et al., ICLR 2025). A generative diffusion foundation model for antibody and nanobody engineering. One model covers antibody-antigen complex structure prediction, CDR design, framework redesign / humanization, affinity maturation, and inverse (sequence-from-structure) design, all epitope-guided against the antigen you upload.

When it fits:

• You have an antibody or nanobody sequence and want to redesign its CDRs (or framework) against a specific antigen and epitope.
• You want to humanize a framework or mature affinity from a wild-type reference.
• You want a fast antibody-antigen complex structure prediction before committing to a wet-lab campaign.

Use the score legend below to read results. Each tool reports a subset of these depending on whether it does design, sequence recovery, or structure prediction.

ipTM

Predicted confidence at an interface. Higher is better; aim above roughly 0.7 on a tractable target. On a multi-chain target it may cover the target's own chain–chain interface as well as the binder's.

pLDDT

Per-residue confidence in the predicted fold. Higher means the model is more sure of that part of the structure.

i_pAE and pAE

Predicted alignment error, at the interface (i_pAE) or across the whole structure (pAE). Lower is better.

ProteinMPNN recovery

Fraction of native residues recovered when ProteinMPNN redesigns a known sequence on its native backbone. Higher is better; well calibrated above roughly 0.4 on diverse folds.

~2 min to scales with samples x masked positions min per run on a dedicated GPU. You pay only for the compute a job delivers, drawn from your wallet balance.

If you are picking between IgGM and a sibling algorithm, these run on the same hub against the same target.

Run RFantibody online

Pick RFantibody when you need a VHH (nanobody) scaffold against a target PDB. For de novo non-antibody binders, use BindCraft. For designs involving modified residues or glycans, use BoltzGen.

Run BoltzGen online

Pick BoltzGen when you want one model that can design mini-proteins, nanobodies, antibodies, or peptides against the same target, or when your target involves glycans, post-translational modifications, or non-canonical residues.

Run Boltz-2 online

Pick Boltz-2 to validate a designed binder against your antigen. Single-sequence cofold with interface confidence (ipTM), antibody-trained and orthogonal to AF2-multimer. For sequence design, use ProteinMPNN; for de novo backbones, use RFantibody, BindCraft, or BoltzGen first.

Wang et al., ICLR 2025