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 in the binder to target interface. Higher is better. Aim above roughly 0.7 on a tractable target.

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