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.

BoltzGen (Wohlwend et al., MIT 2024). Boltz-2 binder design — jointly generates a binder backbone against a target, refolds each candidate end-to-end, and scores affinity via ipTM and pLDDT. Ships four design protocols (mini-protein, nanobody, antibody, peptide) and handles glycans, post-translational modifications, and non-canonical residues natively.

When it fits:

• You want one model that can target the same epitope with mini-proteins, nanobodies, antibodies, or peptides.
• Your target has glycans, PTMs, modified residues, or non-canonical chemistry.
• You want refolding RMSD as a self-consistency signal alongside ipTM and pLDDT.
• You need ~5 to 60 min per run and a budget-tunable number of candidates.

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.

15-60 min per run on a dedicated GPU. Billing is by the second of compute, so a faster preset costs less.

We keep a plain-English overview of BoltzGen on the main Ranomics site — what the model does under the hood, the kinds of targets it works on, and where it fits inside a full design-to-wet-lab campaign.

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

Run RFdiffusion online

Pick RFdiffusion when you want general de novo binder design scored by AF2 multimer (ipTM / pLDDT / i_pAE). For antibody and nanobody scaffolds use RFantibody, for AF2-IG initial-guess scoring use PXDesign, and for hallucination-driven binders without AF2 filtering use BindCraft.

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 BindCraft online

Pick BindCraft when you have a target PDB plus known hotspot residues and want de novo 60-150 aa protein binders.

Can I run BoltzGen online without setting up the model locally?

Yes. Ranomics Tools runs BoltzGen on a dedicated GPU through your browser. Upload a target PDB, pick a scaffold class (mini-binder, VHH, scFv, or peptide), and candidates come back with structure + affinity-like scores per hit.

Which modalities does BoltzGen design against the same target?

BoltzGen can design mini-proteins, nanobodies, antibodies (scFv-class), or peptides against the same target with glycan and PTM support. The form switches scaffold class per run, so you can A/B different modalities cheaply before committing to wet-lab.

How long does a BoltzGen pilot run take?

Pilot runs typically finish in 30 to 90 minutes on a dedicated A100, depending on target size and modality. Billing is by the second of GPU time.

Wohlwend et al., MIT (2024)