---
title: Boltz-2 on the platform
description: Co-fold a multi-chain complex from sequence, with optional binding-affinity prediction.
tags:
  - boltz2
---

Boltz-2 co-folds a complex: you give it the chains — protein, DNA, RNA and small-molecule ligands as SMILES — and it predicts the assembled 3D structure in one pass, with per-chain and interface confidence scores. It needs no receptor structure, which is the difference from [DiffDock](/docs/diffdock), where you supply a receptor and dock a ligand into it.

Running `boltz2_predict` needs a signed-in account; it is not part of the guest tool set.

## What Boltz-2 does here

- **Predicts one complex from sequence.** 1 to 20 chain entries per job, each typed `protein`, `dna`, `rna` or `ligand`.
- **Scores the interfaces, not just the fold.** Per-chain pLDDT and pTM, per-chain interface pTM, and one `complex_iptm` for the whole assembly.
- **Optionally predicts binding affinity** for a complex that contains a ligand chain. The number is reported as log10(IC50) with IC50 in µM — lower is stronger. See [results](/docs/boltz2/results) for what that value is and is not.
- **Runs on GPU as an async job**, typically 5–30 minutes depending on chain count and length.

## When to use it

- You have **sequences and no trustworthy receptor structure** — an assembly with no deposited structure, or a target whose close homologs are well represented in the PDB. Accuracy degrades on targets with no close PDB relative, and the model is documented not to capture ligand-induced conformational change, so a mutant or induced-fit pocket is the weakest case rather than the strongest. See [translatability](/docs/boltz2/translatability).
- You want the **whole complex geometry**: protein–protein, antibody–antigen, protein–DNA/RNA, or protein–ligand.
- You want an **orthogonal check** on a docked pose: co-fold the same receptor sequence plus ligand and compare the pocket geometry with the [DiffDock](/docs/diffdock) pose.
- You want a **model-based affinity estimate** alongside the structure, knowing it is a prediction and not a measurement.

## When to use something else

| Goal | Tool |
|---|---|
| Fold one protein chain | [ESMFold](/docs/esmfold) — faster, single-chain |
| Dock a ligand into a receptor structure you already trust | [DiffDock](/docs/diffdock) |
| Rescore a pose with physics | [MM-GBSA](/docs/mmgbsa) |
| A second co-folding model for the same protein complex | [Chai-1](/docs/chai) |

## Limits to know before you run

- **Chain count:** at most 20 chain entries per job. `chain_id` is a single character.
- **MSAs are off by default.** Without `use_msa_server`, protein chains run in single-sequence mode (`msa: empty`), and the result flags this as `single_sequence_mode`. Upstream describes predictions in this mode as suboptimal. Turning the server on sends your sequences to the public ColabFold MMseqs2 service and can add 30 seconds to 10 minutes.
- **Affinity needs a ligand chain and more than one chain.** With a single chain, or with no `ligand` entry, no affinity is produced.
- **Affinity has a ligand-size ceiling of 128 heavy atoms.** Above it, the platform submits the job with affinity turned off and tells you so, so the structure still comes back instead of the whole run failing.
- **Affinity is a predicted log10(IC50 [µM])**, not an assay readout. Compare it with measured IC50/Kd on your own series before you lean on it. Published testing finds it useful for ranking *within* one congeneric series and unreliable *across* series — see [translatability](/docs/boltz2/translatability).
- **One structure per job, with no steering potentials.** The platform requests a single diffusion sample and does not enable Boltz's physics-based steering, templates, pocket constraints or experimental-method conditioning. Published accuracy figures were generally obtained with MSAs on and many samples per system, so they describe a richer configuration than this one.

## Citation

> Passaro S, Corso G, Wohlwend J, et al. Boltz-2: Towards Accurate and Efficient Binding Affinity Prediction. _bioRxiv_ (2025). doi: [10.1101/2025.06.14.659707](https://doi.org/10.1101/2025.06.14.659707)

Source code: [jwohlwend/boltz](https://github.com/jwohlwend/boltz).

## Read next