---
title: Chai-1 on the platform
description: Co-fold a multi-chain protein complex from sequence with interface confidence scores.
tags:
  - chai
---

Chai-1 co-folds a complex from sequence: you give it the chains and it predicts the assembled structure in one pass, ranked by its own confidence scores. On the platform it serves as a second co-folding model alongside [Boltz-2](/docs/boltz2), built on a different architecture — though two co-folding models agreeing is weaker evidence than it sounds, because their errors are correlated. Read [how Chai-1 results compare to the bench](/docs/chai/translatability) before you act on a predicted interface.

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

## What Chai-1 does here

- **Predicts one complex from protein sequences** and returns the top-ranked candidate.
- **Scores the whole complex and its interface:** `ptm` for the complex, `iptm` for the interface, an `aggregate_score` used for ranking, and a mean pLDDT per chain.
- **Runs on GPU as an async job**, typically 5–30 minutes.
- **Fails loudly rather than returning a hollow result.** If scoring succeeds but no structure was written, the job fails instead of shipping confidence numbers next to an empty structure.

## When to use it

- You want a **second opinion on a predicted complex**. Co-fold the same chains with Chai-1 and [Boltz-2](/docs/boltz2). Treat a disagreement as a warning; treat an agreement as weak support, not confirmation — published pose errors correlate strongly between co-folding methods and much less between a co-folding method and a physics-based one.
- You are working on a **protein–protein assembly** and have sequences rather than a structure, and the chains have close structural relatives in the PDB.
- You want an **interface hypothesis to guide an experiment** — mutagenesis, epitope mapping, or a crystallography or cryo-EM request.

For an **antibody–antigen or nanobody–antigen** complex, read [translatability](/docs/chai/translatability) first. That is the hardest published case for every co-folding model, and the configuration here — no MSAs, a fixed seed, one returned structure — has none of the mitigations the literature uses.

## When to use something else

| Goal | Tool |
|---|---|
| Fold one protein chain | [ESMFold](/docs/esmfold) — faster, single-chain |
| A complex containing DNA or RNA | [Boltz-2](/docs/boltz2) — see the limit below |
| A predicted binding affinity alongside the structure | [Boltz-2](/docs/boltz2) |
| Dock a ligand into a receptor structure you already trust | [DiffDock](/docs/diffdock) |

## Limits to know before you run

- **Protein chains and one ligand.** A chain entry typed `protein` is folded as a protein chain and an entry typed `ligand` as a small molecule from its SMILES, at most one per job. An entry typed `dna`, `rna` or `glycan` is refused rather than folded as a protein — use [Boltz-2](/docs/boltz2) for a complex that contains DNA or RNA.
- **Sampling is yours; the output format is not.** `num_trunk_recycles` (1–20, default 3) and `num_diffusion_steps` (10–500, default 200) are applied and reported back, and both scale what the run costs. The seed is fixed. `output_format` is **not** applied: the structure always comes back as mmCIF — see [inputs](/docs/chai/inputs).
- **No MSAs, no templates, no restraints.** The run uses ESM embeddings only. There is no way to supply a multiple sequence alignment, a structural template, or an epitope or distance restraint — all three are documented levers on Chai-1's accuracy, and none is reachable here. See [translatability](/docs/chai/translatability).
- **One structure per job.** The top-ranked candidate comes back, not a pose ensemble.
- **A confidence score is not an affinity.** Chai-1 returns no binding-energy or affinity value at all.

## Citation

> Chai Discovery. Chai-1: Decoding the molecular interactions of life. _bioRxiv_ (2024). doi: [10.1101/2024.10.10.615955](https://doi.org/10.1101/2024.10.10.615955)

Source code: [chaidiscovery/chai-lab](https://github.com/chaidiscovery/chai-lab).

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