Chain entries are chain_id, sequence_type and sequence; protein only. Three sampling parameters are not read — the run is fixed at 3 recycles, 200 steps.

A Chai-1 job is a list of chain entries. There is no receptor-structure input and no pocket definition: the chains go in as sequences and the model folds them together.

Chain entries

sequences is required. Each entry carries three fields.

chain_idstringrequired

Chain identifier, for example A, B, L.

sequence_type"protein" | "dna" | "rna" | "ligand"required

The declared entity type.

sequencestringrequired

The sequence for that chain.

sequence_type decides how the entry is folded. An entry typed protein, or one that declares no type, becomes a protein record, in the order supplied, with chain IDs assigned A, B, C… An entry typed ligand is folded as a small molecule from its SMILES, and a job carries at most one of those. An entry typed dna, rna or glycan is refused rather than folded: there is no record for those entities here, and folding one as a protein chain would return a scored structure of a different molecule. Use Boltz-2 for a complex containing DNA or RNA.

A job with nothing to fold is rejected at parameter-parse time rather than returning a confidently scored structure of some default — one chain entry is the minimum.

Sampling parameters

num_trunk_recyclesintegerdefault: 3

Range 1–20. More recycles generally sharpen the prediction and cost proportionally more GPU time.

num_diffusion_stepsintegerdefault: 200

Range 10–500. Fewer steps trade accuracy for speed; more steps cost proportionally more GPU time.

output_format"pdb" | "mmcif"default: pdb

Not applied: the structure always comes back as mmCIF text, whichever value you send.

Both sampling parameters are applied, and the result reports the values the run used alongside the scores, so two runs of the same complex can be compared. They also both scale runtime: a request near the top of both ranges on a large complex can exhaust the job's time budget and fail terminally rather than returning a slower answer. The run also uses a fixed seed and ESM embeddings, and folds in single-sequence mode — no MSA and no templates, so nothing leaves the platform.

Example chain list

{
  "sequences": [
    { "chain_id": "A", "sequence_type": "protein", "sequence": "MKWVTFISLLLLFSSAYSRGVFRR" },
    { "chain_id": "B", "sequence_type": "protein", "sequence": "GAMGSEFDLMRYWLQAGLSGIHTFCRGKPAGKYV" }
  ]
}

A protein–ligand complex adds one entry typed ligand, whose sequence is the SMILES:

{
  "sequences": [
    { "chain_id": "A", "sequence_type": "protein", "sequence": "MKWVTFISLLLLFSSAYSRGVFRR" },
    { "chain_id": "B", "sequence_type": "ligand", "sequence": "CC(=O)Oc1ccccc1C(=O)O" }
  ]
}

Chai-1 inputs and parameters

Chain entries

Sampling parameters

Example chain list

Read next

Chai-1 results

Chai-1 overview

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