Chai-1 Protein Structure Prediction

SkillAI & models

Predict protein structures with Chai-1 from sequence or FASTA input and return model scoring summaries.

Available today. Use it from your connected AI after setup.

Connect ahel once, and every AI you use reads what you have installed.

Then ask your AI: use the Chai-1 Protein Structure Prediction skill

What this skill tells your AI

The instructions your AI receives, as published by internscience/molclaw in skills/L1_tools/molclaw-chai1-predict/SKILL.md and read by ahel’s review.

Note:

  • Local files are not directly accessible by the server. Please upload them to the server using molclaw-file-transfer before execution.
  • For PDB file inputs, it is recommended to preprocess them using molclaw-pdbfixer before execution.
  • Please refer to skill molclaw-scp-server to complete tool invocation.

Usage

1. Chai-1 Prediction (Sequence/FASTA)

The description of tool chai1_predict.

Predict protein structures with Chai-1 from sequence or FASTA input, run inference (unless dry-run), and return per-model scoring summaries for downstream selection.
Args:
    mode (str): One of 'sequence', 'fasta', or 'info'; API also accepts 'predict' as an alias of 'sequence'.
    seq (str|None): Comma-separated protein sequence(s) for sequence mode, e.g., "MKFL...,AIQR...".
    name (str|None): Comma-separated chain names corresponding to `seq`; defaults to chain_1, chain_2, ... if omitted.
    fasta_path (str|None): Path to an input FASTA file for fasta mode.
    samples (int): Number of models/samples to generate, must be >= 1. Default: 5.
    dry_run (bool): If True, only prepare inputs and write `input.fasta` without running Chai-1 inference.
Return:
    status (str): 'success' or 'error'.
    msg (str): Human-readable summary or error message.
    output_dir (str|None): Run artifact directory path.
    model_scores (List[dict]|None): Per-model summaries with keys 'model_idx', 'cif_path', 'scores', and 'score_path'.
    best_model (dict|None): Top model summary with keys 'model_idx', 'aggregate_score', and 'cif_path'.

How to use tool chai1_predict :

response = await client.session.call_tool(
    "chai1_predict",
    arguments={
        "mode": "sequence",
        "seq": "MKFLILLFNILCLFPVLAADNHGVS",
        "name": "my_protein",
        "samples": 5,
        "dry_run": True
    }
)
result = client.parse_result(response)
best_model = result["best_model"]

Example parameter sets
# 1) Sequence mode (README/tool_factory validated; main mode)
{
    "mode": "predict",  # alias of sequence
    "seq": "MKFLILLFNILCLFPVLAADNHGVS",
    "name": "my_protein",
    "dry_run": True
}

# 2) FASTA mode (wrapper/API supported variant mode)
{
    "mode": "fasta",
    "fasta_path": "/abs/path/input.fasta",
    "samples": 5,
    "dry_run": True
}

# 3) Info mode (source code run_chai1 behavior)
{
    "mode": "info"
}

Signals

GitHub stars
33
Forks
3
Last commit
Aug 2026
Advanced
Catalog kind
skill
Gateway key
molclaw-chai1-predict
Source
github.com/internscience/molclaw