Chai-1 Protein Structure Prediction
SkillAI & modelsPredict 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.
No other account needed.
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-transferbefore execution. - For PDB file inputs, it is recommended to preprocess them using
molclaw-pdbfixerbefore execution. - Please refer to skill
molclaw-scp-serverto 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