| name | molclaw-admet |
| description | Predict the ADMET (absorption, distribution, metabolism, excretion, and toxicity) properties of the input molecules. |
| license | MIT license |
| metadata | {"skill-author":"PJLab"} |
ADMET Properties Prediction
The description of tool pred_mol_admet.
Predict the ADMET (absorption, distribution, metabolism, excretion, and toxicity) properties of the input molecules from smiles list or file.
Args:
smiles_list (List[str]): List of input SMILES strings, (e.g., ["N[C@@H](Cc1ccc(O)cc1)C(=O)O", "CC(C)C1=CC=CC=C1"]), default is []
smiles_file (str): Path to a file containing SMILES strings (TXT or CSV format), default is ''
Return:
status (str): success/error
msg (str): message
json_content (List[Dcit]): List of dict, each containing the keys 'smiles', 'physicochemical', 'druglikeness' and 'admet_predictions', where 'admet_predictions' includes over 90 key-value pairs representing various molecular properties
json_file (str): Path to the json file saving the ADMET prediction results
How to use tool pred_mol_admet :
response = await client.session.call_tool(
"pred_mol_admet",
arguments={
"smiles_list": smiles_list,
"smiles_file": ''
}
)
result = client.parse_result(response)
admet_predictions = result["json_content"]
⚠ Computation-First Declaration (L3 Principles 10, 13)
ADMET predictions from pred_mol_admet are Level 1 direct tool computations — the highest authority for ADMET data. When reporting these values:
- Label as: "ADMET-AI predicted CYP3A4 inhibition probability: 0.72 (statistical prediction)" — Category 1 tool-computed fact.
- NEVER say: "This molecule inhibits CYP3A4" — ADMET-AI provides probabilities, not binary facts.
- NEVER substitute with literature IC50/EC50 values unless explicitly comparing computational vs. experimental. If literature values are cited, label them: "⚠️ LITERATURE VALUE: ..."
- All probabilities must be in [0, 1]. Values outside this range indicate tool error — re-run, do not accept.
