ADMET Properties Prediction

SkillDev tools

Predict the ADMET (absorption, distribution, metabolism, excretion, and toxicity) properties of the input molecules.

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 ADMET Properties Prediction skill

What this skill tells your AI

The instructions your AI receives, as published by internscience/molclaw in skills/L1_tools/molclaw-admet/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.

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]): Required list of input SMILES strings; pass [] when using smiles_file
    smiles_file (str): Required path to a TXT/CSV SMILES file; pass '' when using smiles_list
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"]

Signals

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