Molclaw Dleps

DLEPS Score Calculation

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 calculatedlepsscore.

Enter a list of candidate small molecules. Based on the input disease name, identify upregulated and downregulated genes associated with the disease state, and predict a reversal score for each small molecule. Generally, a score above 0.2 indicates effectiveness, with higher scores being better.
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"])
    disease_name (str): Supportes diseases, e.g., "Aging", "Gout", "Pulmonary fibrosis", "Non-alcoholic fatty liver disease", "Obesity" 
Return:
    status (str): success/error
    msg (str): message
    pred_scores (List[dict]): List of dict, each containing the keys 'smiles' and 'cs_score'. 
        --smiles (str): A SMILES string of smiles_list 
        --cs_score (float): Predicted reverse score

How to use tool calculatedlepsscore :

response = await client.session.call_tool(
    "calculate_dleps_score",
    arguments={
        "smiles_list": smiles_list,
        "disease_name": disease_name
    }
)
result = client.parse_result(response)
pred_scores = result["pred_scores"]

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: InternScience
• Source: InternScience/MolClaw
• License: MIT

Install and usage instructions live in the source repository linked above.