| name | molclaw-dleps |
| description | Calculate disease reversal scores for the provided molecules relative to a specific disease. |
| license | MIT license |
| metadata | {"skill-author":"PJLab"} |
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 calculate_dleps_score.
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 calculate_dleps_score :
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"]
