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Calculate disease reversal scores for the provided molecules relative to a specific disease.
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Predict the ADMET (absorption, distribution, metabolism, excretion, and toxicity) properties of the input molecules.
Predict binding affinity between target protein sequence and small molecule SMILES using Boltz-2.
Predict protein structures with Chai-1 from sequence or FASTA input and return model scoring summaries.
Chroma toolkit skill covering chroma_monomer for single-chain generation, chroma_complex for multi-chain assembly generation, and chroma_symmetry for symmetry-constrained protein design.
Retrieve SMILES strings from PubChem database using compound names.
Generate new molecules de novo.
Based on SOC occupation classification
| name | molclaw-dleps |
| description | Calculate disease reversal scores for the provided molecules relative to a specific disease. |
| license | MIT license |
| metadata | {"skill-author":"PJLab"} |
Note:
molclaw-file-transfer before execution.molclaw-pdbfixer before execution.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"]