Menu
Calculate disease reversal scores for the provided molecules relative to a specific disease.
Instalar com Codex ou Claude Copie este prompt, cole no Codex, Claude ou outro assistente e deixe que ele revise a página da skill e instale para você.
Baseado na classificação ocupacional SOC
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 entirely new drug-like molecules from scratch without any starting molecule, using REINVENT4's de novo prior.
| name | molclaw-dleps |
| description | Calculate disease reversal scores for the provided molecules relative to a specific disease. |
| license | MIT license |
| metadata | {"skill-author":"PJLab"} |
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"]