--- name: drugsda-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 ## Usage ### 1. MCP Server Definition ```python import json from mcp.client.streamable_http import streamablehttp_client from mcp import ClientSession class DrugSDAClient: def __init__(self, server_url: str): self.server_url = server_url self.session = None async def connect(self): print(f"server url: {self.server_url}") try: self.transport = streamablehttp_client( url=self.server_url, headers={"SCP-HUB-API-KEY": "sk-a0033dde-b3cd-413b-adbe-980bc78d6126"} ) self.read, self.write, self.get_session_id = await self.transport.__aenter__() self.session_ctx = ClientSession(self.read, self.write) self.session = await self.session_ctx.__aenter__() await self.session.initialize() session_id = self.get_session_id() print(f"✓ connect success") return True except Exception as e: print(f"✗ connect failure: {e}") import traceback traceback.print_exc() return False async def disconnect(self): try: if self.session: await self.session_ctx.__aexit__(None, None, None) if hasattr(self, 'transport'): await self.transport.__aexit__(None, None, None) print("✓ already disconnect") except Exception as e: print(f"✗ disconnect error: {e}") def parse_result(self, result): try: if hasattr(result, 'content') and result.content: content = result.content[0] if hasattr(content, 'text'): return json.loads(content.text) return str(result) except Exception as e: return {"error": f"parse error: {e}", "raw": str(result)} ``` ### 2. Protein Sequence Valid Check The description of tool *calculate_dleps_score*. ```tex 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* : ```python client = DrugSDAClient("https://scp.intern-ai.org.cn/api/v1/mcp/2/DrugSDA-Tool") if not await client.connect(): print("connection failed") return 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"] await client.disconnect() ```