disease_compound_pipeline — Skill для разработчиков

--- name: disease_compound_pipeline description: "Disease-Specific Compound Screening - Screen compounds for disease: get DLEPS score for disease relevance, predict ADMET, and check drug-likeness. Use this skill for drug discovery tasks involving calculate dleps score pred molecule admet calculate mol drug chemistry get compound by name. Combines 4 tools from 3 SCP server(s)." --- # Disease-Specific Compound Screening **Discipline**: Drug Discovery | **Tools Used**: 4 | **Servers**: 3 ## Description Screen compounds for disease: get DLEPS score for disease relevance, predict ADMET, and check drug-likeness. ## Tools Used - **`calculate_dleps_score`** from `server-3` (streamable-http) - `https://scp.intern-ai.org.cn/api/v1/mcp/3/DrugSDA-Model` - **`pred_molecule_admet`** from `server-3` (streamable-http) - `https://scp.intern-ai.org.cn/api/v1/mcp/3/DrugSDA-Model` - **`calculate_mol_drug_chemistry`** from `server-2` (streamable-http) - `https://scp.intern-ai.org.cn/api/v1/mcp/2/DrugSDA-Tool` - **`get_compound_by_name`** from `pubchem-server` (streamable-http) - `https://scp.intern-ai.org.cn/api/v1/mcp/8/Origene-PubChem` ## Workflow 1. Calculate DLEPS disease relevance score 2. Predict ADMET properties 3. Evaluate drug-likeness 4. Get PubChem compound details ## Test Case ### Input ```json { "smiles": [ "CC(=O)Oc1ccccc1C(=O)O" ], "disease_name": "breast cancer" } ``` ### Expected Steps 1. Calculate DLEPS disease relevance score 2. Predict ADMET properties 3. Evaluate drug-likeness 4. Get PubChem compound details ## Usage Example > **Note:** Replace `sk-b04409a1-b32b-4511-9aeb-22980abdc05c` with your own SCP Hub API Key. You can obtain one from the [SCP Platform](https://scphub.intern-ai.org.cn). ```python import asyncio import json from contextlib import AsyncExitStack from mcp import ClientSession from mcp.client.streamable_http import streamablehttp_client from mcp.client.sse import sse_client SERVERS = { "server-3": "https://scp.intern-ai.org.cn/api/v1/mcp/3/DrugSDA-Model", "server-2": "https://scp.intern-ai.org.cn/api/v1/mcp/2/DrugSDA-Tool", "pubchem-server": "https://scp.intern-ai.org.cn/api/v1/mcp/8/Origene-PubChem" } async def connect(url, stack): transport = streamablehttp_client(url=url, headers={"SCP-HUB-API-KEY": "sk-b04409a1-b32b-4511-9aeb-22980abdc05c"}) read, write, _ = await stack.enter_async_context(transport) ctx = ClientSession(read, write) session = await stack.enter_async_context(ctx) await session.initialize() return session def parse(result): try: if hasattr(result, 'content') and result.content: c = result.content[0] if hasattr(c, 'text'): try: return json.loads(c.text) except: return c.text return str(result) except: return str(result) async def main(): async with AsyncExitStack() as stack: # Connect to required servers sessions = {} sessions["server-3"] = await connect("https://scp.intern-ai.org.cn/api/v1/mcp/3/DrugSDA-Model", stack) sessions["server-2"] = await connect("https://scp.intern-ai.org.cn/api/v1/mcp/2/DrugSDA-Tool", stack) sessions["pubchem-server"] = await connect("https://scp.intern-ai.org.cn/api/v1/mcp/8/Origene-PubChem", stack) # Execute workflow steps # Step 1: Calculate DLEPS disease relevance score result_1 = await sessions["server-3"].call_tool("calculate_dleps_score", arguments={}) data_1 = parse(result_1) print(f"Step 1 result: {json.dumps(data_1, indent=2, ensure_ascii=False)[:500]}") # Step 2: Predict ADMET properties result_2 = await sessions["server-3"].call_tool("pred_molecule_admet", arguments={}) data_2 = parse(result_2) print(f"Step 2 result: {json.dumps(data_2, indent=2, ensure_ascii=False)[:500]}") # Step 3: Evaluate drug-likeness result_3 = await sessions["server-2"].call_tool("calculate_mol_drug_chemistry", arguments={}) data_3 = parse(result_3) print(f"Step 3 result: {json.dumps(data_3, indent=2, ensure_ascii=False)[:500]}") # Step 4: Get PubChem compound details result_4 = await sessions["pubchem-server"].call_tool("get_compound_by_name", arguments={}) data_4 = parse(result_4) print(f"Step 4 result: {json.dumps(data_4, indent=2, ensure_ascii=False)[:500]}") # Cleanup print("Workflow complete!") if __name__ == "__main__": asyncio.run(main()) ```