compound_to_drug_pipeline: Скилл анализа и разработки лекарств

--- name: compound_to_drug_pipeline description: "Compound-to-Drug Analysis Pipeline - Full compound-to-drug pipeline: name-to-SMILES conversion, structure analysis, drug-likeness, and FDA drug lookup. Use this skill for drug development tasks involving NameToSMILES ChemicalStructureAnalyzer calculate mol drug chemistry get drug by name. Combines 4 tools from 4 SCP server(s)." --- # Compound-to-Drug Analysis Pipeline **Discipline**: Drug Development | **Tools Used**: 4 | **Servers**: 4 ## Description Full compound-to-drug pipeline: name-to-SMILES conversion, structure analysis, drug-likeness, and FDA drug lookup. ## Tools Used - **`NameToSMILES`** from `server-31` (sse) - `https://scp.intern-ai.org.cn/api/v1/mcp/31/SciToolAgent-Chem` - **`ChemicalStructureAnalyzer`** from `server-28` (sse) - `https://scp.intern-ai.org.cn/api/v1/mcp/28/InternAgent` - **`calculate_mol_drug_chemistry`** from `server-2` (streamable-http) - `https://scp.intern-ai.org.cn/api/v1/mcp/2/DrugSDA-Tool` - **`get_drug_by_name`** from `chembl-server` (streamable-http) - `https://scp.intern-ai.org.cn/api/v1/mcp/4/Origene-ChEMBL` ## Workflow 1. Convert name to SMILES 2. Analyze chemical structure 3. Calculate drug-likeness 4. Search in ChEMBL drug database ## Test Case ### Input ```json { "compound_name": "caffeine" } ``` ### Expected Steps 1. Convert name to SMILES 2. Analyze chemical structure 3. Calculate drug-likeness 4. Search in ChEMBL drug database ## 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-31": "https://scp.intern-ai.org.cn/api/v1/mcp/31/SciToolAgent-Chem", "server-28": "https://scp.intern-ai.org.cn/api/v1/mcp/28/InternAgent", "server-2": "https://scp.intern-ai.org.cn/api/v1/mcp/2/DrugSDA-Tool", "chembl-server": "https://scp.intern-ai.org.cn/api/v1/mcp/4/Origene-ChEMBL" } 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-31"] = await connect("https://scp.intern-ai.org.cn/api/v1/mcp/31/SciToolAgent-Chem", stack) sessions["server-28"] = await connect("https://scp.intern-ai.org.cn/api/v1/mcp/28/InternAgent", stack) sessions["server-2"] = await connect("https://scp.intern-ai.org.cn/api/v1/mcp/2/DrugSDA-Tool", stack) sessions["chembl-server"] = await connect("https://scp.intern-ai.org.cn/api/v1/mcp/4/Origene-ChEMBL", stack) # Execute workflow steps # Step 1: Convert name to SMILES result_1 = await sessions["server-31"].call_tool("NameToSMILES", arguments={}) data_1 = parse(result_1) print(f"Step 1 result: {json.dumps(data_1, indent=2, ensure_ascii=False)[:500]}") # Step 2: Analyze chemical structure result_2 = await sessions["server-28"].call_tool("ChemicalStructureAnalyzer", arguments={}) data_2 = parse(result_2) print(f"Step 2 result: {json.dumps(data_2, indent=2, ensure_ascii=False)[:500]}") # Step 3: Calculate 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: Search in ChEMBL drug database result_4 = await sessions["chembl-server"].call_tool("get_drug_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()) ```