chemical-structure-comparison

# Chemical Structure Comparison **Discipline**: Cheminformatics | **Tools Used**: 4 | **Servers**: 4 ## Description Compare chemical structures: get SMILES, analyze structures, compute similarity, and check PubChem records. ## 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_smiles_similarity`** 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. Convert names to SMILES 2. Analyze both structures 3. Compute similarity 4. Get PubChem compound data ## Test Case ### Input ```json { "compound_a": "aspirin", "compound_b": "ibuprofen" } ``` ### Expected Steps 1. Convert names to SMILES 2. Analyze both structures 3. Compute similarity 4. Get PubChem compound data ## Usage Example > **Note:** Replace `<YOUR_SCP_HUB_API_KEY>` 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 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", "pubchem-server": "https://scp.intern-ai.org.cn/api/v1/mcp/8/Origene-PubChem" } async def connect(url, transport_type): transport = streamablehttp_client(url=url, headers={"SCP-HUB-API-KEY": "<YOUR_SCP_HUB_API_KEY>"}) read, write, _ = await transport.__aenter__() ctx = ClientSession(read, write) session = await ctx.__aenter__() await session.initialize() return session, ctx, transport 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(): # Connect to required servers sessions = {} sessions["server-31"], _, _ = await connect("https://scp.intern-ai.org.cn/api/v1/mcp/31/SciToolAgent-Chem", "sse") sessions["server-28"], _, _ = await connect("https://scp.intern-ai.org.cn/api/v1/mcp/28/InternAgent", "sse") sessions["server-2"], _, _ = await connect("https://scp.intern-ai.org.cn/api/v1/mcp/2/DrugSDA-Tool", "streamable-http") sessions["pubchem-server"], _, _ = await connect("https://scp.intern-ai.org.cn/api/v1/mcp/8/Origene-PubChem", "streamable-http") # Execute workflow steps # Step 1: Convert names 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 both structures 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: Compute similarity result_3 = await sessions["server-2"].call_tool("calculate_smiles_similarity", 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 data 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()) ```