combinatorial-chemistry

# Combinatorial Chemistry Library Design **Discipline**: Combinatorial Chemistry | **Tools Used**: 4 | **Servers**: 2 ## Description Design combinatorial library: validate core SMILES, generate variants, compute properties, and predict ADMET for library. ## Tools Used - **`is_valid_smiles`** from `server-2` (streamable-http) - `https://scp.intern-ai.org.cn/api/v1/mcp/2/DrugSDA-Tool` - **`calculate_mol_basic_info`** from `server-2` (streamable-http) - `https://scp.intern-ai.org.cn/api/v1/mcp/2/DrugSDA-Tool` - **`calculate_mol_drug_chemistry`** from `server-2` (streamable-http) - `https://scp.intern-ai.org.cn/api/v1/mcp/2/DrugSDA-Tool` - **`pred_molecule_admet`** from `server-3` (streamable-http) - `https://scp.intern-ai.org.cn/api/v1/mcp/3/DrugSDA-Model` ## Workflow 1. Validate all SMILES 2. Calculate properties for library 3. Evaluate drug-likeness 4. Predict ADMET for top candidates ## Test Case ### Input ```json { "core_smiles": "c1ccc(N)cc1", "variants": [ "c1ccc(NC(=O)C)cc1", "c1ccc(NC(=O)CC)cc1" ] } ``` ### Expected Steps 1. Validate all SMILES 2. Calculate properties for library 3. Evaluate drug-likeness 4. Predict ADMET for top candidates ## 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-2": "https://scp.intern-ai.org.cn/api/v1/mcp/2/DrugSDA-Tool", "server-3": "https://scp.intern-ai.org.cn/api/v1/mcp/3/DrugSDA-Model" } 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-2"], _, _ = await connect("https://scp.intern-ai.org.cn/api/v1/mcp/2/DrugSDA-Tool", "streamable-http") sessions["server-3"], _, _ = await connect("https://scp.intern-ai.org.cn/api/v1/mcp/3/DrugSDA-Model", "streamable-http") # Execute workflow steps # Step 1: Validate all SMILES result_1 = await sessions["server-2"].call_tool("is_valid_smiles", arguments={}) data_1 = parse(result_1) print(f"Step 1 result: {json.dumps(data_1, indent=2, ensure_ascii=False)[:500]}") # Step 2: Calculate properties for library result_2 = await sessions["server-2"].call_tool("calculate_mol_basic_info", 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: Predict ADMET for top candidates result_4 = await sessions["server-3"].call_tool("pred_molecule_admet", 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()) ```