combinatorial_chemistry: Скилл для дизайна и анализа библиотек

--- name: combinatorial_chemistry description: "Combinatorial Chemistry Library Design - Design combinatorial library: validate core SMILES, generate variants, compute properties, and predict ADMET for library. Use this skill for combinatorial chemistry tasks involving is valid smiles calculate mol basic info calculate mol drug chemistry pred molecule admet. Combines 4 tools from 2 SCP server(s)." --- # 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 `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-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, 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-2"] = await connect("https://scp.intern-ai.org.cn/api/v1/mcp/2/DrugSDA-Tool", stack) sessions["server-3"] = await connect("https://scp.intern-ai.org.cn/api/v1/mcp/3/DrugSDA-Model", stack) # 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()) ```