chemical_property_profiling: Скилл для анализа химических свойств

--- name: chemical_property_profiling description: "Chemical Property Profiling - Profile chemical properties: basic info, hydrophobicity, H-bonds, charges, and molecular complexity. Use this skill for physical chemistry tasks involving calculate mol basic info calculate mol hydrophobicity calculate mol hbond calculate mol charge calculate mol complexity. Combines 5 tools from 1 SCP server(s)." --- # Chemical Property Profiling **Discipline**: Physical Chemistry | **Tools Used**: 5 | **Servers**: 1 ## Description Profile chemical properties: basic info, hydrophobicity, H-bonds, charges, and molecular complexity. ## Tools Used - **`calculate_mol_basic_info`** from `server-2` (streamable-http) - `https://scp.intern-ai.org.cn/api/v1/mcp/2/DrugSDA-Tool` - **`calculate_mol_hydrophobicity`** from `server-2` (streamable-http) - `https://scp.intern-ai.org.cn/api/v1/mcp/2/DrugSDA-Tool` - **`calculate_mol_hbond`** from `server-2` (streamable-http) - `https://scp.intern-ai.org.cn/api/v1/mcp/2/DrugSDA-Tool` - **`calculate_mol_charge`** from `server-2` (streamable-http) - `https://scp.intern-ai.org.cn/api/v1/mcp/2/DrugSDA-Tool` - **`calculate_mol_complexity`** from `server-2` (streamable-http) - `https://scp.intern-ai.org.cn/api/v1/mcp/2/DrugSDA-Tool` ## Workflow 1. Calculate basic molecular info 2. Compute hydrophobicity descriptors 3. Analyze H-bond properties 4. Calculate partial charges 5. Compute molecular complexity ## Test Case ### Input ```json { "smiles": "CC(=O)Oc1ccccc1C(=O)O" } ``` ### Expected Steps 1. Calculate basic molecular info 2. Compute hydrophobicity descriptors 3. Analyze H-bond properties 4. Calculate partial charges 5. Compute molecular complexity ## 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" } 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) # Execute workflow steps # Step 1: Calculate basic molecular info result_1 = await sessions["server-2"].call_tool("calculate_mol_basic_info", arguments={}) data_1 = parse(result_1) print(f"Step 1 result: {json.dumps(data_1, indent=2, ensure_ascii=False)[:500]}") # Step 2: Compute hydrophobicity descriptors result_2 = await sessions["server-2"].call_tool("calculate_mol_hydrophobicity", arguments={}) data_2 = parse(result_2) print(f"Step 2 result: {json.dumps(data_2, indent=2, ensure_ascii=False)[:500]}") # Step 3: Analyze H-bond properties result_3 = await sessions["server-2"].call_tool("calculate_mol_hbond", arguments={}) data_3 = parse(result_3) print(f"Step 3 result: {json.dumps(data_3, indent=2, ensure_ascii=False)[:500]}") # Step 4: Calculate partial charges result_4 = await sessions["server-2"].call_tool("calculate_mol_charge", arguments={}) data_4 = parse(result_4) print(f"Step 4 result: {json.dumps(data_4, indent=2, ensure_ascii=False)[:500]}") # Step 5: Compute molecular complexity result_5 = await sessions["server-2"].call_tool("calculate_mol_complexity", arguments={}) data_5 = parse(result_5) print(f"Step 5 result: {json.dumps(data_5, indent=2, ensure_ascii=False)[:500]}") # Cleanup print("Workflow complete!") if __name__ == "__main__": asyncio.run(main()) ```