comparative_drug_analysis: Скилл для сравнительного анализа лекарств

--- name: comparative_drug_analysis description: "Comparative Drug Analysis - Compare drugs: structure analysis, PubChem data, FDA safety, and ChEMBL bioactivity. Use this skill for comparative pharmacology tasks involving ChemicalStructureAnalyzer get compound by name get adverse reactions by drug name search activity. Combines 4 tools from 4 SCP server(s)." --- # Comparative Drug Analysis **Discipline**: Comparative Pharmacology | **Tools Used**: 4 | **Servers**: 4 ## Description Compare drugs: structure analysis, PubChem data, FDA safety, and ChEMBL bioactivity. ## Tools Used - **`ChemicalStructureAnalyzer`** from `server-28` (sse) - `https://scp.intern-ai.org.cn/api/v1/mcp/28/InternAgent` - **`get_compound_by_name`** from `pubchem-server` (streamable-http) - `https://scp.intern-ai.org.cn/api/v1/mcp/8/Origene-PubChem` - **`get_adverse_reactions_by_drug_name`** from `fda-drug-server` (streamable-http) - `https://scp.intern-ai.org.cn/api/v1/mcp/14/Origene-FDADrug` - **`search_activity`** from `chembl-server` (streamable-http) - `https://scp.intern-ai.org.cn/api/v1/mcp/4/Origene-ChEMBL` ## Workflow 1. Analyze structures of both drugs 2. Get PubChem data for both 3. Compare FDA safety profiles 4. Compare ChEMBL bioactivity ## Test Case ### Input ```json { "drug_a": "aspirin", "drug_b": "ibuprofen" } ``` ### Expected Steps 1. Analyze structures of both drugs 2. Get PubChem data for both 3. Compare FDA safety profiles 4. Compare ChEMBL bioactivity ## 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-28": "https://scp.intern-ai.org.cn/api/v1/mcp/28/InternAgent", "pubchem-server": "https://scp.intern-ai.org.cn/api/v1/mcp/8/Origene-PubChem", "fda-drug-server": "https://scp.intern-ai.org.cn/api/v1/mcp/14/Origene-FDADrug", "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-28"] = await connect("https://scp.intern-ai.org.cn/api/v1/mcp/28/InternAgent", stack) sessions["pubchem-server"] = await connect("https://scp.intern-ai.org.cn/api/v1/mcp/8/Origene-PubChem", stack) sessions["fda-drug-server"] = await connect("https://scp.intern-ai.org.cn/api/v1/mcp/14/Origene-FDADrug", stack) sessions["chembl-server"] = await connect("https://scp.intern-ai.org.cn/api/v1/mcp/4/Origene-ChEMBL", stack) # Execute workflow steps # Step 1: Analyze structures of both drugs result_1 = await sessions["server-28"].call_tool("ChemicalStructureAnalyzer", arguments={}) data_1 = parse(result_1) print(f"Step 1 result: {json.dumps(data_1, indent=2, ensure_ascii=False)[:500]}") # Step 2: Get PubChem data for both result_2 = await sessions["pubchem-server"].call_tool("get_compound_by_name", arguments={}) data_2 = parse(result_2) print(f"Step 2 result: {json.dumps(data_2, indent=2, ensure_ascii=False)[:500]}") # Step 3: Compare FDA safety profiles result_3 = await sessions["fda-drug-server"].call_tool("get_adverse_reactions_by_drug_name", arguments={}) data_3 = parse(result_3) print(f"Step 3 result: {json.dumps(data_3, indent=2, ensure_ascii=False)[:500]}") # Step 4: Compare ChEMBL bioactivity result_4 = await sessions["chembl-server"].call_tool("search_activity", 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()) ```