comparative-drug-analysis

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

{
    "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 <YOUR_SCP_HUB_API_KEY> with your own SCP Hub API Key. You can obtain one from the SCP Platform.

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-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, 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-28"], _, _ = await connect("https://scp.intern-ai.org.cn/api/v1/mcp/28/InternAgent", "sse")
    sessions["pubchem-server"], _, _ = await connect("https://scp.intern-ai.org.cn/api/v1/mcp/8/Origene-PubChem", "streamable-http")
    sessions["fda-drug-server"], _, _ = await connect("https://scp.intern-ai.org.cn/api/v1/mcp/14/Origene-FDADrug", "streamable-http")
    sessions["chembl-server"], _, _ = await connect("https://scp.intern-ai.org.cn/api/v1/mcp/4/Origene-ChEMBL", "streamable-http")

    # 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())