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drug_repurposing_screen

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Drug Repurposing Screening - Screen existing drugs for new indications by querying FDA indications, ChEMBL mechanisms, and OpenTargets drug-disease associations. Use this skill for drug discovery tasks involving get indications by drug name get mechanism of action by drug name get drug by name get associated drugs by target name. Combines 4 tools from 3 SCP server(s).

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Drug Repurposing Screening

Discipline: Drug Discovery | Tools Used: 4 | Servers: 3

Description

Screen existing drugs for new indications by querying FDA indications, ChEMBL mechanisms, and OpenTargets drug-disease associations.

Tools Used

  • get_indications_by_drug_name from fda-drug-server (streamable-http) - https://scp.intern-ai.org.cn/api/v1/mcp/14/Origene-FDADrug
  • get_mechanism_of_action_by_drug_name from fda-drug-server (streamable-http) - https://scp.intern-ai.org.cn/api/v1/mcp/14/Origene-FDADrug
  • get_drug_by_name from chembl-server (streamable-http) - https://scp.intern-ai.org.cn/api/v1/mcp/4/Origene-ChEMBL
  • get_associated_drugs_by_target_name from opentargets-server (streamable-http) - https://scp.intern-ai.org.cn/api/v1/mcp/15/Origene-OpenTargets

Workflow

  1. Get current indications from FDA
  2. Get mechanism of action
  3. Get ChEMBL drug data
  4. Search OpenTargets for new target associations

Test Case

Input

{
    "drug_name": "metformin"
}

Expected Steps

  1. Get current indications from FDA
  2. Get mechanism of action
  3. Get ChEMBL drug data
  4. Search OpenTargets for new target associations

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.

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 = {
    "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",
    "opentargets-server": "https://scp.intern-ai.org.cn/api/v1/mcp/15/Origene-OpenTargets"
}

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["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)
        sessions["opentargets-server"] = await connect("https://scp.intern-ai.org.cn/api/v1/mcp/15/Origene-OpenTargets", stack)

        # Execute workflow steps
        # Step 1: Get current indications from FDA
        result_1 = await sessions["fda-drug-server"].call_tool("get_indications_by_drug_name", arguments={})
        data_1 = parse(result_1)
        print(f"Step 1 result: {json.dumps(data_1, indent=2, ensure_ascii=False)[:500]}")

        # Step 2: Get mechanism of action
        result_2 = await sessions["fda-drug-server"].call_tool("get_mechanism_of_action_by_drug_name", arguments={})
        data_2 = parse(result_2)
        print(f"Step 2 result: {json.dumps(data_2, indent=2, ensure_ascii=False)[:500]}")

        # Step 3: Get ChEMBL drug data
        result_3 = await sessions["chembl-server"].call_tool("get_drug_by_name", arguments={})
        data_3 = parse(result_3)
        print(f"Step 3 result: {json.dumps(data_3, indent=2, ensure_ascii=False)[:500]}")

        # Step 4: Search OpenTargets for new target associations
        result_4 = await sessions["opentargets-server"].call_tool("get_associated_drugs_by_target_name", 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())