protein-drug-interaction

name	protein_drug_interaction
description	Protein-Drug Interaction Profiling - Profile protein-drug interactions: protein properties, drug structure, binding affinity prediction, and interaction data. Use this skill for molecular pharmacology tasks involving calculate protein sequence properties ChemicalStructureAnalyzer boltz binding affinity PredictDrugTargetInteraction. Combines 4 tools from 4 SCP server(s).

Protein-Drug Interaction Profiling

Discipline: Molecular Pharmacology | Tools Used: 4 | Servers: 4

Description

Profile protein-drug interactions: protein properties, drug structure, binding affinity prediction, and interaction data.

Tools Used

• calculate_protein_sequence_properties from server-2 (streamable-http) - https://scp.intern-ai.org.cn/api/v1/mcp/2/DrugSDA-Tool
• ChemicalStructureAnalyzer from server-28 (sse) - https://scp.intern-ai.org.cn/api/v1/mcp/28/InternAgent
• boltz_binding_affinity from server-3 (streamable-http) - https://scp.intern-ai.org.cn/api/v1/mcp/3/DrugSDA-Model
• PredictDrugTargetInteraction from server-29 (sse) - https://scp.intern-ai.org.cn/api/v1/mcp/29/SciToolAgent-Bio

Workflow

1. Calculate protein properties
2. Analyze drug structure
3. Predict binding affinity
4. Predict drug-target interaction

Test Case

Input

{
    "sequence": "MKTIIALSYIFCLVFA",
    "drug": "caffeine"
}

Expected Steps

1. Calculate protein properties
2. Analyze drug structure
3. Predict binding affinity
4. Predict drug-target interaction

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 = {
    "server-2": "https://scp.intern-ai.org.cn/api/v1/mcp/2/DrugSDA-Tool",
    "server-28": "https://scp.intern-ai.org.cn/api/v1/mcp/28/InternAgent",
    "server-3": "https://scp.intern-ai.org.cn/api/v1/mcp/3/DrugSDA-Model",
    "server-29": "https://scp.intern-ai.org.cn/api/v1/mcp/29/SciToolAgent-Bio"
}

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:  c.text
         (result)
    :  (result)

  ():
      AsyncExitStack()  stack:
        
        sessions = {}
        sessions[] =  connect(, stack)
        sessions[] =  connect(, stack)
        sessions[] =  connect(, stack)
        sessions[] =  connect(, stack)

        
        
        result_1 =  sessions[].call_tool(, arguments={})
        data_1 = parse(result_1)
        ()

        
        result_2 =  sessions[].call_tool(, arguments={})
        data_2 = parse(result_2)
        ()

        
        result_3 =  sessions[].call_tool(, arguments={})
        data_3 = parse(result_3)
        ()

        
        result_4 =  sessions[].call_tool(, arguments={})
        data_4 = parse(result_4)
        ()

        
        ()

 __name__ == :
    asyncio.run(main())