disease-compound-pipeline

name	disease_compound_pipeline
description	Disease-Specific Compound Screening - Screen compounds for disease: get DLEPS score for disease relevance, predict ADMET, and check drug-likeness. Use this skill for drug discovery tasks involving calculate dleps score pred molecule admet calculate mol drug chemistry get compound by name. Combines 4 tools from 3 SCP server(s).

Disease-Specific Compound Screening

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

Description

Screen compounds for disease: get DLEPS score for disease relevance, predict ADMET, and check drug-likeness.

Tools Used

• calculate_dleps_score from server-3 (streamable-http) - https://scp.intern-ai.org.cn/api/v1/mcp/3/DrugSDA-Model
• pred_molecule_admet from server-3 (streamable-http) - https://scp.intern-ai.org.cn/api/v1/mcp/3/DrugSDA-Model
• calculate_mol_drug_chemistry from server-2 (streamable-http) - https://scp.intern-ai.org.cn/api/v1/mcp/2/DrugSDA-Tool
• get_compound_by_name from pubchem-server (streamable-http) - https://scp.intern-ai.org.cn/api/v1/mcp/8/Origene-PubChem

Workflow

1. Calculate DLEPS disease relevance score
2. Predict ADMET properties
3. Evaluate drug-likeness
4. Get PubChem compound details

Test Case

Input

{
    "smiles": [
        "CC(=O)Oc1ccccc1C(=O)O"
    ],
    "disease_name": "breast cancer"
}

Expected Steps

1. Calculate DLEPS disease relevance score
2. Predict ADMET properties
3. Evaluate drug-likeness
4. Get PubChem compound details

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-3": "https://scp.intern-ai.org.cn/api/v1/mcp/3/DrugSDA-Model",
    "server-2": "https://scp.intern-ai.org.cn/api/v1/mcp/2/DrugSDA-Tool",
    "pubchem-server": "https://scp.intern-ai.org.cn/api/v1/mcp/8/Origene-PubChem"
}

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)
    :  (result)

  ():
      AsyncExitStack()  stack:
        
        sessions = {}
        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())