enzyme-inhibitor-design

name	enzyme_inhibitor_design
description	Enzyme Inhibitor Design - Design enzyme inhibitor: target structure, pocket prediction, compound screening, and ADMET assessment. Use this skill for enzyme pharmacology tasks involving retrieve protein data by pdbcode pred pocket prank quick molecule docking pred molecule admet calculate mol drug chemistry. Combines 5 tools from 2 SCP server(s).
i18n	{"zh":{"description":"酶抑制剂设计：靶点、口袋、筛选。"}}

Enzyme Inhibitor Design

Discipline: Enzyme Pharmacology | Tools Used: 5 | Servers: 2

Description

Design enzyme inhibitor: target structure, pocket prediction, compound screening, and ADMET assessment.

Tools Used

• retrieve_protein_data_by_pdbcode from server-2 (streamable-http) - https://scp.intern-ai.org.cn/api/v1/mcp/2/DrugSDA-Tool
• pred_pocket_prank from server-3 (streamable-http) - https://scp.intern-ai.org.cn/api/v1/mcp/3/DrugSDA-Model
• quick_molecule_docking 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

Workflow

1. Get enzyme structure
2. Predict active site pockets
3. Dock inhibitor candidates
4. Predict ADMET
5. Check drug-likeness

Test Case

Input

{
    "pdb_code": "1AKE",
    "ligand_smiles": "CC(=O)Oc1ccccc1C(=O)O"
}

Expected Steps

1. Get enzyme structure
2. Predict active site pockets
3. Dock inhibitor candidates
4. Predict ADMET
5. Check drug-likeness

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

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():
    
    sessions = {}
    sessions[], _, _ =  connect(, )
    sessions[], _, _ =  connect(, )

    
    
    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)
    ()

    
    result_5 =  sessions[].call_tool(, arguments={})
    data_5 = parse(result_5)
    ()

    
    ()

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