gene-to-drug-pipeline

name	gene_to_drug_pipeline
description	Gene-to-Drug Discovery Pipeline - Full gene-to-drug pipeline: gene lookup, protein structure, binding pocket, virtual screening, and drug-likeness. Use this skill for translational medicine tasks involving get gene metadata by gene name pred protein structure esmfold run fpocket boltz binding affinity calculate mol drug chemistry. Combines 5 tools from 3 SCP server(s).
i18n	{"zh":{"description":"基因到药物发现全流程。"}}

Gene-to-Drug Discovery Pipeline

Discipline: Translational Medicine | Tools Used: 5 | Servers: 3

Description

Full gene-to-drug pipeline: gene lookup, protein structure, binding pocket, virtual screening, and drug-likeness.

Tools Used

• get_gene_metadata_by_gene_name from ncbi-server (streamable-http) - https://scp.intern-ai.org.cn/api/v1/mcp/9/Origene-NCBI
• pred_protein_structure_esmfold from server-3 (streamable-http) - https://scp.intern-ai.org.cn/api/v1/mcp/3/DrugSDA-Model
• run_fpocket from server-3 (streamable-http) - https://scp.intern-ai.org.cn/api/v1/mcp/3/DrugSDA-Model
• boltz_binding_affinity 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 gene info from NCBI
2. Predict protein structure
3. Identify binding pockets
4. Predict ligand binding
5. Assess drug-likeness

Test Case

Input

{
    "gene": "BRAF",
    "sequence": "MAALSGPGPGA"
}

Expected Steps

1. Get gene info from NCBI
2. Predict protein structure
3. Identify binding pockets
4. Predict ligand binding
5. Assess 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 = {
    "ncbi-server": "https://scp.intern-ai.org.cn/api/v1/mcp/9/Origene-NCBI",
    "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"
}

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)

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