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gene_to_drug_pipeline

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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).

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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 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 = {
    "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, 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["ncbi-server"] = await connect("https://scp.intern-ai.org.cn/api/v1/mcp/9/Origene-NCBI", stack)
        sessions["server-3"] = await connect("https://scp.intern-ai.org.cn/api/v1/mcp/3/DrugSDA-Model", stack)
        sessions["server-2"] = await connect("https://scp.intern-ai.org.cn/api/v1/mcp/2/DrugSDA-Tool", stack)

        # Execute workflow steps
        # Step 1: Get gene info from NCBI
        result_1 = await sessions["ncbi-server"].call_tool("get_gene_metadata_by_gene_name", arguments={})
        data_1 = parse(result_1)
        print(f"Step 1 result: {json.dumps(data_1, indent=2, ensure_ascii=False)[:500]}")

        # Step 2: Predict protein structure
        result_2 = await sessions["server-3"].call_tool("pred_protein_structure_esmfold", arguments={})
        data_2 = parse(result_2)
        print(f"Step 2 result: {json.dumps(data_2, indent=2, ensure_ascii=False)[:500]}")

        # Step 3: Identify binding pockets
        result_3 = await sessions["server-3"].call_tool("run_fpocket", arguments={})
        data_3 = parse(result_3)
        print(f"Step 3 result: {json.dumps(data_3, indent=2, ensure_ascii=False)[:500]}")

        # Step 4: Predict ligand binding
        result_4 = await sessions["server-3"].call_tool("boltz_binding_affinity", arguments={})
        data_4 = parse(result_4)
        print(f"Step 4 result: {json.dumps(data_4, indent=2, ensure_ascii=False)[:500]}")

        # Step 5: Assess drug-likeness
        result_5 = await sessions["server-2"].call_tool("calculate_mol_drug_chemistry", arguments={})
        data_5 = parse(result_5)
        print(f"Step 5 result: {json.dumps(data_5, indent=2, ensure_ascii=False)[:500]}")

        # Cleanup
        print("Workflow complete!")

if __name__ == "__main__":
    asyncio.run(main())