pubchem_deep_dive

PubChem Deep Dive Analysis - Deep dive into PubChem: compound info, bioassay summary, 3D conformers, synonyms, and general description. Use this skill for chemical databases tasks involving get pubche

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PubChem Deep Dive Analysis

Discipline: Chemical Databases | Tools Used: 5 | Servers: 1

Description

Deep dive into PubChem: compound info, bioassay summary, 3D conformers, synonyms, and general description.

Tools Used

  • get_pubchem_compound_by_cid from pubchem-server (streamable-http) - https://scp.intern-ai.org.cn/api/v1/mcp/8/Origene-PubChem
  • get_assay_summary_by_cid from pubchem-server (streamable-http) - https://scp.intern-ai.org.cn/api/v1/mcp/8/Origene-PubChem
  • get_conformers_by_cid from pubchem-server (streamable-http) - https://scp.intern-ai.org.cn/api/v1/mcp/8/Origene-PubChem
  • get_compound_synonyms_by_name from pubchem-server (streamable-http) - https://scp.intern-ai.org.cn/api/v1/mcp/8/Origene-PubChem
  • get_general_info_by_compound_name from pubchem-server (streamable-http) - https://scp.intern-ai.org.cn/api/v1/mcp/8/Origene-PubChem

Workflow

  1. Get full compound info
  2. Get bioassay summary
  3. Get 3D conformers
  4. Get all synonyms
  5. Get general description

Test Case

Input

{
    "compound_name": "aspirin",
    "cid": 2244
}

Expected Steps

  1. Get full compound info
  2. Get bioassay summary
  3. Get 3D conformers
  4. Get all synonyms
  5. Get general description

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 = {
    "pubchem-server": "https://scp.intern-ai.org.cn/api/v1/mcp/8/Origene-PubChem"
}

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():
    # Connect to required servers
    sessions = {}
    sessions["pubchem-server"], _, _ = await connect("https://scp.intern-ai.org.cn/api/v1/mcp/8/Origene-PubChem", "streamable-http")

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

    # Step 2: Get bioassay summary
    result_2 = await sessions["pubchem-server"].call_tool("get_assay_summary_by_cid", arguments={})
    data_2 = parse(result_2)
    print(f"Step 2 result: {json.dumps(data_2, indent=2, ensure_ascii=False)[:500]}")

    # Step 3: Get 3D conformers
    result_3 = await sessions["pubchem-server"].call_tool("get_conformers_by_cid", arguments={})
    data_3 = parse(result_3)
    print(f"Step 3 result: {json.dumps(data_3, indent=2, ensure_ascii=False)[:500]}")

    # Step 4: Get all synonyms
    result_4 = await sessions["pubchem-server"].call_tool("get_compound_synonyms_by_name", arguments={})
    data_4 = parse(result_4)
    print(f"Step 4 result: {json.dumps(data_4, indent=2, ensure_ascii=False)[:500]}")

    # Step 5: Get general description
    result_5 = await sessions["pubchem-server"].call_tool("get_general_info_by_compound_name", 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())