Bioassay Data Analysis

SkillSearch

Bioassay Data Analysis - Analyze bioassay data: PubChem assay summary, ChEMBL activity search, compound properties, and target info. Use this skill for bioassay science tasks involving get assay summary by cid search activity calculate mol basic info get target by name. Combines 4 tools from 3 SCP server(s).

Available today. Use it from your connected AI after setup.

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Then ask your AI: use the Bioassay Data Analysis skill

What this skill tells your AI

The instructions your AI receives, as published by internscience/scp in skills/bioassay_analysis/SKILL.md and read by ahel’s review.

Discipline: Bioassay Science | Tools Used: 4 | Servers: 3

Description

Analyze bioassay data: PubChem assay summary, ChEMBL activity search, compound properties, and target info.

Tools Used

  • get_assay_summary_by_cid from pubchem-server (streamable-http) - https://scp.intern-ai.org.cn/api/v1/mcp/8/Origene-PubChem
  • search_activity from chembl-server (streamable-http) - https://scp.intern-ai.org.cn/api/v1/mcp/4/Origene-ChEMBL
  • calculate_mol_basic_info from server-2 (streamable-http) - https://scp.intern-ai.org.cn/api/v1/mcp/2/DrugSDA-Tool
  • get_target_by_name from chembl-server (streamable-http) - https://scp.intern-ai.org.cn/api/v1/mcp/4/Origene-ChEMBL

Workflow

  1. Get PubChem bioassay summary
  2. Search ChEMBL activities
  3. Calculate compound properties
  4. Get target information

Test Case

Input

{
    "cid": 2244,
    "target": "cyclooxygenase"
}

Expected Steps

  1. Get PubChem bioassay summary
  2. Search ChEMBL activities
  3. Calculate compound properties
  4. Get target information

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",
    "chembl-server": "https://scp.intern-ai.org.cn/api/v1/mcp/4/Origene-ChEMBL",
    "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)

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")
    sessions["chembl-server"], _, _ = await connect("https://scp.intern-ai.org.cn/api/v1/mcp/4/Origene-ChEMBL", "streamable-http")
    sessions["server-2"], _, _ = await connect("https://scp.intern-ai.org.cn/api/v1/mcp/2/DrugSDA-Tool", "streamable-http")

    # Execute workflow steps
    # Step 1: Get PubChem bioassay summary
    result_1 = await sessions["pubchem-server"].call_tool("get_assay_summary_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: Search ChEMBL activities
    result_2 = await sessions["chembl-server"].call_tool("search_activity", arguments={})
    data_2 = parse(result_2)
    print(f"Step 2 result: {json.dumps(data_2, indent=2, ensure_ascii=False)[:500]}")

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

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

    # Cleanup
    print("Workflow complete!")

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

Signals

GitHub stars
167
Forks
9
Last commit
Jun 2026
Advanced
Catalog kind
skill
Gateway key
bioassay-analysis
Source
github.com/internscience/scp