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internscience/scp/antibody-drug-development

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PublishedJune 16, 2026 at 11:17 PM

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version: "1.0.0" name: antibody_drug_development description: "Antibody Drug Development - Develop antibody drug: target protein analysis, biotherapeutic lookup, protein properties, and interaction prediction. Use this skill for biologics tasks involving get uniprotkb entry by accession get biotherapeutic by name ComputeProtPara ComputeHydrophilicity. Combines 4 tools from 3 SCP server(s)."

Antibody Drug Development

Discipline: Biologics | Tools Used: 4 | Servers: 3

Description

Develop antibody drug: target protein analysis, biotherapeutic lookup, protein properties, and interaction prediction.

Tools Used

`get_uniprotkb_entry_by_accession` from uniprot-server (streamable-http) - https://scp.intern-ai.org.cn/api/v1/mcp/10/Origene-UniProt
`get_biotherapeutic_by_name` from chembl-server (streamable-http) - https://scp.intern-ai.org.cn/api/v1/mcp/4/Origene-ChEMBL
`ComputeProtPara` from server-29 (sse) - https://scp.intern-ai.org.cn/api/v1/mcp/29/SciToolAgent-Bio
`ComputeHydrophilicity` from server-29 (sse) - https://scp.intern-ai.org.cn/api/v1/mcp/29/SciToolAgent-Bio

Workflow

Get target protein info
Look up biotherapeutic in ChEMBL
Compute protein parameters
Analyze hydrophilicity

Test Case

Input

json

{
    "target_accession": "P04637",
    "biotherapeutic": "trastuzumab",
    "sequence": "MKTIIALSYIFCLVFA"
}

Expected Steps

Get target protein info
Look up biotherapeutic in ChEMBL
Compute protein parameters
Analyze hydrophilicity

Usage Example

Note: Replace <YOUR_SCP_HUB_API_KEY> with your own SCP Hub API Key. You can obtain one from the SCP Platform.

python

import asyncio
import json
from mcp import ClientSession
from mcp.client.streamable_http import streamablehttp_client
from mcp.client.sse import sse_client
SERVERS = {
    "uniprot-server": "https://scp.intern-ai.org.cn/api/v1/mcp/10/Origene-UniProt",
    "chembl-server": "https://scp.intern-ai.org.cn/api/v1/mcp/4/Origene-ChEMBL",
    "server-29": "https://scp.intern-ai.org.cn/api/v1/mcp/29/SciToolAgent-Bio"
}
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["uniprot-server"], _, _ = await connect("https://scp.intern-ai.org.cn/api/v1/mcp/10/Origene-UniProt", "streamable-http")
    sessions["chembl-server"], _, _ = await connect("https://scp.intern-ai.org.cn/api/v1/mcp/4/Origene-ChEMBL", "streamable-http")
    sessions["server-29"], _, _ = await connect("https://scp.intern-ai.org.cn/api/v1/mcp/29/SciToolAgent-Bio", "sse")
    # Execute workflow steps
    # Step 1: Get target protein info
    result_1 = await sessions["uniprot-server"].call_tool("get_uniprotkb_entry_by_accession", arguments={})
    data_1 = parse(result_1)
    print(f"Step 1 result: {json.dumps(data_1, indent=2, ensure_ascii=False)[:500]}")
    # Step 2: Look up biotherapeutic in ChEMBL
    result_2 = await sessions["chembl-server"].call_tool("get_biotherapeutic_by_name", arguments={})
    data_2 = parse(result_2)
    print(f"Step 2 result: {json.dumps(data_2, indent=2, ensure_ascii=False)[:500]}")
    # Step 3: Compute protein parameters
    result_3 = await sessions["server-29"].call_tool("ComputeProtPara", arguments={})
    data_3 = parse(result_3)
    print(f"Step 3 result: {json.dumps(data_3, indent=2, ensure_ascii=False)[:500]}")
    # Step 4: Analyze hydrophilicity
    result_4 = await sessions["server-29"].call_tool("ComputeHydrophilicity", 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())

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