Drug Target Identification Pipeline - Identify drug targets for a disease by querying OpenTargets for associated targets, then retrieve detailed target info from ChEMBL and protein data from UniProt. Use this skill for drug discovery tasks involving get associated targets by disease efoId get target by name get general info by protein or gene name. Combines 3 tools from 3 SCP server(s).
Discipline: Drug Discovery | Tools Used: 3 | Servers: 3
Identify drug targets for a disease by querying OpenTargets for associated targets, then retrieve detailed target info from ChEMBL and protein data from UniProt.
get_associated_targets_by_disease_efoId from opentargets-server (streamable-http) - https://scp.intern-ai.org.cn/api/v1/mcp/15/Origene-OpenTargetsget_target_by_name from chembl-server (streamable-http) - https://scp.intern-ai.org.cn/api/v1/mcp/4/Origene-ChEMBLget_general_info_by_protein_or_gene_name from uniprot-server (streamable-http) - https://scp.intern-ai.org.cn/api/v1/mcp/10/Origene-UniProt{
"disease_efo_id": "EFO_0000311",
"disease_name": "lung cancer"
}
Note: Replace
sk-b04409a1-b32b-4511-9aeb-22980abdc05cwith 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 = {
"opentargets-server": "https://scp.intern-ai.org.cn/api/v1/mcp/15/Origene-OpenTargets",
"chembl-server": "https://scp.intern-ai.org.cn/api/v1/mcp/4/Origene-ChEMBL",
"uniprot-server": "https://scp.intern-ai.org.cn/api/v1/mcp/10/Origene-UniProt"
}
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["opentargets-server"] = await connect("https://scp.intern-ai.org.cn/api/v1/mcp/15/Origene-OpenTargets", stack)
sessions["chembl-server"] = await connect("https://scp.intern-ai.org.cn/api/v1/mcp/4/Origene-ChEMBL", stack)
sessions["uniprot-server"] = await connect("https://scp.intern-ai.org.cn/api/v1/mcp/10/Origene-UniProt", stack)
# Execute workflow steps
# Step 1: Query OpenTargets for lung cancer targets
result_1 = await sessions["opentargets-server"].call_tool("get_associated_targets_by_disease_efoId", arguments={})
data_1 = parse(result_1)
print(f"Step 1 result: {json.dumps(data_1, indent=2, ensure_ascii=False)[:500]}")
# Step 2: Get EGFR target details from ChEMBL
result_2 = await sessions["chembl-server"].call_tool("get_target_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: Get EGFR protein info from UniProt
result_3 = await sessions["uniprot-server"].call_tool("get_general_info_by_protein_or_gene_name", arguments={})
data_3 = parse(result_3)
print(f"Step 3 result: {json.dumps(data_3, indent=2, ensure_ascii=False)[:500]}")
# Cleanup
print("Workflow complete!")
if __name__ == "__main__":
asyncio.run(main())