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Skill v1.0.1
currentAutomated scan100/100internscience/scp/polypharmacology-analysis
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PublishedJune 16, 2026 at 11:17 PM
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version: "1.0.1" name: polypharmacology_analysis description: "Polypharmacology Analysis - Analyze a drug's multi-target pharmacology: get targets from ChEMBL, functional enrichment from STRING, and pathway links from KEGG. Use this skill for pharmacology tasks involving get target by name get functional enrichment kegg link get mechanism by id. Combines 4 tools from 3 SCP server(s)."
Polypharmacology Analysis
Discipline: Pharmacology | Tools Used: 4 | Servers: 3
Description
Analyze a drug's multi-target pharmacology: get targets from ChEMBL, functional enrichment from STRING, and pathway links from KEGG.
Tools Used
- `get_target_by_name` from
chembl-server(streamable-http) -https://scp.intern-ai.org.cn/api/v1/mcp/4/Origene-ChEMBL - `get_functional_enrichment` from
string-server(streamable-http) -https://scp.intern-ai.org.cn/api/v1/mcp/6/Origene-STRING - `kegg_link` from
kegg-server(streamable-http) -https://scp.intern-ai.org.cn/api/v1/mcp/5/Origene-KEGG - `get_mechanism_by_id` from
chembl-server(streamable-http) -https://scp.intern-ai.org.cn/api/v1/mcp/4/Origene-ChEMBL
Workflow
- Get drug targets from ChEMBL
- Run functional enrichment on targets
- Link to KEGG pathways
- Get mechanism details
Test Case
Input
json
{"drug_name": "imatinib"}
Expected Steps
- Get drug targets from ChEMBL
- Run functional enrichment on targets
- Link to KEGG pathways
- Get mechanism details
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 asyncioimport jsonfrom mcp import ClientSessionfrom mcp.client.streamable_http import streamablehttp_clientfrom mcp.client.sse import sse_clientSERVERS = {"chembl-server": "https://scp.intern-ai.org.cn/api/v1/mcp/4/Origene-ChEMBL","string-server": "https://scp.intern-ai.org.cn/api/v1/mcp/6/Origene-STRING","kegg-server": "https://scp.intern-ai.org.cn/api/v1/mcp/5/Origene-KEGG"}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, transportdef 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.textreturn str(result)except: return str(result)async def main():# Connect to required serverssessions = {}sessions["chembl-server"], _, _ = await connect("https://scp.intern-ai.org.cn/api/v1/mcp/4/Origene-ChEMBL", "streamable-http")sessions["string-server"], _, _ = await connect("https://scp.intern-ai.org.cn/api/v1/mcp/6/Origene-STRING", "streamable-http")sessions["kegg-server"], _, _ = await connect("https://scp.intern-ai.org.cn/api/v1/mcp/5/Origene-KEGG", "streamable-http")# Execute workflow steps# Step 1: Get drug targets from ChEMBLresult_1 = await sessions["chembl-server"].call_tool("get_target_by_name", arguments={})data_1 = parse(result_1)print(f"Step 1 result: {json.dumps(data_1, indent=2, ensure_ascii=False)[:500]}")# Step 2: Run functional enrichment on targetsresult_2 = await sessions["string-server"].call_tool("get_functional_enrichment", arguments={})data_2 = parse(result_2)print(f"Step 2 result: {json.dumps(data_2, indent=2, ensure_ascii=False)[:500]}")# Step 3: Link to KEGG pathwaysresult_3 = await sessions["kegg-server"].call_tool("kegg_link", arguments={})data_3 = parse(result_3)print(f"Step 3 result: {json.dumps(data_3, indent=2, ensure_ascii=False)[:500]}")# Step 4: Get mechanism detailsresult_4 = await sessions["chembl-server"].call_tool("get_mechanism_by_id", arguments={})data_4 = parse(result_4)print(f"Step 4 result: {json.dumps(data_4, indent=2, ensure_ascii=False)[:500]}")# Cleanupprint("Workflow complete!")if __name__ == "__main__":asyncio.run(main())