Systems Pharmacology Analysis - Systems pharmacology: drug targets, protein interactions, pathway enrichment, and gene expression. Use this skill for systems pharmacology tasks involving get target by name get string network interaction get functional enrichment get gene expression across cancers. Combines 4 tools from 3 SCP server(s).
Discipline: Systems Pharmacology | Tools Used: 4 | Servers: 3
Systems pharmacology: drug targets, protein interactions, pathway enrichment, and gene expression.
get_target_by_name from chembl-server (streamable-http) - https://scp.intern-ai.org.cn/api/v1/mcp/4/Origene-ChEMBLget_string_network_interaction from string-server (streamable-http) - https://scp.intern-ai.org.cn/api/v1/mcp/6/Origene-STRINGget_functional_enrichment from string-server (streamable-http) - https://scp.intern-ai.org.cn/api/v1/mcp/6/Origene-STRINGget_gene_expression_across_cancers from (streamable-http) - tcga-serverhttps://scp.intern-ai.org.cn/api/v1/mcp/11/Origene-TCGA{
"target": "EGFR",
"species": 9606
}
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 = {
"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",
"tcga-server": "https://scp.intern-ai.org.cn/api/v1/mcp/11/Origene-TCGA"
}
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["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["tcga-server"], _, _ = await connect("https://scp.intern-ai.org.cn/api/v1/mcp/11/Origene-TCGA", "streamable-http")
# Execute workflow steps
# Step 1: Get drug target info
result_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: Build interaction network
result_2 = await sessions["string-server"].call_tool("get_string_network_interaction", arguments={})
data_2 = parse(result_2)
print(f"Step 2 result: {json.dumps(data_2, indent=2, ensure_ascii=False)[:500]}")
# Step 3: Run pathway enrichment
result_3 = await sessions["string-server"].call_tool("get_functional_enrichment", arguments={})
data_3 = parse(result_3)
print(f"Step 3 result: {json.dumps(data_3, indent=2, ensure_ascii=False)[:500]}")
# Step 4: Check expression across cancers
result_4 = await sessions["tcga-server"].call_tool("get_gene_expression_across_cancers", 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())