internscience/drclaw/compound-to-drug-pipeline

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'Compound-to-Drug Analysis Pipeline - Full compound-to-drug pipeline:

Install

npx vskill@latest install internscience/drclaw/compound-to-drug-pipeline

SKILL.md

Compound-to-Drug Analysis Pipeline

Discipline: Drug Development | Tools Used: 4 | Servers: 4

Description

Full compound-to-drug pipeline: name-to-SMILES conversion, structure analysis, drug-likeness, and FDA drug lookup.

Tools Used

  • `NameToSMILES` from server-31 (sse) - https://scp.intern-ai.org.cn/api/v1/mcp/31/SciToolAgent-Chem
  • `ChemicalStructureAnalyzer` from server-28 (sse) - https://scp.intern-ai.org.cn/api/v1/mcp/28/InternAgent
  • `calculate_mol_drug_chemistry` from server-2 (streamable-http) - https://scp.intern-ai.org.cn/api/v1/mcp/2/DrugSDA-Tool
  • `get_drug_by_name` from chembl-server (streamable-http) - https://scp.intern-ai.org.cn/api/v1/mcp/4/Origene-ChEMBL

Workflow

  1. Convert name to SMILES
  2. Analyze chemical structure
  3. Calculate drug-likeness
  4. Search in ChEMBL drug database

Test Case

Input
json
{
"compound_name": "caffeine"
}
Expected Steps
  1. Convert name to SMILES
  2. Analyze chemical structure
  3. Calculate drug-likeness
  4. Search in ChEMBL drug database

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 = {
"server-31": "https://scp.intern-ai.org.cn/api/v1/mcp/31/SciToolAgent-Chem",
"server-28": "https://scp.intern-ai.org.cn/api/v1/mcp/28/InternAgent",
"server-2": "https://scp.intern-ai.org.cn/api/v1/mcp/2/DrugSDA-Tool",
"chembl-server": "https://scp.intern-ai.org.cn/api/v1/mcp/4/Origene-ChEMBL"
}
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["server-31"], _, _ = await connect("https://scp.intern-ai.org.cn/api/v1/mcp/31/SciToolAgent-Chem", "sse")
sessions["server-28"], _, _ = await connect("https://scp.intern-ai.org.cn/api/v1/mcp/28/InternAgent", "sse")
sessions["server-2"], _, _ = await connect("https://scp.intern-ai.org.cn/api/v1/mcp/2/DrugSDA-Tool", "streamable-http")
sessions["chembl-server"], _, _ = await connect("https://scp.intern-ai.org.cn/api/v1/mcp/4/Origene-ChEMBL", "streamable-http")
# Execute workflow steps
# Step 1: Convert name to SMILES
result_1 = await sessions["server-31"].call_tool("NameToSMILES", arguments={})
data_1 = parse(result_1)
print(f"Step 1 result: {json.dumps(data_1, indent=2, ensure_ascii=False)[:500]}")
# Step 2: Analyze chemical structure
result_2 = await sessions["server-28"].call_tool("ChemicalStructureAnalyzer", arguments={})
data_2 = parse(result_2)
print(f"Step 2 result: {json.dumps(data_2, indent=2, ensure_ascii=False)[:500]}")
# Step 3: Calculate drug-likeness
result_3 = await sessions["server-2"].call_tool("calculate_mol_drug_chemistry", arguments={})
data_3 = parse(result_3)
print(f"Step 3 result: {json.dumps(data_3, indent=2, ensure_ascii=False)[:500]}")
# Step 4: Search in ChEMBL drug database
result_4 = await sessions["chembl-server"].call_tool("get_drug_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())

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