ADMET & Drug-Likeness Report

Discipline: Medicinal Chemistry | Tools Used: 5 | Servers: 2

Description

Generate comprehensive ADMET and drug-likeness report: molecular properties, H-bond analysis, hydrophobicity, topology, and ADMET prediction.

Tools Used

• calculate_mol_basic_info from server-2 (streamable-http) - https://scp.intern-ai.org.cn/api/v1/mcp/2/DrugSDA-Tool
• calculate_mol_hbond from server-2 (streamable-http) - https://scp.intern-ai.org.cn/api/v1/mcp/2/DrugSDA-Tool
• calculate_mol_hydrophobicity from server-2 (streamable-http) - https://scp.intern-ai.org.cn/api/v1/mcp/2/DrugSDA-Tool
• calculate_mol_topology from server-2 (streamable-http) - https://scp.intern-ai.org.cn/api/v1/mcp/2/DrugSDA-Tool
• pred_molecule_admet from server-3 (streamable-http) - https://scp.intern-ai.org.cn/api/v1/mcp/3/DrugSDA-Model

Workflow

1. Calculate basic molecular info
2. Analyze H-bonds
3. Compute hydrophobicity
4. Calculate topology descriptors
5. Predict ADMET

Test Case

Input

{
    "smiles": "c1ccc(CC(=O)O)cc1"
}

Expected Steps

1. Calculate basic molecular info
2. Analyze H-bonds
3. Compute hydrophobicity
4. Calculate topology descriptors
5. Predict ADMET

Usage Example

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 = {
    "server-2": "https://scp.intern-ai.org.cn/api/v1/mcp/2/DrugSDA-Tool",
    "server-3": "https://scp.intern-ai.org.cn/api/v1/mcp/3/DrugSDA-Model"
}

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-2"], _, _ = await connect("https://scp.intern-ai.org.cn/api/v1/mcp/2/DrugSDA-Tool", "streamable-http")
    sessions["server-3"], _, _ = await connect("https://scp.intern-ai.org.cn/api/v1/mcp/3/DrugSDA-Model", "streamable-http")

    # Execute workflow steps
    # Step 1: Calculate basic molecular info
    result_1 = await sessions["server-2"].call_tool("calculate_mol_basic_info", arguments={})
    data_1 = parse(result_1)
    print(f"Step 1 result: {json.dumps(data_1, indent=2, ensure_ascii=False)[:500]}")

    # Step 2: Analyze H-bonds
    result_2 = await sessions["server-2"].call_tool("calculate_mol_hbond", arguments={})
    data_2 = parse(result_2)
    print(f"Step 2 result: {json.dumps(data_2, indent=2, ensure_ascii=False)[:500]}")

    # Step 3: Compute hydrophobicity
    result_3 = await sessions["server-2"].call_tool("calculate_mol_hydrophobicity", arguments={})
    data_3 = parse(result_3)
    print(f"Step 3 result: {json.dumps(data_3, indent=2, ensure_ascii=False)[:500]}")

    # Step 4: Calculate topology descriptors
    result_4 = await sessions["server-2"].call_tool("calculate_mol_topology", arguments={})
    data_4 = parse(result_4)
    print(f"Step 4 result: {json.dumps(data_4, indent=2, ensure_ascii=False)[:500]}")

    # Step 5: Predict ADMET
    result_5 = await sessions["server-3"].call_tool("pred_molecule_admet", arguments={})
    data_5 = parse(result_5)
    print(f"Step 5 result: {json.dumps(data_5, indent=2, ensure_ascii=False)[:500]}")

    # Cleanup
    print("Workflow complete!")

if __name__ == "__main__":
    asyncio.run(main())