combinatorial-chemistry

name	combinatorial_chemistry
description	Combinatorial Chemistry Library Design - Design combinatorial library: validate core SMILES, generate variants, compute properties, and predict ADMET for library. Use this skill for combinatorial chemistry tasks involving is valid smiles calculate mol basic info calculate mol drug chemistry pred molecule admet. Combines 4 tools from 2 SCP server(s).

Combinatorial Chemistry Library Design

Discipline: Combinatorial Chemistry | Tools Used: 4 | Servers: 2

Description

Design combinatorial library: validate core SMILES, generate variants, compute properties, and predict ADMET for library.

Tools Used

• is_valid_smiles from server-2 (streamable-http) - https://scp.intern-ai.org.cn/api/v1/mcp/2/DrugSDA-Tool
• calculate_mol_basic_info from server-2 (streamable-http) - https://scp.intern-ai.org.cn/api/v1/mcp/2/DrugSDA-Tool
• calculate_mol_drug_chemistry 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. Validate all SMILES
2. Calculate properties for library
3. Evaluate drug-likeness
4. Predict ADMET for top candidates

Test Case

Input

{
    "core_smiles": "c1ccc(N)cc1",
    "variants": [
        "c1ccc(NC(=O)C)cc1",
        "c1ccc(NC(=O)CC)cc1"
    ]
}

Expected Steps

1. Validate all SMILES
2. Calculate properties for library
3. Evaluate drug-likeness
4. Predict ADMET for top candidates

Usage Example

Note: Replace sk-b04409a1-b32b-4511-9aeb-22980abdc05c with 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 = {
    "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, 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["server-2"] = await connect("https://scp.intern-ai.org.cn/api/v1/mcp/2/DrugSDA-Tool", stack)
        sessions["server-3"] = await connect("https://scp.intern-ai.org.cn/api/v1/mcp/3/DrugSDA-Model", stack)

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

        # Step 2: Calculate properties for library
        result_2 = await sessions["server-2"].call_tool("calculate_mol_basic_info", arguments={})
        data_2 = parse(result_2)
        print(f"Step 2 result: {json.dumps(data_2, indent=2, ensure_ascii=False)[:500]}")

        # Step 3: Evaluate 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: Predict ADMET for top candidates
        result_4 = await sessions["server-3"].call_tool("pred_molecule_admet", 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())