Enzyme Inhibitor Design

---
name: enzyme_inhibitor_design
description: "Enzyme Inhibitor Design - Design enzyme inhibitor: target structure, pocket prediction, compound screening, and ADMET assessment. Use this skill for enzyme pharmacology tasks involving retrieve protein data by pdbcode pred pocket prank quick molecule docking pred molecule admet calculate mol drug chemistry. Combines 5 tools from 2 SCP server(s)."
---

# Enzyme Inhibitor Design

**Discipline**: Enzyme Pharmacology | **Tools Used**: 5 | **Servers**: 2

## Description

Design enzyme inhibitor: target structure, pocket prediction, compound screening, and ADMET assessment.

## Tools Used

- **`retrieve_protein_data_by_pdbcode`** from `server-2` (streamable-http) - `https://scp.intern-ai.org.cn/api/v1/mcp/2/DrugSDA-Tool`
- **`pred_pocket_prank`** from `server-3` (streamable-http) - `https://scp.intern-ai.org.cn/api/v1/mcp/3/DrugSDA-Model`
- **`quick_molecule_docking`** from `server-3` (streamable-http) - `https://scp.intern-ai.org.cn/api/v1/mcp/3/DrugSDA-Model`
- **`pred_molecule_admet`** from `server-3` (streamable-http) - `https://scp.intern-ai.org.cn/api/v1/mcp/3/DrugSDA-Model`
- **`calculate_mol_drug_chemistry`** from `server-2` (streamable-http) - `https://scp.intern-ai.org.cn/api/v1/mcp/2/DrugSDA-Tool`

## Workflow

1. Get enzyme structure
2. Predict active site pockets
3. Dock inhibitor candidates
4. Predict ADMET
5. Check drug-likeness

## Test Case

### Input
```json
{
    "pdb_code": "1AKE",
    "ligand_smiles": "CC(=O)Oc1ccccc1C(=O)O"
}
```

### Expected Steps
1. Get enzyme structure
2. Predict active site pockets
3. Dock inhibitor candidates
4. Predict ADMET
5. Check drug-likeness

## 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](https://scphub.intern-ai.org.cn).

```python
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: Get enzyme structure
        result_1 = await sessions["server-2"].call_tool("retrieve_protein_data_by_pdbcode", arguments={})
        data_1 = parse(result_1)
        print(f"Step 1 result: {json.dumps(data_1, indent=2, ensure_ascii=False)[:500]}")

        # Step 2: Predict active site pockets
        result_2 = await sessions["server-3"].call_tool("pred_pocket_prank", arguments={})
        data_2 = parse(result_2)
        print(f"Step 2 result: {json.dumps(data_2, indent=2, ensure_ascii=False)[:500]}")

        # Step 3: Dock inhibitor candidates
        result_3 = await sessions["server-3"].call_tool("quick_molecule_docking", 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
        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]}")

        # Step 5: Check drug-likeness
        result_5 = await sessions["server-2"].call_tool("calculate_mol_drug_chemistry", 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())
```