# Structural Pharmacogenomics

> Structural Pharmacogenomics - Link structure to pharmacogenomics: variant effect, protein structure change, drug binding, and clinical data. Use this skill for pharmacogenomics tasks involving get vep hgvs pred protein structure esmfold boltz binding affinity get pharmacogenomics info by drug name. Combines 4 tools from 3 SCP server(s).

- Skill: `gabrielmoreira/structural-pharmacogenomics` (Agent Skill)
- Install (CLI): `npx skillmds@latest add gabrielmoreira/structural-pharmacogenomics`
- Raw SKILL.md: https://api.skillmd.com/api/skills/gabrielmoreira/structural-pharmacogenomics/raw
- Safety review: pending (external: skill-scanner PASS, skillspector PASS)
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Coding & Dev Tools
- Author: gabrielmoreira (https://skillmd.com/u/gabrielmoreira)
- Updated: 2026-09-09
- Page: https://skillmd.com/skills/gabrielmoreira/structural-pharmacogenomics

---


# Structural Pharmacogenomics

**Discipline**: Pharmacogenomics | **Tools Used**: 4 | **Servers**: 3

## Description

Link structure to pharmacogenomics: variant effect, protein structure change, drug binding, and clinical data.

## Tools Used

- **`get_vep_hgvs`** from `ensembl-server` (streamable-http) - `https://scp.intern-ai.org.cn/api/v1/mcp/12/Origene-Ensembl`
- **`pred_protein_structure_esmfold`** from `server-3` (streamable-http) - `https://scp.intern-ai.org.cn/api/v1/mcp/3/DrugSDA-Model`
- **`boltz_binding_affinity`** from `server-3` (streamable-http) - `https://scp.intern-ai.org.cn/api/v1/mcp/3/DrugSDA-Model`
- **`get_pharmacogenomics_info_by_drug_name`** from `fda-drug-server` (streamable-http) - `https://scp.intern-ai.org.cn/api/v1/mcp/14/Origene-FDADrug`

## Workflow

1. Predict variant effect
2. Predict mutant structure
3. Compare binding affinity
4. Get pharmacogenomics data

## Test Case

### Input
```json
{
    "variant": "ENSP00000227163.5:p.Pro227Ser",
    "sequence": "MKTIIALSYIFCLVFA",
    "drug": "warfarin"
}
```

### Expected Steps
1. Predict variant effect
2. Predict mutant structure
3. Compare binding affinity
4. Get pharmacogenomics data

## 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 = {
    "ensembl-server": "https://scp.intern-ai.org.cn/api/v1/mcp/12/Origene-Ensembl",
    "server-3": "https://scp.intern-ai.org.cn/api/v1/mcp/3/DrugSDA-Model",
    "fda-drug-server": "https://scp.intern-ai.org.cn/api/v1/mcp/14/Origene-FDADrug"
}

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["ensembl-server"] = await connect("https://scp.intern-ai.org.cn/api/v1/mcp/12/Origene-Ensembl", stack)
        sessions["server-3"] = await connect("https://scp.intern-ai.org.cn/api/v1/mcp/3/DrugSDA-Model", stack)
        sessions["fda-drug-server"] = await connect("https://scp.intern-ai.org.cn/api/v1/mcp/14/Origene-FDADrug", stack)

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

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

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

        # Step 4: Get pharmacogenomics data
        result_4 = await sessions["fda-drug-server"].call_tool("get_pharmacogenomics_info_by_drug_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())
```