compound_to_drug_pipeline
Compound-to-Drug Analysis Pipeline - Full compound-to-drug pipeline: name-to-SMILES conversion, structure analysis, drug-likeness, and FDA drug lookup. Use this skill for drug development tasks involving NameToSMILES ChemicalStructureAnalyzer calculate mol drug chemistry get drug by name. Combines 4 tools from 4 SCP server(s).
Best use case
compound_to_drug_pipeline is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Compound-to-Drug Analysis Pipeline - Full compound-to-drug pipeline: name-to-SMILES conversion, structure analysis, drug-likeness, and FDA drug lookup. Use this skill for drug development tasks involving NameToSMILES ChemicalStructureAnalyzer calculate mol drug chemistry get drug by name. Combines 4 tools from 4 SCP server(s).
Teams using compound_to_drug_pipeline should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/compound_to_drug_pipeline/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How compound_to_drug_pipeline Compares
| Feature / Agent | compound_to_drug_pipeline | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Compound-to-Drug Analysis Pipeline - Full compound-to-drug pipeline: name-to-SMILES conversion, structure analysis, drug-likeness, and FDA drug lookup. Use this skill for drug development tasks involving NameToSMILES ChemicalStructureAnalyzer calculate mol drug chemistry get drug by name. Combines 4 tools from 4 SCP server(s).
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# 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](https://scphub.intern-ai.org.cn).
```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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