drugsda-drug-likeness
Compute the drug-likeness metrics (QED score and Number of violations of Lipinski's Rule of Five) of the input candidate molecules (SMILES format).
Best use case
drugsda-drug-likeness is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Compute the drug-likeness metrics (QED score and Number of violations of Lipinski's Rule of Five) of the input candidate molecules (SMILES format).
Teams using drugsda-drug-likeness 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/drugsda-drug-likeness/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How drugsda-drug-likeness Compares
| Feature / Agent | drugsda-drug-likeness | 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?
Compute the drug-likeness metrics (QED score and Number of violations of Lipinski's Rule of Five) of the input candidate molecules (SMILES format).
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
# Molecular Drug-likeness Metrics Calculation
## Usage
### 1. MCP Server Definition
```python
import json
from mcp.client.streamable_http import streamablehttp_client
from mcp import ClientSession
class DrugSDAClient:
def __init__(self, server_url: str):
self.server_url = server_url
self.session = None
async def connect(self):
print(f"server url: {self.server_url}")
try:
self.transport = streamablehttp_client(
url=self.server_url,
headers={"SCP-HUB-API-KEY": "sk-a0033dde-b3cd-413b-adbe-980bc78d6126"}
)
self.read, self.write, self.get_session_id = await self.transport.__aenter__()
self.session_ctx = ClientSession(self.read, self.write)
self.session = await self.session_ctx.__aenter__()
await self.session.initialize()
session_id = self.get_session_id()
print(f"✓ connect success")
return True
except Exception as e:
print(f"✗ connect failure: {e}")
import traceback
traceback.print_exc()
return False
async def disconnect(self):
try:
if self.session:
await self.session_ctx.__aexit__(None, None, None)
if hasattr(self, 'transport'):
await self.transport.__aexit__(None, None, None)
print("✓ already disconnect")
except Exception as e:
print(f"✗ disconnect error: {e}")
def parse_result(self, result):
try:
if hasattr(result, 'content') and result.content:
content = result.content[0]
if hasattr(content, 'text'):
return json.loads(content.text)
return str(result)
except Exception as e:
return {"error": f"parse error: {e}", "raw": str(result)}
```
### 2. Drug-likeness Calculation
The description of tool *calculate_mol_drug_chemistry*.
```tex
Compute key drug-likeness metrics for each SMILES.
Args:
smiles_list (List[str]): List of input SMILES strings, (e.g., ["N[C@@H](Cc1ccc(O)cc1)C(=O)O", "CC(C)C1=CC=CC=C1"])
Return:
status (str): success/error
msg (str): message
metrics (List[dict]): List of dict, each containing feature keys.
--smiles (str): A SMILES string of smiles_list
--qed (float): Quantitative Estimate of Drug-likeness (QED) score
--lipinski_rule_of_5_violations (int): Number of violations of Lipinski's Rule of Five
```
How to use tool *calculate_mol_drug_chemistry* :
```python
client = DrugSDAClient("https://scp.intern-ai.org.cn/api/v1/mcp/2/DrugSDA-Tool")
if not await client.connect():
print("connection failed")
return
response = await client.session.call_tool(
"calculate_mol_drug_chemistry",
arguments={
"smiles_list": smiles_list
}
)
result = client.parse_result(response)
druglikeness_metrics = result["metrics"]
await client.disconnect()
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