internscience/scp/admet-druglikeness-report

Trust tier 2
  • community
Source check pending

Checking that the SKILL.md file is still in the repository.

ADMET & Drug-Likeness Report - Generate comprehensive ADMET and drug-likeness report: molecular properties, H-bond analysis, hydrophobicity, topology, and ADMET prediction. Use this skill for medicinal chemistry tasks involving calculate mol basic info calculate mol hbond calculate mol hydrophobicity calculate mol topology pred molecule admet. Combines 5 tools from 2 SCP server(s).

Install

npx vskill@latest install internscience/scp/admet-druglikeness-report

SKILL.md

ADMET & Drug-Likeness Report

Discipline: Medicinal Chemistry | Tools Used: 5 | Servers: 2

Description

Generate comprehensive ADMET and drug-likeness report: molecular properties, H-bond analysis, hydrophobicity, topology, and ADMET prediction.

Tools Used

  • `calculate_mol_basic_info` from server-2 (streamable-http) - https://scp.intern-ai.org.cn/api/v1/mcp/2/DrugSDA-Tool
  • `calculate_mol_hbond` from server-2 (streamable-http) - https://scp.intern-ai.org.cn/api/v1/mcp/2/DrugSDA-Tool
  • `calculate_mol_hydrophobicity` from server-2 (streamable-http) - https://scp.intern-ai.org.cn/api/v1/mcp/2/DrugSDA-Tool
  • `calculate_mol_topology` 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. Calculate basic molecular info
  2. Analyze H-bonds
  3. Compute hydrophobicity
  4. Calculate topology descriptors
  5. Predict ADMET

Test Case

Input
json
{
"smiles": "c1ccc(CC(=O)O)cc1"
}
Expected Steps
  1. Calculate basic molecular info
  2. Analyze H-bonds
  3. Compute hydrophobicity
  4. Calculate topology descriptors
  5. Predict ADMET

Usage Example

Note: Replace <YOUR_SCP_HUB_API_KEY> with your own SCP Hub API Key. You can obtain one from the SCP Platform.
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-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, 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-2"], _, _ = await connect("https://scp.intern-ai.org.cn/api/v1/mcp/2/DrugSDA-Tool", "streamable-http")
sessions["server-3"], _, _ = await connect("https://scp.intern-ai.org.cn/api/v1/mcp/3/DrugSDA-Model", "streamable-http")
# Execute workflow steps
# Step 1: Calculate basic molecular info
result_1 = await sessions["server-2"].call_tool("calculate_mol_basic_info", arguments={})
data_1 = parse(result_1)
print(f"Step 1 result: {json.dumps(data_1, indent=2, ensure_ascii=False)[:500]}")
# Step 2: Analyze H-bonds
result_2 = await sessions["server-2"].call_tool("calculate_mol_hbond", arguments={})
data_2 = parse(result_2)
print(f"Step 2 result: {json.dumps(data_2, indent=2, ensure_ascii=False)[:500]}")
# Step 3: Compute hydrophobicity
result_3 = await sessions["server-2"].call_tool("calculate_mol_hydrophobicity", arguments={})
data_3 = parse(result_3)
print(f"Step 3 result: {json.dumps(data_3, indent=2, ensure_ascii=False)[:500]}")
# Step 4: Calculate topology descriptors
result_4 = await sessions["server-2"].call_tool("calculate_mol_topology", arguments={})
data_4 = parse(result_4)
print(f"Step 4 result: {json.dumps(data_4, indent=2, ensure_ascii=False)[:500]}")
# Step 5: Predict ADMET
result_5 = await sessions["server-3"].call_tool("pred_molecule_admet", 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())

Security

  • Security scanPassed, 100 of 100Pattern checks on the skill's files.
  • Model reviewPassed

Quality evaluation

  • Task evaluationWarning, 57 of 100Marginal. 1 run. Evaluated 7 months ago.

Source

Popularity

Repository stars
150
Repository forks
10
7-day trend score
25

Stars and forks are for the whole GitHub repository, not this skill alone. Refreshed 9 Apr 2026, 6 months ago.

Works with

Any agent vskill installs to: 56 targets in all. These 39 have a page on this site.

  • Claude Code
  • Cursor
  • GitHub Copilot
  • Windsurf
  • Codex
  • Gemini CLI
  • Cline
  • Amp
  • Roo Code
  • Goose
  • Continue
  • Kilo Code
  • Junie
  • Trae
  • OpenHands
  • Qwen Code
  • AI Agent
  • Tabnine Chat
  • Sourcegraph Cody
  • Amazon Q
  • Firebase Genkit
  • Sweep
  • Devin
  • Phind
  • Mentat
  • Claude Desktop
  • Aider
  • GPT Pilot
  • Plandex
  • bolt.new
  • v0
  • Replit Agent
  • Anse
  • TypingMind
  • BetterChatGPT
  • LibreChat
  • Warp AI
  • Pieces
  • Fig AI
Embed badge
vskill VERIFIED badge
[![vskill VERIFIED](https://verified-skill.com/api/v1/skills/internscience/scp/admet-druglikeness-report/badge)](https://verified-skill.com/skills/internscience/scp/admet-druglikeness-report)

Something wrong with this skill or its page? Tell us.