admet-druglikeness-report

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).

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仓库: SpectrAI-Initiative/InnoClaw
最近来源活动: 2026年4月1日 17:03
检测到的 SKILL.md 语言: 英语
星标: 390
分支: 28

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name	admet_druglikeness_report
description	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).

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

Calculate basic molecular info
Analyze H-bonds
Compute hydrophobicity

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: 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())

admet-druglikeness-report

安装方式

检查来源文件

ADMET & Drug-Likeness Report

Description

Tools Used

Workflow

ADMET & Drug-Likeness Report

Description

Tools Used

Workflow

Test Case

Input

Expected Steps

Usage Example

admet-druglikeness-report

来源信息

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同仓库更多 Skills

同仓库更多 Skills

ADMET & Drug-Likeness Report

Description

Tools Used

Workflow

ADMET & Drug-Likeness Report

Description

Tools Used

Workflow

Test Case

Input

Expected Steps

Usage Example