How do I install smiles_comprehensive_analysis?

smiles_comprehensive_analysis is a Skill hosted on GitHub at https://github.com/InternScience/scp. Visit the ImAiFox page at https://imaifox.com/boosters/internscience-scp-smiles-comprehensive-analysis for the AI-ready install prompt you can copy directly into Claude Code, Cursor, or Windsurf.

How popular is smiles_comprehensive_analysis?

smiles_comprehensive_analysis has 109 GitHub stars and 6 forks. It is actively maintained with recent commits.

Is smiles_comprehensive_analysis free?

Yes — smiles_comprehensive_analysis is open source and free to use under the MIT license. The source code is publicly available on GitHub at https://github.com/InternScience/scp.

Skill

smiles_comprehensive_analysis

Name: smiles_comprehensive_analysis
Author: InternScience

by InternScience

AI Summary

This booster enables comprehensive SMILES molecular analysis for cheminformatics tasks, including validation, descriptor computation, and ADMET prediction. It's useful for chemists, drug developers, and AI assistants working with molecular structures.

Install

Copy this and paste it into Claude Code, Cursor, or any AI assistant:

I want to install the "smiles_comprehensive_analysis" skill in my project.

Please run this command in my terminal:
# Install skill into the correct directory
mkdir -p .claude/skills/smiles_comprehensive_analysis && curl --retry 3 --retry-delay 2 --retry-all-errors -o .claude/skills/smiles_comprehensive_analysis/SKILL.md "https://raw.githubusercontent.com/InternScience/scp/main/skills/smiles_comprehensive_analysis/SKILL.md"

Then restart Claude Code (or reload the window in Cursor) so the skill is picked up.

skill

Description

SMILES Comprehensive Analysis - Comprehensive SMILES analysis: validate, convert name, compute all molecular descriptors, and predict ADMET. Use this skill for cheminformatics tasks involving is valid smiles ChemicalStructureAnalyzer calculate mol basic info pred molecule admet. Combines 4 tools from 3 SCP server(s).

Tools Used

• is_valid_smiles from server-2 (streamable-http) - https://scp.intern-ai.org.cn/api/v1/mcp/2/DrugSDA-Tool • ChemicalStructureAnalyzer from server-28 (sse) - https://scp.intern-ai.org.cn/api/v1/mcp/28/InternAgent • calculate_mol_basic_info 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

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-28": "https://scp.intern-ai.org.cn/api/v1/mcp/28/InternAgent", "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-28"], _, _ = await connect("https://scp.intern-ai.org.cn/api/v1/mcp/28/InternAgent", "sse") sessions["server-3"], _, _ = await connect("https://scp.intern-ai.org.cn/api/v1/mcp/3/DrugSDA-Model", "streamable-http") # Execute workflow steps # Step 1: Validate SMILES result_1 = await sessions["server-2"].call_tool("is_valid_smiles", arguments={}) data_1 = parse(result_1) print(f"Step 1 result: {json.dumps(data_1, indent=2, ensure_ascii=False)[:500]}") # Step 2: Analyze 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 molecular descriptors result_3 = await sessions["server-2"].call_tool("calculate_mol_basic_info", arguments={}) data_3 = parse(result_3) print(f"Step 3 result: {json.dumps(data_3, indent=2, ensure_ascii=False)[:500]}") # Step 4: Predict ADMET result_4 = await sessions["server-3"].call_tool("pred_molecule_admet", 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()) `