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currentAutomated scan100/100internscience/scp/smiles-comprehensive-analysis
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version: "1.0.1" name: smiles_comprehensive_analysis 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)."
SMILES Comprehensive Analysis
Discipline: Cheminformatics | Tools Used: 4 | Servers: 3
Description
Comprehensive SMILES analysis: validate, convert name, compute all molecular descriptors, and predict ADMET.
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
Workflow
- Validate SMILES
- Analyze structure
- Calculate molecular descriptors
- Predict ADMET
Test Case
Input
json
{"smiles": "CC(=O)Oc1ccccc1C(=O)O"}
Expected Steps
- Validate SMILES
- Analyze structure
- Calculate molecular descriptors
- 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 asyncioimport jsonfrom mcp import ClientSessionfrom mcp.client.streamable_http import streamablehttp_clientfrom mcp.client.sse import sse_clientSERVERS = {"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, transportdef 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.textreturn str(result)except: return str(result)async def main():# Connect to required serverssessions = {}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 SMILESresult_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 structureresult_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 descriptorsresult_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 ADMETresult_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]}")# Cleanupprint("Workflow complete!")if __name__ == "__main__":asyncio.run(main())