--- name: smiles-comprehensive-analysis-6506d9a63520 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 1. Validate SMILES 2. Analyze structure 3. Calculate molecular descriptors 4. Predict ADMET ## Test Case ### Input ```json { "smiles": "CC(=O)Oc1ccccc1C(=O)O" } ``` ### Expected Steps 1. Validate SMILES 2. Analyze structure 3. Calculate molecular descriptors 4. Predict ADMET ## Usage Example > **Note:** Replace `` with your own SCP Hub API Key. You can obtain one from the [SCP Platform](https://scphub.intern-ai.org.cn). ```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": ""}) 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()) ``` --- Source attribution: InternScience Original project: https://github.com/InternScience/scp/blob/cea5398564032aea65a78e246d06c30ae945e03f/skills/smiles_comprehensive_analysis/SKILL.md License: MIT License information: https://opensource.org/license/mit Original upstream instructions; downloaded from Dave AI. ## Upstream license notice MIT License Copyright (c) 2024 Anthropic, PBC Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. # Dave AI resource metadata Creator / publisher: InternScience Original project: https://github.com/InternScience/scp/blob/cea5398564032aea65a78e246d06c30ae945e03f/skills/smiles_comprehensive_analysis/SKILL.md Attribution basis: Source repository record Fields of work: Data & Analytics, Development & Engineering, Education & Research Roles: Data Scientist, ML / AI Engineer, Researcher Specialties: API Tools: Chemicalstructureanalyzer, Calculate_Mol_Basic_Info, Is_Valid_Smiles, Pred_Molecule_Admet Country applicability: Country not specified Classification basis: Source catalog labels Instruction SHA-256: 157c732065518aef4733e3e35e41d065d439029f852608345f444fc91f327cd8