chemical_patent_analysis: Скилл анализа патентов и новизны

--- name: chemical_patent_analysis description: "Chemical Patent & Novelty Analysis - Analyze chemical novelty: PubChem substructure CAS search, ChEMBL similarity search, compound synonyms, and literature. Use this skill for patent chemistry tasks involving get substructure cas get similarity by smiles get compound synonyms by name search literature. Combines 4 tools from 3 SCP server(s)." --- # Chemical Patent & Novelty Analysis **Discipline**: Patent Chemistry | **Tools Used**: 4 | **Servers**: 3 ## Description Analyze chemical novelty: PubChem substructure CAS search, ChEMBL similarity search, compound synonyms, and literature. ## Tools Used - **`get_substructure_cas`** from `pubchem-server` (streamable-http) - `https://scp.intern-ai.org.cn/api/v1/mcp/8/Origene-PubChem` - **`get_similarity_by_smiles`** from `chembl-server` (streamable-http) - `https://scp.intern-ai.org.cn/api/v1/mcp/4/Origene-ChEMBL` - **`get_compound_synonyms_by_name`** from `pubchem-server` (streamable-http) - `https://scp.intern-ai.org.cn/api/v1/mcp/8/Origene-PubChem` - **`search_literature`** from `server-1` (sse) - `https://scp.intern-ai.org.cn/api/v1/mcp/1/VenusFactory` ## Workflow 1. Search CAS by substructure 2. Search ChEMBL by similarity 3. Get compound synonyms 4. Search patent literature ## Test Case ### Input ```json { "smiles": "c1ccc(-c2ccccc2)cc1", "compound_name": "biphenyl" } ``` ### Expected Steps 1. Search CAS by substructure 2. Search ChEMBL by similarity 3. Get compound synonyms 4. Search patent literature ## Usage Example > **Note:** Replace `sk-b04409a1-b32b-4511-9aeb-22980abdc05c` 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 contextlib import AsyncExitStack from mcp import ClientSession from mcp.client.streamable_http import streamablehttp_client from mcp.client.sse import sse_client SERVERS = { "pubchem-server": "https://scp.intern-ai.org.cn/api/v1/mcp/8/Origene-PubChem", "chembl-server": "https://scp.intern-ai.org.cn/api/v1/mcp/4/Origene-ChEMBL", "server-1": "https://scp.intern-ai.org.cn/api/v1/mcp/1/VenusFactory" } 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["pubchem-server"] = await connect("https://scp.intern-ai.org.cn/api/v1/mcp/8/Origene-PubChem", stack) sessions["chembl-server"] = await connect("https://scp.intern-ai.org.cn/api/v1/mcp/4/Origene-ChEMBL", stack) sessions["server-1"] = await connect("https://scp.intern-ai.org.cn/api/v1/mcp/1/VenusFactory", stack) # Execute workflow steps # Step 1: Search CAS by substructure result_1 = await sessions["pubchem-server"].call_tool("get_substructure_cas", arguments={}) data_1 = parse(result_1) print(f"Step 1 result: {json.dumps(data_1, indent=2, ensure_ascii=False)[:500]}") # Step 2: Search ChEMBL by similarity result_2 = await sessions["chembl-server"].call_tool("get_similarity_by_smiles", arguments={}) data_2 = parse(result_2) print(f"Step 2 result: {json.dumps(data_2, indent=2, ensure_ascii=False)[:500]}") # Step 3: Get compound synonyms result_3 = await sessions["pubchem-server"].call_tool("get_compound_synonyms_by_name", arguments={}) data_3 = parse(result_3) print(f"Step 3 result: {json.dumps(data_3, indent=2, ensure_ascii=False)[:500]}") # Step 4: Search patent literature result_4 = await sessions["server-1"].call_tool("search_literature", 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()) ```