orphan_drug_analysis: Скилл анализа редких заболеваний

--- name: orphan_drug_analysis description: "Orphan Drug & Rare Disease Analysis - Analyze orphan drugs: Monarch disease phenotypes, OpenTargets targets, FDA drug data, and clinical studies. Use this skill for orphan drug development tasks involving get joint associated diseases by HPO ID list get associated targets by disease efoId get clinical studies info by drug name pubmed search. Combines 4 tools from 4 SCP server(s)." --- # Orphan Drug & Rare Disease Analysis **Discipline**: Orphan Drug Development | **Tools Used**: 4 | **Servers**: 4 ## Description Analyze orphan drugs: Monarch disease phenotypes, OpenTargets targets, FDA drug data, and clinical studies. ## Tools Used - **`get_joint_associated_diseases_by_HPO_ID_list`** from `monarch-server` (streamable-http) - `https://scp.intern-ai.org.cn/api/v1/mcp/16/Origene-Monarch` - **`get_associated_targets_by_disease_efoId`** from `opentargets-server` (streamable-http) - `https://scp.intern-ai.org.cn/api/v1/mcp/15/Origene-OpenTargets` - **`get_clinical_studies_info_by_drug_name`** from `fda-drug-server` (streamable-http) - `https://scp.intern-ai.org.cn/api/v1/mcp/14/Origene-FDADrug` - **`pubmed_search`** from `search-server` (streamable-http) - `https://scp.intern-ai.org.cn/api/v1/mcp/7/Origene-Search` ## Workflow 1. Map phenotypes to diseases 2. Find drug targets 3. Get clinical studies 4. Search literature ## Test Case ### Input ```json { "hpo_ids": [ "HP:0001250" ], "disease_efo": "MONDO_0010075", "query": "orphan drug seizure disorder" } ``` ### Expected Steps 1. Map phenotypes to diseases 2. Find drug targets 3. Get clinical studies 4. Search 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 = { "monarch-server": "https://scp.intern-ai.org.cn/api/v1/mcp/16/Origene-Monarch", "opentargets-server": "https://scp.intern-ai.org.cn/api/v1/mcp/15/Origene-OpenTargets", "fda-drug-server": "https://scp.intern-ai.org.cn/api/v1/mcp/14/Origene-FDADrug", "search-server": "https://scp.intern-ai.org.cn/api/v1/mcp/7/Origene-Search" } 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["monarch-server"] = await connect("https://scp.intern-ai.org.cn/api/v1/mcp/16/Origene-Monarch", stack) sessions["opentargets-server"] = await connect("https://scp.intern-ai.org.cn/api/v1/mcp/15/Origene-OpenTargets", stack) sessions["fda-drug-server"] = await connect("https://scp.intern-ai.org.cn/api/v1/mcp/14/Origene-FDADrug", stack) sessions["search-server"] = await connect("https://scp.intern-ai.org.cn/api/v1/mcp/7/Origene-Search", stack) # Execute workflow steps # Step 1: Map phenotypes to diseases result_1 = await sessions["monarch-server"].call_tool("get_joint_associated_diseases_by_HPO_ID_list", arguments={}) data_1 = parse(result_1) print(f"Step 1 result: {json.dumps(data_1, indent=2, ensure_ascii=False)[:500]}") # Step 2: Find drug targets result_2 = await sessions["opentargets-server"].call_tool("get_associated_targets_by_disease_efoId", arguments={}) data_2 = parse(result_2) print(f"Step 2 result: {json.dumps(data_2, indent=2, ensure_ascii=False)[:500]}") # Step 3: Get clinical studies result_3 = await sessions["fda-drug-server"].call_tool("get_clinical_studies_info_by_drug_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 literature result_4 = await sessions["search-server"].call_tool("pubmed_search", 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()) ```