disease_drug_landscape: Скилл анализа лекарств и болезней

--- name: disease_drug_landscape description: "Disease-Drug Landscape Analysis - Map the drug landscape for a disease: OpenTargets disease drugs, FDA indications, and clinical studies. Use this skill for drug discovery tasks involving get associated drugs by target name get drug names by indication get clinical studies info by drug name. Combines 3 tools from 2 SCP server(s)." --- # Disease-Drug Landscape Analysis **Discipline**: Drug Discovery | **Tools Used**: 3 | **Servers**: 2 ## Description Map the drug landscape for a disease: OpenTargets disease drugs, FDA indications, and clinical studies. ## Tools Used - **`get_associated_drugs_by_target_name`** from `opentargets-server` (streamable-http) - `https://scp.intern-ai.org.cn/api/v1/mcp/15/Origene-OpenTargets` - **`get_drug_names_by_indication`** from `fda-drug-server` (streamable-http) - `https://scp.intern-ai.org.cn/api/v1/mcp/14/Origene-FDADrug` - **`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` ## Workflow 1. Get associated drugs from OpenTargets 2. Find drugs by indication in FDA 3. Get clinical studies for top drug ## Test Case ### Input ```json { "target_name": "EGFR", "indication": "non-small cell lung cancer" } ``` ### Expected Steps 1. Get associated drugs from OpenTargets 2. Find drugs by indication in FDA 3. Get clinical studies for top drug ## 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 = { "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" } 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["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) # Execute workflow steps # Step 1: Get associated drugs from OpenTargets result_1 = await sessions["opentargets-server"].call_tool("get_associated_drugs_by_target_name", arguments={}) data_1 = parse(result_1) print(f"Step 1 result: {json.dumps(data_1, indent=2, ensure_ascii=False)[:500]}") # Step 2: Find drugs by indication in FDA result_2 = await sessions["fda-drug-server"].call_tool("get_drug_names_by_indication", 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 for top drug 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]}") # Cleanup print("Workflow complete!") if __name__ == "__main__": asyncio.run(main()) ```