--- name: systems-pharmacology-0354b7913a20 description: "Systems Pharmacology Analysis - Systems pharmacology: drug targets, protein interactions, pathway enrichment, and gene expression. Use this skill for systems pharmacology tasks involving get target by name get string network interaction get functional enrichment get gene expression across cancers. Combines 4 tools from 3 SCP server(s)." --- # Systems Pharmacology Analysis **Discipline**: Systems Pharmacology | **Tools Used**: 4 | **Servers**: 3 ## Description Systems pharmacology: drug targets, protein interactions, pathway enrichment, and gene expression. ## Tools Used - **`get_target_by_name`** from `chembl-server` (streamable-http) - `https://scp.intern-ai.org.cn/api/v1/mcp/4/Origene-ChEMBL` - **`get_string_network_interaction`** from `string-server` (streamable-http) - `https://scp.intern-ai.org.cn/api/v1/mcp/6/Origene-STRING` - **`get_functional_enrichment`** from `string-server` (streamable-http) - `https://scp.intern-ai.org.cn/api/v1/mcp/6/Origene-STRING` - **`get_gene_expression_across_cancers`** from `tcga-server` (streamable-http) - `https://scp.intern-ai.org.cn/api/v1/mcp/11/Origene-TCGA` ## Workflow 1. Get drug target info 2. Build interaction network 3. Run pathway enrichment 4. Check expression across cancers ## Test Case ### Input ```json { "target": "EGFR", "species": 9606 } ``` ### Expected Steps 1. Get drug target info 2. Build interaction network 3. Run pathway enrichment 4. Check expression across cancers ## 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 = { "chembl-server": "https://scp.intern-ai.org.cn/api/v1/mcp/4/Origene-ChEMBL", "string-server": "https://scp.intern-ai.org.cn/api/v1/mcp/6/Origene-STRING", "tcga-server": "https://scp.intern-ai.org.cn/api/v1/mcp/11/Origene-TCGA" } 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["chembl-server"], _, _ = await connect("https://scp.intern-ai.org.cn/api/v1/mcp/4/Origene-ChEMBL", "streamable-http") sessions["string-server"], _, _ = await connect("https://scp.intern-ai.org.cn/api/v1/mcp/6/Origene-STRING", "streamable-http") sessions["tcga-server"], _, _ = await connect("https://scp.intern-ai.org.cn/api/v1/mcp/11/Origene-TCGA", "streamable-http") # Execute workflow steps # Step 1: Get drug target info result_1 = await sessions["chembl-server"].call_tool("get_target_by_name", arguments={}) data_1 = parse(result_1) print(f"Step 1 result: {json.dumps(data_1, indent=2, ensure_ascii=False)[:500]}") # Step 2: Build interaction network result_2 = await sessions["string-server"].call_tool("get_string_network_interaction", arguments={}) data_2 = parse(result_2) print(f"Step 2 result: {json.dumps(data_2, indent=2, ensure_ascii=False)[:500]}") # Step 3: Run pathway enrichment result_3 = await sessions["string-server"].call_tool("get_functional_enrichment", arguments={}) data_3 = parse(result_3) print(f"Step 3 result: {json.dumps(data_3, indent=2, ensure_ascii=False)[:500]}") # Step 4: Check expression across cancers result_4 = await sessions["tcga-server"].call_tool("get_gene_expression_across_cancers", 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/systems_pharmacology/SKILL.md License: MIT License information: https://opensource.org/license/mit Original upstream instructions; downloaded from Mega AI Hub. ## 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. # Mega AI Hub resource metadata Creator / publisher: InternScience Original project: https://github.com/InternScience/scp/blob/cea5398564032aea65a78e246d06c30ae945e03f/skills/systems_pharmacology/SKILL.md Attribution basis: Source repository record Fields of work: Education & Research, Healthcare & Life Sciences Roles: Clinical Research, Health Informatics, Researcher Specialties: API Tools: Chembl Server, String Server, Tcga Server Country applicability: Country not specified Classification basis: Source catalog labels Instruction SHA-256: 918fce5057d080a2451e9cde84c7967c9b434125ee700eaaa82df557e035bb2c