gene_to_drug_pipeline: Скилл для поиска лекарств

--- name: gene_to_drug_pipeline description: "Gene-to-Drug Discovery Pipeline - Full gene-to-drug pipeline: gene lookup, protein structure, binding pocket, virtual screening, and drug-likeness. Use this skill for translational medicine tasks involving get gene metadata by gene name pred protein structure esmfold run fpocket boltz binding affinity calculate mol drug chemistry. Combines 5 tools from 3 SCP server(s)." --- # Gene-to-Drug Discovery Pipeline **Discipline**: Translational Medicine | **Tools Used**: 5 | **Servers**: 3 ## Description Full gene-to-drug pipeline: gene lookup, protein structure, binding pocket, virtual screening, and drug-likeness. ## Tools Used - **`get_gene_metadata_by_gene_name`** from `ncbi-server` (streamable-http) - `https://scp.intern-ai.org.cn/api/v1/mcp/9/Origene-NCBI` - **`pred_protein_structure_esmfold`** from `server-3` (streamable-http) - `https://scp.intern-ai.org.cn/api/v1/mcp/3/DrugSDA-Model` - **`run_fpocket`** from `server-3` (streamable-http) - `https://scp.intern-ai.org.cn/api/v1/mcp/3/DrugSDA-Model` - **`boltz_binding_affinity`** from `server-3` (streamable-http) - `https://scp.intern-ai.org.cn/api/v1/mcp/3/DrugSDA-Model` - **`calculate_mol_drug_chemistry`** from `server-2` (streamable-http) - `https://scp.intern-ai.org.cn/api/v1/mcp/2/DrugSDA-Tool` ## Workflow 1. Get gene info from NCBI 2. Predict protein structure 3. Identify binding pockets 4. Predict ligand binding 5. Assess drug-likeness ## Test Case ### Input ```json { "gene": "BRAF", "sequence": "MAALSGPGPGA" } ``` ### Expected Steps 1. Get gene info from NCBI 2. Predict protein structure 3. Identify binding pockets 4. Predict ligand binding 5. Assess drug-likeness ## 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 = { "ncbi-server": "https://scp.intern-ai.org.cn/api/v1/mcp/9/Origene-NCBI", "server-3": "https://scp.intern-ai.org.cn/api/v1/mcp/3/DrugSDA-Model", "server-2": "https://scp.intern-ai.org.cn/api/v1/mcp/2/DrugSDA-Tool" } 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["ncbi-server"] = await connect("https://scp.intern-ai.org.cn/api/v1/mcp/9/Origene-NCBI", stack) sessions["server-3"] = await connect("https://scp.intern-ai.org.cn/api/v1/mcp/3/DrugSDA-Model", stack) sessions["server-2"] = await connect("https://scp.intern-ai.org.cn/api/v1/mcp/2/DrugSDA-Tool", stack) # Execute workflow steps # Step 1: Get gene info from NCBI result_1 = await sessions["ncbi-server"].call_tool("get_gene_metadata_by_gene_name", arguments={}) data_1 = parse(result_1) print(f"Step 1 result: {json.dumps(data_1, indent=2, ensure_ascii=False)[:500]}") # Step 2: Predict protein structure result_2 = await sessions["server-3"].call_tool("pred_protein_structure_esmfold", arguments={}) data_2 = parse(result_2) print(f"Step 2 result: {json.dumps(data_2, indent=2, ensure_ascii=False)[:500]}") # Step 3: Identify binding pockets result_3 = await sessions["server-3"].call_tool("run_fpocket", arguments={}) data_3 = parse(result_3) print(f"Step 3 result: {json.dumps(data_3, indent=2, ensure_ascii=False)[:500]}") # Step 4: Predict ligand binding result_4 = await sessions["server-3"].call_tool("boltz_binding_affinity", arguments={}) data_4 = parse(result_4) print(f"Step 4 result: {json.dumps(data_4, indent=2, ensure_ascii=False)[:500]}") # Step 5: Assess drug-likeness result_5 = await sessions["server-2"].call_tool("calculate_mol_drug_chemistry", arguments={}) data_5 = parse(result_5) print(f"Step 5 result: {json.dumps(data_5, indent=2, ensure_ascii=False)[:500]}") # Cleanup print("Workflow complete!") if __name__ == "__main__": asyncio.run(main()) ```