drug-target-structure

Drug-Target Structural Biology - Integrate drug and target structure: get drug from ChEMBL, target structure from PDB, dock them, and predict ADMET. Use this skill for structural pharmacology tasks involving get drug by name retrieve protein data by pdbcode quick molecule docking pred molecule admet. Combines 4 tools from 3 SCP server(s).

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Repository: SpectrAI-Initiative/InnoClaw
Last source activity: April 1, 2026 at 17:03
Detected SKILL.md language: English
Stars: 390
Forks: 28

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name	drug_target_structure
description	Drug-Target Structural Biology - Integrate drug and target structure: get drug from ChEMBL, target structure from PDB, dock them, and predict ADMET. Use this skill for structural pharmacology tasks involving get drug by name retrieve protein data by pdbcode quick molecule docking pred molecule admet. Combines 4 tools from 3 SCP server(s).

Drug-Target Structural Biology

Discipline: Structural Pharmacology | Tools Used: 4 | Servers: 3

Description

Integrate drug and target structure: get drug from ChEMBL, target structure from PDB, dock them, and predict ADMET.

Tools Used

get_drug_by_name from chembl-server (streamable-http) - https://scp.intern-ai.org.cn/api/v1/mcp/4/Origene-ChEMBL
retrieve_protein_data_by_pdbcode from server-2 (streamable-http) - https://scp.intern-ai.org.cn/api/v1/mcp/2/DrugSDA-Tool
quick_molecule_docking from server-3 (streamable-http) - https://scp.intern-ai.org.cn/api/v1/mcp/3/DrugSDA-Model
pred_molecule_admet from server-3 (streamable-http) - https://scp.intern-ai.org.cn/api/v1/mcp/3/DrugSDA-Model

Workflow

Get drug data from ChEMBL
Download target structure
Perform molecular docking
Predict ADMET for drug

Test Case

Input

{
    "drug":

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 = { "chembl-server": "https://scp.intern-ai.org.cn/api/v1/mcp/4/Origene-ChEMBL", "server-2": "https://scp.intern-ai.org.cn/api/v1/mcp/2/DrugSDA-Tool", "server-3": "https://scp.intern-ai.org.cn/api/v1/mcp/3/DrugSDA-Model" } 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["chembl-server"] = await connect("https://scp.intern-ai.org.cn/api/v1/mcp/4/Origene-ChEMBL", stack) sessions["server-2"] = await connect("https://scp.intern-ai.org.cn/api/v1/mcp/2/DrugSDA-Tool", stack) sessions["server-3"] = await connect("https://scp.intern-ai.org.cn/api/v1/mcp/3/DrugSDA-Model", stack) # Execute workflow steps # Step 1: Get drug data from ChEMBL result_1 = await sessions["chembl-server"].call_tool("get_drug_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: Download target structure result_2 = await sessions["server-2"].call_tool("retrieve_protein_data_by_pdbcode", arguments={}) data_2 = parse(result_2) print(f"Step 2 result: {json.dumps(data_2, indent=2, ensure_ascii=False)[:500]}") # Step 3: Perform molecular docking result_3 = await sessions["server-3"].call_tool("quick_molecule_docking", arguments={}) data_3 = parse(result_3) print(f"Step 3 result: {json.dumps(data_3, indent=2, ensure_ascii=False)[:500]}") # Step 4: Predict ADMET for drug result_4 = await sessions["server-3"].call_tool("pred_molecule_admet", 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())

drug-target-structure

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Review the source files

Drug-Target Structural Biology

Description

Tools Used

Workflow

Test Case

Input

Drug-Target Structural Biology

Description

Tools Used

Workflow

Test Case

Input

Expected Steps

Usage Example

drug-target-structure

Source facts

Install options

Review the source files

More from this repository

More from this repository

Drug-Target Structural Biology

Description

Tools Used

Workflow

Test Case

Input

Drug-Target Structural Biology

Description

Tools Used

Workflow

Test Case

Input

Expected Steps

Usage Example