Comprehensive drug screening pipeline from molecular filtering through QED/ADMET criteria to protein-ligand docking, identifying promising drug candidates.
import json
from contextlib import AsyncExitStack
from mcp.client.streamable_http import streamablehttp_client
from mcp import ClientSession
class DrugSDAClient:
def __init__(self, server_url: str):
self.server_url = server_url
self.session = None
async def connect(self):
print(f"server url: {self.server_url}")
try:
self.transport = streamablehttp_client(
url=self.server_url,
headers={"SCP-HUB-API-KEY": "<your-api-key>"}
)
self._stack = AsyncExitStack()
await self._stack.__aenter__()
self.read, self.write, self.get_session_id = await self._stack.enter_async_context(self.transport)
self.session_ctx = ClientSession(self.read, self.write)
self.session = await self._stack.enter_async_context(self.session_ctx)
await self.session.initialize()
session_id = self.get_session_id()
print(f"✓ connect success")
return True
except Exception as e:
print(f"✗ connect failure: {e}")
import traceback
traceback.print_exc()
return False
async def disconnect(self):
"""Disconnect from server"""
try:
if hasattr(self, '_stack'):
await self._stack.aclose()
print("✓ already disconnect")
except Exception as e:
print(f"✗ disconnect error: {e}")
def parse_result(self, result):
try:
if hasattr(result, 'content') and result.content:
content = result.content[0]
if hasattr(content, 'text'):
return json.loads(content.text)
return str(result)
except Exception as e:
return {"error": f"parse error: {e}", "raw": str(result)}
This workflow screens candidate molecules using drug-likeness and ADMET criteria, then performs molecular docking with a target protein to identify promising drug candidates.
Workflow Steps:
Implementation:
## Initialize clients for both Tool and Model servers
tool_client = DrugSDAClient("https://scp.intern-ai.org.cn/api/v1/mcp/2/DrugSDA-Tool")
model_client = DrugSDAClient("https://scp.intern-ai.org.cn/api/v1/mcp/3/DrugSDA-Model")
if not await tool_client.connect() or not await model_client.connect():
print("connection failed")
return
## Input: List of candidate SMILES strings
smiles_list = ['O=C(Nc1cccc2c1CCCC2)N1CCc2c([nH]c3ccccc23)C1c1cccc(F)c1F', ...]
## Step 1: Calculate QED scores
result = await tool_client.session.call_tool(
"calculate_mol_drug_chemistry",
arguments={"smiles_list": smiles_list}
)
QED_result = tool_client.parse_result(result)["metrics"]
## Step 2: Predict ADMET properties (LD50)
result = await model_client.session.call_tool(
"pred_molecule_admet",
arguments={"smiles_list": smiles_list}
)
LD50_result = model_client.parse_result(result)["admet_preds"]
## Step 3: Filter molecules by QED and LD50 criteria
select_smiles_list = []
for i in range(len(smiles_list)):
QED = QED_result[i]["qed"]
LD50 = LD50_result[i]["LD50_Zhu"]
if QED >= 0.6 and LD50 >= 3.0:
select_smiles_list.append(smiles_list[i])
## Step 4: Retrieve protein structure by PDB code
pdb_code = "6vkv"
result = await tool_client.session.call_tool(
"retrieve_protein_data_by_pdbcode",
arguments={"pdb_code": pdb_code}
)
pdb_path = tool_client.parse_result(result)["pdb_path"]
## Step 5: Extract main chain
result = await tool_client.session.call_tool(
"save_main_chain_pdb",
arguments={"pdb_file": pdb_path, "main_chain_id": ""}
)
pdb_path = tool_client.parse_result(result)["out_file"]
## Step 6: Fix PDB file for docking
result = await tool_client.session.call_tool(
"fix_pdb_dock",
arguments={"pdb_file_path": pdb_path}
)
pdb_path = tool_client.parse_result(result)["fix_pdb_file_path"]
## Step 7: Identify binding pocket
result = await model_client.session.call_tool(
"run_fpocket",
arguments={"pdb_path": pdb_path}
)
best_pocket = tool_client.parse_result(result)["pockets"][0]
## Step 8: Convert SMILES to PDBQT format
result = await tool_client.session.call_tool(
"convert_smiles_to_other_format",
arguments={"inputs": select_smiles_list, "target_format": "pdbqt"}
)
ligand_paths = [x["output_file"] for x in tool_client.parse_result(result)["convert_results"]]
## Step 9: Convert protein PDB to PDBQT
result = await tool_client.session.call_tool(
"convert_pdb_to_pdbqt_dock",
arguments={"input_pdb_path": pdb_path}
)
receptor_path = tool_client.parse_result(result)["output_file"]
## Step 10: Perform molecular docking
result = await model_client.session.call_tool(
"quick_molecule_docking",
arguments={
"receptor_path": receptor_path,
"ligand_paths": ligand_paths,
"center_x": best_pocket["center_x"],
"center_y": best_pocket["center_y"],
"center_z": best_pocket["center_z"],
"size_x": best_pocket["size_x"],
"size_y": best_pocket["size_y"],
"size_z": best_pocket["size_z"]
}
)
docking_results = model_client.parse_result(result)["docking_results"]
## Step 11: Filter by binding affinity
final_smiles_list = []
for item in docking_results:
if item['affinity'] <= -7.0:
final_smiles_list.append(select_smiles_list[item['index']])
print(f"Final candidates: {final_smiles_list}")
await tool_client.disconnect()
await model_client.disconnect()
DrugSDA-Tool Server Tools:
calculate_mol_drug_chemistry: Compute QED score and Lipinski's Rule of Five violationsretrieve_protein_data_by_pdbcode: Download protein structure from RCSB PDBsave_main_chain_pdb: Extract main protein chainfix_pdb_dock: Repair PDB file using PDBFixerconvert_smiles_to_other_format: Convert SMILES to various formats (PDBQT, SDF, etc.)convert_pdb_to_pdbqt_dock: Convert PDB to PDBQT format for dockingDrugSDA-Model Server Tools:
pred_molecule_admet: Predict ADMET properties including LD50 toxicityrun_fpocket: Identify protein binding pocketsquick_molecule_docking: Perform AutoDock Vina molecular dockingInput:
smiles_list: List of SMILES strings representing candidate moleculespdb_code: PDB code of target protein structureOutput:
final_smiles_list: SMILES strings of molecules with QED ≥ 0.6, LD50 ≥ 3.0, and binding affinity ≤ -7.0 kcal/molAdjust these thresholds based on your specific requirements.