Predict protein-ligand binding affinity using Boltz-2 model to assess molecular interactions and binding probability for drug discovery.
import asyncio
import json
from mcp.client.streamable_http import streamablehttp_client
from mcp import ClientSession
class DrugSDAClient:
"""DrugSDA-Model MCP Client"""
def __init__(self, server_url: str, api_key: str):
self.server_url = server_url
self.api_key = api_key
self.session = None
async def connect(self):
"""Establish connection and initialize session"""
print(f"server url: {self.server_url}")
try:
self.transport = streamablehttp_client(
url=self.server_url,
headers={"SCP-HUB-API-KEY": self.api_key}
)
self.read, self.write, self.get_session_id = await self.transport.__aenter__()
self.session_ctx = ClientSession(self.read, self.write)
self.session = await self.session_ctx.__aenter__()
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 self.session:
await self.session_ctx.__aexit__(None, None, None)
if hasattr(self, 'transport'):
await self.transport.__aexit__(None, None, None)
print("✓ already disconnect")
except Exception as e:
print(f"✗ disconnect error: {e}")
def parse_result(self, result):
"""Parse MCP tool call 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 predicts protein-ligand binding affinity using the Boltz-2 deep learning model, providing affinity probabilities and 3D complex structures.
Workflow Steps:
Implementation:
## Initialize client
client = DrugSDAClient(
"https://scp.intern-ai.org.cn/api/v1/mcp/3/DrugSDA-Model",
"<your-api-key>"
)
if not await client.connect():
print("connection failed")
exit()
## Input: Protein sequence and ligand SMILES
sequence = 'PIVQNLQGQMVHQCISPRTLNAWVKVVEEKAFSPEVIPMFSALSCGATPQDLNTMLNTVGGHQAAMQMLKETINEEAAEWDRLHPVHAGPIAPGQMREPRGSDIAGTTSTLQEQIGWMTHNPPIPVGEIYKRWIILGLNKIVRMYSPTSILDIRQGPKEPFRDYVDRFYKTLRAEQASQEVKNAATETLLVQNANPDCKTILKALGPGATLEEMMTACQG'
protein = [{'chain': 'A', 'sequence': sequence}]
smiles_list = ['N[C@@H](Cc1ccc(O)cc1)C(=O)O', "CC(C)C1=CC=CC=C1"]
## Execute Boltz-2 binding affinity prediction
result = await client.session.call_tool(
"boltz_binding_affinity",
arguments={
"protein": protein,
"smiles_list": smiles_list
}
)
result_data = client.parse_result(result)
boltz_res = result_data["boltz_res"]
## Display results
for i, item in enumerate(boltz_res, 1):
print(f"{i}. SMILES: {item['smiles']}")
print(f" Affinity Probability: {item['affinity_probability']:.4f}")
print(f" Structure File: {item['cif_file']}\n")
await client.disconnect()
DrugSDA-Model Server:
boltz_binding_affinity: Predict protein-ligand binding affinity using Boltz-2
protein (list): List of protein chains with sequence information
{'chain': str, 'sequence': str}smiles_list (list): List of ligand SMILES stringsboltz_res (list): List of binding predictions
smiles (str): Ligand SMILES stringaffinity_probability (float): Binding affinity probability (0-1)cif_file (str): Path to predicted complex structureInput:
protein: List of protein chains
chain: Chain identifier (e.g., 'A', 'B')sequence: Amino acid sequence in single-letter codesmiles_list: List of SMILES strings for ligand moleculesOutput:
smiles: Ligand SMILES stringaffinity_probability: Binding probability (0-1, higher is better)cif_file: Path to predicted protein-ligand complex structure in CIF format