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version: "1.0.0" name: boltz2-binding-affinity description: Predict protein-ligand binding affinity using Boltz-2 model to assess molecular interactions and binding probability for drug discovery. license: MIT license metadata: skill-author: PJLab

Boltz-2 Protein-Ligand Binding Affinity Prediction

Usage

1. MCP Server Definition

python

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)}

2. Boltz-2 Binding Affinity Workflow

This workflow predicts protein-ligand binding affinity using the Boltz-2 deep learning model, providing affinity probabilities and 3D complex structures.

Workflow Steps:

Prepare Input - Define protein sequence and SMILES list for ligands
Run Boltz-2 Prediction - Calculate binding affinity probability for each ligand
Analyze Results - Extract affinity scores and structure files

Implementation:

python

## 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()

Tool Descriptions

DrugSDA-Model Server:

boltz_binding_affinity: Predict protein-ligand binding affinity using Boltz-2
Args:
protein (list): List of protein chains with sequence information
Each chain: {'chain': str, 'sequence': str}
smiles_list (list): List of ligand SMILES strings
Returns:
boltz_res (list): List of binding predictions
smiles (str): Ligand SMILES string
affinity_probability (float): Binding affinity probability (0-1)
cif_file (str): Path to predicted complex structure

Input/Output

Input:

protein: List of protein chains
chain: Chain identifier (e.g., 'A', 'B')
sequence: Amino acid sequence in single-letter code
smiles_list: List of SMILES strings for ligand molecules

Output:

List of binding predictions, each containing:
smiles: Ligand SMILES string
affinity_probability: Binding probability (0-1, higher is better)
cif_file: Path to predicted protein-ligand complex structure in CIF format

Affinity Interpretation

Probability > 0.5: Strong binding likelihood
Probability 0.3-0.5: Moderate binding potential
Probability < 0.3: Weak or no binding expected

Use Cases

Virtual screening of compound libraries
Lead optimization in drug discovery
Protein-ligand binding mode prediction
Structure-based drug design
Comparative binding analysis across ligands

Performance Notes

Execution time: 30-120 seconds per ligand depending on protein size
Protein length: Best for proteins <1000 amino acids
Multiple ligands: Processes sequentially, allow sufficient time
Structure output: CIF files can be visualized in PyMOL, ChimeraX, or similar tools

All versions