drugsda-mol-properties

name	drugsda-mol-properties
description	Calculate different types of molecular properties based on SMILES strings, covering basic physicochemical properties, hydrophobicity, hydrogen bonding capability, molecular complexity, topological structures, charge distribution, and custom complexity metrics, respectively.
license	MIT license
metadata	{"skill-author":"PJLab"}

Molecular Properties Calculation

Usage

1. MCP Server Definition

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": "sk-a0033dde-b3cd-413b-adbe-980bc78d6126"}
            )
            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)}

2. Tool Description

Tool 1: calculate_mol_basic_info

Compute a set of basic molecular properties for each SMILES.
Args:
    smiles_list (List[str]): List of input SMILES strings, (e.g., ["N[C@@H](Cc1ccc(O)cc1)C(=O)O", "CC(C)C1=CC=CC=C1"])
Return:
    status (str): success/error
    msg (str): message
    metrics (List[dict]): List of dict, each containing feature keys.
        --smiles (str): A SMILES string of smiles_list
        --molecular_formula (str): Molecular formula, e.g. "C9H11NO3"
        --exact_molecular_weight (float): Exact molecular weight
        --molecular_weight (float): Average molecular weight
        --num_heavy_atoms (int): Number of heavy atoms
        --num_atoms (int): Number of total atoms
        --num_bonds (int): Number of bonds
        --num_valence_electrons (int): Number of valence electrons
        --formal_charge (int): Number of formal charge

Tool 2: calculate_mol_hydrophobicity

Compute hydrophobicity-related molecular descriptors for each SMILES.
Args:
    smiles_list (List[str]): List of input SMILES strings, (e.g., ["N[C@@H](Cc1ccc(O)cc1)C(=O)O", "CC(C)C1=CC=CC=C1"])
Return:
    status (str): success/error
    msg (str): message
    metrics (List[dict]): List of dict, each containing feature keys.
        --smiles (str): A SMILES string of smiles_list
        --logp (float): The octanol-water partition coefficient (logP)
        --molar_refractivity (float): Molar refractivity

Tool 3: calculate_mol_hbond

Compute hydrogen bonding-related properties for each SMILES.
Args:
    smiles_list (List[str]): List of input SMILES strings, (e.g., ["N[C@@H](Cc1ccc(O)cc1)C(=O)O", "CC(C)C1=CC=CC=C1"])
Return:
    status (str): success/error
    msg (str): message
    metrics (List[dict]): List of dict, each containing several feature keys.
        --smiles (str): A SMILES string of smiles_list
        --num_h_donors (int): Number of hydrogen bond donors
        --num_h_acceptors (int): Number of hydrogen bond acceptors

Tool 4: calculate_mol_structure_complexity

Compute a set of molecular complexity descriptors for each SMILES.
Args:
    smiles_list (List[str]): List of input SMILES strings, (e.g., ["N[C@@H](Cc1ccc(O)cc1)C(=O)O", "CC(C)C1=CC=CC=C1"])
Return:
    status (str): success/error
    msg (str): message
    metrics (List[dict]): List of dict, each containing feature keys.
        --smiles (str): A SMILES string of smiles_list
        --num_rotatable_bonds (int): Number of rotatable bonds
        --num_rings (int): Number of total rings
        --num_aromatic_rings (int): Number of aromatic rings
        --num_aliphatic_rings (int): Number of aliphatic rings
        --num_saturated_rings (int): Number of saturated rings
        --num_heteroatoms (int): Number of heteroatoms
        --fraction_csp3 (float): The fraction of sp³-hybridized carbon atoms (Fsp³)
        --num_bridgehead_atoms (int): Number of bridgehead atoms

Tool 5: calculate_mol_topology

Compute a set of topological descriptors for each SMILES.
Args:
    smiles_list (List[str]): List of input SMILES strings, (e.g., ["N[C@@H](Cc1ccc(O)cc1)C(=O)O", "CC(C)C1=CC=CC=C1"])
Return:
    status (str): success/error
    msg (str): message
    metrics (List[dict]): List of dict, each containing several feature keys.
        --smiles (str): A SMILES string of smiles_list
        --tpsa (float): Topological polar surface area
        --chi0v (float): Non-valence molecular connectivity index
        --chi1v (float): Non-valence molecular connectivity index
        --chi2v (float): Non-valence molecular connectivity index
        --chi3v (float): Non-valence molecular connectivity index
        --chi4v (float): Non-valence molecular connectivity index
        --chi0n (float): Non-valence molecular connectivity index
        --chi1n (float): Non-valence molecular connectivity index
        --chi2n (float): Non-valence molecular connectivity index
        --chi3n (float): Non-valence molecular connectivity index
        --chi4n (float): Non-valence molecular connectivity index
        --hall_kier_alpha (float): Hall–Kier alpha value
        --kappa1 (float): Kappa shape index
        --kappa2 (float): Kappa shape index
        --kappa3 (float): Kappa shape index

Tool 6: calculate_mol_charge

Compute Gasteiger partial charges and formal charge for each SMILES.
Args:
    smiles_list (List[str]): List of input SMILES strings, (e.g., ["N[C@@H](Cc1ccc(O)cc1)C(=O)O", "CC(C)C1=CC=CC=C1"])
Return:
    status (str): success/error
    msg (str): message
    metrics (List[dict]): List of dict, each containing several feature keys.
        --smiles (str): A SMILES string of smiles_list
        --min_gasteiger_charge (float): Minimum of Gasteiger charges
        --max_gasteiger_charge (float): Maximum of Gasteiger charges
        --avg_gasteiger_charge (float): Average of Gasteiger charges
        --gasteiger_charge_range (float): Range of Gasteiger charges
        --formal_charge (int): Formal charge

Tool 7: calculate_mol_complexity

Compute custom molecular complexity-related descriptors for each SMILES.
Args:
    smiles_list (List[str]): List of input SMILES strings, (e.g., ["N[C@@H](Cc1ccc(O)cc1)C(=O)O", "CC(C)C1=CC=CC=C1"])
Return:
    status (str): success/error
    msg (str): message
    metrics (List[dict]): List of dict, each containing feature keys.
        --smiles (str): A SMILES string of smiles_list
        --molecular_complexity (int): Molecular complexity
        --aromatic_proportion (float): Aromatic proportion 
        --asphericity (float): Asphericity

3. Example Code

How to use these tools:

client = DrugSDAClient("https://scp.intern-ai.org.cn/api/v1/mcp/2/DrugSDA-Tool")
if not await client.connect():
    print("connection failed")
    return

## The tool can be replaced with another based on actual requirements.
response = await client.session.call_tool(
    "calculate_mol_basic_info",			
    arguments={
        "smiles_list": smiles_list
    }
)
result = client.parse_result(response)
metrics = result["metrics"]

await client.disconnect()