drugsda-mol-properties

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.

Best use case

drugsda-mol-properties is best used when you need a repeatable AI agent workflow instead of a one-off prompt.

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.

Teams using drugsda-mol-properties should expect a more consistent output, faster repeated execution, less prompt rewriting.

When to use this skill

  • You want a reusable workflow that can be run more than once with consistent structure.

When not to use this skill

  • You only need a quick one-off answer and do not need a reusable workflow.
  • You cannot install or maintain the underlying files, dependencies, or repository context.

Installation

Claude Code / Cursor / Codex

$curl -o ~/.claude/skills/drugsda-mol-properties/SKILL.md --create-dirs "https://raw.githubusercontent.com/SpectrAI-Initiative/InnoClaw/main/.claude/skills/drugsda-mol-properties/SKILL.md"

Manual Installation

  1. Download SKILL.md from GitHub
  2. Place it in .claude/skills/drugsda-mol-properties/SKILL.md inside your project
  3. Restart your AI agent — it will auto-discover the skill

How drugsda-mol-properties Compares

Feature / Agentdrugsda-mol-propertiesStandard Approach
Platform SupportNot specifiedLimited / Varies
Context Awareness High Baseline
Installation ComplexityUnknownN/A

Frequently Asked Questions

What does this skill do?

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.

Where can I find the source code?

You can find the source code on GitHub using the link provided at the top of the page.

SKILL.md Source

# Molecular Properties Calculation

## Usage

### 1. MCP Server Definition

```python
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*

```tex
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

```tex
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

```tex
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

```tex
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

```tex
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

```tex
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

```tex
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:

```python
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() 
```

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