# Molclaw Drug Likeness

> Compute the drug-likeness metrics (QED score and Number of violations of Lipinski's Rule of Five) of the input candidate molecules (SMILES format).

- Skill: `internscience/molclaw-drug-likeness` (Agent Skill)
- Install (CLI): `npx skillmds@latest add internscience/molclaw-drug-likeness`
- Raw SKILL.md: https://api.skillmd.com/api/skills/internscience/molclaw-drug-likeness/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Coding & Dev Tools
- License: MIT license
- Author: internscience (https://skillmd.com/u/internscience)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/internscience/molclaw-drug-likeness

---


# Molecular Drug-likeness Metrics Calculation

Note: 
- Local files are not directly accessible by the server. Please upload them to the server using `molclaw-file-transfer` before execution. 
- For PDB file inputs, it is recommended to preprocess them using `molclaw-pdbfixer` before execution.
- Please refer to skill `molclaw-scp-server` to complete tool invocation.

The description of tool *calculate_mol_drug_chemistry*.

```tex
Compute key drug-likeness metrics 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
        --qed (float): Quantitative Estimate of Drug-likeness (QED) score
        --lipinski_rule_of_5_violations (int): Number of violations of Lipinski's Rule of Five
```

How to use tool *calculate_mol_drug_chemistry* :

```python
response = await client.session.call_tool(
    "calculate_mol_drug_chemistry",
    arguments={
        "smiles_list": smiles_list
    }
)
result = client.parse_result(response)
druglikeness_metrics = result["metrics"]
```