# Molclaw Dleps

> Calculate disease reversal scores for the provided molecules relative to a specific disease.

- Skill: `internscience/molclaw-dleps` (Agent Skill)
- Install (CLI): `npx skillmds@latest add internscience/molclaw-dleps`
- Raw SKILL.md: https://api.skillmd.com/api/skills/internscience/molclaw-dleps/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-dleps

---


# DLEPS Score 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_dleps_score*.

```tex
Enter a list of candidate small molecules. Based on the input disease name, identify upregulated and downregulated genes associated with the disease state, and predict a reversal score for each small molecule. Generally, a score above 0.2 indicates effectiveness, with higher scores being better.
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"])
    disease_name (str): Supportes diseases, e.g., "Aging", "Gout", "Pulmonary fibrosis", "Non-alcoholic fatty liver disease", "Obesity" 
Return:
    status (str): success/error
    msg (str): message
    pred_scores (List[dict]): List of dict, each containing the keys 'smiles' and 'cs_score'. 
        --smiles (str): A SMILES string of smiles_list 
        --cs_score (float): Predicted reverse score
```

How to use tool *calculate_dleps_score* :

```python
response = await client.session.call_tool(
    "calculate_dleps_score",
    arguments={
        "smiles_list": smiles_list,
        "disease_name": disease_name
    }
)
result = client.parse_result(response)
pred_scores = result["pred_scores"]
```