# Molclaw Mol Similarity

> Calculate both Tanimoto similarities and the count of shared structural fragments between a target molecule and a list of candidate molecules via Morgan fingerprints.

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

---


# Molecule Similarity 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.

**Scene 1**: Compute the Tanimoto similarities between a target molecule and a list of candidate molecules using Morgan fingerprints. Need to use the tool *calculate_morgan_fingerprint_similarity*.

The description of tool *calculate_morgan_fingerprint_similarity*.

```tex
Compute the Tanimoto similarities between a target molecule and a list of candidate molecules using Morgan fingerprints.
Args:
    target_smiles (str): SMILES string of the target molecule
    candidate_smiles_list (List[str]): List of candidate molecule SMILES strings
    radius (int): Required Morgan fingerprint radius (commonly 2)
    nBits (int): Required Morgan fingerprint vector bit count (commonly 2048)
Return:
    status (str): success/error
    msg (str): message
    similarities (List[dict]): List of dict, each containing the keys 'smiles' and 'score'.
        --smiles (str): A SMILES string of candidate_smiles_list
        --score (float): Similarity value between the candidate SMILES and the target SMILES
```

How to use tool *calculate_morgan_fingerprint_similarity* :

```python
response = await client.session.call_tool(
    "calculate_morgan_fingerprint_similarity",
    arguments={
        "target_smiles": target_smiles,
        "candidate_smiles_list": candidate_smiles_list,
        "radius": radius,
        "nBits": nBits
    }
)
result = client.parse_result(response)
similarities = result["similarities"]
```

**Scene 2**: Compute the count of shared structural fragments between a target molecule and a list of candidate molecules using Morgan fingerprints. Need to use the tool *calculate_common_fragments*.

The description of tool *calculate_common_fragments*.

```tex
Compute the count of shared structural fragments between a target molecule and a list of candidate molecules using Morgan fingerprints.
Args:
    target_smiles (str): SMILES string of the target molecule
    candidate_smiles_list (List[str]): List of candidate molecule SMILES strings
    radius (int): Required Morgan fingerprint radius (commonly 2)
Return:
    status (str): success/error
    msg (str): message
    fragments_info (List[dict]): List of dict, each containing the keys 'smiles' and 'common_fragment_count'.
        --smiles (str): A SMILES string of candidate_smiles_list
        --common_fragment_count (float): Number of structural fragments shared between the candidate SMILES and the target SMILES
```

How to use tool *calculate_common_fragments* :

```python
response = await client.session.call_tool(
    "calculate_common_fragments",
    arguments={
        "target_smiles": target_smiles,
        "candidate_smiles_list": candidate_smiles_list,
        "radius": radius
    }
)
result = client.parse_result(response)
fragments_info = result["fragments_info"]
```