| name | molclaw-mol-similarity |
| description | Calculate both Tanimoto similarities and the count of shared structural fragments between a target molecule and a list of candidate molecules via Morgan fingerprints. |
| license | MIT license |
| metadata | {"skill-author":"PJLab"} |
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.
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): Morgan fingerprint radius, default is 2
nBits (int): Morgan fingerprint vector bits number, default is 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 :
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.
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): Morgan fingerprint radius, default is 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 :
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"]
