القائمة
Calculate both Tanimoto similarities and the count of shared structural fragments between a target molecule and a list of candidate molecules via Morgan fingerprints.
التثبيت باستخدام Codex أو Claude انسخ هذا Prompt والصقه في Codex أو Claude أو مساعد آخر ليراجع صفحة Skill ويثبّتها لك.
استنادا إلى تصنيف SOC المهني
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| 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"} |
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"]