StructureCandidateScored.md

StructureCandidateScored

Properties

Name	Type	Description	Notes
inchi_key	str		[optional]
smiles	str		[optional]
structure_name	str		[optional]
structure_svg	str	SVG graphics of the structure candidate OPTIONAL: needs to be added by parameter	[optional]
db_links	List[DBLink]	List of structure database links belonging to this structure candidate OPTIONAL: needs to be added by parameter	[optional]
spectral_library_matches	List[SpectralLibraryMatch]	List of spectral library matches belonging to this structure candidate OPTIONAL: needs to be added by parameter	[optional]
xlog_p	float		[optional]
rank	int	the overall rank of this candidate among all candidates of this feature	[optional]
csi_score	float	CSI:FingerID score of the fingerprint of this compound to the predicted fingerprint of CSI:FingerID This is the score used for ranking structure candidates	[optional]
tanimoto_similarity	float	Tanimoto similarly of the fingerprint of this compound to the predicted fingerprint of CSI:FingerID	[optional]
mces_dist_to_top_hit	float	Maximum Common Edge Subgraph (MCES) distance to the top scoring hit (CSI:FingerID) in a candidate list.	[optional]
fingerprint	BinaryFingerprint		[optional]

Example

from PySirius.models.structure_candidate_scored import StructureCandidateScored

# TODO update the JSON string below
json = "{}"
# create an instance of StructureCandidateScored from a JSON string
structure_candidate_scored_instance = StructureCandidateScored.from_json(json)
# print the JSON string representation of the object
print(StructureCandidateScored.to_json())

# convert the object into a dict
structure_candidate_scored_dict = structure_candidate_scored_instance.to_dict()
# create an instance of StructureCandidateScored from a dict
structure_candidate_scored_from_dict = StructureCandidateScored.from_dict(structure_candidate_scored_dict)

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