Molecular formula candidate that holds a unique identifier (molecular formula + adduct). It can be extended with optional scoring metrics and the raw results such as fragmentation trees and simulated isotope pattern.
| Name | Type | Description | Notes |
|---|---|---|---|
| formula_id | str | Unique identifier of this formula candidate | [optional] |
| molecular_formula | str | molecular formula of this formula candidate | [optional] |
| adduct | str | Adduct of this formula candidate | [optional] |
| rank | int | [optional] | |
| sirius_score_normalized | float | Normalized Sirius Score of the formula candidate. If NULL result is not available | [optional] |
| sirius_score | float | Sirius Score (isotope + tree score) of the formula candidate. If NULL result is not available | [optional] |
| isotope_score | float | [optional] | |
| tree_score | float | [optional] | |
| zodiac_score | float | Zodiac Score of the formula candidate. If NULL result is not available | [optional] |
| num_of_explained_peaks | int | [optional] | |
| num_of_explainable_peaks | int | [optional] | |
| total_explained_intensity | float | [optional] | |
| median_mass_deviation | Deviation | [optional] | |
| fragmentation_tree | FragmentationTree | [optional] | |
| annotated_spectrum | AnnotatedSpectrum | [optional] | |
| isotope_pattern_annotation | IsotopePatternAnnotation | [optional] | |
| lipid_annotation | LipidAnnotation | [optional] | |
| predicted_fingerprint | List[Optional[float]] | Probabilistic molecular fingerprint predicted by CSI:FingerID | [optional] |
| compound_classes | CompoundClasses | [optional] | |
| canopus_prediction | CanopusPrediction | [optional] |
from PySirius.models.formula_candidate import FormulaCandidate
# TODO update the JSON string below
json = "{}"
# create an instance of FormulaCandidate from a JSON string
formula_candidate_instance = FormulaCandidate.from_json(json)
# print the JSON string representation of the object
print(FormulaCandidate.to_json())
# convert the object into a dict
formula_candidate_dict = formula_candidate_instance.to_dict()
# create an instance of FormulaCandidate from a dict
formula_candidate_from_dict = FormulaCandidate.from_dict(formula_candidate_dict)