Given that only approximately 10% of features are commonly annotated using experimental spectral DB alone (e.g. GNPS), several computational tools have emerged to bridge this gap by enabling metabolite annotation through in silico spectrum prediction and structural candidates identification . These tools serve as a link between MS 2 spectra and molecular structure DB. Broadly, two distinct approaches are employed: 1) predicting an in silico MS 2 spectrum based on a molecular structure, and 2) predicting a spectral fingerprint from an MS spectrum and then matching it to a molecular structure from DBs . The first approach is the base for the strategy developed by Allard et al. (2016) , allowing the integration of NPs in silico DB (ISDB) and MN in the dereplication pipeline. An additional improvement in this strategy was introduced by Rutz et al. (2019) by considering the taxonomic distance between the candidate structure's biological source and the annotated NE's biological sources. This approach was be automated (e.g., TimaR). This was shown to systematically improve the annotation results quality of several computational metabolite annotation approaches (ISDB, MSFinder, Sirius). The comparison of experimental and in silico MS 2 spectra is done through spectral similarity algorithms. The second approach, fingerprint matching, is the basis of the SIRIUS workflow and its dependencies. It starts by predicting the molecular formula (MF) of a feature using precursor m/z, isotope pattern, and MS 2 spectrum. These MF candidates are further refined by the ZODIAC module and then used to find potential structures from DB. Then, the CSI:FingerID 40 algorithm generates molecular fingerprints from the experimental MS 2 spectra by using support vector machine models. These molecular fingerprints are used to identify potential molecular structures by matching them in DB like KEGG , HMDB , and PubChem . Because MS annotations rely on MF and fragmentation patterns, this form of spectrometric data primarily emphasizes the atoms' connectivity. However, it does not provide information about spatial configuration. As a result, any structural annotations generated using these annotation methods are presented as 2D planar structures. Determining the 3D structure often involves making hypotheses or inferences, particularly when dereplication outcomes identify a constituent that has already been described and fully characterized within the same botanical species or taxonomically related samples . The CANOPUS module from Sirius systematically annotates the chemical classes directly from the MS 2 spectral fingerprint without the necessity of a structural annotation . The chemical class taxonomy is based on NPClassifier , a deep neural networkbased NP classification tool. This tool produces a classification structured into three hierarchical levels (pathway, superclass, and class), which are determined based on expert knowledge .