Our tool needs MS/MS spectra derived from [M+H] + entities. Indeed, during the exploration of the data of the real case, despite the use of a feature-based detection tool (i.e., MZmine4), the resulting processed file that has been submitted to MS2DECIDE still contained artifactual features such as ion source fragmentations, adducts and isotopologues (Table ). Unfortunately, such features, if not correctly filtered or recognized as such, are often misclassified as distinct molecular entities . Therefore, MS/MS data processing must include a curation step leading to remove or identify such artefacts. Furthermore, since MS2DECIDE performs a prioritization ranking of all the features of a MN, the practitioner may apply a consistent mass range filtration in regard to the metabolites class under exploration. Another interesting improvement for MS2DECIDE would include the use of semi-quantitative information 37 derived from a feature to assess the feasibility of its targeted isolation.