MAW is divided into three components. The first component is MAW-R, which represents the R section of the workflow. It takes the .mzML LC-MS 2 spectra files (available in any spectral data submission repository such as MetaboLights repository ) and three spectral databases (GNPS , HMDB , and MassBank ) stored as Spectra objects in separate Robject files and available on Zenodo . MAW-R generates results from spectral database dereplication as CSV files and also creates TXT parameter files for MetFrag (an in silico fragmentation-based annotation tool), which are used in the second component of the workflow called MAW-MetFrag. Considering that most metabolic studies search for novel natural products, we have provided a CSV (currently hosted on Zenodo) from the COlleCtion of Open Natural ProdUcTs (COCONUT) database SDF file from January 2022. MetFrag generates result CSV files for each precursor mass [m/z] with an annotated structure using the local CSV file for COCONUT, which serves as a compound database for dereplication. Alternatively, MAW also integrates SIRIUS , but SIRIUS 5 version requires registration, and thus, we cannot enable sharing or reproducibility using it; hence, this study does not discuss it. The third component is MAW-Py, the workflow's Python section. The CSV files from spectral database dereplication results and MetFrag compound database dereplication results are analysed with MAW-Py, which performs candidate selection and, for each .mzML file, generates a CSV file consisting of precursor masses [m/z] and their corresponding top 1st candidate structure. Figure briefly overviews the workflow components, inputs, intermediary results, and outputs. Figure : Overview of three MAW components. MAW is divided into three sections termed MAW-R (spectral database dereplication), MAW-MetFrag (compound database dereplication, here as an example, we provide COCONUT database), and MAW-Py (cheminformatics post-processing and candidate selection). The inputs for the first component MAW-R are .mzML input files, which can be uploaded to MetaboLights. The spectral databases (GNPS, HMDB, and MassBank) are downloaded and written as spectra R objects and then uploaded to Zenodo. These inputs are used to run MAW-R, which generates CSV results from spectral database dereplication and TXT files for MetFrag parameters. MAW-MetFrag, the second component, takes these input TXT files and generates CSV outputs from compound database dereplication. Once we have results from the first two components, MAW-Py, which is the third component, takes the CSV files from MAW-R and MAW-MetFrag to select candidate structures and post-process the results to give a final list of precursor masses with their corresponding top candidate structure.