The creation of theories about the organisation and regulation of metabolic networks hinges on the discovery of molecular components and their functions. The path that leads to accurate computational modelling and prediction of metabolic outcomes is through iterative experimental testing of such assumptions. This information is particularly useful for comprehending the biology of natural compounds, whose metabolism is sometimes only vaguely characterised. A collection of reliable time-resolved and geographically characterised metabolite abundance data and accompanying information is essential to realising this objective. One of the most difficult aspects of metabolite profiling is the intricacy and analytical limitations of determining an organism’s whole metabolome. Furthermore, metabolomics data must be curated in publically accessible metabolomics databases in order for it to be effectively used by the scientific community. To incorporate genomic system-scale datasets, such databases require clear, consistent formats, easy access to data and metadata, data download, and accessible computational tools. Although transcriptomics and proteomics combine the genome’s linear predictive capability, the metabolome represents the genome’s nonlinear, final biochemical outputs, which are the outcome of the complex system(s) that control genome expression. For example, duplicated links between metabolites and gene-products muddle the relationship between metabolomics data and the metabolic network. The rules of chemistry, on the other hand, predict the linkages between metabolites. As a result, improving the metabolome’s ability to integrate with anchor points in the transcriptome and proteome will improve the predictive value of genomics data.