Despite the continuous growth in metabolomic databases, typically more than 50% of the signals detected and quantified in the typical mass spectroscopy experiment remain unknown. While some of these unknowns correspond to metabolites that have not yet been identified, others may correspond to chemical transformations of known metabolites during sample preparation or during the experimental set up. Hence the interest in the development of tools that extend the known metabolome through In silico simulation of chemical transformations of known metabolites. Two such tools are MINE and BioTransformer. Metabolic In silico Network Expansions (MINEs) extends the compounds from several databases (KEGG, EcoCyc, YMDB and Chemical Damage SEED) using the Biochemical Network Integrated Computational Explorer (BNICE) framework with hand-curated reaction rules. BioTransformer predicts small molecule metabolism in mammals, their gut microbiota, as well as the soil/aquatic microbiota. Moreover, it allows for metabolism-based compound identification.