They are often also referred to as "metabolic hotspots". Today, several SoM predictors have become available, most of which involve machine learning algorithms. These include the ADMET Predictor Metabolism module, 2 FAME, MetaSpot, MetaSite, the P450 SoM Predictor of the Schrödinger platform, SMARTCyp, SOMP, the StarDrop P450 Metabolism Prediction module, 9 and the XenoSite platform. Once the (likely) SoMs in a molecule are identified, medicinal chemists are often able to devise strategies for optimizing the metabolic properties while maintaining the bioactivity on the biomacromolecular target. Importantly, SoM predictors have also become increasingly relevant in the context of metabolite structure prediction. The primary limitations of the existing metabolite structure predictors are their high false-positive prediction rates and, associated with that, their insufficient capacity to rank metabolites according to their likelihood or relevance. In a bid to mitigate these limitations, researchers have explored the possibility of enhancing metabolite structure prediction by using predicted SoMs as a means for filtering and ranking predicted metabolites. Metabolite structure predictors making use of predicted SoMs include GLORYx, Meteor, and XenoNet. SoM predictors are generally found to perform well. Based on our own experience, these tools are capable of identifying, for more than 80% of drug-like test compounds, at least one experimentally observed SoM among the two top-ranked atom positions in a molecule. Of note, this is a conservative estimate since by far not all existing metabolites are reported in the scientific literature (most commonly, only the major products confidently detected by experimental techniques are reported), let alone recorded in the data sets used for model building and evaluation.