SoM predictors are also characterized by comparably large applicability domains (as compared to many predictors of molecular properties). This is related to the fact that metabolic lability is primarily a function of the proximate atom environment, and these local environments are, to some extent, redundant across chemical spaces. But, as always, there is a catch: the vast majority of SoM predictors are trained on a single, fairly small data set of drugs and drug-like compounds with their annotated SoMs. This data set was compiled by Zaretzki et al. and is composed of 680 compounds. Although of enormous value to the scientific community, the Zaretzki data set covers only cytochrome P450 (CYP) mediated metabolism, and while CYPs certainly are the most relevant xenobiotic-metabolizing enzymes, there are many other phase 1 enzymes (e.g. reductases and hydrolases) and also phase 2 enzymes (mainly transferases) that are of high relevance to small-molecule research. One of the very few SoM predictors with comprehensive coverage of phase 1 and phase 2 metabolism is FAst MEtabolizer in its 3rd generation (FAME 3). This SoM predictor, developed by some of us, is based on a set of more than 2000 parent compounds with their expert-annotated, experimentally confirmed SoMs for phase 1 and phase 2 metabolic enzymes (i.e., the MetaQSAR database ). Substantial advances in the accuracy and applicability of SoM predictors will depend on the availability of additional, measured data on distinct atom environments, in particular on those involved in rare, complex biotransformation events. Given the high costs associated with the generation and annotation of such data and the fact that metabolism data is primarily generated within the pharmaceutical industry and not publicly available, it is unlikely that the rate at which measured data is becoming available will improve substantially over the next few years. Therefore, strategies for increasing the efficiency of data acquisition (e.g. by guiding experimental efforts to yield the highest information gain) and machine learning are needed. Active learning has the potential to address both of these issues.