Although that work was a quantitative study, it was descriptive. The increase in the publicly available chemogenomic data for all target classes over the years opened up the opportunity for the construction of ligand-based machine learning models to assist target prediction of small molecules. Some of these methods are currently available as easy-to-access web applications, such as Similarity Ensemble Approach (SEA), HitPick, Polypharmacology Browser (PPB), TargetHunter, and SwissTargetPrediction, to name a few examples. These methods usually assign the targets for a given small molecule from the known targets of the most similar ligands in their datasets, employing different descriptions and metrics for the similarity assessment, and often making use of additional statistical models to estimate the significance of the predictions. Despite of the increasing number of chemogenomic databases related to epigenetics, this data still represents a minimal amount when compared to other protein families such as kinases (KINs), ion channels or G protein-coupled receptors. This suggests that epigenetic targets are commonly underrepresented in the current target prediction methods, and that unless the similarity of a known ligand is high enough, they are less likely to be predicted as potential targets of small molecules, which points out the need of developing predictive models focused on epigenetic targets to assist medicinal chemistry efforts in this area.