Considering the chemical features that make these substances P, M and T, and the provided criteria for classification, PMT/vPvM substances can be identified and grouped accordingly. Readacross can be applied to identify PMT substances on the basis that they have similar (sub)structures, similar properties and available toxicological data. This can be done through a fragment-based approach, which relies on the identification of small similar fragments or functional groups with similar properties . Substance grouping approaches take the fragment models one step further. Substances can have similar properties due to the similarity in structures this includes functional groups, common precursors or reaction products, and a constant pattern in the changing of the potency of the properties across the group . The substance grouping approach is used in read-across techniques and alternative assessments [104]. This has been supported by the use of various models that predict physical-chemical property information such as the . This is a fragment-based model that relates substructures and mobility (KOC) based on appropriate training data [120].. The mobility (KOC) of new chemicals can be predicted using the established relationship (when they are within the applicability domain). Similarly, the ReadyBiodegradable model in OPERA and CERAPP and CoMPARA models can be used to predict the biodegradability and toxicity, respectively, of many new PMT/vPvM substances.