Estimating the number of individuals and groups of chemicals that fit the PMT/vPvM classification is a challenging task. Most substances lack readily available persistence, mobility and toxicity data. Due to the limited data availability, the prediction models of these properties are also limited in their accuracy and applicability domains. However, some data and models are available as a starting point, especially for log KOW. For example, both XlogP and the newer XlogP3 and the KOWWIN [129] module in EPI Suite [130] use a multivariate regression approach to KOW prediction. While KOC data is more limited than KOW data, there are also several models which attempt to predict KOC, including KOCWIN from EPI Suite and the KOC module of OPERA , which both utilise the training data from the PHYSPROP database . For persistence, OPERA also contains three modules for predicting biodegradability (BiodegHL, ReadyBiodegradable and Km) as well as one module predicting rate constants for gas-phase reactions with hydroxyl-radicals, though with more limited applicability domains compared to the KOC module . The prediction of toxicity is even more challenging due to the limited availability of training data. However, models already exist for several toxicological endpoint predictions such as endocrine disruption, mutagenicity and developmental toxicity for many possible structures . For example, the CERAPP and CoMPARA models in OPERA can be used to predict estrogen and androgen receptor interactions .