Applicability Domain (area of reliable predictions) based on the Euclidean distances. The applicability domain must be defined to flag compounds/samples for which predictions of a developed model may be unreliable. In this node similarity measurements are used to define the domain of applicability of the model based on the Euclidean distances among all training compounds and the test or virtual screening compounds. A threshold value is calculated and a prediction is considered reliable only if distances between train and test/screening compounds are lower than this threshold (Zhang et al. 2006).
Input Training set table and test/virtual screening set table (only the descriptors involved in modelling)
Output A table containing for each compound of the test/virtual screening set, the result "reliable"/"unreliable"
