Feature filtering is commonly used to select the most informative features from the calculated descriptors. Here, the feature filters currently implemented in QSPRpred will be discussed, however, new feature filters can be easily added by the user. Filters in QSPRpred are always calculated using only the training set or the training fold of the data set to avoid data leakage (see subsection 2.1.5). The first filter that is implemented is the LowVarianceFilter, which calculates the variance within a feature on the training set and removes it if it is below a threshold specified by the user.