The term 'data augmentation' refers to approaches that artificially inflate the data available for training. This is usually done by generating multiple (and different) instances of the same molecule to be used as input for a deep learning model (Fig. ). Data augmentation can be applied to the entire training set, or selectively to mitigate the presence of imbalanced classes, e.g., lack of negative data.