Another factor highly affecting QSAR and PCM models is data imbalance, since the class definitions based on bioactivity data can result in highly skewed labels. In this regard, Zakharov et al explored how data balancing affected self-consistent regression QSAR models using highly imbalanced PubChem bioassays. The study proposed a method including costsensitive learning and under-sampling approaches to obtain more accurate predictions. Using the same data, Korkmaz explored how data balancing affected DL-based QSAR models. The study concluded that imbalance has indeed a negative impact on the performance of the models, but that this impact could be alleviated by applying oversampling methods like SMOTE (Synthetic Minority Oversampling Technique) on the fingeprint representations of the molecules. Besides, oversampling methods could also serve the purpose of augmenting the original dataset.