Due to costs and time limitations, typical drug discovery datasets are comprised of only several hundreds of molecules -a number drastically smaller compared to other deep learning applications. Moreover, drug discovery datasets are often characterized by limited structural diversity, and insufficient 'negative data' (e.g., inactive molecules) , which restricts the information accessible for learning. Finally, the need to represent molecules as 'computerreadable' formats inevitably leads to information loss (e.g., about molecular systems dynamics), which might hamper the performance, for instance, with highly nonlinear structure-activity landscapes , , , .