As with any method, validation -and how to perform validation in a practically relevant manner -has been discussed actively . In the early stages of deep generative models, many researchers only concentrated on how the model produced novel compounds efficiently by copying the distribution of the training dataset, referred to as distribution-learning. Therefore, the principle performance metrics developed were validity, uniqueness, novelty, and diversity which are included in benchmarks such as MOSES and Fréchet ChemNet Distance .