In chemistry, molecules have previously been featurized with descriptors and fingerprints , but recently other formats such as images , strings and graphs have been used as input to deep neural networks which solve chemically related tasks such as property prediction. Moreover, the graphs and strings can also be sampled autoregressively and thus used to predict molecular structures , . This particular generative or molecular read-out capability has enabled a wide range of advanced algorithms ranging from autoencoders to molecular transformers for e.g. reaction informatics , representation learning and molecular optimizations .