Several other projects and frameworks contain code for working with SMILES based deep learning for chemistry. One such project is SMILES-enumeration () . It is a more or less monolithic class for both tokenization and data augmentation aimed at providing tensors for Keras training. It only supports single-character based tokenization, Page 7 of 8 one-hot encoding and fixed-length padding. It has been superseded by MolVecGen, which also contains SMILES based tensor generation as well as objects for creating chemception images or tensors based on RDKit fingerprints (). The wellestablished DeepChem project has a SmilesToSeq class amongst the featurizers (), which supports character-based tokenization and fixed width padding, where it uses indices-based encodings. Augmentation utilities seem not to be available and would have to be coded independently.