In comparison with the other frameworks, PySMILESUtils aims to only handle the SMILES conversions and datamodel in a flexible and extendible way and is fully model agnostic, so that it can be adapted to the needs of the task and deep learning algorithm. Many other projects aimed for single models or algorithms, also contain code for tokenization and handling, but are not aiming to be libraries or frameworks. As examples can be mentioned Reinvent and Molecular Transformer , for de novo design and synthesis prediction. A full review of all projects using SMILES based deep learning is however out of scope of this application note.