The preprocessing of each molecule involved using the SELFIES library to convert SMILES strings into SELFIES. Each SELFIE string was then augmented with "<start>" and "<end>" tokens to mark the sequence boundaries. These augmented SELFIES were tokenized to a maximum length of 45 tokens, ensuring a consistent input size for the model. This tokenization process yielded a vocabulary of 69 unique tokens, covering the full range of molecular substructures in the dataset. The tokenized representations were used as the target output for the molecule decoder, which was trained to predict these sequences of tokens representing molecular structures.