The tokenisation methods for SMILES followed the procedures outlined in Appendix 1 and Figure . IUPAC names were tokenised both by meaningful segments and character-wise, resulting in additional training datasets. Here, characterwise splitting means that each character in the IUPAC name has been separated into a token, increasing the maximum length of the tokenised IUPAC names. The effect of increasing dataset size on the number of unique tokens and the maximum length of split strings was also analysed. Table summarises the datasets used for training the models for SMILES strings to IUPAC name translations, with the IUPAC names split by characters or tokens. In total, 6 combinations of training datasets were generated. The final model selected from experiment 1 was trained on 110 million compounds and corresponding IUPAC names retrieved from PubChem. The datasets were retained as they are. However, SMILES strings longer than 600 characters were removed from the training dataset along with the corresponding IUPAC names, which retained a dataset size of 111,104,000 molecules. From this, one million molecules were selected using the chemfp implementation of the MaxMin algorithm to test the final model.