The retranslation fails for longer IUPAC names generated by the 100 million data points model due to errors such as missing spaces, incorrect group order, formatting issues, incorrect valency, and other OPSIN-related errors encountered during the retranslation of these names. As seen in Figure , the poor quality of the retranslation decreases the overall average Tanimoto similarity. Similarly, the IUPAC to SMILES translation models, trained on 100 million and approximately 1 billion data points, were also compared using the ChEBI dataset. As seen in Table the models trained on 100 million and 1 billion data points perform almost similarly. However, the model trained on 1 billion data points, which included IUPAC names longer than 600 characters, had to learn more complex IUPAC names. This complexity slightly reduced the model's performance compared to the 100 million data point model. In general, the performance of both models is comparable. To understand how well the model handles slightly different IUPAC names, names that did not match the original were retranslated using OPSIN, and the resulting structures were compared. Table summarises a few examples: