In the development of our model for embedding SMILES (Simplified Molecular Input Line Entry System) strings, we opted for a character-level encoding approach to construct the vocabulary. This decision was driven by the simplicity and directness of treating each character in the SMILES notation as an independent token, which simplifies the model architecture and training process. Character-level tokenization ensures comprehensive coverage of the chemical space without the risk of missing rare or novel substructures that might not be included in a predefined word dictionary. However, it is acknowledged that utilizing a group-based word dictionary, such as that employed in ChemBerta : Model architecture based on the vanilla attention only model , the hidden-size was set to be 512, the number of heads was 16 and the layers was 24. model's performance, particularly in the accurate prediction of valid molecular structures. ChemBerta and similar approaches leverage groupings of characters that represent common chemical substructures, thus encapsulating more contextual information per token than individual characters. This method could reduce the complexity of the model and improve its learning efficiency by decreasing the sequence length of the inputs and capturing more meaningful chemical patterns.