In this work, we present a comparative analysis of SMILES, DeepSMILES, and SELFIES string representations for chemical structures in neural machine translation tasks in cheminformatics. Using transformer-based models, we systematically evaluated their effectiveness in translating between these representations and the corresponding linguistic IUPAC nomenclature. The experimental results demonstrate comparable performance for all three string representations, with SMILES achieving a marginally higher accuracy (99.30% with stereochemical information, 99.21% without) compared to its alternatives. In scaling experiments with 1, 10, and 50 million compounds, the performance differences remained small, though the performance gap narrowed with larger datasets. These findings suggest that researchers can confidently continue using SMILES for neural machine translation tasks with transformers, which benefits from their extensive support in existing chemical libraries, tools, and databases, rather than adopting newer representations. This work has a significant impact on developing more efficient chemical language models in drug discovery, material science, and chemical database curation.