The development of STOUT V2 demonstrates the transformer models' capacity and the fact that neural networks can accurately perform the non-trivial task of converting SMILES to IUPAC names. This work was solely possible due to the access of much-established rule-based systems like OpenEye's Lexichem software. Our primary goal of this work is to make this work available to a wider audience, including those without a programming background, to simplify their IUPAC naming tasks. The development process was centred on creating an accurate, user-friendly tool that leverages the capabilities of the transformer-based models rather than competing with any existing rule-based systems.