In our previous work , we introduced STOUT (SMILES-TO-IUPAC-name translator), a deep learning model based on a sequence-to-sequence (seq2seq) architecture with an encoder and decoder using Recurrent Neural Networks (RNNs) with Gated Recurrent Units (GRUs) . STOUT was trained on a dataset of 60 million molecules from PubChem and corresponding IUPAC names generated with ChemAxon's molconvert software . The model achieved promising results, with an average BLEU score of approximately 90% and a Tanimoto similarity index of over 0.9, indicating high accuracy in predicting IUPAC names from SMILES strings and vice versa. However, there were still areas for improvement, particularly in model architecture, tokenisation strategies, and the handling of stereochemical information. This work presents STOUT V2.0, a transformer-based model for SMILES to IUPAC name translation (see Figure ). The model was trained on a dataset of nearly 1 billion SMILES and corresponding IUPAC names, generated using OpenEye's Lexichem software. This new version achieves very high accuracy on test and benchmark datasets, demonstrating improved capability in producing longer IUPAC names with fewer errors overall. The models were trained entirely on TPU VMs, and after finalising the training, they were optimised using TensorFlow to run efficiently on CPUs. and have been made available along with the accompanying code as open-source resources. To enhance accessibility for users with limited or no programming experience, a web application has been developed and is accessible at . We demonstrate that neural networks can accurately perform the non-trivial task of converting SMILES to IUPAC names. However, we accomplished this with the generous access to Lexichem software that OpenEye provided us. The use of deterministic algorithms for generating IUPAC names, such as Lexichem, in production environments is always recommended due to the higher error rates associated with neural machine translation.