Struct2IUPAC -- Transformer-Based Artificial Neural Network for the Conversion Between Chemical Notations

Providing IUPAC chemical names is necessary for chemical information exchange. We developed a Transformer-based artificial neural architecture to translate between SMILES and IUPAC chemical notations: Struct2IUPAC and IUPAC2Struct. Our models demonstrated the performance that is comparable to rule-based solutions. We proved that both accuracy, speed of computations, and the model's robustness allow us to use it in production. Our showcase demonstrates that a neural-based solution can encourage rapid development keeping the same performance. We believe that our findings will inspire other developers to reduce development costs by replacing complex rule-based solutions with neural-based ones. The demonstration of Struct2IUPAC model is available online on Syntelly platform https://app.syntelly.com/smiles2iupac