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

submitted on 23.11.2020, 14:24 and posted on 24.11.2020, 05:12 by Lev Krasnov, Ivan Khokhlov, Maxim Fedorov, Sergey Sosnin
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


Email Address of Submitting Author


Skolkovo Institute of Science and Technology



ORCID For Submitting Author


Declaration of Conflict of Interest

Maxim Fedorov and Sergey Sosnin are co-founders of Syntelly LLC. Lev Krasnov and Ivan Khokhlov are employees of Syntelly LLC

Version Notes

version 0.0.1