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Transformer-based artificial neural networks for the conversion between chemical notations
Lev Krasnov1,2,3, Ivan Khokhlov2, Maxim V Fedorov1,2
1Center for Computational and Data-Intensive Science and Engineering, Skolkovo Institute of Science and Technology , Bolshoy Boulevard 30, bld. 1, Moscow, 121205, Russia.
Abstract:
We developed a Transformer-based artificial neural approach to translate between SMILES and IUPAC chemical notations: Struct2IUPAC and IUPAC2Struct. The overall performance level of our model is comparable to the rule-based solutions. We proved that the accuracy and speed of computations as well as the robustness of the model allow to use it in production. Our showcase demonstrates that a neural-based solution can facilitate rapid development keeping the required level of accuracy. We believe that our findings will inspire other developers to reduce development costs by replacing complex rule-based solutions with neural-based ones.
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