Transformer-based artificial neural networks for the conversion between chemical notations
| Autor: | Sergey Sosnin, Lev Krasnov, Maxim V. Fedorov, Ivan Khokhlov |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
0301 basic medicine
Multidisciplinary Artificial neural network Computer science Computation Science Cheminformatics Control engineering Information technology Notation 01 natural sciences Article 0104 chemical sciences 010404 medicinal & biomolecular chemistry 03 medical and health sciences Chemistry 030104 developmental biology Robustness (computer science) Production (economics) Medicine Overall performance Transformer (machine learning model) |
| Zdroj: | Scientific Reports Scientific Reports, Vol 11, Iss 1, Pp 1-10 (2021) |
| ISSN: | 2045-2322 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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