| Autor: |
Daniel Farfán, Carolina Gómez-Márquez, Dania Sandoval-Nuñez, Omar Paredes, J. Alejandro Morales |
| Jazyk: |
angličtina |
| Rok vydání: |
2024 |
| Předmět: |
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| Zdroj: |
Franklin Open, Vol 7, Iss , Pp 100106- (2024) |
| Druh dokumentu: |
article |
| ISSN: |
2773-1863 |
| DOI: |
10.1016/j.fraope.2024.100106 |
| Popis: |
We introduce Bidirectional and Auto-Regressive Transformer for Reactions (BARTReact), a self-supervised deep learning model designed to predict chemical reactions. Built on the powerful Bidirectional and Auto-Regressive Transformer (BART) architecture, BARTReact is trained using the SELF-referencIng Embedded Strings (SELFIES), a molecular representation that ensures the production of only viable molecules, achieving an outstanding accuracy of 98.6 %. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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