Automated extraction of chemical synthesis actions from experimental procedures

Extracting experimental operations for chemical synthesis from procedures reported in prose is a tedious task. Here the authors develop a deep-learning model based on the transformer architecture to translate experimental procedures from the field of organic chemistry into synthesis actions.

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Detalles Bibliográficos
Autores principales: Alain C. Vaucher, Federico Zipoli, Joppe Geluykens, Vishnu H. Nair, Philippe Schwaller, Teodoro Laino
Formato: article
Lenguaje:EN
Publicado: Nature Portfolio 2020
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Acceso en línea:https://doaj.org/article/c608946fef074490b3844156d983475b
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Descripción
Sumario:Extracting experimental operations for chemical synthesis from procedures reported in prose is a tedious task. Here the authors develop a deep-learning model based on the transformer architecture to translate experimental procedures from the field of organic chemistry into synthesis actions.