Translating the InChI: adapting neural machine translation to predict IUPAC names from a chemical identifier

We present a sequence-to-sequence machine learning model for predicting the IUPAC name of a chemical from its standard International Chemical Identifier (InChI). The model uses two stacks of transformers in an encoder-decoder architecture, a setup similar to the neural networks used in state-of-the-...

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Detalles Bibliográficos
Autores principales: Handsel, Jennifer, Matthews, Brian, Knight, Nicola J., Coles, Simon J.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer International Publishing 2021
Materias:
Research Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8496104/
https://www.ncbi.nlm.nih.gov/pubmed/34620215
http://dx.doi.org/10.1186/s13321-021-00535-x

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8496104/
https://www.ncbi.nlm.nih.gov/pubmed/34620215
http://dx.doi.org/10.1186/s13321-021-00535-x

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