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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-...
| Autores principales: | , , , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
Springer International Publishing
2021
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| Materias: | |
| 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 |