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

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

Abstract 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 stat...

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Bibliographic Details
Main Authors: Jennifer Handsel, Brian Matthews, Nicola J. Knight, Simon J. Coles
Format: Article
Language:English
Published: BMC 2021-10-01
Series:Journal of Cheminformatics
Subjects:
seq2seq
InChI
IUPAC
Transformer
Attention
GPU
Online Access:https://doi.org/10.1186/s13321-021-00535-x

Internet

https://doi.org/10.1186/s13321-021-00535-x

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