Translating the Molecules: Adapting Neural Machine Translation to Predict IUPAC Names from a Chemical Identifier Article Swipe
Jennifer Handsel
,
Brian W. Matthews
,
Nicola Knight
,
Simon J. Coles
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.26434/chemrxiv.14170472.v1
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.26434/chemrxiv.14170472.v1
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-art machine translation. Unlike neural machine translation, which usually tokenizes input and output into words or sub-words, our model processes the InChI and predicts the 2 IUPAC name character by character. The model was trained on a dataset of 10 million InChI/IUPAC name pairs freely downloaded from the National Library of Medicine’s online PubChem service. Training took five days on a Tesla K80 GPU, and the model achieved test-set accuracies of 95% (character-level) and 91% (whole name). The model performed particularly well on organics, with the exception of macrocycles. The predictions were less accurate for inorganic compounds, with a character-level accuracy of 71%. This can be explained by inherent limitations in InChI for representing inorganics, as well as low coverage (1.4 %) of the training data.
Related Topics
Concepts
Chemical nomenclature
Computer science
Identifier
Natural language processing
Artificial intelligence
Machine translation
Character (mathematics)
Cheminformatics
Artificial neural network
Encoder
Chemical database
Training set
Machine learning
Chemistry
Programming language
Mathematics
Geometry
Operating system
Organic chemistry
Computational chemistry
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.26434/chemrxiv.14170472.v1
- https://chemrxiv.org/engage/api-gateway/chemrxiv/assets/orp/resource/item/60c755d5567dfe21ffec6368/original/translating-the-molecules-adapting-neural-machine-translation-to-predict-iupac-names-from-a-chemical-identifier.pdf
- OA Status
- gold
- Cited By
- 4
- References
- 15
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4235912386
All OpenAlex metadata
Raw OpenAlex JSON
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https://openalex.org/W4235912386Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.26434/chemrxiv.14170472.v1Digital Object Identifier
- Title
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Translating the Molecules: Adapting Neural Machine Translation to Predict IUPAC Names from a Chemical IdentifierWork title
- Type
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preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
-
2021Year of publication
- Publication date
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2021-03-08Full publication date if available
- Authors
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Jennifer Handsel, Brian W. Matthews, Nicola Knight, Simon J. ColesList of authors in order
- Landing page
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https://doi.org/10.26434/chemrxiv.14170472.v1Publisher landing page
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https://chemrxiv.org/engage/api-gateway/chemrxiv/assets/orp/resource/item/60c755d5567dfe21ffec6368/original/translating-the-molecules-adapting-neural-machine-translation-to-predict-iupac-names-from-a-chemical-identifier.pdfDirect link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
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goldOpen access status per OpenAlex
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https://chemrxiv.org/engage/api-gateway/chemrxiv/assets/orp/resource/item/60c755d5567dfe21ffec6368/original/translating-the-molecules-adapting-neural-machine-translation-to-predict-iupac-names-from-a-chemical-identifier.pdfDirect OA link when available
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Chemical nomenclature, Computer science, Identifier, Natural language processing, Artificial intelligence, Machine translation, Character (mathematics), Cheminformatics, Artificial neural network, Encoder, Chemical database, Training set, Machine learning, Chemistry, Programming language, Mathematics, Geometry, Operating system, Organic chemistry, Computational chemistryTop concepts (fields/topics) attached by OpenAlex
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4Total citation count in OpenAlex
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2022: 1, 2021: 2, 2020: 1Per-year citation counts (last 5 years)
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15Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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