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polyBERT: a chemical language model to enable fully machine-driven ultrafast polymer informatics

Polymers are a vital part of everyday life. Their chemical universe is so large that it presents unprecedented opportunities as well as significant challenges to identify suitable application-specific candidates. We present a complete end-to-end machine-driven polymer informatics pipeline that can s...

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Detalles Bibliográficos
Autores principales: Kuenneth, Christopher, Ramprasad, Rampi
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10336012/
https://www.ncbi.nlm.nih.gov/pubmed/37433807
http://dx.doi.org/10.1038/s41467-023-39868-6
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author Kuenneth, Christopher
Ramprasad, Rampi
author_facet Kuenneth, Christopher
Ramprasad, Rampi
author_sort Kuenneth, Christopher
collection PubMed
description Polymers are a vital part of everyday life. Their chemical universe is so large that it presents unprecedented opportunities as well as significant challenges to identify suitable application-specific candidates. We present a complete end-to-end machine-driven polymer informatics pipeline that can search this space for suitable candidates at unprecedented speed and accuracy. This pipeline includes a polymer chemical fingerprinting capability called polyBERT (inspired by Natural Language Processing concepts), and a multitask learning approach that maps the polyBERT fingerprints to a host of properties. polyBERT is a chemical linguist that treats the chemical structure of polymers as a chemical language. The present approach outstrips the best presently available concepts for polymer property prediction based on handcrafted fingerprint schemes in speed by two orders of magnitude while preserving accuracy, thus making it a strong candidate for deployment in scalable architectures including cloud infrastructures.
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spelling pubmed-103360122023-07-13 polyBERT: a chemical language model to enable fully machine-driven ultrafast polymer informatics Kuenneth, Christopher Ramprasad, Rampi Nat Commun Article Polymers are a vital part of everyday life. Their chemical universe is so large that it presents unprecedented opportunities as well as significant challenges to identify suitable application-specific candidates. We present a complete end-to-end machine-driven polymer informatics pipeline that can search this space for suitable candidates at unprecedented speed and accuracy. This pipeline includes a polymer chemical fingerprinting capability called polyBERT (inspired by Natural Language Processing concepts), and a multitask learning approach that maps the polyBERT fingerprints to a host of properties. polyBERT is a chemical linguist that treats the chemical structure of polymers as a chemical language. The present approach outstrips the best presently available concepts for polymer property prediction based on handcrafted fingerprint schemes in speed by two orders of magnitude while preserving accuracy, thus making it a strong candidate for deployment in scalable architectures including cloud infrastructures. Nature Publishing Group UK 2023-07-11 /pmc/articles/PMC10336012/ /pubmed/37433807 http://dx.doi.org/10.1038/s41467-023-39868-6 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Kuenneth, Christopher
Ramprasad, Rampi
polyBERT: a chemical language model to enable fully machine-driven ultrafast polymer informatics
title polyBERT: a chemical language model to enable fully machine-driven ultrafast polymer informatics
title_full polyBERT: a chemical language model to enable fully machine-driven ultrafast polymer informatics
title_fullStr polyBERT: a chemical language model to enable fully machine-driven ultrafast polymer informatics
title_full_unstemmed polyBERT: a chemical language model to enable fully machine-driven ultrafast polymer informatics
title_short polyBERT: a chemical language model to enable fully machine-driven ultrafast polymer informatics
title_sort polybert: a chemical language model to enable fully machine-driven ultrafast polymer informatics
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10336012/
https://www.ncbi.nlm.nih.gov/pubmed/37433807
http://dx.doi.org/10.1038/s41467-023-39868-6
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