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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...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Nature Publishing Group UK
2023
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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. |
format | Online Article Text |
id | pubmed-10336012 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
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 |
work_keys_str_mv | AT kuennethchristopher polybertachemicallanguagemodeltoenablefullymachinedrivenultrafastpolymerinformatics AT ramprasadrampi polybertachemicallanguagemodeltoenablefullymachinedrivenultrafastpolymerinformatics |