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Spectral deep learning for prediction and prospective validation of functional groups
State-of-the-art identification of the functional groups present in an unknown chemical entity requires the expertise of a skilled spectroscopist to analyse and interpret Fourier transform infra-red (FTIR), mass spectroscopy (MS) and/or nuclear magnetic resonance (NMR) data. This process can be time...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
The Royal Society of Chemistry
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8152587/ https://www.ncbi.nlm.nih.gov/pubmed/34122917 http://dx.doi.org/10.1039/c9sc06240h |
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author | Fine, Jonathan A. Rajasekar, Anand A. Jethava, Krupal P. Chopra, Gaurav |
author_facet | Fine, Jonathan A. Rajasekar, Anand A. Jethava, Krupal P. Chopra, Gaurav |
author_sort | Fine, Jonathan A. |
collection | PubMed |
description | State-of-the-art identification of the functional groups present in an unknown chemical entity requires the expertise of a skilled spectroscopist to analyse and interpret Fourier transform infra-red (FTIR), mass spectroscopy (MS) and/or nuclear magnetic resonance (NMR) data. This process can be time-consuming and error-prone, especially for complex chemical entities that are poorly characterised in the literature, or inefficient to use with synthetic robots producing molecules at an accelerated rate. Herein, we introduce a fast, multi-label deep neural network for accurately identifying all the functional groups of unknown compounds using a combination of FTIR and MS spectra. We do not use any database, pre-established rules, procedures, or peak-matching methods. Our trained neural network reveals patterns typically used by human chemists to identify standard groups. Finally, we experimentally validated our neural network, trained on single compounds, to predict functional groups in compound mixtures. Our methodology showcases practical utility for future use in autonomous analytical detection. |
format | Online Article Text |
id | pubmed-8152587 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | The Royal Society of Chemistry |
record_format | MEDLINE/PubMed |
spelling | pubmed-81525872021-06-11 Spectral deep learning for prediction and prospective validation of functional groups Fine, Jonathan A. Rajasekar, Anand A. Jethava, Krupal P. Chopra, Gaurav Chem Sci Chemistry State-of-the-art identification of the functional groups present in an unknown chemical entity requires the expertise of a skilled spectroscopist to analyse and interpret Fourier transform infra-red (FTIR), mass spectroscopy (MS) and/or nuclear magnetic resonance (NMR) data. This process can be time-consuming and error-prone, especially for complex chemical entities that are poorly characterised in the literature, or inefficient to use with synthetic robots producing molecules at an accelerated rate. Herein, we introduce a fast, multi-label deep neural network for accurately identifying all the functional groups of unknown compounds using a combination of FTIR and MS spectra. We do not use any database, pre-established rules, procedures, or peak-matching methods. Our trained neural network reveals patterns typically used by human chemists to identify standard groups. Finally, we experimentally validated our neural network, trained on single compounds, to predict functional groups in compound mixtures. Our methodology showcases practical utility for future use in autonomous analytical detection. The Royal Society of Chemistry 2020-03-13 /pmc/articles/PMC8152587/ /pubmed/34122917 http://dx.doi.org/10.1039/c9sc06240h Text en This journal is © The Royal Society of Chemistry https://creativecommons.org/licenses/by/3.0/ |
spellingShingle | Chemistry Fine, Jonathan A. Rajasekar, Anand A. Jethava, Krupal P. Chopra, Gaurav Spectral deep learning for prediction and prospective validation of functional groups |
title | Spectral deep learning for prediction and prospective validation of functional groups |
title_full | Spectral deep learning for prediction and prospective validation of functional groups |
title_fullStr | Spectral deep learning for prediction and prospective validation of functional groups |
title_full_unstemmed | Spectral deep learning for prediction and prospective validation of functional groups |
title_short | Spectral deep learning for prediction and prospective validation of functional groups |
title_sort | spectral deep learning for prediction and prospective validation of functional groups |
topic | Chemistry |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8152587/ https://www.ncbi.nlm.nih.gov/pubmed/34122917 http://dx.doi.org/10.1039/c9sc06240h |
work_keys_str_mv | AT finejonathana spectraldeeplearningforpredictionandprospectivevalidationoffunctionalgroups AT rajasekarananda spectraldeeplearningforpredictionandprospectivevalidationoffunctionalgroups AT jethavakrupalp spectraldeeplearningforpredictionandprospectivevalidationoffunctionalgroups AT chopragaurav spectraldeeplearningforpredictionandprospectivevalidationoffunctionalgroups |