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Virtual screening of antimicrobial plant extracts by machine-learning classification of chemical compounds in semantic space
Plant extract is a mixture of diverse phytochemicals, and considered as an important resource for drug discovery. However, large-scale exploration of the bioactive extracts has been hindered by various obstacles until now. In this research, we have introduced and evaluated a new computational screen...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10184910/ https://www.ncbi.nlm.nih.gov/pubmed/37186641 http://dx.doi.org/10.1371/journal.pone.0285716 |
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author | Yabuuchi, Hiroaki Hayashi, Kazuhito Shigemoto, Akihiko Fujiwara, Makiko Nomura, Yuhei Nakashima, Mayumi Ogusu, Takeshi Mori, Megumi Tokumoto, Shin-ichi Miyai, Kazuyuki |
author_facet | Yabuuchi, Hiroaki Hayashi, Kazuhito Shigemoto, Akihiko Fujiwara, Makiko Nomura, Yuhei Nakashima, Mayumi Ogusu, Takeshi Mori, Megumi Tokumoto, Shin-ichi Miyai, Kazuyuki |
author_sort | Yabuuchi, Hiroaki |
collection | PubMed |
description | Plant extract is a mixture of diverse phytochemicals, and considered as an important resource for drug discovery. However, large-scale exploration of the bioactive extracts has been hindered by various obstacles until now. In this research, we have introduced and evaluated a new computational screening strategy that classifies bioactive compounds and plants in semantic space generated by word embedding algorithm. The classifier showed good performance in binary (presence/absence of bioactivity) classification for both compounds and plant genera. Furthermore, the strategy led to the discovery of antimicrobial activity of essential oils from Lindera triloba and Cinnamomum sieboldii against Staphylococcus aureus. The results of this study indicate that machine-learning classification in semantic space can be a highly efficient approach for exploring bioactive plant extracts. |
format | Online Article Text |
id | pubmed-10184910 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-101849102023-05-16 Virtual screening of antimicrobial plant extracts by machine-learning classification of chemical compounds in semantic space Yabuuchi, Hiroaki Hayashi, Kazuhito Shigemoto, Akihiko Fujiwara, Makiko Nomura, Yuhei Nakashima, Mayumi Ogusu, Takeshi Mori, Megumi Tokumoto, Shin-ichi Miyai, Kazuyuki PLoS One Research Article Plant extract is a mixture of diverse phytochemicals, and considered as an important resource for drug discovery. However, large-scale exploration of the bioactive extracts has been hindered by various obstacles until now. In this research, we have introduced and evaluated a new computational screening strategy that classifies bioactive compounds and plants in semantic space generated by word embedding algorithm. The classifier showed good performance in binary (presence/absence of bioactivity) classification for both compounds and plant genera. Furthermore, the strategy led to the discovery of antimicrobial activity of essential oils from Lindera triloba and Cinnamomum sieboldii against Staphylococcus aureus. The results of this study indicate that machine-learning classification in semantic space can be a highly efficient approach for exploring bioactive plant extracts. Public Library of Science 2023-05-15 /pmc/articles/PMC10184910/ /pubmed/37186641 http://dx.doi.org/10.1371/journal.pone.0285716 Text en © 2023 Yabuuchi et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Yabuuchi, Hiroaki Hayashi, Kazuhito Shigemoto, Akihiko Fujiwara, Makiko Nomura, Yuhei Nakashima, Mayumi Ogusu, Takeshi Mori, Megumi Tokumoto, Shin-ichi Miyai, Kazuyuki Virtual screening of antimicrobial plant extracts by machine-learning classification of chemical compounds in semantic space |
title | Virtual screening of antimicrobial plant extracts by machine-learning classification of chemical compounds in semantic space |
title_full | Virtual screening of antimicrobial plant extracts by machine-learning classification of chemical compounds in semantic space |
title_fullStr | Virtual screening of antimicrobial plant extracts by machine-learning classification of chemical compounds in semantic space |
title_full_unstemmed | Virtual screening of antimicrobial plant extracts by machine-learning classification of chemical compounds in semantic space |
title_short | Virtual screening of antimicrobial plant extracts by machine-learning classification of chemical compounds in semantic space |
title_sort | virtual screening of antimicrobial plant extracts by machine-learning classification of chemical compounds in semantic space |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10184910/ https://www.ncbi.nlm.nih.gov/pubmed/37186641 http://dx.doi.org/10.1371/journal.pone.0285716 |
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