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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...

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Detalles Bibliográficos
Autores principales: Yabuuchi, Hiroaki, Hayashi, Kazuhito, Shigemoto, Akihiko, Fujiwara, Makiko, Nomura, Yuhei, Nakashima, Mayumi, Ogusu, Takeshi, Mori, Megumi, Tokumoto, Shin-ichi, Miyai, Kazuyuki
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2023
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
Descripción
Sumario: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.