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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Bibliographic Details
Published in:PloS one 2023-05, Vol.18 (5), p.e0285716-e0285716
Main Authors: Yabuuchi, Hiroaki, Hayashi, Kazuhito, Shigemoto, Akihiko, Fujiwara, Makiko, Nomura, Yuhei, Nakashima, Mayumi, Ogusu, Takeshi, Mori, Megumi, Tokumoto, Shin-Ichi, Miyai, Kazuyuki
Format: Article
Language:English
Subjects:
Algorithms
Analysis
Anti-Bacterial Agents - chemistry
Anti-Bacterial Agents - pharmacology
Anti-infective agents
Anti-Infective Agents - pharmacology
Antiinfectives and antibacterials
Antimicrobial activity
Antimicrobial agents
Bacteria
Bioactive compounds
Biological activity
Biological products
Biology and Life Sciences
Chemical compounds
Chromatography
Classification
Drug discovery
Embedding
Essential oils
Genera
Health aspects
Herbal medicine
Learning algorithms
Machine Learning
Mass spectrometry
Medicine and Health Sciences
Microbial Sensitivity Tests
Oils & fats
Physical Sciences
Phytochemicals
Plant extracts
Plant Extracts - chemistry
Plant Extracts - pharmacology
Plants
Properties
Scientific imaging
Screening
Semantics
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Summary: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.
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0285716