Virtual screening of antimicrobial plant extracts by machine-learning classification of chemical compounds in semantic space

• Published in: PloS one Vol. 18; no. 5; p. 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: Journal Article
• Language: English
• Published: United States Public Library of Science 15.05.2023; Public Library of Science (PLoS)
• 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; Japan; United States > US
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0285716

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.