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

Author:

Yabuuchi HiroakiORCID,Hayashi Kazuhito,Shigemoto AkihikoORCID,Fujiwara Makiko,Nomura Yuhei,Nakashima Mayumi,Ogusu Takeshi,Mori Megumi,Tokumoto Shin-ichi,Miyai Kazuyuki

Abstract

AbstractPlant 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 fromLindera trilobaandCinnamomum sieboldiiagainstStaphylococcus 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.

Publisher

Cold Spring Harbor Laboratory

Cited by 1 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Phytochemicals in Drug Discovery—A Confluence of Tradition and Innovation;International Journal of Molecular Sciences;2024-08-13

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