通过图形嵌入方法在体外和体内预测精油之间的抗菌相互作用
Hiroaki Yabuuchi1,2, Kazuhito Hayashi3,4, Akihiko Shigemoto5
1Department of Pharmaceutical Industry, Industrial Technology Center of Wakayama Prefecture, Wakayama, Japan. yabuuchi_h0002@pref.wakayama.lg.jp.
Scientific reports
|November 3, 2023
概括
机器学习可以预测精油的相互作用. 一种图形嵌入方法准确地确定了协同作用的抗菌作用,通过体外测定得到证实,有助于开发新型抗菌剂.
科学领域:
- 自然产品 化学 化学
- 计算生物学 计算生物学
- 微生物学 微生物学
背景情况:
- 精油具有多样化的挥发性代谢物,具有潜在的应用,如抗菌剂,昆虫驱虫剂和除草剂.
- 由于众多化合物之间复杂的协同作用和对抗作用,预测精油的综合作用具有挑战性.
研究的目的:
- 开发和评估一种机器学习 (ML) 方法,用于分类精油之间的抗菌相互作用 (协同作用,对抗作用或不存在).
- 评估图形嵌入在捕获交互网络特征中的有效性,以改进in silico预测.
主要方法:
- 利用图形嵌入来分析文献数据,并捕捉精油相互作用网络的结构特征.
- 在体外对黄金葡萄球菌进行了抗菌检测,以验证ML预测.
主要成果:
- 图形嵌入方法显著改善了用于分类协同相互作用的in silico预测性能.
- 包括Origanum compactum-Trachyspermum ammi和Cymbopogon citratus-Thujopsis dolabrata在内的四种精油对显示出协同抗菌活性,正如模型预测的那样.
- 该模型成功地预测了Cinnamomum verum-Cymbopogon citratus和Trachyspermum ammi-Zingiber officinale的协同作用.
结论:
- 基于图形嵌入的ML方法是识别抗菌精油之间的协同作用的有效工具.
- 这种方法可以加速基于精油组合的新抗微生物配方的发现和开发.
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