In vitro and in silico prediction of antibacterial interaction between essential oils via graph embedding approach.

ABSTRACT: Essential oils contain a variety of volatile metabolites, and are expected to be utilized in wide fields such as antimicrobials, insect repellents and herbicides. However, it is difficult to foresee the effect of oil combinations because hundreds of compounds can be involved in synergistic and antagonistic interactions. In this research, it was developed and evaluated a machine learning method to classify types of (synergistic/antagonistic/no) antibacterial interaction between essential oils. Graph embedding was employed to capture structural features of the interaction network from literature data, and was found to improve in silico predicting performances to classify synergistic interactions. Furthermore, in vitro antibacterial assay against a standard strain of Staphylococcus aureus rev

SUBMITTER: Yabuuchi H 

PROVIDER: S-EPMC10622510 | biostudies-literature | 2023 Nov

REPOSITORIES: biostudies-literature