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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 revealed that four essential oil pairs (Origanum compactum—Trachyspermum ammi, Cymbopogon citratus—Thujopsis dolabrata, Cinnamomum verum—Cymbopogon citratus and Trachyspermum ammi—Zingiber officinale) exhibited synergistic interaction as predicted. These results indicate that graph embedding approach can efficiently find synergistic interactions between antibacterial essential oils

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 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

Prediction of antibacterial interaction between essential oils via graph embedding approach

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 mixing the oils because hundreds of compounds can be involved in synergistic and antagonistic interactions. For efficient formula optimization, we have developed and evaluated a machine learning method to classify antibacterial interactions between the oils. Cross-validation showed that graph embedding improved areas under the ROC curves for synergistic-versus-rest classification. Furthermore, antibacterial assay against Staphylococcus aureus revealed that oregano–ajowan, lemongrass–hiba, cinnamon–lemongrass and ajowan–ginger combinations exhibited synergistic interaction as predicted. These results indicate that graph embedding approach is useful for predicting synergistic interaction between antibacterial essential oils

Dispersion of Single-Walled Carbon Nanotube Bundles in Non-Aqueous Solution

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