Work

A machine learning-assisted analysis of essential oil constituents targeting salmonella typhimurium

Year: 2025

Type: article

Abstract: Some essential oils (EOs) have exhibited high efficacy against Salmonella Typhimurium, suggesting that specific molecular structures within their constituents could inform the development of alternative antimicrobial agents, potentially addressing the rise of antibiotic resistance in this bacterium. Through feature permutation analysis on a dataset of 171 EO samples encompassing 682 molecular substructures, we identified ten key predictors of antibacterial activity against S. Typhimurium. These predictors were used to train a logistic regression-based machine learning model. Hydroxyl-substituted benzene rings characteristic of phenolic compounds - such as carvacrol, thymol and eugenol - emerged as strong predictors of antibacterial activity. In contrast, non-aromatic bicyclic structures present in monoterpenoids like alpha-pinene, beta-pinene and delta-3-carene were associated with a lack of efficacy against Salmonella. The molecular features identified align with existing research on the antimicrobial properties of phenolic compounds, thereby validating the use of machine learning approaches in guiding the discovery of naturally occurring antimicrobial agents.

Source: Comprehensive Plant Biology

DOI: 10.2298/cpb2501087l

Authors: Maria Lagerholm, Mirjana Pesic, Milos Jovanovic, Marija Kuzmanovic, Viktor Nedovic

Institution: University of Belgrade

Language: English

Topic: Essential Oils and Antimicrobial Activity

Subfield: Food Science

Field: Agricultural and Biological Sciences

Domain: Life Sciences

SDG: Good health and well-being

Open Access status: diamond

Funder: Ministarstvo Prosvete, Nauke i Tehnološkog Razvoja