Application of Predictive Modeling and Molecular Simulations to Elucidate the Mechanisms Underlying the Antimicrobial Activity of Sage (Salvia officinalis L.) Components in Fresh Cheese Production

Plant-derived materials from <i>Salvia officinalis</i> L. (sage) have demonstrated significant antimicrobial potential when applied during fresh cheese production. In this study, the mechanism of action of sage components against <i>Listeria monocytogenes, Escherichia coli</i>, and <i>Staphylococcus aureus</i> was investigated through the development of predictive models that describe the influence of key parameters on antimicrobial efficacy. Molecular modeling techniques were employed to identify the major constituents responsible for the observed inhibitory activity. Epirosmanol, carvacrol, limonene, and thymol were identified as the primary compounds contributing to the antimicrobial effects during cheese production. The highest weighted predicted binding energy was observed for thymol against the KdpD histidine kinase from <i>Staphylococcus aureus</i>, with a value of -33.93 kcal/mol. To predict the binding affinity per unit mass of these sage-derived compounds against the target pathogens, machine learning models-including Artificial Neural Networks (ANN), Support Vector Machines (SVM), and Boosted Trees Regression (BTR)-were developed and evaluated. Among these, the ANN model demonstrated the highest predictive accuracy and robustness, showing minimal bias and a strong coefficient of determination (R² = 0.934). These findings underscore the value of integrating molecular modeling and machine learning approaches for the identification of bioactive compounds in functional food systems.