Supplementary Material : "Machine learning decision tree-based models for predicting the antibacterial activity of Lamiaceae essential oils against Staphylococcus aureus"

This study applies machine learning to predict the antibacterial activity of Lamiaceae essential oils (EOs) against Staphylococcus aureus. A curated dataset (NAEOL) was developed, incorporating chemical composition and MIC values. Decision tree models and artificial neural networks classified EOs as active or inactive, with J48 showing the best performance. Key terpenoids—estragole, bicyclogermacrene, o-cymene, γ-caryophyllene, and sabinene hydrate—were identified as antibacterial biomarkers, demonstrating the potential of computational approaches in bioactive EO discovery..