Interpretable machine learning models for predicting hormesis of binary antibiotic mixtures on Vibrio qinghaiensis...

The hormesis phenomenon of antibiotic mixtures hinders environmental risk assessment owing to the lack of predictive models for chemical mixtures. This study developed a novel machine learning classification approach to predict the hormesis effect of 275 binary antibiotic mixtures on Vibrio qinghaiensis sp.-Q67. Time-dependent toxicities (12, 18, and 24 h) indicate that 81.1 % of mixtures demonstrate hormesis at 24 h (compared with 54.1 % at 12 h), revealing a time-dependent amplification. A machine learning framework incorporating nine algorithms was developed to distinguish between hormesis and non-hormesis responses. For each exposure duration, 27 base models and 243 ensemble models were developed via stacked generalisation. The Random Forest model achieved 92.8 % accuracy (area under the receiver operating characteristic curve > 0.985), outperforming eight algorithms. Shapley additive explanations method employs a game-theoretic approach for feature interpretation. This analysis revealed three critical molecular determinants, namely, SsssN, SpMax6_Bhs and nHCsatu, which collectively explain the bioactivity profiles of antibiotic mixtures. The results revealed that steric hindrance, electron delocalisation and the hydrogen-bond network were key factors in predicting the biological activity of antibiotic mixtures exhibiting the hormesis effect. This study introduces a machine learning framework for predicting the hormesis effect of antibiotic mixtures, providing vital tools for ecological risk assessment and more intelligent management of aquatic contaminants.