No abstract

Background: Infectious diseases not only cause severe health issues but also burden the healthcare system. Therefore, the effective treatment of those diseases is crucial. A variety of conventional therapies including antibiotics as well as novel treatment methods namely antimicrobial peptides (AMP) are utilized for the treatment of infections. However, because of the drawbacks of current therapies such as the risk of the emergence of drug-resistant microorganisms, low stability of the agent in its environment as well as toxicity problems, new solutions are still being investigated. A recent approach is the usage of these two different antimicrobial agents in combination. Nonetheless, the determination of synergism is time-consuming and depends on several experimental studies. Prediction of different biological outcomes with machine learning (ML) algorithms is a widespread research field recently, and AMP with ML studies is one of the most researched areas further to understand the underlying associations of the mechanism of interest. Although many characteristics and biological outcomes of AMPs and antibiotics have been studied with different ML methods, no investigation reported on predicting the synergistic effects of AMP and antibiotics. Results: Supervised ML models were implemented to accurately classify the synergistic effect of AMPs and antibiotics within the scope of the proposed study. The results demonstrated that the hyperparameter-optimized Light Gradient Boosted Machine Classifier (LGBMC) yielded the best test accuracy of 76.92% for predicting the synergistic effect when compared to other trained models. Also, the feature analysis of the LGBMC model reveals that the target microbial strain, the minimum inhibitory concentrations (MIC) of the AMP and the antibiotic, and the used antibiotic are the most important features for the prediction of synergistic effect. Moreover, the results of the feature importance analysis are consistent with recently reported experimental studies in the literature. Conclusion: This study presents a novel and effective approach that aims to automatically and accurately predict the interactions between AMPs and antibiotics by various supervised ML algorithms, for the first time. Furthermore, this study reveals that ML algorithms can predict the synergistic activity of two different antimicrobial agents without the need for complex and time-consuming experimental procedures.