Prediction of Potential Natural Antibiotics based on Jamu Formula Using Machine Learning Approach

In order to address antibiotics resistance, multidrug resistance, and superbugs phenomena, our research explored the utility of Jamu ingredients on the molecular level to predict new natural antibiotic candidates. Jamu is one of the popular traditional medicines from Indonesia, with different therapeutics usage including curing diseases caused by bacterial infection. We used three types of machine learning methods, such as Random Forest (RF), Support Vector Machine (SVM), Deep Learning (DL), to classify Jamu formulas according to their effectiveness against different types of bacterial diseases. The best accuracy for RF, SVM, and DL models are 89%, 84%, and 80%, respectively. We extracted the potential compounds based on the best model as candidate antibiotics corresponding to five groups of efficacies, e.g., digestive systems, respiratory systems, reproductive systems, skin and soft tissue, and urinary systems. Overall, we mined 111 compounds, and many of them could be validated by published literature, and considering structural similarities with known antibiotics.

Automated summary

This research used machine learning methods to classify Jamu formulas and predict their effectiveness against bacterial diseases. It identified 111 potential antibiotic compounds for various systems.