Machine learning-based quantitative structure–activity relationship model for antibiotic prediction and discovery

Abstract

Antimicrobial resistance (AMR) is a critical global health problem that has become increasingly alarming in recent years. The discovery of new antibiotics is one approach for alleviating AMR, however, screening for novel drugs is time consuming and expensive. To accelerate antibiotic discovery, the integration of machine learning algorithms with Quantitative Structure–Activity Relationship (QSAR) calculations could provide a rapid solution. Thus, this study combines a QSAR model and machine learning algorithms to predict antibacterial activities of potential novel drugs based on chemical information. Information on compounds that are reportedly active and inactive against bacteria was downloaded from the PubChem database and manually curated to create positive and negative datasets. The decision tree (DT), support vector machine (SVM), and naïve Bayesian (NB) algorithms were employed to predict the antibacterial activities of chemical compounds from their Simplified Molecular Input Line Entry System (SMILES) information. The models were then evaluated quantitatively and tuned. DT and SVM exhibited comparable predictive performance and outperformed the NB model, achieving accuracy, precision, sensitivity, and AUC-ROC values exceeding 0.90. DT was chosen for further analysis because of its simplicity and effectiveness. This revealed that descriptors relating to the electrotopology and β-lactam structures of compounds were the top contributors to model predictability. The model was then further tested against different classes of antibiotics and achieved high accuracy in all classes. The model is freely available as a web application at: https://antibacterial-predictor-model-ocogzqyibervrqb7trvfev.streamlit.app/.