Interpretable machine learning models for QSAR-based prediction of anti-Salmonella typhi activity

AIMS: This study aimed to develop a robust machine learning (ML)-based quantitative structure-activity relationship (QSAR) model to identify potential drug candidates active against multidrug-resistant Salmonella typhi. MATERIALS & METHODS: A curated ChEMBL-derived dataset was assessed for modelability, yielding a high MODI value of 0.89. A hybrid feature selection workflow was applied to retain 20 chemically interpretable molecular descriptors, and eight diverse ML classifiers were systematically trained and benchmarked. RESULTS: The Support Vector Machine (SVM) model achieved the highest performance (MCC = 0.61, ROC-AUC = 0.90) on the hold-out test set. CONCLUSIONS: Overall, rigorous ML-QSAR modeling offers a reliable and efficient framework for virtual screening and prioritization of novel anti-S. typhi agents discovery.