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

Key Points

• Offers a reliable framework for predicting anti-Salmonella activity with interpretable machine learning.
• The approach aids in virtual screening, enhancing the discovery of new agents to combat Salmonella typhi.
• Utilizes rigorous QSAR modeling techniques to improve efficiency and accuracy in drug development.
• Highlights the importance of prioritization in the discovery of novel therapeutic agents for Salmonella typhi.