Machine learning-driven antiviral libraries targeting respiratory viruses

Viral infections represent a significant global health concern; therefore, herein we seek to design compound libraries with prospective antiviral activity. Viral diseases can range from mild symptoms to life-threatening conditions, and the impact of these infections has grown due to increased contagious rates driven by globalization. A prime example is the SARS-CoV-2 pandemic, which emphasized the urgent need to design and develop new antiviral drugs. This study aimed to generate a curated data set of compounds relevant to respiratory infections, focusing on predicting their antiviral activity. Specifically, the study leverages ML classification models to evaluate focused and on-demand compound libraries targeting pathways associated with viral respiratory infections. ML models were trained based on the antiviral biological activity related to respiratory diseases deposited on a major public compound database annotated with biological activity. The models were validated and retrained to classify and design antiviral-focused libraries on seven respiratory targets.