ABSTRACT
Human Respiratory Syncytial Virus (hRSV) infection results in death and hospitalization of thousands of people worldwide each year. Unfortunately, there are no vaccines or specific treatments for hRSV infections. Screening hundreds or even thousands of promising molecules is a challenge for science. We integrated biological, structural, and physicochemical properties to train and to apply the concept of artificial intelligence (AI) able to predict flavonoids with potential anti-hRSV activity. During the training and simulation steps, the AI produced results with hit rates of more than 83%. The better AIs were able to predict active or inactive flavonoids against hRSV. In the future, in vitro and/or in vivo evaluations of these flavonoids may accelerate trials for new anti-RSV drugs, reduce hospitalizations, deaths, and morbidity caused by this infection worldwide, and be used as input in these networks to determine which parameter is more important for their decision.
