The prediction of antiviral activity for the compounds from the assembled general screening data set (VS data set) (Table ) led to the design of seven individual antiviral focused libraries for each target. The design consisted in the calculation of two outputs using PyCaret's prediction function: binary values (0/1) for labeling, indicating whether a compound is predicted (classified) to be active (1) or inactive (0); and a prediction score, which represents the probability of the compound belonging to the active class (for further details, see Section 2.5). Table summarizes the results of these predictions from the VS data set for the seven antiviral targets. The table includes the number of compounds predicted to be active by one, two, and three models and for the top, second and third best models, as well as its relative frequency. a Number in parenthesis is the relative percentage frequency.