To date, the discovery process of more than seventy commercialized drugs has included the rational use of at least a computational method . Computer-aided drug design (CADD) has the potential to reduce the billionaire cost and decrease the time through the drug design process, e.g., the hit identification rate for highthroughput screening (HTS) to discover novel inhibitors for the enzyme protein tyrosine phosphatase-1B is only 0.021% and the one for molecular docking is 34.8% . A crucial resource in CADD are the databases of chemical compounds including NP databases. From the compound databases, it is possible to identify potential hit molecules through several virtual screening (VS) techniques , including the training of artificial intelligence (AI) algorithms . When the compound databases are annotated with biological activity (or other property of relevance), it is possible to use the data to perform structure-activity (property) relationships and develop predictive models. From 2003 to 2018, 104 research articles reported the identification of potential drug candidates from NP databases by using computational tools .