The demand for new therapies has encouraged the development of faster and cheaper methods of drug design. Considering the number of potential biological targets for new drugs, the docking-based virtual screening (DBVS) approach has occupied a prominent role among modern strategies for identifying new bioactive substances. Some tools have been developed to validate docking methodologies and identify false positives, such as the receiver operating characteristic (ROC) curve. In this context, a database with 31 molecular targets called the Our Own Molecular Targets Data Bank (OOMT) was validated using the root-mean-square deviation (RMSD) and the area under the ROC curve (AUC) with two different docking methodologies: AutoDock Vina and DOCK 6. Sixteen molecular targets showed AUC values of >0.8, and those targets were selected for molecular docking studies. The drug-likeness properties were then determined for 473 Brazilian natural compounds that were obtained from the ZINC database. Ninety-six compounds showed similar drug-likeness property values to the marked drugs (positive values). These compounds were submitted to DBVS for 16 molecular targets. Our results showed that AutoDock Vina was more appropriate than DOCK 6 for performing DBVS experiments. Furthermore, this work suggests that three compounds-ZINC13513540, ZINC06041137, and ZINC1342926-are inhibitors of the three molecular targets 1AGW, 2ZOQ, and 3EYG, respectively, which are associated with cancer. Finally, since ZINC and the PDB were solely created to store biomolecule structures, their utilization requires the application of filters to improve the first steps of the drug development process. Graphical Abstract Evaluation of docking methods used for virtual screening.

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