Octopus is an automated workflow management tool that is scalable for virtual high-throughput screening (vHTS). It integrates MOPAC2016, MGLTools, PyMOL, and AutoDock Vina. In contrast to other platforms, Octopus can perform docking simulations of an unlimited number of compounds into a set of molecular targets. After generating the ligands in a drawing package in the Protein Data Bank (PDB) format, Octopus can carry out geometry refinement using the semi-empirical method PM7 implemented in MOPAC2016. Docking simulations can be performed using AutoDock Vina and can utilize the Our Own Molecular Targets (OOMT) databank. Finally, the proposed software compiles the best binding energies into a standard table. Here, we describe two successful case studies that were verified by biological assay. In the first case study, the vHTS process was carried out for 22 (phenylamino)urea derivatives. The vHTS process identified a metalloprotease with the PDB code 1GKC as a molecular target for derivative LE&007. In a biological assay, compound LE&007 was found to inhibit 80% of the activity of this enzyme. In the second case study, compound Tx001 was submitted to the Octopus routine, and the results suggested that Plasmodium falciparum ATP6 (PfATP6) as a molecular target for this compound. Following an antimalarial assay, Tx001 was found to have an inhibitory concentration (IC) of 8.2 μM against PfATP6. These successful examples illustrate the utility of this software for finding appropriate molecular targets for compounds. Hits can then be identified and optimized as new antineoplastic and antimalarial drugs. Finally, Octopus has a friendly Linux-based user interface, and is available at www.drugdiscovery.com.br . Graphical Abstract Octopus: A platform for inverse virtual screening (IVS) to search new molecular targets for drugs.

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.

The main challenge in the control of malaria has been the emergence of drug-resistant parasites. The presence of drug-resistant Plasmodium sp. has raised the need for new antimalarial drugs. Molecular modelling techniques have been used as tools to develop new drugs. In this study, we employed virtual screening of a pyrazol derivative (Tx001) against four malaria targets: plasmepsin-IV, plasmepsin-II, falcipain-II, and PfATP6. The receiver operating characteristic curves and area under the curve (AUC) were established for each molecular target. The AUC values obtained for plasmepsin-IV, plasmepsin-II, and falcipain-II were 0.64, 0.92, and 0.94, respectively. All docking simulations were carried out using AutoDock Vina software. The ligand Tx001 exhibited a better interaction with PfATP6 than with the reference compound (-12.2 versus -6.8 Kcal/mol). The Tx001-PfATP6 complex was submitted to molecular dynamics simulations in vacuum implemented on an NAMD program. The ligand Tx001 docked at the same binding site as thapsigargin, which is a natural inhibitor of PfATP6. Compound TX001 was evaluated in vitro with a P. falciparum strain (W2) and a human cell line (WI-26VA4). Tx001 was discovered to be active against P. falciparum (IC50 = 8.2 µM) and inactive against WI-26VA4 (IC50 > 200 µM). Further ligand optimisation cycles generated new prospects for docking and biological assays.