Zika virus (ZIKV) is an emergent flavivirus, causing several neurological disorders in some infected patients[1–3]. In 2018, ZIKV was listed as a prioritizing disease for research and development in public health emergency contexts by the World Health Organization[4]. The ZIKV NS2B-NS3 protease (NS3prot) promotes the viral polyprotein cleavage and is involved in the virus life cycle, therefore is considered a promising target for the development of antivirals[1]. The OpenZika project (http://openzika.ufg.br/)[5] on IBM’s World Community Grid (https://www.worldcommunitygrid.org/research/zika/overview.do) is an open science collaboration, which uses the computational power of numbers of volunteers to perform virtual screening calculations, aimed to identify candidates against ZIKV to be biologically evaluated. In this study, as part of the OpenZika project, we performed a virtual screening (VS) of a commercial database against ZIKV NS3prot, using molecular docking and machine learning models. To perform the VS we used the following filters: (I) drug-like properties and aggregators removal; (II) molecular docking at active and allosteric site’s; (III) machine learning and Bayesian models for ZIKV phenotypic data and (IV) predicted pharmacokinetic properties. The VS workflow allowed us to prioritize 59 virtual hits that were experimentally tested in vitro in glioblastoma stem cells infected with ZIKV. From the cell-based assays, 16 compounds inhibited ZIKV replication, showing EC50 in the range of 0.003 μM to 84.4 μM and low cytotoxicity (CC50> 100 μM). Biophysical and enzymatic assays with ZIKV NS3prot revealed that one compound inhibited the enzyme with IC50 7.4 ± 0.3 μM. In addition, molecular dynamics simulations have been performed to investigate the stability of protein-ligand complexes predicted by docking and to study the possible binding mode of this ligand on NS3prot. Moreover, kinetics assays were performed to investigate the compound mechanism of inhibition. In conclusion, the OpenZika project enabled the identification of promising hits and one experimental potent inhibitor against ZIKV NS2B-NS3 protease. 

References
1.	Mottin, M. et al. The A–Z of Zika drug discovery. Drug Discov. Today 23, 1833–1847 (2018).
2.	Cao-Lormeau, V. M. et al. Guillain-Barré Syndrome outbreak associated with Zika virus infection in French Polynesia: A case-control study. Lancet 387, 1531–1539 (2016).
3.	Cugola, F. R. et al. The Brazilian Zika virus strain causes birth defects in experimental models. Nature 534, 267–271 (2016).
4.	World Health Organization. Prioritizing diseases for research and development in emergency contexts. https://www.who.int/activities/prioritizing-diseases-for-research-and-development-in-emergency-contexts (2020).
5.	Ekins, S., Perryman, A. L. & Horta Andrade, C. OpenZika: An IBM World Community Grid Project to Accelerate Zika Virus Drug Discovery. PLoS Negl. Trop. Dis. 10, e0005023 (2016).

OpenZika: an open science collaboration project enables the discovery of potent NS3 protease inhibitors as new antiviral candidates against Zika Virus

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