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Virtual Discovery utilizes the latest and most complete version of VirtualFlow to conduct its in silico screens. Specifically, we use VirtualFlow 2.0 to screen an expanded version of the REAL Space from Enamine, which contains ~69 billion drug-like molecules in a "ready-to-dock" format.

This is the largest library of its kind available to date.

VirtualFlow 2.0 can consider a range of targets, including RNA/DNA, transmembrane proteins (e.g., GPCR), and kinases, to name just a few. Moreover, the applications of this platform are diverse, ranging from drug discovery to agritech.


The following publications are from various research institutions, labs, and companies that have applied VirtualFlow technology to carry out virtual screens, validating its efficacy in drug discovery. If you are interested in other ways VirtualFlow has been validated, please explore here. 



Protein-Protein Interaction

"To demonstrate the power of VirtualFlow, we screened more than 1 billion compounds and identified a set of structurally diverse molecules that bind to KEAP1 with submicromolar affinity. "

Gorgulla, C., Boeszoermenyi, A., Wang, ZF. et al. An open-source drug discovery platform enables ultra-large virtual screens. Nature 580, 663–668 (2020).


Active Sites & Protein-Protein Interactions

"In this unprecedented structure-based virtual campaign, we screened roughly 1 billion molecules against each of 40 different target sites on 17 different potential viral and host targets. In addition to targeting the active sites of viral enzymes, we also targeted critical auxiliary sites such as functionally important protein-protein interactions. "

Gorgulla C, Padmanabha Das KM, Leigh KE, Cespugli M, et al. A multi-pronged approach targeting SARS-CoV-2 proteins using ultra-large virtual screening. iScience. 2021 Feb 19;24(2):102021. doi: 10.1016/j.isci.2020.102021. Epub 2021 Jan 5. PMID: 33426509; PMCID: PMC7783459.



NADPH oxidases

"Here, we describe fully validated human NOX inhibitors, obtained from an in silico screen, targeting the active site of Cylindrospermum stagnale NOX5 (csNOX5). The hits are validated by in vitro and in cellulo enzymatic and binding assays, and their binding modes to the dehydrogenase domain of csNOX5 studied via high-resolution crystal structures. "

Reis, J., Gorgulla, C., Massari, M. et al. Targeting ROS production through inhibition of NADPH oxidases. Nat Chem Biol 19, 1540–1550 (2023).

Additional Applications of Virtual Flow

μ-opioid receptor

"Here, this multi-objective optimal affinity approach is presented, together with a virtual drug discovery pipeline for its practical implementation. When applied to finding pH-specific drug candidates, it combines protonation state-dependent structure and ligand preparation with high-throughput virtual screening. We employ this pipeline to characterize a set of MOR agonists identifying a morphine-like opioid derivative with higher predicted binding affinities to the MOR at low pH compared to neutral pH." - Secker, 2023

Secker, C., Fackeldey, K., Weber, M. et al. Novel multi-objective affinity approach allows to identify pH-specific μ-opioid receptor agonists. J Cheminform 15, 85 (2023).

hDLG1-Tax-1 protein-protein interaction

"In order to identify potential inhibitors of the Tax-1-hDLG1 interaction, we performed an ultra-large virtual screening of small molecule libraries using crystal structures of hDLG1 PDZ-peptide complexes as an input. In particular, we screened over 10 million commercially-available compounds from the ZINC15 library using VirtualFlow platform (Gorgulla et al., 2020) and selected 212 candidate molecules with high docking scores for further experimental validation." - Maseko, 2023


Sibusiso B. Maseko, Yasmine Brammerloo, Inge Van Molle, Adrià Sogues, Charlotte Martin, Christoph Gorgulla, Estelle Plant, Julien Olivet, Jeremy Blavier, Thandokuhle Ntombela, Frank Delvigne, Haribabu Arthanari, Hiba El Hajj, Ali Bazarbachi, Carine Van Lint, Kourosh Salehi-Ashtiani, Han Remaut, Steven Ballet, Alexander N. Volkov, Jean-Claude Twizere, Identification of small molecule antivirals against HTLV-1 by targeting the hDLG1-Tax-1 protein-protein interaction, Antiviral Research, Volume 217, 2023, 105675, ISSN 0166-3542,

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