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Peer-reviewedCC BY-NC-NDnpj Drug Discovery.

V-SYNTHES2—the next generation tool for structure-based virtual screening of giga-scale chemical spaces

  • Antonina L. Nazarova1,2,3
  • Anastasiia Sadybekov1,2,3
  • Arman A. Sadybekov1,2,3
  • Mykola Protopopov4,5
  • Dmytro S. Radchenko6,7
  • Yurii S. Moroz6,8,7
  • Olga O. Tarkhanova4,7
  • Vsevolod Katritch9,10,11,12,3
12 affiliations
  1. 1Center for New Technologies in Drug Discovery and Development, University of Southern California, Los Angeles, CA, USA
  2. 2Department of Quantitative & Computational Biology, University of Southern California, Los Angeles, CA, USA
  3. 3University of Southern California
  4. 4Chemspace LLC, Kyiv, Ukraine
  5. 5Chemspace LLC
  6. 6Enamine Ltd., Kyiv, Ukraine
  7. 7Enamine Ltd
  8. 8Taras Shevchenko National University of Kyiv, Kyiv, Ukraine
  9. 9Center for New Technologies in Drug Discovery and Development, University of Southern California, Los Angeles, CA, USA. [email protected]
  10. 10Department of Chemistry, Bridge Institute, USC Michelson Center for Convergent Biosciences, University of Southern California, Los Angeles, CA, USA. [email protected]
  11. 11Department of Pharmacology and Pharmaceutical Sciences, University of Southern California, Los Angeles, CA, USA. [email protected]
  12. 12Department of Quantitative & Computational Biology, University of Southern California, Los Angeles, CA, USA. [email protected]
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Abstract

The recent advent of synthesizable on-demand chemical spaces of drug-like compounds opened new horizons in the discovery of ligands and drug candidates for clinically relevant targets, but exposed the scalability of computational screening as a key bottleneck. The modular V-SYNTHES approach has shown highly efficient > 1000-fold accelerated virtual screening, but its initial implementation was not fully automated, limited to the initial version of Enamine REAL space (11 billion), and its validation was limited to only two targets. Here we present an upgraded V-SYNTHES2 workflow with improved automation features and scalability, expanded REAL Space of 36 billion readily available compounds, and assessing its performance on new, more challenging targets. As the original method, V-SYNTHES2 employs initial docking of the Minimal Enumeration Library (MEL) of fragments that represent all scaffolds and synthons of the REAL space. The best fragments are iteratively enumerated with corresponding synthons, and the intermediates redocked, until the fully enumerated molecules are docked and selected for synthesis. V-SYNTHES2 introduces a new geometry-based CapSelect method, allowing us to fully automate MEL fragment selection based on docking score and optimal binding pose. The method shows excellent enrichment and binding pose reproducibility in computational benchmarks, including challenging targets with shallow pockets, RNA-binding sites, G-protein-coupled receptors (GPCRs), and phospholipid-binding enzymes. Experimental testing shows the utility of this workflow in prospective screening campaigns for two new targets. The fully automated V-SYNTHES2 workflow (https://github.com/KatritchLab/V-SYNTHES2_pipeline/) can be deployed on computing clusters or clouds, offering a powerful tool for effective screening of giga-scale chemical spaces.

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Cite

BibTeXRIS
BibTeX
@article{nazarova2026vsynthes2the,
  title = {{V-SYNTHES2—the next generation tool for structure-based virtual screening of giga-scale chemical spaces}},
  author = {Antonina L. Nazarova and Anastasiia Sadybekov and Arman A. Sadybekov and Mykola Protopopov and Dmytro S. Radchenko and Yurii S. Moroz and Olga O. Tarkhanova and Vsevolod Katritch},
  journal = {npj Drug Discovery.},
  year = {2026},
  month = jul,
  doi = {10.1038/s44386-026-00053-6},
  url = {https://doi.org/10.1038/s44386-026-00053-6},
  note = {CC BY-NC-ND},
}
RIS
TY  - JOUR
TI  - V-SYNTHES2—the next generation tool for structure-based virtual screening of giga-scale chemical spaces
AU  - Antonina L. Nazarova
AU  - Anastasiia Sadybekov
AU  - Arman A. Sadybekov
AU  - Mykola Protopopov
AU  - Dmytro S. Radchenko
AU  - Yurii S. Moroz
AU  - Olga O. Tarkhanova
AU  - Vsevolod Katritch
PY  - 2026
DA  - 2026/07/03
JO  - npj Drug Discovery.
DO  - 10.1038/s44386-026-00053-6
UR  - https://doi.org/10.1038/s44386-026-00053-6
AB  - The recent advent of synthesizable on-demand chemical spaces of drug-like compounds opened new horizons in the discovery of ligands and drug candidates for clinically relevant targets, but exposed the scalability of computational screening as a key bottleneck. The modular V-SYNTHES approach has shown highly efficient > 1000-fold accelerated virtual screening, but its initial implementation was not fully automated, limited to the initial version of Enamine REAL space (11 billion), and its validation was limited to only two targets. Here we present an upgraded V-SYNTHES2 workflow with improved automation features and scalability, expanded REAL Space of 36 billion readily available compounds, and assessing its performance on new, more challenging targets. As the original method, V-SYNTHES2 employs initial docking of the Minimal Enumeration Library (MEL) of fragments that represent all scaffolds and synthons of the REAL space. The best fragments are iteratively enumerated with corresponding synthons, and the intermediates redocked, until the fully enumerated molecules are docked and selected for synthesis. V-SYNTHES2 introduces a new geometry-based CapSelect method, allowing us to fully automate MEL fragment selection based on docking score and optimal binding pose. The method shows excellent enrichment and binding pose reproducibility in computational benchmarks, including challenging targets with shallow pockets, RNA-binding sites, G-protein-coupled receptors (GPCRs), and phospholipid-binding enzymes. Experimental testing shows the utility of this workflow in prospective screening campaigns for two new targets. The fully automated V-SYNTHES2 workflow (https://github.com/KatritchLab/V-SYNTHES2_pipeline/) can be deployed on computing clusters or clouds, offering a powerful tool for effective screening of giga-scale chemical spaces.
SN  - 3005-1452
ER  -