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

作者：Antonina L. Nazarova, Anastasiia Sadybekov, Arman A. Sadybekov, Mykola Protopopov, Dmytro S. Radchenko, Yurii S. Moroz, Olga O. Tarkhanova, Vsevolod Katritch · 发表于：npj Drug Discovery. · 年份：2026 · DOI：10.1038/s44386-026-00053-6 · 被引用次数：2 · 研究领域：Catalysis and Oxidation Reactions、Machine Learning in Materials Science、Computational Drug Discovery Methods

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 phosp...