G-screen: Scalable Receptor-Aware Virtual Screening through Flexible Ligand Alignment

Abstract

Virtual screening has long been a central computational tool for rational ligand discovery, enabling the systematic prioritization of candidate molecules from large chemical libraries. Although docking and related approaches that explicitly account for protein-ligand interactions have been developed and refined over several decades, achieving both reliable protein-aware interaction modeling and computational scalability remains an open challenge, particularly for ultra-large chemical spaces. Ligand-based methods are fast and robust but do not explicitly incorporate protein structure, whereas docking-based approaches model protein-ligand interactions more directly at substantially higher computational cost. Here, we present G-screen, a freely available and scalable protein-aware virtual screening framework designed for cases in which an experimentally determined or predicted reference protein-ligand complex structure is available. Rather than performing computationally intensive full docking with explicit pose sampling and optimization, G-screen rapidly generates alignment-guided pose hypotheses using a flexible global alignment algorithm (G-align). The resulting aligned poses are subsequently evaluated using protein-aware pharmacophore interactions derived from the reference complex, enabling explicit atomic-level interaction analysis while retaining the scalability and robustness of ligand-based alignment methods. Benchmarking on DUD-E, LIT-PCBA, and MUV datasets demonstrates that G-screen achieves competitive discrimination and early enrichment relative to representative ligand-based and docking-based methods, while maintaining millisecond-scale per-molecule runtimes under multi-threaded execution. These results position G-screen as a practical and scalable intermediate strategy between conventional ligand-based virtual screening and computationally intensive docking workflows for efficiently filtering ultra-large chemical libraries when a reference complex structure is available. G-screen and G-align are freely available at https://github.com/seoklab/gscreen and https://github.com/seoklab/galign, respectively. Scientific ContributionWe have developed a scalable virtual screening framework for efficiently filtering ultra-large chemical libraries using a flexible global alignment algorithm combined with protein- aware pharmacophore evaluations and alignment-guided pose hypotheses. Despite explicitly capturing atomic-level interactions, the method remains highly efficient, maintaining millisecond-scale per-molecule runtimes under parallel execution. It achieves competitive discrimination and early enrichment, serving as an intermediate strategy between conventional ligand-based virtual screening and computationally intensive docking while combining the speed of ligand-based approaches with the structural context of traditional docking.