UniDock-Pro: A Unified GPU-Accelerated Platform for High-Throughput Structure-Based, Ligand-Based, and Synergistic Hybrid Virtual Screening
The screening of ultralarge chemical libraries requires high-throughput computational tools that can efficiently exploit all available structural and chemical information. We present UniDock-Pro, a unified platform based on the GPU-accelerated Uni-Dock architecture that integrates structure-based virtual screening (SBVS), ligand-based virtual screening (LBVS), and a novel hybrid virtual screening mode. By capitalizing on inter-ligand (batch) parallelism, UniDock-Pro achieves substantial throughput gains, enabling the processing of millions of compounds per day on a single GPU. We significantly enhanced the LBVS methodology by implementing a smooth, Lennard-Jones-like potential optimized for gradient-based search, which replaces the rugged recursive model employed in our previous work. This optimization yields a substantial 2.42-fold improvement in early enrichment (EF<sub>1%</sub>) over the legacy AutoDock-SS on the DUDE-Z benchmark. The Hybrid mode combines complementary information by integrating receptor- and ligand-derived grid maps on-the-fly during the conformational search, delivering strong early enrichment on DUDE-Z and competitive performance on the more stringent VSDS-vd TrueDecoy benchmark. To understand the mechanisms underlying this synergy, we introduce Force Field Complementarity Analysis (FFCA), a method for quantifying the spatial alignment between receptor and ligand force fields. UniDock-Pro offers a robust, versatile, and highly efficient solution for accelerating drug discovery campaigns across the expansive modern chemical space.
Authors
- Douglas R. Houston (ORCID: https://orcid.org/0000-0002-3469-1546)
- Boyang Ni (ORCID: https://orcid.org/0009-0008-0483-8886)
Institutions
- Quantitative BioSciences (US)
- University of Edinburgh (GB)
Publication Details
- Journal
- Journal of Chemical Information and Modeling
- Published
- 2026-02-24
- DOI
- https://doi.org/10.1021/acs.jcim.5c02587
- Primary Topic
- Computational Drug Discovery Methods
- Type
- article
- Field-Weighted Citation Impact
- 0.00