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.

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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
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article

UniDock-Pro: A Unified GPU-Accelerated Platform for High-Throughput Structure-Based, Ligand-Based, and Synergistic Hybrid Virtual Screening

Douglas R. Houston, Boyang Ni
Journal of Chemical Information and Modeling
Computational Drug Discovery Methods
article

UniDock-Pro: A Unified GPU-Accelerated Platform for High-Throughput Structure-Based, Ligand-Based, and Synergistic Hybrid Virtual Screening

Douglas R. Houston, Boyang Ni
article en

Abstract

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.

Journal of Chemical Information and ModelingVol. 66(5)
Quantitative BioSciences (US), University of Edinburgh (GB)
Openalex Percentile: Top 44%
Computational Drug Discovery Methods
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