虚拟筛选
计算机科学
利用
膨胀的
网格
分布式计算
互补性(分子生物学)
药物发现
领域(数学)
吞吐量
建筑
计算机体系结构
缩小
虚拟现实
实施
作者
Boyang Ni,Douglas R. Houston
标识
DOI:10.1021/acs.jcim.5c02587
摘要
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 (EF1%) 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.
科研通智能强力驱动
Strongly Powered by AbleSci AI