Trillion Ligands per Day: Performance-Portable Virtual Screening via Compound Database Optimization and Multi-Target Docking
Xiaohui Duan, Cheng Shen, Gaowei Chen, Shanshan Wu, Yizhen Wang, Yizhen Chen, Qixin Chang, Qiancheng Xia, Zekun Yin, Lin Gan, Yibing Shan, Guangwen Yang
摘要
Structure-based virtual screening confronts a grand challenge in scaling to trillion-ligand libraries for drug discovery. We present SWDOCKP2, a performance-portable virtual screening framework achieving 1.9 trillion ligand-receptor pairs daily across eight targets on the Sunway OceanLight supercomputer with 39-million cores — 10× faster than prior state-of-the-art. Key innovations combine (1) a ligand database optimizer with conformational sorting and merging, (2) multi-receptor grid alignment enabling parallel target screening and SIMD-accelerated trilinear interpolation, and (3) a Sunway architecture emulator for cross-platform efficiency. These advancements bridge computational scalability with novel drug discovery demands, offering a blueprint for next-generation supercomputing in structure-based drug design. Additionally, SWDOCKP2 will generate an unprecedented dataset of predicted protein-ligand interactions, creating a transformative resource for machine learning applications. By addressing experimental data scarcity, this dataset empowers accurate ligand prediction, generative chemistry, and AI-driven drug discovery.
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