虚拟筛选
计算机科学
可扩展性
超级计算机
蓝图
药物发现
网格
化学数据库
计算机体系结构
化学信息学
数据库
大数据
钥匙(锁)
高分辨率
网格计算
分类
对接(动物)
资源(消歧)
数据挖掘
精密医学
桥(图论)
分解
分类器(UML)
计算科学
分析
仿真
数据存取
超大数据库
虚拟机
合成孔径声纳
建筑
缩放比例
作者
Xiaohui Duan,Cheng Shen,G Chen,Shanshan Wu,Yizhen Wang,Yizhen Chen,Qixin Chang,Qiancheng Xia,Zekun Yin,Lin Gan,Yibing Shan,Guangwen Yang,Weiguo Liu,Niu Huang
标识
DOI:10.1145/3712285.3759833
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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