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High-throughput virtual laboratory for drug discovery using massive datasets

机译:使用大规模数据集进行药物发现的高通量虚拟实验室

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Time-to-solution for structure-based screening of massive chemical databases for COVID-19 drug discovery has been decreased by an order of magnitude, and a virtual laboratory has been deployed at scale on up to 27,612 GPUs on the Summit supercomputer, allowing an average molecular docking of 19,028 compounds per second. Over one billion compounds were docked to two SARS-CoV-2 protein structures with full optimization of ligand position and 20 poses per docking, each in under 24 hours. GPU acceleration and high-throughput optimizations of the docking program produced 350× mean speedup over the CPU version (50× speedup per node). GPU acceleration of both feature calculation for machine-learning based scoring and distributed database queries reduced processing of the 2.4 TB output by orders of magnitude. The resulting 50× speedup for the full pipeline reduces an initial 43 day runtime to 21 hours per protein for providing high-scoring compounds to experimental collaborators for validation assays.
机译:基于结构的基于结构的筛选的时间 - 用于Covid-19药物发现的大规模化学数据库的筛查已经减少了一个数量级,虚拟实验室已经在峰会超级计算机上以高达27,612个GPU的规模部署,允许平均分子对接为每秒19,028化合物。超过10亿化合物对接到两种SARS-COV-2蛋白质结构,全面优化配体位置,每次对接20件,每次在24小时内都有20个。 GPU加速和扩展程序的高通量优化在CPU版本上产生了350×平均加速(每个节点的50×加速)。基于机器学习的评分和分布式数据库的GPU加速功能计算逐次数减少了2.4 TB输出的处理。得到的50倍用于全管线的加速将初始43天的运行时间降低至每种蛋白质的21小时,以为实验性合作者提供高分化合物进行验证测定。

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