在HPC分子对接微型应用程序中控制解决方案时间的可调近似
Tunable Approximations to Control Time-to-Solution in an HPC Molecular Docking Mini-App
摘要：
药物发现过程涉及在体内、体外和计算机上进行的几项任务。分子对接是通常在计算机中执行的任务。它的目的是找到一个给定的分子与靶蛋白结合位点相互作用时的三维姿态。这项任务通常用于虚拟筛选一组巨大的分子，以找到最有希望的分子，这些分子将被转发到药物发现过程的后期阶段。鉴于问题的巨大复杂性，分子对接不能通过探索配体的整个空间来解决。最先进的方法通过对配体位姿的空间进行采样以在合理的时间预算内生成结果来面对该问题。在这项工作中，我们通过引入可调近似来改进分子对接的几何方法。特别是，我们分析和丰富了可调软件旋钮的原始实现，以探索和控制性能精度的权衡。我们将虚拟筛选任务的求解时间建模为软件旋钮、输入数据特征和可用计算资源的函数。因此，应用程序可以根据用户定义的时间预算自动调整其配置。我们使用LiGenDock衍生的Mini-App-一种最先进的分子对接应用程序-来验证所提出的方法。我们通过使用非常大的口袋和配体数据库在HPC系统上运行增强的Mini-App。所提出的方法暴露了一个时间到解决方案的间隔跨越一个以上的数量级与准确度下降高达30%，更一般地提供不同的准确度水平，根据需要的虚拟筛选活动。
英文摘要：
The drug discovery process involves several tasks to be performed in vivo, in vitro and in silico. Molecular docking is a task typically performed in silico. It aims at finding the three-dimensional pose of a given molecule when it interacts with the target protein binding site. This task is often done for virtual screening a huge set of molecules to find the most promising ones, which will be forwarded to the later stages of the drug discovery process. Given the huge complexity of the problem, molecular docking cannot be solved by exploring the entire space of the ligand poses. State-of-the-art approaches face the problem by sampling the space of the ligand poses to generate results in a reasonable time budget. In this work, we improve the geometric approach to molecular docking by introducing tunable approximations. In particular, we analyzed and enriched the original implementation with tunable software knobs to explore and control the performance-accuracy tradeoffs. We modeled time-to-solution of the virtual screening task as a function of software knobs, input data features, and available computational resources. Therefore, the application can autotune its configuration according to a user-defined time budget. We used a Mini-App derived by LiGenDock - a state-of-the-art molecular docking application - to validate the proposed approach. We run the enhanced Mini-App on an HPC system by using a very large database of pockets and ligands. The proposed approach exposes a time-to-solution interval spanning more than one order of magnitude with accuracy degradation up to 30%, more in general providing different accuracy levels according to the needs of the virtual screening campaign.
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