The race to meet the challenges of the global pandemic has served as a reminder that the existing drug discovery process is expensive, inefficient and slow. There is a major bottleneck screening the vast number of potential small molecules to shortlist lead compounds for antiviral drug development. New opportunities to accelerate drug discovery lie at the interface between machine learning methods, in this case developed for linear accelerators, and physics-based methods. The two in silico methods, each have their own advantages and limitations which, interestingly, complement each other. Here, we present an innovative method that combines both approaches to accelerate drug discovery. The scale of the resulting workflow is such that it is dependent on high performance computing. We have demonstrated the applicability of this workflow on four COVID-19 target proteins and our ability to perform the required large-scale calculations to identify lead compounds on a variety of supercomputers.

中文翻译：

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大流行药物的流行速度：在高性能计算机上通过基于机器学习和物理的混合模拟来加速COVID-19药物的发现

应对全球大流行挑战的竞赛提醒我们，现有的药物发现过程昂贵，低效且缓慢。筛选大量潜在的小分子以筛选用于抗病毒药物开发的先导化合物存在很大的瓶颈。加速药物发现的新机遇在于机器学习方法（基于线性加速器）与基于物理学的方法之间的接口。两种计算机方法各有各的优点和局限性，有趣的是，它们相互补充。在这里，我们提出了一种创新的方法，该方法结合了两种方法来加速药物发现。最终工作流程的规模取决于高性能计算。