以流行病速度的流行病药物：在高性能计算机上使用混合机器学习和基于物理的模拟加速COVID-19药物发现的基础设施
Pandemic Drugs at Pandemic Speed: Infrastructure for Accelerating COVID-19 Drug Discovery with Hybrid Machine Learning- and Physics-based Simulations on High Performance Computers
摘要：
应对全球大流行挑战的竞赛提醒人们，现有的药物发现过程昂贵、效率低下且缓慢。筛选大量潜在小分子以入围抗病毒药物开发的先导化合物存在一个主要瓶颈。加速药物发现的新机会在于机器学习方法（在这种情况下是为线性加速器开发的）和基于物理学的方法之间的接口。这两种计算机方法各有其优点和局限性，有趣的是，它们相辅相成。在这里，我们提出了一个创新的基础设施的发展，结合这两种方法来加速药物发现。潜在的工作流程的规模是这样的，它依赖于超级计算来实现极高的吞吐量。我们已经证明了这一工作流程在研究四种COVID-19靶蛋白抑制剂方面的可行性，以及我们通过在各种超级计算机上重新利用来执行所需的大规模计算以识别主要抗病毒化合物的能力。
英文摘要：
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 infrastructural development that combines both approaches to accelerate drug discovery. The scale of the potential resulting workflow is such that it is dependent on supercomputing to achieve extremely high throughput. We have demonstrated the viability of this workflow for the study of inhibitors for four COVID-19 target proteins and our ability to perform the required large-scale calculations to identify lead antiviral compounds through repurposing on a variety of supercomputers.
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