论文标题
可伸缩的HPC和AI基础架构，用于COVID-19
Scalable HPC and AI Infrastructure for COVID-19 Therapeutics
论文作者
论文摘要
Covid-19夺去了100万人的生命，并导致超过4000万感染。迫切需要鉴定可以抑制SARS-COV-2的药物。作为回应，美国能源部最近建立了医学治疗项目，作为国家虚拟生物技术实验室的一部分，并任务是创建计算基础架构和推进治疗剂开发所需的方法。我们讨论了加速和推进药物设计的计算基础设施和方法的创新。具体而言，我们描述了几种整合人工智能和基于仿真方法的方法，以及计算基础架构的设计，以大规模支持这些方法。我们讨论了他们的实施并表征他们的绩效，并强调了这些能力已启用的科学进步。
COVID-19 has claimed more 1 million lives and resulted in over 40 million infections. There is an urgent need to identify drugs that can inhibit SARS-CoV-2. In response, the DOE recently established the Medical Therapeutics project as part of the National Virtual Biotechnology Laboratory, and tasked it with creating the computational infrastructure and methods necessary to advance therapeutics development. We discuss innovations in computational infrastructure and methods that are accelerating and advancing drug design. Specifically, we describe several methods that integrate artificial intelligence and simulation-based approaches, and the design of computational infrastructure to support these methods at scale. We discuss their implementation and characterize their performance, and highlight science advances that these capabilities have enabled.
