Scalable HPC and AI Infrastructure for COVID-19 Therapeutics

TL;DR

This paper discusses the development of scalable HPC and AI infrastructure that accelerates COVID-19 drug discovery by integrating advanced computational methods and infrastructure at scale.

Contribution

It introduces novel scalable infrastructure and integrated AI-simulation methods specifically designed to enhance COVID-19 therapeutics development.

Findings

Improved computational performance for drug design workflows

Successful integration of AI and simulation methods at scale

Enabled new scientific insights into SARS-CoV-2 therapeutics

Abstract

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.