Scalable HPC & AI infrastructure for COVID-19 therapeutics
Hyungro Lee, André Merzky, Li Lynn Tan, Mikhail Titov, Matteo Turilli, Dario Alfè, Agastya P. Bhati, Alex Brace, Austin Clyde, Peter V. Coveney, Heng Ma, Arvind Ramanathan, Rick Stevens, Anda Trifan, Hubertus J. J. van Dam, Shunzhou Wan, Sean R. Wilkinson, Shantenu Jha
Abstract
COVID-19 has claimed more than 2.7 × 106 lives and resulted in over 124 × 106 infections. There is an urgent need to identify drugs that can inhibit SARS-CoV-2. 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, characterize their performance, and highlight science advances that these capabilities have enabled.