Scalable HPC & AI infrastructure for COVID-19 therapeutics

Conference · 01 July 2021

DOI:https://doi.org/10.1145/3468267.3470573· OSTI ID:1895213

Lee, Hyungro ^[1]; Merzky, Andre ^[1]; Tan, Li ^[2]; Titov, Mikhail ^[1]; Turilli, Matteo ^[1]; Alfe, Dario ^[3]; Bhati, Agastya ^[3]; Brace, Alexander ^[4]; Clyde, Austin ^[5]; Coveney, Peter ^[3]; Ma, Heng ^[6]; Ramanathan, Arvind ^[6]; Stevens, Rick ^[6]; Trifan, Anda ^[7]; Van Dam, Hubertus ^[2]; Wan, Shunzhou ^[3]; Wilkinson, Sean ^[8]; Jha, Shantenu ^[1]

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1. Rutgers University
2. Brookhaven National Laboratory (BNL)
3. University College London (UCL), UK
4. Argonne National Laboratory
5. University of Chicago
6. Argonne National Laboratory (ANL)
7. University of Illinois at Urbana-Champaign
8. ORNL

COVID-19 has claimed more than 2.7 × 10^6 lives and resulted in over 124 × 10^6 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.

• Research Organization: Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
• Sponsoring Organization: USDOE; USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR) (SC-21)
• DOE Contract Number: AC05-00OR22725
• OSTI ID: 1895213
• Country of Publication: United States
• Language: English