Author: Lee, H.; Merzky, A.; Tan, L.; Titov, M.; Turilli, M.; Alfe, D.; Bhati, A.; Brace, A.; Clyde, A.; Coveney, P.; Ma, H.; Ramanathan, A.; Stevens, R.; Trifan, A.; Van Dam, H.; Wan, S.; Wilkinson, S.; Jha, S.
Title: Scalable HPC & AI infrastructure for COVID-19 therapeutics Cord-id: pe7at2ew Document date: 2021_1_1
ID: pe7at2ew
Snippet: 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 implemen
Document: 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. © 2021 ACM.
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