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.
Scalable HPC & AI infrastructure for COVID-19 therapeutics / 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.. - (2021), pp. 1-13. (Intervento presentato al convegno 2021 Platform for Advanced Scientific Computing Conference, PASC 2021 tenutosi a che nel 2021) [10.1145/3468267.3470573].
Scalable HPC & AI infrastructure for COVID-19 therapeutics
Alfe' D.;
2021
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.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
