Lee, H;
Merzky, A;
Tan, L;
Titov, M;
Turilli, M;
Alfe, D;
Bhati, A;
... Jha, S; + view all
(2021)
Scalable HPC & AI infrastructure for COVID-19 therapeutics.
PASC '21: Proceedings of the Platform for Advanced Scientific Computing Conference
, Article 2. 10.1145/3468267.3470573.
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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.
| Type: | Article |
|---|---|
| Title: | Scalable HPC & AI infrastructure for COVID-19 therapeutics |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1145/3468267.3470573 |
| Publisher version: | https://doi.org/10.1145/3468267.3470573 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Dept of Chemistry UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Dept of Earth Sciences |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10135465 |
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