Scalable HPC & AI Infrastructure for COVID-19 Therapeutics

被引：3

作者：

Lee, Hyungro ^{[1
]}

Merzky, Andre ^{[1
]}

Tan, Li ^{[2
]}

Titov, Mikhail ^{[1
]}

Turilli, Matteo ^{[1
]}

Alfe, Dario ^{[3
]}

Bhati, Agastya ^{[3
]}

Brace, Alex ^{[4
]}

Clyde, Austin ^{[5
]}

Coveney, Peter ^{[3
]}

Ma, Heng ^{[4
]}

Ramanathan, Arvind ^{[4
]}

Stevens, Rick ^{[5
]}

Trifan, Anda ^{[6
]}

Van Dam, Hubertus ^{[2
]}

Wan, Shunzhou ^{[3
]}

Wilkinson, Sean ^{[7
]}

Jha, Shantenu ^{[1
,2
]}

机构：

[1] Rutgers State Univ, Piscataway, NJ 08854 USA

[2] Brookhaven Natl Lab, Upton, NY 11973 USA

[3] UCL, London, England

[4] Argonne Natl Lab, Lemont, IL USA

[5] Univ Chicago, Chicago, IL 60637 USA

[6] Univ Illinois, Champaign, IL USA

[7] Oak Ridge Natl Lab, Knoxville, TN USA

来源：

PROCEEDINGS OF THE PLATFORM FOR ADVANCED SCIENTIFIC COMPUTING CONFERENCE (PASC '21) | 2021年

基金：

美国能源部; 欧盟地平线“2020”;

关键词：

high-performance computing; machine learning; workflows; docking molecular dynamics; free energy estimation; COVID-19; FREE-ENERGY; PREDICTION;

D O I：

10.1145/3468267.3470573

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

COVID-19 has claimed more than 2.7 x 10(6) lives and resulted in over 124 x 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.

引用

页数：13

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