002807914 001__ 2807914 002807914 003__ SzGeCERN 002807914 005__ 20221027183910.0 002807914 0247_ $$2DOI$$a10.1098/rsfs.2021.0018 002807914 0248_ $$aoai:cds.cern.ch:2807914$$pcerncds:CERN 002807914 035__ $$9https://inspirehep.net/api/oai2d$$aoai:inspirehep.net:2072734$$d2022-04-28T14:57:59Z$$h2022-04-29T04:00:08Z$$mmarcxml 002807914 035__ $$9Inspire$$a2072734 002807914 041__ $$aeng 002807914 100__ $$aBhati, Agastya P$$uUniversity Coll. London 002807914 245__ $$9submitter$$aPandemic drugs at pandemic speed: infrastructure for accelerating COVID-19 drug discovery with hybrid machine learning- and physics-based simulations on high-performance computers 002807914 260__ $$c2021 002807914 300__ $$a12 p 002807914 520__ $$9submitter$$aThe race to meet the challenges of the global pandemic has served as a reminder that the existing drug discovery process is expensive, inefficient and slow. There is a major bottleneck screening the vast number of potential small molecules to shortlist lead compounds for antiviral drug development. New opportunities to accelerate drug discovery lie at the interface between machine learning methods, in this case, developed for linear accelerators, and physics-based methods. The two in silico methods, each have their own advantages and limitations which, interestingly, complement each other. Here, we present an innovative infrastructural development that combines both approaches to accelerate drug discovery. The scale of the potential resulting workflow is such that it is dependent on supercomputing to achieve extremely high throughput. We have demonstrated the viability of this workflow for the study of inhibitors for four COVID-19 target proteins and our ability to perform the required large-scale calculations to identify lead antiviral compounds through repurposing on a variety of supercomputers. 002807914 540__ $$3publication$$aCC-BY-4.0$$uhttp://creativecommons.org/licenses/by/4.0/ 002807914 542__ $$3publication$$dThe Authors$$f© 2021 The Authors$$g2021 002807914 65017 $$2INSPIRE$$aComputing and Computers 002807914 690C_ $$aARTICLE 002807914 690C_ $$aCERN 002807914 700__ $$aWan, Shunzhou$$uU. Coll. London 002807914 700__ $$aAlfè, Dario$$uU. Coll. London 002807914 700__ $$aClyde, Austin R$$uChicago U. 002807914 700__ $$aBode, Mathis$$uRWTH Aachen U. 002807914 700__ $$aTan, Li$$uBrookhaven Natl. Lab. 002807914 700__ $$aTitov, Mikhail$$uRutgers U., Piscataway 002807914 700__ $$aMerzky, Andre$$uRutgers U., Piscataway 002807914 700__ $$aTurilli, Matteo$$uRutgers U., Piscataway 002807914 700__ $$aJha, Shantenu$$uBrookhaven Natl. Lab.$$uRutgers U., Piscataway 002807914 700__ $$aHighfield, Roger R$$uUnlisted, GB 002807914 700__ $$aRocchia, Walter$$uItalian Inst. Tech., Genoa 002807914 700__ $$aScafuri, Nicola$$uItalian Inst. Tech., Genoa 002807914 700__ $$aSucci, Sauro$$uItalian Inst. Tech., Genoa 002807914 700__ $$aKranzlmüller, Dieter$$uLeibniz Rechenzentrum, Garching 002807914 700__ $$aMathias, Gerald$$uLeibniz Rechenzentrum, Garching 002807914 700__ $$aWifling, David$$uLeibniz Rechenzentrum, Garching 002807914 700__ $$aDonon, Yann$$uCERN 002807914 700__ $$aDi Meglio, Alberto 002807914 700__ $$aVallecorsa, Sofia$$uCERN 002807914 700__ $$aMa, Heng$$uArgonne 002807914 700__ $$aTrifan, Anda$$uArgonne 002807914 700__ $$aRamanathan, Arvind$$uArgonne 002807914 700__ $$aBrettin, Tom$$uArgonne 002807914 700__ $$aPartin, Alexander$$uArgonne 002807914 700__ $$aXia, Fangfang$$uArgonne 002807914 700__ $$aDuan, Xiaotan$$uChicago U. 002807914 700__ $$aStevens, Rick$$uArgonne 002807914 700__ $$aCoveney, Peter V$$uU. Coll. London$$uAmsterdam U. 002807914 773__ $$c20210018$$n6$$pInterface Focus$$v11$$y2021 002807914 960__ $$a13 002807914 980__ $$aARTICLE