EXSCALATE: An Extreme-Scale Virtual Screening Platform for Drug Discovery Targeting Polypharmacology to Fight SARS-CoV-2
IEEE Transactions on Emerging Topics in Computing
; : 1-12, 2022.
Article
in English
| Scopus | ID: covidwho-1961439
ABSTRACT
The social and economic impact of the COVID-19 pandemic demands a reduction of the time required to find a therapeutic cure. In this paper, we describe the EXSCALATE molecular docking platform capable to scale on an entire modern supercomputer for supporting extreme-scale virtual screening campaigns. Such virtual experiments can provide in short time information on which molecules to consider in the next stages of the drug discovery pipeline, and it is a key asset in case of a pandemic. The EXSCALATE platform has been designed to benefit from heterogeneous computation nodes and to reduce scaling issues. In particular, we maximized the accelerators’usage, minimized the communications between nodes, and aggregated the I/O requests to serve them more efficiently. Moreover, we balanced the computation across the nodes by designing an ad-hoc workflow based on the execution time prediction of each molecule. We deployed the platform on two HPC supercomputers, with a combined computational power of 81 PFLOPS, to evaluate the interaction between 70 billion of small molecules and 15 binding-sites of 12 viral proteins of SARS-CoV-2. The experiment lasted 60 hours and it performed more than one trillion ligand-pocket evaluations, setting a new record on the virtual screening scale. IEEE
Chemicals; COVID-19; Drugs; extreme-scale virtual screening; GPU; Graphics processing units; HPC; Libraries; molecular docking; Proteins; SARS-CoV-2; Supercomputers; Throughput; Binding sites; Digital libraries; Graphics processing unit; Molecular modeling; Molecules; Program processors; Virtual reality; % reductions; Drug; Drug discovery; Extreme scale; Social and economic impacts; Virtual experiments; Virtual Screening
Full text:
Available
Collection:
Databases of international organizations
Database:
Scopus
Language:
English
Journal:
IEEE Transactions on Emerging Topics in Computing
Year:
2022
Document Type:
Article
Similar
MEDLINE
...
LILACS
LIS