Grid-enabled High-throughput in silico Screening against Influenza A Neuraminidase
Identifieur interne : 000120 ( Hal/Corpus ); précédent : 000119; suivant : 000121Grid-enabled High-throughput in silico Screening against Influenza A Neuraminidase
Auteurs : H.-C. Lee ; J. Salzemann ; N. Jacq ; H.-Y. Chen ; Li-Yung Ho ; I. Merelli ; L. Milanesi ; Vincent Breton ; S. C. Lin ; Y.-T. WuSource :
- IEEE Transactions on NanoBioscience [ 1536-1241 ] ; 2006.
English descriptors
- mix :
Abstract
Encouraged by the success of the first EGEE biomedical data challenge against malaria (WISDOM) [1], the second data challenge battling avian flu kicked off in April 2006 to identify new drugs for the potential variants of the Influenza A virus. Mobilizing thousands of CPUs on the Grid, the 6-weeks long high-throughput screening activity has fulfilled over 100 CPU years of computing power and produced around 600 Gigabytes of results on the Grid for further biological analysis and testing. In the paper, we demonstrate the impact of a world-wide Grid infrastructure to efficiently deploy large scale virtual screening [2] to speed up the drug design process. Lessons learned through the data challenge activity are also discussed.
Url:
DOI: 10.1109/TNB.2006.887943
Links to Exploration step
Hal:in2p3-00114129Le document en format XML
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<front><div type="abstract" xml:lang="en"> <p>Encouraged by the success of the first EGEE biomedical data challenge against malaria (WISDOM) [1], the second data challenge battling avian flu kicked off in April 2006 to identify new drugs for the potential variants of the Influenza A virus. Mobilizing thousands of CPUs on the Grid, the 6-weeks long high-throughput screening activity has fulfilled over 100 CPU years of computing power and produced around 600 Gigabytes of results on the Grid for further biological analysis and testing. In the paper, we demonstrate the impact of a world-wide Grid infrastructure to efficiently deploy large scale virtual screening [2] to speed up the drug design process. Lessons learned through the data challenge activity are also discussed.</p>
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<notesStmt> <note type="commentary">8 pages,6 figures, to appear in IEEE Transactions on Nanobioscience. - PCSV, à paraître dans IEEE Transactions on Nanobioscience (2006). This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible.</note>
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<abstract xml:lang="en"> <p>Encouraged by the success of the first EGEE biomedical data challenge against malaria (WISDOM) [1], the second data challenge battling avian flu kicked off in April 2006 to identify new drugs for the potential variants of the Influenza A virus. Mobilizing thousands of CPUs on the Grid, the 6-weeks long high-throughput screening activity has fulfilled over 100 CPU years of computing power and produced around 600 Gigabytes of results on the Grid for further biological analysis and testing. In the paper, we demonstrate the impact of a world-wide Grid infrastructure to efficiently deploy large scale virtual screening [2] to speed up the drug design process. Lessons learned through the data challenge activity are also discussed.</p>
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