GPU accelerated molecular docking simulation with genetic algorithms

dc.authorscopusid 57201897023
dc.authorscopusid 6601990115
dc.authorscopusid 6601906472
dc.contributor.author Bozkuş, Zeki
dc.contributor.author Bozkus,Z.
dc.contributor.author Fraguela,B.B.
dc.contributor.other Computer Engineering
dc.date.accessioned 2024-10-15T19:41:53Z
dc.date.available 2024-10-15T19:41:53Z
dc.date.issued 2016
dc.department Kadir Has University en_US
dc.department-temp Altuntaş S., Department of Computer Engineering, Kadir Has Üniversitesi, Istanbul, Turkey; Bozkus Z., Department of Computer Engineering, Kadir Has Üniversitesi, Istanbul, Turkey; Fraguela B.B., Depto. De Electrónica e Sistemas, Universidade da Coruña, A Coruña, Spain en_US
dc.description Camara Municipal do Porto, Portugal; Institute for Informatics and Digital Innovation at Edinburgh Napier University, UK; Turismo do Porto, Portugal; University of Coimbra, Portugal; World Federation on Soft Computing (technical sponsor of the EvoCOMNET track) en_US
dc.description.abstract Receptor-Ligand Molecular Docking is a very computationally expensive process used to predict possible drug candidates for many diseases. A faster docking technique would help life scientists to discover better therapeutics with less effort and time. The requirement of long execution times may mean using a less accurate evaluation of drug candidates potentially increasing the number of false-positive solutions, which require expensive chemical and biological procedures to be discarded. Thus the development of fast and accurate enough docking algorithms greatly reduces wasted drug development resources, helping life scientists discover better therapeutics with less effort and time. In this article we present the GPU-based acceleration of our recently developed molecular docking code. We focus on offloading the most computationally intensive part of any docking simulation, which is the genetic algorithm, to accelerators, as it is very well suited to them. We show how the main functions of the genetic algorithm can be mapped to the GPU. The GPU-accelerated system achieves a speedup of around ~ 14x with respect to a single CPU core. This makes it very productive to use GPU for small molecule docking cases. © Springer International Publishing Switzerland 2016. en_US
dc.description.sponsorship Galician Government, (GRC2013-055); TUBITAK, (112E191); European Commission, EC, (TIN2013-42148-P); Ministerio de Economía y Competitividad, MINECO; Türkiye Bilimsel ve Teknolojik Araştirma Kurumu, TÜBITAK; European Regional Development Fund, ERDF en_US
dc.identifier.citationcount 8
dc.identifier.doi 10.1007/978-3-319-31153-1_10
dc.identifier.endpage 146 en_US
dc.identifier.isbn 978-331931152-4
dc.identifier.issn 0302-9743
dc.identifier.scopus 2-s2.0-84962246245
dc.identifier.scopusquality Q3
dc.identifier.startpage 134 en_US
dc.identifier.uri https://doi.org/10.1007/978-3-319-31153-1_10
dc.identifier.uri https://hdl.handle.net/20.500.12469/6479
dc.identifier.volume 9598 en_US
dc.identifier.wosquality N/A
dc.language.iso en en_US
dc.publisher Springer Verlag en_US
dc.relation.ispartof Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) -- 19th European Conference on Applications of Evolutionary Computation, EvoApplications 2016 -- 30 March 2016 through 1 April 2016 -- Porto -- 172609 en_US
dc.relation.publicationcategory Konferans Öğesi - Uluslararası - Kurum Öğretim Elemanı en_US
dc.rights info:eu-repo/semantics/closedAccess en_US
dc.scopus.citedbyCount 8
dc.subject Genetic algorithm en_US
dc.subject GPU en_US
dc.subject Molecular docking en_US
dc.subject OpenCL en_US
dc.subject Parallelization en_US
dc.title GPU accelerated molecular docking simulation with genetic algorithms en_US
dc.type Conference Object en_US
dspace.entity.type Publication
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