dc.contributor.authorHandoko, Stephanus Daniel
dc.contributor.authorOuyang, Xuchang
dc.contributor.authorSu, Chinh Tran To
dc.contributor.authorKwoh, Chee Keong
dc.contributor.authorOng, Yew Soon
dc.date.accessioned2013-10-16T04:41:49Z
dc.date.available2013-10-16T04:41:49Z
dc.date.copyright2012en_US
dc.date.issued2012
dc.identifier.citationHandoko, S. D., Ouyang, X., Su, C. T. T., Kwoh, C. K., & Ong, Y. S. (2012). QuickVina : accelerating AutoDock Vina using gradient-based heuristics for global optimization. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 9(5), 1266-1272 .
dc.identifier.issn1545-5963en_US
dc.identifier.urihttp://hdl.handle.net/10220/16520
dc.description.abstractPredicting binding between macromolecule and small molecule is a crucial phase in the field of rational drug design. AutoDock Vina, one of the most widely used docking software released in 2009, uses an empirical scoring function to evaluate the binding affinity between the molecules and employs the iterated local search global optimizer for global optimization, achieving a significantly improved speed and better accuracy of the binding mode prediction compared its predecessor, AutoDock 4. In this paper, we propose further improvement in the local search algorithm of Vina by heuristically preventing some intermediate points from undergoing local search. Our improved version of Vina-dubbed QVina-achieved a maximum acceleration of about 25 times with the average speed-up of 8.34 times compared to the original Vina when tested on a set of 231 protein-ligand complexes while maintaining the optimal scores mostly identical. Using our heuristics, larger number of different ligands can be quickly screened against a given receptor within the same time frame.en_US
dc.language.isoenen_US
dc.relation.ispartofseriesIEEE/ACM Transactions on Computational Biology and Bioinformaticsen_US
dc.rights© 2012 IEEEen_US
dc.subjectDRNTU::Engineering::Computer science and engineering::Software
dc.titleQuickVina : accelerating AutoDock Vina using gradient-based heuristics for global optimizationen_US
dc.typeJournal Article
dc.contributor.researchBioinformatics Research Centreen_US
dc.contributor.researchCentre for Computational Intelligenceen_US
dc.contributor.schoolSchool of Computer Engineeringen_US
dc.identifier.doihttp://dx.doi.org/10.1109/TCBB.2012.82


Files in this item

FilesSizeFormatView

There are no files associated with this item.

This item appears in the following Collection(s)

Show simple item record