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Issue No.05 - Sept.-Oct. (2012 vol.9)
pp: 1266-1272
Stephanus Daniel Handoko , Centre for Comput. Intell., Nanyang Technol. Univ., Singapore, Singapore
Xuchang Ouyang , Bioinf. Res. Centre, Nanyang Technol. Univ., Singapore, Singapore
Chinh Tran To Su , Bioinf. Res. Centre, Nanyang Technol. Univ., Singapore, Singapore
Chee Keong Kwoh , Sch. of Comput. Eng., Nanyang Technol. Univ., Singapore, Singapore
Yew Soon Ong , Sch. of Comput. Eng., Nanyang Technol. Univ., Singapore, Singapore
ABSTRACT
Predicting 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.
INDEX TERMS
proteins, bioinformatics, drugs, gradient methods, heuristic programming, macromolecules, molecular biophysics, optimisation, bioinformatics, QuickVina, AutoDock Vina, gradient-based heuristics, global optimization, macromolecule, rational drug design, docking software, empirical scoring function, binding affinity, AutoDock 4, local search algorithm, acceleration, protein-ligand complexes, Optimization, Databases, Proteins, Algorithm design and analysis, Bioinformatics, Computational biology, Drugs, gradient methods., Artificial intelligence, bioinformatics, global optimization
CITATION
Stephanus Daniel Handoko, Xuchang Ouyang, Chinh Tran To Su, Chee Keong Kwoh, Yew Soon Ong, "QuickVina: Accelerating AutoDock Vina Using Gradient-Based Heuristics for Global Optimization", IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol.9, no. 5, pp. 1266-1272, Sept.-Oct. 2012, doi:10.1109/TCBB.2012.82
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