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QuickVina: Accelerating AutoDock Vina Using Gradient-Based Heuristics for Global Optimization

Authors:

Stephanus Daniel Handoko,

Xuchang Ouyang,

Chinh Tran To Su,

Chee Keong Kwoh,

Yew Soon OngAuthors Info & Claims

IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB), Volume 9, Issue 5

Pages 1266 - 1272

https://doi.org/10.1109/TCBB.2012.82

Published: 01 September 2012 Publication History

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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.

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Ling MLin QChen RQi HLin MZhu YWu J(2023)Vina-FPGA: A Hardware-Accelerated Molecular Docking Tool With Fixed-Point Quantization and Low-Level ParallelismIEEE Transactions on Very Large Scale Integration (VLSI) Systems10.1109/TVLSI.2022.321727531:4(484-497)Online publication date: 1-Apr-2023
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Published In

IEEE/ACM Transactions on Computational Biology and Bioinformatics Volume 9, Issue 5

September 2012

287 pages

ISSN:1545-5963

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Publisher

IEEE Computer Society Press

Washington, DC, United States

Publication History

Published: 01 September 2012

Published in TCBB Volume 9, Issue 5

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Ling MLin QChen RQi HLin MZhu YWu J(2023)Vina-FPGA: A Hardware-Accelerated Molecular Docking Tool With Fixed-Point Quantization and Low-Level ParallelismIEEE Transactions on Very Large Scale Integration (VLSI) Systems10.1109/TVLSI.2022.321727531:4(484-497)Online publication date: 1-Apr-2023
https://dl.acm.org/doi/10.1109/TVLSI.2022.3217275
Andrianov ANikolaev GKornoushenko YUsanov S(2020)In Silico Design and Evaluation of Novel Triazole-Based Compounds as Promising Drug Candidates Against Breast CancerBioinformatics Research and Applications10.1007/978-3-030-57821-3_29(312-318)Online publication date: 1-Dec-2020
https://dl.acm.org/doi/10.1007/978-3-030-57821-3_29
Nikolaev GShuldov NAnischenko ATuzikov AAndrianov A(2020)Development of a Neural Network-Based Approach for Prediction of Potential HIV-1 Entry Inhibitors Using Deep Learning and Molecular Modeling MethodsBioinformatics Research and Applications10.1007/978-3-030-57821-3_28(304-311)Online publication date: 1-Dec-2020
https://dl.acm.org/doi/10.1007/978-3-030-57821-3_28
Bjerrum E(2016)Machine learning optimization of cross docking accuracyComputational Biology and Chemistry10.1016/j.compbiolchem.2016.04.00562:C(133-144)Online publication date: 1-Jun-2016
https://dl.acm.org/doi/10.1016/j.compbiolchem.2016.04.005
Handoko SOuyang XSu CKwoh COng Y(2012)Erratum to "QuickVinaIEEE/ACM Transactions on Computational Biology and Bioinformatics10.1109/TCBB.2012.1569:6(1853)Online publication date: 1-Nov-2012
https://dl.acm.org/doi/10.1109/TCBB.2012.156

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