QuickVina: accelerating AutoDock Vina using gradient-based heuristics for global optimization.
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.
Centre for Computational Intelligence, School of Computer Engineering, Nanyang Technological University, Singapore. sdhandoko@ntu.edu.sg
Stephanus Daniel
SD
Handoko
Xuchang
X
Ouyang
Chinh Tran To
CT
Su
Chee Keong
CK
Kwoh
Yew Soon
YS
Ong
D000465:Algorithms; D001665:Binding Sites; D015195:Drug Design; D008024:Ligands; D011506:Proteins / Q000737:chemistry* / Q000378:metabolism; D012984:Software; D013816:Thermodynamics
D008024:Ligands; D011506:Proteins