Journal of Computational Chemistry
Classification-Assisted Memetic Algorithms for Equality-Constrained Optimization Problems
AI '09 Proceedings of the 22nd Australasian Joint Conference on Advances in Artificial Intelligence
Feasibility structure modeling: an effective chaperone for constrained memetic algorithms
IEEE Transactions on Evolutionary Computation - Special issue on preference-based multiobjective evolutionary algorithms
Meta-Lamarckian learning in memetic algorithms
IEEE Transactions on Evolutionary Computation
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
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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.