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.

[1]  Renxiao Wang,et al.  The PDBbind database: collection of binding affinities for protein-ligand complexes with known three-dimensional structures. , 2004, Journal of medicinal chemistry.

[2]  David S. Goodsell,et al.  Automated docking using a Lamarckian genetic algorithm and an empirical binding free energy function , 1998, J. Comput. Chem..

[3]  D. Shanno.  Conditioning of Quasi-Newton Methods for Function Minimization , 1970 .

[4]  Thomas Lengauer,et al.  A fast flexible docking method using an incremental construction algorithm. , 1996, Journal of molecular biology.

[5]  Chee Keong Kwoh,et al.  Classification-assisted memetic algorithms for solving optimization problems with restricted equality constraint function mapping , 2011, 2011 IEEE Congress of Evolutionary Computation (CEC).

[6]  Arthur J. Olson,et al.  AutoDock Vina: Improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading , 2009, J. Comput. Chem..

[7]  Ruth Nussinov,et al.  Principles of docking: An overview of search algorithms and a guide to scoring functions , 2002, Proteins.

[8]  Chee Keong Kwoh,et al.  Classification-Assisted Memetic Algorithms for Equality-Constrained Optimization Problems , 2009, Australasian Conference on Artificial Intelligence.

[9]  P Willett,et al.  Development and validation of a genetic algorithm for flexible docking. , 1997, Journal of molecular biology.

[10]  Ruben Abagyan,et al.  ICM—A new method for protein modeling and design: Applications to docking and structure prediction from the distorted native conformation , 1994, J. Comput. Chem..

[11]  Chee Keong Kwoh,et al.  Feasibility Structure Modeling: An Effective Chaperone for Constrained Memetic Algorithms , 2010, IEEE Transactions on Evolutionary Computation.

[12]  Andy J. Keane,et al.  Meta-Lamarckian learning in memetic algorithms , 2004, IEEE Transactions on Evolutionary Computation.

[13]  Pedro Alexandrino Fernandes,et al.  Protein–ligand docking: Current status and future challenges , 2006, Proteins.