GPU-accelerated high-accuracy molecular docking using guided differential evolution: real world applications

The objective in molecular docking is to determine the best binding mode of two molecules in silico. A common application of molecular docking is in drug discovery where a large number of ligands are docked against a protein to identify potential drug candidates. This is a computationally intensive problem especially if flexibility of the molecules are taken into account. In this paper we show how MolDock, which is a high accuracy method for flexible molecular docking using a variant of differential evolution, can be parallelised on both CPU and GPU. The methods presented for parallelising the workload result in an average speedup of 3.9x on a 4-core CPU and 27.4x on a comparable CUDA enabled GPU when docking 133 ligands of different sizes. Furthermore, the presented parallelisation schemes are generally applicable and can easily be adapted to other common flexible docking methods.

[1]  Rainer Storn,et al.  Differential Evolution – A Simple and Efficient Heuristic for global Optimization over Continuous Spaces , 1997, J. Glob. Optim..

[2]  Martin C. Herbordt,et al.  GPU acceleration of a production molecular docking code , 2009, GPGPU-2.

[3]  Klaus Schulten,et al.  GPU-accelerated molecular modeling coming of age. , 2010, Journal of molecular graphics & modelling.

[4]  Takuji Nishimura,et al.  Mersenne twister: a 623-dimensionally equidistributed uniform pseudo-random number generator , 1998, TOMC.

[5]  Mauro Birattari,et al.  Dm63 Heuristics for Combinatorial Optimization Ant Colony Optimization Exercises Outline Ant Colony Optimization: the Metaheuristic Application Examples Generalized Assignment Problem (gap) Connection between Aco and Other Metaheuristics Encodings Capacited Vehicle Routing Linear Ordering Ant Colony , 2022 .

[6]  Marco Dorigo,et al.  Ant colony optimization , 2006, IEEE Computational Intelligence Magazine.

[7]  Matthias Rarey,et al.  Small Molecule Docking and Scoring , 2001 .

[8]  I ScottKirkpatrick.  Optimization by Simulated Annealing: Quantitative Studies , 1984 .

[9]  Thomas Stützle,et al.  PLANTS: Application of Ant Colony Optimization to Structure-Based Drug Design , 2006, ANTS Workshop.

[10]  Manuel López-Ibáñez,et al.  Ant colony optimization , 2010, GECCO '10.

[11]  Jinn-Moon Yang,et al.  GEMDOCK: A generic evolutionary method for molecular docking , 2004, Proteins.

[12]  M. Matsumoto,et al.  Parallel Mersenne Twister , 2007 .

[13]  Sarnath Kannan,et al.  Porting Autodock to CUDA , 2010, IEEE Congress on Evolutionary Computation.

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

[15]  D. Goodsell,et al.  Automated docking of substrates to proteins by simulated annealing , 1990, Proteins.

[16]  L. Dagum,et al.  OpenMP: an industry standard API for shared-memory programming , 1998 .

[17]  René Thomsen,et al.  MolDock: a new technique for high-accuracy molecular docking. , 2006, Journal of medicinal chemistry.

[18]  D S Goodsell,et al.  Automated docking of flexible ligands: Applications of autodock , 1996, Journal of molecular recognition : JMR.

[19]  Oliver Korb,et al.  Efficient ant colony optimization algorithms for structure- and ligand-based drug design , 2009 .

[20]  Robin Taylor,et al.  A new test set for validating predictions of protein–ligand interaction , 2002, Proteins.

[21]  Stephen R. Comeau,et al.  PIPER: An FFT‐based protein docking program with pairwise potentials , 2006, Proteins.