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GPU-Accelerated High-Accuracy Molecular Docking using Guided Differential Evolution

Martin Simonsen, Mikael H. Christensen, Rene Thomsen, Christian N. S. Pedersen
CLC Bio, Finlandsgade 10-12, Katrinebjerg, DK-8200 Aarhus N
Massively Parallel Evolutionary Computation on GPGPUs, Natural Computing Series, Springer, 2013
@article{simonsen2013gpu,

   title={GPU-Accelerated High-Accuracy Molecular Docking using Guided Differential Evolution},

   author={Simonsen, Martin and Christensen, Mikael H. and Thomsen, Rene and Pedersen, Christian N. S.},

   year={2013}

}

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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 into a protein to identify potential drug candidates. This is a computationally intensive problem especially if the flexibility of the molecules is taken into account. 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 flexible docking methods.
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