GPU-Accelerated High-Accuracy Molecular Docking using Guided Differential Evolution
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}
}
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.
February 5, 2013 by hgpu
