Abstract
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 3.9× on a four-core CPU and 27.4× 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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Notes
- 1.
This work was done while the author “Martin Simonsen” was affiliated to the Bioinformatics Research Centre (BiRC), Aarhus University, Aarhus, Denmark.
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Simonsen, M., Christensen, M.H., Thomsen, R., Pedersen, C.N.S. (2013). GPU-Accelerated High-Accuracy Molecular Docking Using Guided Differential Evolution. In: Tsutsui, S., Collet, P. (eds) Massively Parallel Evolutionary Computation on GPGPUs. Natural Computing Series. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37959-8_16
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DOI: https://doi.org/10.1007/978-3-642-37959-8_16
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