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.
We have presented methods for parallelising flexible molecular docking with MolDock on both CPUs and CUDA enabled GPUs which are applicable to other common flexible molecular docking methods. When docking multiple ligands against a target protein on a four core CPU, we achieved an average speedup of 3.3x with multi-threading and 3.9x by running multiple instances of the program concurrently. Using a comparable GPU with 112 cores, the average speedup was increased to 27.4x which translates to an average docking time of less than a second per ligand. The modifications made to the MolDock method did not decrease the accuracy on neither the CPU or GPU.
The reduction in running time per ligand achieved with a cheap consumer GPU, reduces the cost and amount of hardware needed to perform virtual screening with MolDock significantly. We plan to further increase the performance of the GPU implementation by using e.g. concurrent evaluation of multiple ligands which would also provide better utilization of GPUs with 500+ cores.
Martin Simonsen, Christian N.S. Pedersen, Mikael H. Christensen, and René Thomsen. 2011. GPU-accelerated high-accuracy molecular docking using guided differential evolution: real world applications. In Proceedings of the 13th annual conference on Genetic and evolutionary computation (GECCO ’11), Natalio Krasnogor (Ed.). ACM, New York, NY, USA, 1803-1810. [DOI: 10.1145/2001576.2001818]