GPU-accelerated high-accuracy molecular docking using guided differential evolution

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.