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Accelerating AutoDock4 with GPUs and Gradient-Based Local Search

Diogo Santos-Martins, Leonardo Solis-Vasquez, Andreas Koch, and Stefano Forli
Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, California, USA
J. Chem. Theory Comput. 2021, 17, 2, 1060–1073

@article{santos2021accelerating,

   title={Accelerating autodock4 with gpus and gradient-based local search},

   author={Santos-Martins, Diogo and Solis-Vasquez, Leonardo and Tillack, Andreas F and Sanner, Michel F and Koch, Andreas and Forli, Stefano},

   journal={Journal of Chemical Theory and Computation},

   volume={17},

   number={2},

   pages={1060–1073},

   year={2021},

   publisher={ACS Publications}

}

AutoDock4 is a widely used program for docking small molecules to macromolecular targets. It describes ligand–receptor interactions using a physics-inspired scoring function that has been proven useful in a variety of drug discovery projects. However, compared to more modern and recent software, AutoDock4 has longer execution times, limiting its applicability to large scale dockings. To address this problem, we describe an OpenCL implementation of AutoDock4, called AutoDock-GPU, that leverages the highly parallel architecture of GPU hardware to reduce docking runtime by up to 350-fold with respect to a single-threaded process. Moreover, we introduce the gradient-based local search method ADADELTA, as well as an improved version of the Solis-Wets random optimizer from AutoDock4. These efficient local search algorithms significantly reduce the number of calls to the scoring function that are needed to produce good results. The improvements reported here, both in terms of docking throughput and search efficiency, facilitate the use of the AutoDock4 scoring function in large scale virtual screening.
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