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Accelerating AutoDock VINA with GPUs

Shidi Tang, Ruiqi Chen, Mengru Lin, Qingde Lin, Yanxiang Zhu, Jiansheng Wu, Haifeng Hu, Ming Ling
School of Geographic and Biological Information, Nanjing University of Posts and Telecommunications, Nanjing 210023, China
ChemRxiv. Cambridge: Cambridge Open Engage, 2021

@article{shidi2021accelerating,

   title={Accelerating AutoDock VINA with GPUs},

   author={Shidi, Tang and Ruiqi, Chen and Mengru, Lin and Qingde, Lin and Yanxiang, Zhu and Jiansheng, Wu and Haifeng, Hu and Ming, Ling},

   year={2021}

}

AutoDock VINA is one of the most-used docking tools in the early stage of modern drug discovery. It uses a Monte-Carlo based iterated search method and multithreading parallelism scheme on multicore machines to improve docking accuracy and speed. However, virtual screening from huge compound databases is common for modern drug discovery, which puts forward a great demand for higher docking speed of AutoDock VINA. Therefore, we propose a fast method VINA-GPU, which expands the Monte-Carlo based docking lanes into thousands of ones coupling with a largely reduced number of search steps in each lane. Furthermore, we develop a heterogeneous OpenCL implementation of VINA-GPU that leverages thousands of computational cores of a GPU, and obtains a maximum of 403-fold acceleration on docking runtime when compared with a quad-threaded AutoDock VINA implementation. In addition, a heuristic function was fitted to determine the proper size of search steps in each lane for a convenient usage. The VINA-GPU code can be freely available at for academic usage.
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