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 https://github.com/DeltaGroupNJUPT/VINA-GPU for academic usage.

AutoDock Vina is one of the most popular molecular docking tools. In the latest benchmark CASF-2016 for comparative assessment of scoring functions, AutoDock Vina won the best docking power among all the docking tools. Modern drug discovery is facing the most common scenario on large virtual screening of drug hits from huge compound databases. Due to the seriality characteristic of the AutoDock Vina algorithm, there is no successful report on its parallel acceleration with GPUs. Current acceleration of AutoDock Vina typically relies on the stack of computing power as well as the allocation of resource and tasks, such as the VirtualFlow platform. The vast resource expenditure and the high access threshold of users will seriously limit the popularity of AutoDock Vina and the flexibility of usage in modern drug discovery. Thus, the design of a new method for accelerating AutoDock Vina with GPUs is greatly needed for reducing the investment for large virtual screens, and also for a wide application in large-scale virtual screening on personal computers, station servers orcloud computing etc. Our proposed method Vina-GPU greatly raises the number of initial random conformations and reduces the search depth of each lane, and then a heterogeneous OpenCL implementation was developed to realize its parallel acceleration leveraging thousands of GPU cores. Large benchmarks show that Vina-GPU reaches a maximum of 403- fold docking acceleration against the original AutoDock Vina while ensuring their comparable docking accuracy, indicating its potential of pushing the popularization of AutoDock Vina in large virtual screens. The Vina-GPU code and tool can be freely available at http:// www.noveldelta.com/Vina_GPU for academic usage.

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 https://github.com/DeltaGroupNJUPT/VINA-GPU for academic usage.

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 https://github.com/DeltaGroupNJUPT/Vina-GPU for academic usage.

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 https://github.com/DeltaGroupNJUPT/Vina-GPU for academic usage.