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• W4312669283 abstract "Molecular docking (MD) is one of the core steps in the expensive and time-consuming process of drug design, which is basically an optimization problem based on scoring functions. AutoDock series MD software is widely accepted by academia and industry, among which AutoDock Vina (Vina) is the latest and most popular version due to its accuracy and relatively high speed. However, contrast to its prior version, i.e., AutoDock4, hardware acceleration approaches of Vina are rarely reported. In this article, we propose Vina-field-programmable gate array (FPGA), a hardware-accelerated Vina implementation with FPGA that exploits the low-level parallelism. First, the fixed-point quantization is analyzed and realized to accelerate the MD algorithm with a better energy efficiency in hardware. To boost the performance of the module-level computation, multiple in- module hardware pipelines have been designed and implemented. Besides, a strategy for fast accessing to block RAM (BRAM) is implemented by utilizing the layout of data, which brings four times memory access speed to the intermolecular and intramolecular energy computing modules. Under the same 140 ligand–receptor benchmarks, Vina-FPGA performs up to <inline-formula xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink> <tex-math notation=LaTeX>$6.9times $ </tex-math></inline-formula> (average <inline-formula xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink> <tex-math notation=LaTeX>$3.7times$ </tex-math></inline-formula> ) faster than a state-of-the-art CPU does while consuming only 2.5% energy with similar docking accuracies. Compared to the GPU-accelerated implementation or Vina-GPU, the average energy consumption of Vina-FPGA is merely 45%." @default.
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• W4312669283 date "2023-04-01" @default.
• W4312669283 modified "2023-10-15" @default.
• W4312669283 title "Vina-FPGA: A Hardware-Accelerated Molecular Docking Tool With Fixed-Point Quantization and Low-Level Parallelism" @default.
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• W4312669283 doi "https://doi.org/10.1109/tvlsi.2022.3217275" @default.
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