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Vina-CUDA: An Efficient Program with in-Depth Utilization of GPU to Accelerate Molecular Docking.

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  • 1School of Computer Science and Technology, Beijing Institute of Technology, No. 5 Zhongguancun South Street, Haidian District, Beijing 100081, China.

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We developed Vina-CUDA, a GPU-accelerated molecular docking tool, to speed up drug discovery. This enhanced computational capability significantly accelerates virtual screening of large chemical libraries, improving drug development efficiency.

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Area of Science:

  • Computational chemistry
  • Drug discovery
  • Bioinformatics

Background:

  • Molecular docking is crucial for identifying drug leads.
  • Growing chemical databases challenge existing docking tools.
  • Efficient virtual screening is vital for drug development.

Purpose of the Study:

  • To accelerate molecular docking using GPU hardware.
  • To optimize AutoDock Vina's core algorithm for speed.
  • To develop a multi-GPU framework for large-scale virtual screening.

Main Methods:

  • Leveraged GPU features for computational capability, memory access, and resource utilization.
  • Implemented a hybrid parallel optimization strategy.
  • Developed Vina-CUDA, QuickVina2-CUDA, and QuickVina-W-CUDA.

Main Results:

  • Vina-CUDA achieved average accelerations of 3.71×, QuickVina2-CUDA 6.19×, and QuickVina-W-CUDA 1.46×.
  • Accelerations reached up to 6.89× without compromising docking accuracy.
  • Demonstrated comparable docking, scoring, and ranking power to baseline programs.

Conclusions:

  • Vina-CUDA and its derivatives significantly enhance molecular docking efficiency.
  • The tools offer excellent scalability and portability for virtual screening.
  • GPU acceleration is key to overcoming challenges posed by large chemical databases.