Modern drug discovery typically faces
large virtual screens from
huge compound databases where multiple docking tools are involved
for meeting various real scenes or improving the precision of virtual
screens. Among these tools, AutoDock Vina and its numerous derivatives
are the most popular and have become the standard pipeline for molecular
docking in modern drug discovery. Our recent Vina-GPU method realized
14-fold acceleration against AutoDock Vina on a piece of NVIDIA RTX
3090 GPU in one virtual screening case. Further speedup of AutoDock
Vina and its derivatives with graphics processing units (GPUs) is
beneficial to systematically push their popularization in large-scale
virtual screens due to their high benefit–cost ratio and easy
operation for users. Thus, we proposed the Vina-GPU 2.0 method to
further accelerate AutoDock Vina and the most common derivatives with
new docking algorithms (QuickVina 2 and QuickVina-W) with GPUs. Caused
by the discrepancy in their docking algorithms, our Vina-GPU 2.0 adopts
different GPU acceleration strategies. In virtual screening for two
hot protein kinase targets, RIPK1 and RIPK3, from the DrugBank database,
our Vina-GPU 2.0 reaches an average of 65.6-fold, 1.4-fold, and 3.6-fold
docking acceleration against the original AutoDock Vina, QuickVina
2, and QuickVina-W while ensuring their comparable docking accuracy.
In addition, we develop a friendly and installation-free graphical
user interface tool for their convenient usage. The codes and tools
of Vina-GPU 2.0 are freely available at , coupled with explicit instructions and examples.
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