dataset · Figshare · 1753

DSDP: A Blind Docking\nStrategy Accelerated by GPUs

YuPeng Huang (16472792), Hong Zhang (25820), Siyuan Jiang (7889114), Dajiong Yue (16472795), Xiaohan Lin (8202087), Jun Zhang (48506), Yi Qin Gao (132523)

Cited by 0Open access

Abstract

Virtual screening, including molecular docking, plays\nan essential\nrole in drug discovery. Many traditional and machine-learning-based\nmethods are available to fulfill the docking task. However, the traditional\ndocking methods are normally extensively time-consuming, and their\nperformance in blind docking remains to be improved. Although the\nruntime of docking based on machine learning is significantly decreased,\ntheir accuracy is still limited. In this study, we take advantage\nof both traditional and machine-learning-based methods and present\na method, deep site and docking pose (DSDP), to improve the performance\nof blind docking. For traditional blind docking, the entire protein\nis covered by a cube, and the initial positions of ligands are randomly\ngenerated in this cube. In contrast, DSDP can predict the binding\nsite of proteins and provide an accurate searching shape and initial\npositions for further conformational sampling. The sampling task of\nDSDP makes use of the score function and a similar but modified searching\nstrategy of AutoDock Vina, accelerated by implementation in GPUs.\nWe systematically compare its performance in redocking, blind docking,\nand virtual screening tasks with state-of-the-art methods, including\nAutoDock Vina, GNINA, QuickVina, SMINA, and DiffDock. In the blind\ndocking task, DSDP reaches a 29.8% top-1 success rate (root-mean-squared\ndeviation < 2 Å) on an unbiased and challenging test dataset\nwith 1.2 s wall-clock computational time per system. Its performances\non the DUD-E dataset and the time-split PDBBind dataset used in EquiBind,\nTANKBind, and DiffDock are also evaluated, presenting a 57.2 and 41.8%\ntop-1 success rate with 0.8 and 1.0 s per system, respectively.

Cite this paper

APA 7

(16472792), Y. H., (25820), H. Z., (7889114), S. J., (16472795), D. Y., (8202087), X. L., (48506), J. Z., & (132523), Y. Q. G. (1753). DSDP: A Blind Docking\nStrategy Accelerated by GPUs. Figshare. https://doi.org/10.1021/acs.jcim.3c00519.s001

MLA 9

(16472792), YuPeng Huang, et al. “DSDP: A Blind Docking\nStrategy Accelerated by GPUs.” Figshare, 1753, https://doi.org/10.1021/acs.jcim.3c00519.s001.

Chicago

(16472792), YuPeng Huang, Hong Zhang (25820), Siyuan Jiang (7889114), Dajiong Yue (16472795), Xiaohan Lin (8202087), Jun Zhang (48506), and Yi Qin Gao (132523). 1753. “DSDP: A Blind Docking\nStrategy Accelerated by GPUs.” Figshare. https://doi.org/10.1021/acs.jcim.3c00519.s001.

Harvard

(16472792), Y.H. et al. (1753) ‘DSDP: A Blind Docking\nStrategy Accelerated by GPUs’, Figshare. Available at: https://doi.org/10.1021/acs.jcim.3c00519.s001.

BibTeX

@misc{164727921753dsdp,
  title = {{DSDP: A Blind Docking\nStrategy Accelerated by GPUs}},
  author = {(16472792), YuPeng Huang and (25820), Hong Zhang and (7889114), Siyuan Jiang and (16472795), Dajiong Yue and (8202087), Xiaohan Lin and (48506), Jun Zhang and (132523), Yi Qin Gao},
  journal = {Figshare},
  year = {1753},
  doi = {10.1021/acs.jcim.3c00519.s001}
}

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