Yaacoub Jean Charle, Gleave James, Gentile Francesco, Stern Abraham, Cherkasov Artem
Vancouver Prostate Centre, Department of Urologic Sciences, The University of British Columbia, Vancouver, BC V6H 3Z6, Canada.
NVIDIA Corporation, Santa Clara, CA 95051, USA.
Bioinformatics. 2022 Jan 27;38(4):1146-1148. doi: 10.1093/bioinformatics/btab771.
Deep learning (DL) can significantly accelerate virtual screening of ultra-large chemical libraries, enabling the evaluation of billions of compounds at a fraction of the computational cost and time required by conventional docking. Here, we introduce DD-GUI, the graphical user interface for such DL approach we have previously developed, termed Deep Docking (DD). The DD-GUI allows for quick setups of large-scale virtual screens in an intuitive way, and provides convenient tools to track the progress and analyze the outcomes of a drug discovery project.
DD-GUI is freely available with an MIT license on GitHub at https://github.com/jamesgleave/DeepDockingGUI.
Supplementary data are available at Bioinformatics online.
深度学习（DL）可以显著加速超大型化学文库的虚拟筛选，能够以传统对接所需计算成本和时间的一小部分来评估数十亿种化合物。在此，我们介绍DD-GUI，这是我们之前开发的名为深度对接（DD）的此类深度学习方法的图形用户界面。DD-GUI允许以直观的方式快速设置大规模虚拟筛选，并提供方便的工具来跟踪药物发现项目的进展并分析其结果。
DD-GUI根据麻省理工学院许可在GitHub上免费获取，网址为https://github.com/jamesgleave/DeepDockingGUI。
补充数据可在《生物信息学》在线获取。
