… introduce Deep Docking (DD), a novel deep learning platform that is suitable for docking …
) deep models trained on docking scores of subsets of a chemical library to approximate the …

… have a fair comparison with deep learning models in the blind docking setting, we applied …
docking. For the molecular docking, we use Uni-dock [21], a GPU-accelerated docking tool, for …

… Here, we evaluate our implementation of active learning for molecular docking, using
both the DOCK3.7 and Glide SP programs. All machine-learning models in this work were …

… Hence, various machine learning emulation techniques of docking have been proposed to
perform such tasks without large computing resources. In Supplementary Table 1, we have …

… a deep-learning-based pipeline for the informed selection of a particular molecular docking
… result in a better pose with a predefined docking algorithm. In conclusion, we believe that we …

… , we herein introduce Deep Docking (D 2 ) - a novel deep learning-based approach which
is suited for docking billions of molecular structures. The developed D 2 -platform utilizes …

… The docking power refers to the capacity of a SF to … is used to represent the final docking
power. The screening power … than Surflex-Dock, they showed much poorer docking power and …

… Recently, our group has developed a novel deep learning platform – Deep Docking (DD)
which provides fast prediction of docking scores of Glide (or any other docking program) and, …

… Docking is one of the most important steps in virtual … of learning a deep neural network to
predict the docking output … of magnitude faster than typical docking software, and it returns …

… dockingbased machine learning method called MLDDM (Machince Learning Dockingby-Docking …
, we setup such a machine learning model: MLDDM (Machince Learning Docking-by-…