Emulating Docking Results Using a Deep Neural Network: A New Perspective for Virtual Screening

Docking
is one of the most important steps in virtual screening
pipelines, and it is an established method for examining potential
interactions between ligands and receptors. However, this method is
computationally expensive, and it is often among the last steps of
the process of compound libraries evaluation. In this work, we investigate
the feasibility of learning a deep neural network to predict the docking
output directly from a two-dimensional compound structure. The developed
protocol is orders of magnitude faster than typical docking software,
and it returns ligand–receptor complexes encoded in the form
of the interaction fingerprint. Its speed and efficiency unlock the
application possibilities, such as screening compound libraries of
vast size on the basis of contact patterns or docking score (derived
on the basis of predicted interaction schemes). We tested our approach
on several G protein-coupled receptor targets and 4 CYP enzymes in
retrospective virtual screening experiments, and a variant of graph
convolutional network appeared to be most effective in emulating docking
results. The method can be easily used by the community based on the
code available in the Supporting Information.