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

By Stanislaw Jastrzebski, Maciej Szymczak, Agnieszka Pocha, Stefan Mordalski, Jacek Tabor, Andrzej J. Bojarski, Sabina Podlewska. 2020

26 citations recorded by the source corpus.

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 last steps of the process of compound libraries evaluation. In this work, we investigate the feasibility of an innovative procedure 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 unlocks 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 output. The method can be widely used by the community by using the code available at Supporting Information.

Subfields: Computational Theory and Mathematics, Molecular Biology, Radiology, Nuclear Medicine and Imaging

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Citations in the corpus

EquiBind: Geometric Deep Learning for Drug Binding Structure Prediction (2022, methodology): “, fast virtual screening (Gniewek et al., 2021; Jastrzebski et al., 2020) or de novo binder generation (Masuda et al.”
Artificial intelligence–enabled virtual screening of ultra-large chemical libraries with deep docking (2022, unknown): “Although the steps required for protein, ligand and docking grid preparation are explained using speci ﬁ c tools that we use in house, all of those can be readily adapted to similar programs and computer-aided drug discovery (CADD) packages.”