GraphDTA: Predicting drug–target binding affinity with graph neural networks

Nguyen, Thin, Le, H, Quinn, Thomas, Nguyen, Tri, Le, TD and Venkatesh, Svetha 2020, GraphDTA: Predicting drug–target binding affinity with graph neural networks, Bioinformatics, pp. 1-14, doi: 10.1093/bioinformatics/btaa921.

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Title GraphDTA: Predicting drug–target binding affinity with graph neural networks
Author(s) Nguyen, ThinORCID iD for Nguyen, Thin orcid.org/0000-0003-3467-8963
Le, H
Quinn, ThomasORCID iD for Quinn, Thomas orcid.org/0000-0003-0286-6329
Nguyen, Tri
Le, TD
Venkatesh, SvethaORCID iD for Venkatesh, Svetha orcid.org/0000-0001-8675-6631
Journal name Bioinformatics
Start page 1
End page 14
Total pages 14
Publisher Oxford University Press
Place of publication Oxford, Eng.
Publication date 2020-10-29
ISSN 1367-4803
1460-2059
Keyword(s) binding affinity
drug-target
graph neural networks
Summary The development of new drugs is costly, time consuming, and often accompanied with safety issues. Drug repurposing can avoid the expensive and lengthy process of drug development by finding new uses for already approved drugs. In order to repurpose drugs effectively, it is useful to know which proteins are targeted by which drugs. Computational models that estimate the interaction strength of new drug–target pairs have the potential to expedite drug repurposing. Several models have been proposed for this task. However, these models represent the drugs as strings, which is not a natural way to represent molecules. We propose a new model called GraphDTA that represents drugs as graphs and uses graph neural networks to predict drug–target affinity. We show that graph neural networks not only predict drug–target affinity better than non-deep learning models, but also outperform competing deep learning methods. Our results confirm that deep learning models are appropriate for drug–target binding affinity prediction, and that representing drugs as graphs can lead to further improvements. Availability of data and materials The proposed models are implemented in Python. Related data, pre-trained models, and source code are publicly available at https://github.com/thinng/GraphDTA. All scripts and data needed to reproduce the post-hoc statistical analysis are available from https://doi.org/10.5281/zenodo.3603523.
Notes In Press
Language eng
DOI 10.1093/bioinformatics/btaa921
Indigenous content off
Field of Research 01 Mathematical Sciences
06 Biological Sciences
08 Information and Computing Sciences
HERDC Research category C1.1 Refereed article in a scholarly journal
Persistent URL http://hdl.handle.net/10536/DRO/DU:30144730

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