GraphDTA: predicting drug–target binding affinity with graph neural networks
Abstract 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...
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| Published in | Bioinformatics Vol. 37; no. 8; pp. 1140 - 1147 |
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| Main Authors | , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
Oxford University Press
23.05.2021
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| Online Access | Get full text |
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| Abstract | Abstract
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 implementation
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.
Supplementary information
Supplementary data are available at Bioinformatics online. |
|---|---|
| AbstractList | Abstract
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 implementation
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.
Supplementary information
Supplementary data are available at Bioinformatics online. 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.SUMMARYThe 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.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.AVAILABILITY OF IMPLEMENTATIONThe 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.Supplementary data are available at Bioinformatics online.SUPPLEMENTARY INFORMATIONSupplementary data are available at Bioinformatics online. |
| Author | Nguyen, Thin Quinn, Thomas P Le, Hang Le, Thuc Duy Nguyen, Tri Venkatesh, Svetha |
| Author_xml | – sequence: 1 givenname: Thin surname: Nguyen fullname: Nguyen, Thin email: thin.nguyen@deakin.edu.au organization: Applied Artificial Intelligence Institute, Deakin University, Geelong, VIC, 3216, Australia – sequence: 2 givenname: Hang surname: Le fullname: Le, Hang organization: Faculty of Information Technology, Nha Trang University, Nha Trang, Khanh Hoa, Viet Nam – sequence: 3 givenname: Thomas P orcidid: 0000-0003-0286-6329 surname: Quinn fullname: Quinn, Thomas P organization: Applied Artificial Intelligence Institute, Deakin University, Geelong, VIC, 3216, Australia – sequence: 4 givenname: Tri surname: Nguyen fullname: Nguyen, Tri email: thin.nguyen@deakin.edu.au organization: Applied Artificial Intelligence Institute, Deakin University, Geelong, VIC, 3216, Australia – sequence: 5 givenname: Thuc Duy orcidid: 0000-0002-9732-4313 surname: Le fullname: Le, Thuc Duy organization: School of Information Technology and Mathematical Sciences, University of South Australia, Adelaide, SA, 5095, Australia – sequence: 6 givenname: Svetha surname: Venkatesh fullname: Venkatesh, Svetha organization: Applied Artificial Intelligence Institute, Deakin University, Geelong, VIC, 3216, Australia |
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The development of new drugs is costly, time consuming and often accompanied with safety issues. Drug repurposing can avoid the expensive and... The development of new drugs is costly, time consuming and often accompanied with safety issues. Drug repurposing can avoid the expensive and lengthy process... |
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