Abstract
Computational prediction of drug-target interaction (DTI) is very important for the new drug discovery. Currently, graph convolutional networks (GCNs) have been gained a lot of momentum, as its performance on non-Euclidean data. Although drugs and targets are two typical non-Euclidean data, many problems are also existed when use GCNs in DTI prediction. Firstly, most state-of-the-art GCN models are prone to the vanishing gradient problem, which is more serious in DTI prediction, as the number of interactions is limit. Secondly, a suitable graph is hardly defined for GCN in DTI prediction, as the relationship between the samples is not explicitly provided. To overcome the above problems, in this paper, a multiple output graph convolutional network (MOGCN) based DTI prediction method is designed. MOGCN enhances its learning ability with many new designed auxiliary classifier layers and a new designed graph calculation method. Many auxiliary classifier layers can increase the gradient signal that gets propagated back, utilize multi-level features to train the model, and use the features produced by the higher, middle or lower layers in a unified framework. The graph calculation method can use both the label information and the manifold. The conducted experiments validate the effectiveness of our MOGCN.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Wang, H., Wang, J., Dong, C.: A novel approach for drug-target interactions prediction based on multimodal deep autoencoder. Front. Pharmacol. 10, 1592 (2020)
Sturm, N., et al.: Industry-scale application and evaluation of deep learning for drug target prediction. J. Cheminform. 12(1), 1–13 (2020). https://doi.org/10.1186/s13321-020-00428-5
Xie, L.W., et al.: Deep learning-based transcriptome data classification for drug-target interaction prediction. BMC Genom. 19(667), 93–102 (2018)
Wang, L., et al.: Computational methods for the prediction of drug-target interactions from drug fingerprints and protein sequences by stacked auto-encoder deep neural network. Bioinform. Res. Appl. 10330, 46–58 (2017)
Wen, M., et al.: Deep learning-based drug-target interaction prediction. J. Proteome Res. 16(4), 1401–1409 (2017)
Wang, Y.B. et al. A deep learning-based method for drugtarget interaction prediction based on long short-term memory neural network, BMC Medical Informatics and Decision Making, 20(49), 1–9 (2020)
Lee, I., Keum, J., Nam, H.: DeepConv-DTI: Prediction of drug-target interactions via deep learning with convolution on protein sequences, PLOS Comput. Biol. 15(6) (2019)
Zhang, Y.F., et al.: SPVec: A Word2vec-inspired feature representation method for drug-target interaction prediction. Front. Chem. 7, 1–11 (2020)
Rayhan, F. et al. FRnet-DTI: Deep convolutional neural network for drug-target interaction prediction, Heliyon. 6(3), e03444 (2020)
Hu, S.S. et al. A convolutional neural network system to discriminate drug-target interactions, IEEE/ACM Trans. Comput. Biol. Bioinform. (2019), in press.
Monteiro, N.R.C., Ribeiro, B., Arrais, J.: Drug-target interaction prediction: end-to-end deep learning approach. IEEE/ACM Trans. Comput. Biol. Bioinform. (2020). https://doi.org/10.1109/TCBB.2020.2977335
Rifaioglu, A.S. et al. DEEPScreen: high performance drug–target interaction prediction with convolutional neural networks using 2-D structural compound representations, Chem. Sci. 9, 1–68 (2020)
David, D. et al. Convolutional networks on graphs for learning molecular fingerprints. In: Proceedings of the 28th International Conference on Neural Information Processing Systems, pp. 2224–2232 (2015)
Feng, Q.Y. et al. PADME: A Deep Learning-based Framework for Drug-Target Interaction Prediction. Comput. Sci. arXiv:1807.09741 (2019)
Gao K.Y. et al. Interpretable drug target prediction using deep neural representation. In: International Joint Conference on Artificial Intelligence, pp. 3371–3377 (2018)
Torng, W., Altman, R.B.: Graph convolutional neural networks for predicting drug-target interactions. J. Chem. Inform. Model. 59(10), 4131–4149 (2019). https://doi.org/10.1021/acs.jcim.9b00628
Sun, C., Xuan, P., Zhang, T., Ye, Y.: Graph convolutional autoencoder and generative adversarial network-based method for predicting drug-target interactions. IEEE/ACM Trans. Comput. Biol. Bioinform. (2020). https://doi.org/10.1109/TCBB.2020.2999084
Zhao, T.Y. et al. Identifying drug–target interactions based on graph convolutional network and deep neural network. Brief. Bioinform. bbaa044, 1–10 (2020)
.Yap, C.W.: PaDEL-descriptor: an open source software to calculate molecular descriptors and fingerprints. J. Comput. Chem. 32(7), 1466–1474 (2011)
Sheng, D., Liang, Q.S., Zeng, Y.: Propy: a tool to generate various modes of Chou’s PseAAC. Bioinformatics 29(7), 960–962 (2013)
Li, G.H., Xiong, C.X., Thabet, A., Bernard Ghanem. DeeperGCN: All You Need to Train Deeper GCNs. arXiv:2006.07739 (2020)
Bengio, Y.: Learning deep architectures for AI, in Learning Deep Architectures for AI (2009)
Yamanishi, Y., Araki, M., Gutteridge, A., et al.: Prediction of drug–target interaction networks from the integration of chemical and genomic spaces. Bioinformatics 24(13), 1232–1240 (2008)
DrugBank Release Version 5.1.7, https://go.drugbank.com/releases/latest
Kingma, D.P., Ba, L.J. Adam: A method for stochastic optimization. In: International Conference on Learning Representations (2015)
Acknowledgments
The authors thank the members of Machine Learning and Artificial Intelligence Laboratory, School of Computer Science and Technology, Wuhan University of Science and Technology, for their helpful discussion within seminars. This work was supported by, National Natural Science Foundation of China (No.61972299, 61502356), Zhejiang Provincial Natural Science Foundation (No.LQ18F020006, LQ18F020007), Hubei Province Natural Science Foundation of China (No. 2018CFB526).
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Ye, Q., Zhang, X., Lin, X. (2021). Drug-Target Interaction Prediction via Multiple Output Graph Convolutional Networks. In: Huang, DS., Jo, KH., Li, J., Gribova, V., Premaratne, P. (eds) Intelligent Computing Theories and Application. ICIC 2021. Lecture Notes in Computer Science(), vol 12838. Springer, Cham. https://doi.org/10.1007/978-3-030-84532-2_9
Download citation
DOI: https://doi.org/10.1007/978-3-030-84532-2_9
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-84531-5
Online ISBN: 978-3-030-84532-2
eBook Packages: Computer ScienceComputer Science (R0)