Abstract
Predicting drug-target interactions is very important to shorten the drug development cycle and reduce the cost of drug development. In this paper, we use a prediction framework based on knowledge graphs and binary classification models. Firstly, a knowledge graph is constructed using a drug database. Then, the entities in the knowledge graph are transformed into embedded vectors. Based on a dataset of drug-target interactions, the embedded vectors corresponding to drugs and targets are used as input data, and whether there is an interaction between the drug and the target is used as the label input to a binary classification neural network model for training. The experimental results show that the accuracy of drug-target prediction can be improved, when the improved transR strategy is used to construct the embedding vectors and the BiLSTM binary classification neural network model with attention mechanism.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Mohamed, S.K., Nováček, V., Nounu, A.: Discovering protein drug targets using knowledge graph embeddings. Bioinformatics 36(2), 603–610 (2020)
Xiaoli, L., Shuai, X., Xuan, L., Xiaolong, Z., Jing, H.: Detecting drug-target interactions with feature similarity fusion and molecular graphs. Biology 11(7), 967 (2022)
Xiaoli, L., Xiaolong, Z.: Efficient classification of hot spots and hub protein interfaces by recursive feature elimination and gradient boosting. IEEE/ACM Trans. Comput. Biol. Bioinf. 17(5), 1525–1534 (2020)
Xiaoli, L., Xiaolong, Z.: Prediction of hot regions in PPIs based on improved local community structure detecting. IEEE/ACM Trans. Comput. Biol. Bioinf. 15(5), 1470–1479 (2018)
Shuo, Z., Xiaoli, L., Xiaolong, Z.: Discovering DTI and DDI by knowledge graph with MHRW and improved neural network. In: IEEE International Conference on Bioinformatics and Biomedicine (BIBM2021) (2021)
Ye, Q., Hsieh, C.Y., Yang, Z., et al.: A unified drug–target interaction prediction framework based on knowledge graph and recommendation system. Nat. Commun. 12, 6775 (2021)
Wishart, D.S., Knox, C., Guo, A.C., et al.: DrugBank: a knowledgebase for drugs, drug actions and drug targets. Nucleic Acids Res. 36, D901–D906 (2008)
Kanehisa, M.: The KEGG database. In: Silico’Simulation of Biological Processes: Novartis Foundation Symposium 247. Chichester, UK: John Wiley & Sons Ltd, vol. 247, pp. 91–103 (2002)
Yamanishi, Y., Araki, M., Gutteridge, A., Honda, W., Kanehisa, M.: Prediction of drug-target interaction networks from the integration of chemical and genomic spaces. Bioinformatics 24, i232–i240 (2008)
Mongia, A., Jain, V., Chouzenoux, E., et al.: Deep latent factor model for predicting drug target interactions. In: 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1254–1258. IEEE (2019)
Rayhan, F., Ahmed, S., Mousavian, Z., et al.: FRnet-DTI: Deep convolutional neural network for drug-target interaction prediction. Heliyon 6(3), e03444 (2020)
Zhao, T., Hu, Y., Valsdottir, L.R., et al.: Identifying drug–target interactions based on graph convolutional network and deep neural network. Briefings Bioinform. 22(2), 2141–2150 (2021)
Acknowledgements
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 the Innovation and Entrepreneurship Training Program for University Students (2022169).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Zhang, Y., Cheng, M. (2023). Drug-Target Interaction Prediction Based on Knowledge Graph Embedding and BiLSTM Networks. In: Huang, DS., Premaratne, P., Jin, B., Qu, B., Jo, KH., Hussain, A. (eds) Advanced Intelligent Computing Technology and Applications. ICIC 2023. Lecture Notes in Computer Science, vol 14088. Springer, Singapore. https://doi.org/10.1007/978-981-99-4749-2_68
Download citation
DOI: https://doi.org/10.1007/978-981-99-4749-2_68
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-99-4748-5
Online ISBN: 978-981-99-4749-2
eBook Packages: Computer ScienceComputer Science (R0)