Abstract
The identification of drug-target interaction (DTI) is the crucial challenge in virtual screening, which plays a key role in drug discovery. Recently, large amounts of deep learning methods have been applied to DTI predictions and achieved good performance. However, traditional methods are difficult to effectively extract features of drugs and proteins because of their structural complexity. In order to solve this issue, we propose a novel method based on transformer. Our method only uses the sequence information of drugs and proteins, and it can effectively extract deep features through the attention mechanism in transformer. To improve the performance of our approach, we have done a lot of adjustment experiments and optimized the parameters to analyze and compare the experimental results. Tests on two benchmark datasets demonstrate the effectiveness and accuracy of our method.
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References
Lin, X., Li, X., Lin, X.: A review on applications of computational methods in drug screening and design. Molecules 25(6), 1375 (2020)
Ozturk, H., Ozkirimli, E., Ozgur, A.: DeepDTA: deep drug-target binding affinity prediction. Bioinformatics 34(17), 821–829 (2018)
Ozturk, H., Ozkirimli, E., Ozgur, A.: WideDTA: prediction of drug-target binding affinity. arXiv preprint arXiv:1902.04166 (2019)
Karimi, M., et al.: DeepAffinity: interpretable deep learning of compound-protein affinity through unified recurrent and convolutional neural networks. Bioinformatics 35(18), 3329–3338 (2019)
Gao, K.Y., et al.: Interpretable drug target prediction using deep neural representation. In: Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence, Stockholm, Sweden, pp. 3371–3377 (2018)
Wen, T., Altman, R.B.: Graph convolutional neural networks for predicting drug-target interactions. Chem. Inf. Model. 59(10), 4131–4149 (2019)
Jiang, M., et al.: Drug-target affinity prediction using graph neural network and contact maps. RSC Adv. 10(35), 20701–20712 (2020)
Nguyen, T., et al.: GraphDTA: predicting drug-target binding affinity with graph neural networks. Bioinformatics 37(8), 1040–1047 (2020)
Cheng, S., et al.: GraphMS: drug target prediction using graph representation learning via substructures contrast. Appl. Sci. 11(7), 3239 (2021)
Li, S., et al.: MONN: a multi-objective neural network for predicting compound-protein interactions and affinities. Cell Syst. 10(4), 308–322 (2020)
Zheng, S., et al.: Predicting drug–protein interaction using quasi-visual question answering system. Nat. Mach. Intell. 2(2), 134–140 (2020)
Vaswani, A., et al.: Attention is all you need. In: Advances in Neural Information Processing Systems, Long Beach, USA, pp. 5998–6008 (2017)
Devlin, J., et al.: BERT: pretraining of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (2019)
Liu, H., et al.: Improving compound-protein interaction prediction by building up highly credible negative samples. Bioinformatics 31(12), 221–229 (2015)
Tsubaki, M., Tomii, K., Sese, J.: Compound-protein interaction prediction with end-to-end learning of neural networks for graphs and sequences. Bioinformatics 35(2), 309–318 (2019)
Wishart, D.S., et al.: DrugBank: a knowledgebase for drugs, drug actions and drug targets. Nucleic Acids Res. 36(Database issue), D901–D906 (2008)
Gunther, S., et al.: SuperTarget and Matador: resources for exploring drug-target relationships. Nucleic Acids Res. 36(Database issue), D919–D922 (2008)
Weininger, D.: SMILES, a chemical language and information system. 1. Introduction to methodology and encoding rules. J. Chem. Inf. Comput. Sci. 28(1), 31–36 (1988)
Bento, A.P., et al.: An open source chemical structure curation pipeline using RDKit. J. Cheminf. 12(1), 1–16 (2020). https://doi.org/10.1186/s13321-020-00456-1
Mikolov, T., et al.: Distributed representations of words and phrases and their compositionality. In: Advances in Neural Information Processing Systems, p. 26 (2013)
Kingma, D., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint arXiv: 1412.6980 (2014)
Li, P., et al.: TrimNet: learning molecular representation from triplet messages for biomedicine. Brief. Bioinf. 22(4) (2021)
Acknowledgement
This paper is supported by the National Natural Science Foundation of China (61902272, 62073231, 62176175, 61876217, 61902271), National Research Project (2020YFC2006602), Provincial Key Laboratory for Computer Information Processing Technology, Soochow University (KJS2166), Opening Topic Fund of Big Data Intelligent Engineering Laboratory of Jiangsu Province (SDGC2157).
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Liu, J., Jiang, T., Lu, Y., Wu, H. (2022). Drug-Target Interaction Prediction Based on Transformer. In: Huang, DS., Jo, KH., Jing, J., Premaratne, P., Bevilacqua, V., Hussain, A. (eds) Intelligent Computing Theories and Application. ICIC 2022. Lecture Notes in Computer Science, vol 13394. Springer, Cham. https://doi.org/10.1007/978-3-031-13829-4_25
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