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Drug–Target Interaction Prediction Based on Graph Neural Network and Recommendation System

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Book cover Intelligent Computing Theories and Application (ICIC 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13394))

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Abstract

Drug therapy is an important means to cure diseases. The identification of drugs and target proteins is the key to the development of new drugs. However, due to the limitations of high throughput, low precision and high cost of biological experimental methods, the verification of a large number of drug target interactions has a certain degree of blindness, which makes it difficult to carry out widely in practical applications. Driven by information science, intelligent information processing technologies such as machine learning, data mining and mathematical statistics have been developed and applied rapidly. Predicting the interaction between drugs and target proteins through computer simulation can reduce the research and development cost, shorten the time of new drug development and reduce the blindness of new drug development. It is of great significance for new drug research and development and the improvement of human medical treatment. However, the existing drug-target interactions (DTIs) prediction methods have the problems of low accuracy and high false positive rate. In this paper, a new DTIs prediction method GCN_NFM is proposed by combining graph neural network and recommendation system, the framework first learns the low dimensional representation of drug entities and protein entities in graph neural network (GCN), and then integrates multimodal information through neural factorization machine (NFM). The results show that under the 5-fold cross-validation, the area under the receiver operating characteristic curve (AUROC) obtained by this method is 0.9457, indicating that GCN_NFM can effectively and robustly capture undiscovered DTIs.

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References

  1. Mullard, A.: New drugs cost US[dollar]2.6 billion to develop. Nat. Rev. Drug Discov. 13 (2014)

    Google Scholar 

  2. Nic, F.: How artificial intelligence is changing drug discovery. Nature 557(7707), S55 (2018)

    Article  Google Scholar 

  3. Smalley, E.: AI-powered drug discovery captures pharma interest. Nat. Biotechnol. 35 (2017)

    Google Scholar 

  4. Gschwend, D.A., Good, A.C., Kuntz, I.D.: Molecular Docking Towards Drug Discovery, vol. 9, issue 2, pp. 175–186. Wiley (1996)

    Google Scholar 

  5. Mayr, A., Klambauer, G., Unterthiner, T., et al.: Large-scale comparison of machine learning methods for drug target prediction on ChEMBL. Chem. Sci. 9(24), 5441–5451 (2018)

    Article  Google Scholar 

  6. Sydow, D., Burggraaff, L., Szengel, A., et al.: Advances and challenges in computational target prediction. J. Chem. Inf. Model. 59(5), 1728–1742 (2019)

    Article  Google Scholar 

  7. Li, J., Zheng, S., Chen, B., et al.: A survey of current trends in computational drug repositioning. Brief. Bioinform. 17(1), 2–12 (2016)

    Article  Google Scholar 

  8. Napolitano, F., Zhao, Y., Moreira, V.M., et al.: Drug repositioning: a machine-learning approach through data integration. J. Cheminform. 5(1), 1–9 (2013)

    Article  Google Scholar 

  9. Wu, C., Gudivada, R.C., Aronow, B.J., et al.: Computational drug repositioning through heterogeneous network clustering. BMC Syst. Biol. 7(5), 1–9 (2013)

    Google Scholar 

  10. Kinnings, S.L., Liu, N., Buchmeier, N., et al.: Drug discovery using chemical systems biology: repositioning the safe medicine Comtan to treat multi-drug and extensively drug resistant tuberculosis. PLoS Comput. Biol. 5(7), e1000423 (2009)

    Article  Google Scholar 

  11. Liu, Z., Fang, H., Reagan, K., et al.: In silico drug repositioning–what we need to know. Drug Discov. Today 18(3–4), 110–115 (2013)

    Article  Google Scholar 

  12. Bagherian, M., Sabeti, E., Wang, K., et al.: Machine learning approaches and databases for prediction of drug–target interaction: a survey paper. Brief. Bioinform. 22(1), 247–269 (2021)

    Article  Google Scholar 

  13. Agamah, F.E., Mazandu, G.K., Hassan, R., et al.: Computational/in silico methods in drug target and lead prediction. Brief. Bioinform. 21(5), 1663–1675 (2020)

    Article  Google Scholar 

  14. Manoochehri, H.E., Nourani, M.: Drug-target interaction prediction using semi-bipartite graph model and deep learning. BMC Bioinform. 21(4), 1–16 (2020)

    Google Scholar 

  15. D’Souza, S., Prema, K.V., Balaji, S.: Machine learning models for drug–target interactions: current knowledge and future directions. Drug Discov. Today 25(4), 748–756 (2020)

    Article  Google Scholar 

  16. Xue, H., Li, J., Xie, H., et al.: Review of drug repositioning approaches and resources. Int. J. Biol. Sci. 14(10), 1232 (2018)

    Article  Google Scholar 

  17. Luo, H., Li, M., Yang, M., et al.: Biomedical data and computational models for drug repositioning: a comprehensive review. Brief. Bioinform. 22(2), 1604–1619 (2021)

    Article  Google Scholar 

  18. Yella, J.K., Yaddanapudi, S., Wang, Y., et al.: Changing trends in computational drug repositioning. Pharmaceuticals 11(2), 57 (2018)

    Article  Google Scholar 

  19. Luo, Y., Zhao, X., Zhou, J., et al.: A network integration approach for drug-target interaction prediction and computational drug repositioning from heterogeneous information. Nat. Commun. 8(1), 1–13 (2017)

    Article  Google Scholar 

  20. Wen, M., Zhang, Z., Niu, S., et al.: Deep-learning-based drug–target interaction prediction. J. Proteome Res. 16(4), 1401–1409 (2017)

    Article  Google Scholar 

  21. Ding, Y., Tang, J., Guo, F.: Identification of drug-target interactions via multiple information integration. Inf. Sci. 418, 546–560 (2017)

    Article  Google Scholar 

  22. Thafar, M.A., Olayan, R.S., Ashoor, H., et al.: DTiGEMS+: drug–target interaction prediction using graph embedding, graph mining, and similarity-based techniques. J. Cheminform. 12(1), 1–17 (2020)

    Article  Google Scholar 

  23. Wu, Z., Pan, S., Chen, F., et al.: A comprehensive survey on graph neural networks. IEEE Trans. Neural Networks Learn. Syst. 32(1), 4–24 (2020)

    Article  MathSciNet  Google Scholar 

  24. Cheng, T., Hao, M., Takeda, T., et al.: Large-scale prediction of drug-target interaction: a data-centric review. AAPS J. 19(5), 1264–1275 (2017)

    Article  Google Scholar 

  25. Zhao, T., Hu, Y., Valsdottir, L.R., et al.: Identifying drug–target interactions based on graph convolutional network and deep neural network. Brief. Bioinform. 22(2), 2141–2150 (2021)

    Article  Google Scholar 

  26. Torng, W., Altman, R.B.: Graph convolutional neural networks for predicting drug-target interactions. J. Chem. Inf. Model. 59(10), 4131–4149 (2019)

    Article  Google Scholar 

  27. Lim, H., Poleksic, A., Yao, Y., et al.: Large-scale off-target identification using fast and accurate dual regularized one-class collaborative filtering and its application to drug repurposing. PLoS Comput. Biol. 12(10), e1005135 (2016)

    Article  Google Scholar 

  28. Wishart, D.S., Feunang, Y.D., Guo, A.C., et al.: DrugBank 5.0: a major update to the DrugBank database for 2018. Nucleic Acids Res. 46(D1), D1074–D1082 (2018)

    Google Scholar 

  29. Li, Y., Liu, X., You, Z.H., et al.: A computational approach for predicting drug–target interactions from protein sequence and drug substructure fingerprint information. Int. J. Intell. Syst. 36(1), 593–609 (2021)

    Article  Google Scholar 

  30. Szklarczyk, D., Morris, J.H., Cook, H., et al.: The STRING database in 2017: quality-controlled protein–protein association networks, made broadly accessible. Nucleic Acids Res. 2016, gkw937 (2016)

    Google Scholar 

  31. Rizk, G., Lavenier, D., Chikhi, R.: DSK: k-mer counting with very low memory usage. Bioinformatics 29(5), 652–653 (2013)

    Article  Google Scholar 

  32. Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907 (2016)

  33. Zweig, M.H., Campbell, G.: Receiver-operating characteristic (ROC) plots: a fundamental evaluation tool in clinical medicine. Clin. Chem. 39(4), 561–577 (1993)

    Article  Google Scholar 

  34. Rogers, D., Hahn, M.: Extended-connectivity fingerprints. J. Chem. Inf. Model. 50(5), 742–754 (2010)

    Article  Google Scholar 

  35. Maggiora, G., Vogt, M., Stumpfe, D., et al.: Molecular similarity in medicinal chemistry: miniperspective. J. Med. Chem. 57(8), 3186–3204 (2014)

    Article  Google Scholar 

  36. Chen, Z.H., You, Z.H., Guo, Z.H., et al.: Prediction of drug–target interactions from multi-molecular network based on deep walk embedding model. Front. Bioeng. Biotechnol. 8, 338 (2020)

    Article  Google Scholar 

  37. Ji, B.Y., You, Z.H., Jiang, H.J., et al.: Prediction of drug-target interactions from multi-molecular network based on LINE network representation method. J. Transl. Med. 18(1), 1–11 (2020)

    Article  Google Scholar 

  38. Shen, Z., Zhang, Q., Han, K., et al.: A deep learning model for RNA-protein binding preference prediction based on hierarchical LSTM and attention network. IEEE/ACM Trans. Comput. Biol. Bioinform. (2020)

    Google Scholar 

  39. Zhang, Q., Shen, Z., Huang, D.S.: Predicting in-vitro transcription factor binding sites using DNA sequence+ shape. IEEE/ACM Trans. Comput. Biol. Bioinf. 18(2), 667–676 (2019)

    Article  Google Scholar 

  40. Shen, Z., Deng, S.P., Huang, D.S.: Capsule network for predicting RNA-protein binding preferences using hybrid feature. IEEE/ACM Trans. Comput. Biol. Bioinf. 17(5), 1483–1492 (2019)

    Article  Google Scholar 

  41. Zhu, L., Li, N., Bao, W., et al.: Learning regulatory motifs by direct optimization of Fisher Exact Test Score. In: 2016 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 86–91. IEEE (2016)

    Google Scholar 

  42. Shen, Z., Deng, S.P., Huang, D.S.: RNA-protein binding sites prediction via multi scale convolutional gated recurrent unit networks. IEEE/ACM Trans. Comput. Biol. Bioinf. 17(5), 1741–1750 (2019)

    Article  Google Scholar 

  43. Zhang, Q., Zhu, L., Bao, W., et al.: Weakly-supervised convolutional neural network architecture for predicting protein-DNA binding. IEEE/ACM Trans. Comput. Biol. Bioinf. 17(2), 679–689 (2018)

    Google Scholar 

  44. Zhang, Q., Zhu, L., Huang, D.S.: High-order convolutional neural network architecture for predicting DNA-protein binding sites. IEEE/ACM Trans. Comput. Biol. Bioinf. 16(4), 1184–1192 (2018)

    Article  Google Scholar 

  45. Zhang, Q., Shen, Z., Huang, D.S.: Modeling in-vivo protein-DNA binding by combining multiple-instance learning with a hybrid deep neural network. Sci. Rep. 9(1), 1–12 (2019)

    Google Scholar 

  46. Xu, W., Zhu, L., Huang, D.S.: DCDE: an efficient deep convolutional divergence encoding method for human promoter recognition. IEEE Trans. Nanobiosci. 18(2), 136–145 (2019)

    Article  Google Scholar 

  47. Shen, Z., Bao, W., Huang, D.S.: Recurrent neural network for predicting transcription factor binding sites. Sci. Rep. 8(1), 1–10 (2018)

    Article  Google Scholar 

  48. Zhang, H., Zhu, L., Huang, D.S.: DiscMLA: an efficient discriminative motif learning algorithm over high-throughput datasets. IEEE/ACM Trans. Comput. Biol. Bioinf. 15(6), 1810–1820 (2016)

    Article  Google Scholar 

  49. Zhu, L., Zhang, H.B., Huang, D.: LMMO: a large margin approach for refining regulatory motifs. IEEE/ACM Trans. Comput. Biol. Bioinf. 15(3), 913–925 (2017)

    Article  Google Scholar 

  50. Shen, Z., Zhang, Y.H., Han, K., et al.: miRNA-disease association prediction with collaborative matrix factorization. Complexity 2017, 1–9 (2017)

    Google Scholar 

  51. Zhu, L., Zhang, H.B., Huang, D.S.: Direct AUC optimization of regulatory motifs. Bioinformatics 33(14), i243–i251 (2017)

    Article  Google Scholar 

  52. Zhang, H., Zhu, L., Huang, D.S.: WSMD: weakly-supervised motif discovery in transcription factor ChIP-seq data. Sci. Rep. 7(1), 1–12 (2017)

    Google Scholar 

Download references

Acknowledgements

This work was supported by the grant of National Key R&D Program of China (No. 2018YFA0902600 & 2018AAA0100100) and partly supported by National Natural Science Foundation of China (Grant nos. 61732012, 62002266, 61932008, and 62073231), and Introduction Plan of High-end Foreign Experts (Grant no. G2021033002L) and, respectively, supported by the Key Project of Science and Technology of Guangxi (Grant no. 2021AB20147), Guangxi Natural Science Foundation (Grant nos. 2021JJA170204 & 2021JJA170199) and Guangxi Science and Technology Base and Talents Special Project (Grant nos. 2021AC19354 & 2021AC19394).

Author information

Authors and Affiliations

  1. Institute of Machine Learning and Systems Biology, School of Electronics and Information Engineering, Tongji University, Shanghai, 201804, China

    Peng Lei

  2. Guangxi Academy of Science, Nanning, 530007, China

    Changan Yuan

  3. Guangxi Key Lab of Human-Machine Interaction and Intelligent Decision, Guangxi Academy Sciences, Nanning, China

    Changan Yuan

  4. School of Electronic and Information Engineering, Suzhou University of Science and Technology, Suzhou, 215009, China

    Hongjie Wu

  5. Institute of Science and Technology for Brain Inspired Intelligence (ISTBI), Fudan University, Shanghai, 200433, China

    Xingming Zhao

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  1. Peng Lei
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  2. Changan Yuan
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  3. Hongjie Wu
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  4. Xingming Zhao
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Corresponding author

Correspondence to Peng Lei .

Editor information

Editors and Affiliations

  1. Tongji University, Shanghai, China

    De-Shuang Huang

  2. University of Ulsan, Ulsan, Korea (Republic of)

    Kang-Hyun Jo

  3. Xi’an Polytechnic University, Xi’an, China

    Junfeng Jing

  4. The University of Wollongong, North Wollongong, NSW, Australia

    Prashan Premaratne

  5. Polytecnic of Bari, Bari, Italy

    Vitoantonio Bevilacqua

  6. Liverpool John Moores University, Liverpool, UK

    Abir Hussain

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Lei, P., Yuan, C., Wu, H., Zhao, X. (2022). Drug–Target Interaction Prediction Based on Graph Neural Network and Recommendation System. 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_6

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  • DOI: https://doi.org/10.1007/978-3-031-13829-4_6

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