Skip to main content
SpringerLink
Log in

A Review on Predicting Drug Target Interactions Based on Machine Learning

  • Conference paper
  • First Online:
Health Information Science (HIS 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14305))

Included in the following conference series:

  • 443 Accesses

Abstract

The prediction of drug-target interactions (DTIs) is a key preliminary step for drug discovery and development due to the high risk of failure as well as the long validation period of in vitro and in vivo experiments. Nowadays, with the swiftly growing power in solving scientific problems, machine learning has become an important tool in DTI prediction. By simply categorizing them into traditional machine learning-based approaches and deep learning-based ones, this review discusses some representative approaches in each branch. After a brief introduction on traditional methods, we firstly pay large attention to the data representation of deep learning-based methods, which can be summarized with 5 different representations for drugs and 4 for proteins. Then we introduce a new taxonomy of deep neural network models for DTI prediction. Furthermore, the commonly used datasets and evaluation metrics were also summarized for an easier hands-on practice.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 59.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 74.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Al-Absi, H.R., Refaee, M.A., Rehman, A.U., Islam, M.T., Belhaouari, S.B., Alam, T.: Risk factors and comorbidities associated to cardiovascular disease in Qatar: a machine learning based case-control study. IEEE Access 9, 29929–29941 (2021)

    Article  Google Scholar 

  2. Baek, M., et al.: Accurate prediction of protein structures and interactions using a three-track neural network. Science 373(6557), 871–876 (2021)

    Article  Google Scholar 

  3. Chen, R., Liu, X., Jin, S., Lin, J., Liu, J.: Machine learning for drug-target interaction prediction. Molecules 23(9), 2208 (2018)

    Article  Google Scholar 

  4. Cui, F., Zhang, Z., Zou, Q.: Sequence representation approaches for sequence-based protein prediction tasks that use deep learning. Brief. Funct. Genomics 20(1), 61–73 (2021)

    Article  Google Scholar 

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

    Article  Google Scholar 

  6. Ezzat, A., Wu, M., Li, X.L., Kwoh, C.K.: Drug-target interaction prediction via class imbalance-aware ensemble learning. BMC Bioinform. 17(19), 267–276 (2016)

    Google Scholar 

  7. Faulon, J.L., Misra, M., Martin, S., Sale, K., Sapra, R.: Genome scale enzyme-metabolite and drug-target interaction predictions using the signature molecular descriptor. Bioinformatics 24(2), 225–233 (2008)

    Article  Google Scholar 

  8. Fu, G., Ding, Y., Seal, A., Chen, B., Sun, Y., Bolton, E.: Predicting drug target interactions using meta-path-based semantic network analysis. BMC Bioinform. 17(1), 1–10 (2016)

    Article  Google Scholar 

  9. He, T., Heidemeyer, M., Ban, F., Cherkasov, A., Ester, M.: SimBoost: a read-across approach for predicting drug-target binding affinities using gradient boosting machines. J. Cheminform. 9(1), 1–14 (2017)

    Article  Google Scholar 

  10. Hua, Y., Song, X., Feng, Z., Wu, X.: MFR-DTA: a multi-functional and robust model for predicting drug-target binding affinity and region. Bioinformatics 39(2), btad056 (2023)

    Google Scholar 

  11. Jiang, M., et al.: Drug-target affinity prediction using graph neural network and contact maps. RSC Adv. 10(35), 20701–20712 (2020)

    Article  Google Scholar 

  12. Jiménez, J., Skalic, M., Martinez-Rosell, G., De Fabritiis, G.: K deep: protein-ligand absolute binding affinity prediction via 3D-convolutional neural networks. J. Chem. Inf. Model. 58(2), 287–296 (2018)

    Article  Google Scholar 

  13. Lee, H., Kim, W.: Comparison of target features for predicting drug-target interactions by deep neural network based on large-scale drug-induced transcriptome data. Pharmaceutics 11(8), 377 (2019)

    Article  Google Scholar 

  14. Li, J., Zheng, S., Chen, B., Butte, A.J., Swamidass, S.J., Lu, Z.: A survey of current trends in computational drug repositioning. Brief. Bioinform. 17(1), 2–12 (2016)

    Article  Google Scholar 

  15. Lim, J., Ryu, S., Park, K., Choe, Y.J., Ham, J., Kim, W.Y.: Predicting drug-target interaction using a novel graph neural network with 3D structure-embedded graph representation. J. Chem. Inf. Model. 59(9), 3981–3988 (2019)

    Article  Google Scholar 

  16. Mousavian, Z., Masoudi-Nejad, A.: Drug-target interaction prediction via chemogenomic space: learning-based methods. Expert Opin. Drug Metab. Toxicol. 10(9), 1273–1287 (2014)

    Article  Google Scholar 

  17. Mukherjee, S., Ghosh, M., Basuchowdhuri, P.: DeepGLSTM: deep graph convolutional network and LSTM based approach for predicting drug-target binding affinity. In: Proceedings of the 2022 SIAM International Conference on Data Mining (SDM), pp. 729–737. SIAM (2022)

    Google Scholar 

  18. Nguyen, T., Le, H., Quinn, T.P., Nguyen, T., Le, T.D., Venkatesh, S.: GraphDTA: predicting drug-target binding affinity with graph neural networks. Bioinformatics 37(8), 1140–1147 (2021)

    Article  Google Scholar 

  19. Öztürk, H., Özgür, A., Ozkirimli, E.: DeepDTA: deep drug-target binding affinity prediction. Bioinformatics 34(17), i821–i829 (2018)

    Article  Google Scholar 

  20. Pahikkala, T., et al.: Toward more realistic drug-target interaction predictions. Brief. Bioinform. 16(2), 325–337 (2015)

    Article  Google Scholar 

  21. Pan, L., et al.: MFDNN: multi-channel feature deep neural network algorithm to identify covid19 chest X-ray images. Health Inf. Sci. Syst. 10(1), 4 (2022)

    Article  Google Scholar 

  22. Peng, J., Li, J., Shang, X.: A learning-based method for drug-target interaction prediction based on feature representation learning and deep neural network. BMC Bioinform. 21(13), 1–13 (2020)

    Google Scholar 

  23. Peng, J., et al.: An end-to-end heterogeneous graph representation learning-based framework for drug-target interaction prediction. Briefings Bioinform. 22(5), bbaa430 (2021)

    Google Scholar 

  24. Perlman, L., Gottlieb, A., Atias, N., Ruppin, E., Sharan, R.: Combining drug and gene similarity measures for drug-target elucidation. J. Comput. Biol. 18(2), 133–145 (2011)

    Article  Google Scholar 

  25. Samara, K.A., Al Aghbari, Z., Abusafia, A.: Glimpse: a glioblastoma prognostication model using ensemble learning—a surveillance, epidemiology, and end results study. Health Inf. Sci. Syst. 9, 1–13 (2021)

    Article  Google Scholar 

  26. da Silva Rocha, S.F., Olanda, C.G., Fokoue, H.H., Sant’Anna, C.M.: Virtual screening techniques in drug discovery: review and recent applications. Curr. Top. Med. Chem. 19(19), 1751–1767 (2019)

    Article  Google Scholar 

  27. Stärk, H., Ganea, O., Pattanaik, L., Barzilay, R., Jaakkola, T.: EquiBind: geometric deep learning for drug binding structure prediction. In: International Conference on Machine Learning, pp. 20503–20521. PMLR (2022)

    Google Scholar 

  28. Stokes, J.M., et al.: A deep learning approach to antibiotic discovery. Cell 180(4), 688–702 (2020)

    Article  Google Scholar 

  29. Thafar, M.A., et al.: DTi2Vec: drug-target interaction prediction using network embedding and ensemble learning. J. Cheminform. 13(1), 1–18 (2021)

    Article  Google Scholar 

  30. Tunyasuvunakool, K., et al.: Highly accurate protein structure prediction for the human proteome. Nature 596(7873), 590–596 (2021)

    Article  Google Scholar 

  31. Van Laarhoven, T., Nabuurs, S.B., Marchiori, E.: Gaussian interaction profile kernels for predicting drug-target interaction. Bioinformatics 27(21), 3036–3043 (2011)

    Article  Google Scholar 

  32. Vázquez, J., López, M., Gibert, E., Herrero, E., Luque, F.J.: Merging ligand-based and structure-based methods in drug discovery: an overview of combined virtual screening approaches. Molecules 25(20), 4723 (2020)

    Article  Google Scholar 

  33. Wang, M., Li, P., Qiao, P., et al.: The virtual screening of the drug protein with a few crystal structures based on the adaboost-SVM. Comput. Math. Methods Med. 2016 (2016)

    Google Scholar 

  34. Wang, P., et al.: Structure-aware multimodal deep learning for drug-protein interaction prediction. J. Chem. Inf. Model. 62(5), 1308–1317 (2022)

    Article  MathSciNet  Google Scholar 

  35. Wang, S., Du, Z., Ding, M., Rodriguez-Paton, A., Song, T.: KG-DTI: a knowledge graph based deep learning method for drug-target interaction predictions and Alzheimer’s disease drug repositions. Appl. Intell. 52(1), 846–857 (2022)

    Article  Google Scholar 

  36. Wang, Y.B., You, Z.H., Yang, S., Yi, H.C., Chen, Z.H., Zheng, K.: A deep learning-based method for drug-target interaction prediction based on long short-term memory neural network. BMC Med. Inform. Decis. Mak. 20(2), 1–9 (2020)

    Google Scholar 

  37. Wang, Y., Zeng, J.: Predicting drug-target interactions using restricted Boltzmann machines. Bioinformatics 29(13), i126–i134 (2013)

    Article  Google Scholar 

  38. Wu, J., Lv, X., Jiang, S.: BSageIMC: drug repositioning based on bipartite graph convolutional networks and transcriptomics data. In: Li, X. (ed.) IASC 2021. LNDECT, vol. 80, pp. 376–383. Springer, Cham (2022). https://doi.org/10.1007/978-3-030-81007-8_42

    Chapter  Google Scholar 

  39. Xia, Z., Wu, L.Y., Zhou, X., Wong, S.T.: Semi-supervised drug-protein interaction prediction from heterogeneous biological spaces. In: BMC Systems Biology, vol. 4, pp. 1–16. BioMed Central (2010)

    Google Scholar 

  40. Yang, F., Xue, F., Zhang, Y., Karypis, G.: Kernelized multitask learning method for personalized signaling adverse drug reactions. IEEE Trans. Knowl. Data Eng. (2021)

    Google Scholar 

  41. Yang, Z., Zhong, W., Zhao, L., Chen, C.Y.C.: MGraphDTA: deep multiscale graph neural network for explainable drug-target binding affinity prediction. Chem. Sci. 13(3), 816–833 (2022)

    Article  Google Scholar 

  42. Yin, X.X., et al.: Automatic breast tissue segmentation in MRIs with morphology snake and deep denoiser training via extended stein’s unbiased risk estimator. Health Inf. Sci. Syst. 9, 1–21 (2021)

    Article  Google Scholar 

  43. Yuan, Y., et al.: A novel strategy for prediction of human plasma protein binding using machine learning techniques. Chemom. Intell. Lab. Syst. 199, 103962 (2020)

    Article  Google Scholar 

  44. Zhang, J., Liu, B.: A review on the recent developments of sequence-based protein feature extraction methods. Curr. Bioinform. 14(3), 190–199 (2019)

    Article  Google Scholar 

  45. Zhang, W., Lin, W., Zhang, D., Wang, S., Shi, J., Niu, Y.: Recent advances in the machine learning-based drug-target interaction prediction. Curr. Drug Metab. 20(3), 194–202 (2019)

    Article  Google Scholar 

  46. Zhang, Y.F., et al.: SPVec: a word2vec-inspired feature representation method for drug-target interaction prediction. Front. Chem. 7, 895 (2020)

    Article  Google Scholar 

  47. Zhao, Z., Bourne, P.E.: Harnessing systematic protein-ligand interaction fingerprints for drug discovery. Drug Discovery Today (2022)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. The Cyberspace Institute of Advanced Technology, Guangzhou University, Guangzhou, China

    Wen Shi, Dandan Peng, Jinyuan Luo, Guozhu Chen, Hong Yang, Xiao-Xia Yin & Yanchun Zhang

  2. State Key Laboratory of Proteomics, National Center for Protein Sciences (Beijing), Beijing, 102206, China

    Linhai Xie

Authors
  1. Wen Shi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Dandan Peng
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jinyuan Luo
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Guozhu Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Hong Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Linhai Xie
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Xiao-Xia Yin
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Yanchun Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Hong Yang or Linhai Xie .

Editor information

Editors and Affiliations

  1. University of Southern Queensland, Darling Heights, Australia

    Yan Li

  2. Vrije University, Amsterdam, The Netherlands

    Zhisheng Huang

  3. DAV University Jalandhar, Jalandhar, Punjab, India

    Manik Sharma

  4. Swinburne University of Technology, Hawthorn, VIC, Australia

    Lu Chen

  5. Swinburne University of Technology, Hawthorn, VIC, Australia

    Rui Zhou

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Shi, W. et al. (2023). A Review on Predicting Drug Target Interactions Based on Machine Learning. In: Li, Y., Huang, Z., Sharma, M., Chen, L., Zhou, R. (eds) Health Information Science. HIS 2023. Lecture Notes in Computer Science, vol 14305. Springer, Singapore. https://doi.org/10.1007/978-981-99-7108-4_24

Download citation

  • DOI: https://doi.org/10.1007/978-981-99-7108-4_24

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-99-7107-7

  • Online ISBN: 978-981-99-7108-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation