SpringerLink
Log in

Predicting drug–drug interactions based on multi-view and multichannel attention deep learning

  • Research
  • Published:
Health Information Science and Systems Aims and scope Submit manuscript

Abstract

Predicting drug-drug interactions (DDIs) has become a major concern in the drug research field because it helps explore the pharmacological function of drugs and enables the development of new therapeutic drugs. Existing prediction methods simply integrate multiple drug attributes or perform tasks on a biomedical knowledge graph (KG). Though effective, few methods can fully utilize multi-source drug data information. In this paper, a multi-view and multichannel attention deep learning (MMADL) model is proposed, which not only extracts rich drug features containing both drug attributes and drug-related entity information from multi-source databases, but also considers the consistency and complementarity of different drug feature representation learning approaches to improve the effectiveness and accuracy of DDI prediction. A single-layer perceptron encoder is applied to encode multi-source drug information to obtain multi-view drug representation vectors in the same linear space. Then, the multichannel attention mechanism is introduced to obtain the attention weight by adaptively learning the importance of drug features according to their contributions to DDI prediction. Further, the representation vectors of multi-view drug pairs with attention weights are used as inputs of the deep neural network to predict potential DDI. The accuracy and precision-recall curves of MMADL are 93.05 and 95.94, respectively. The results indicate that the proposed method outperforms other state-of-the-art methods.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (Germany)

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

Data availability

The source codes and the data at https://github.com/Liyu-gx/MMADL.git.

References

  1. Kantor ED, Rehm CD, Haas JS, Chan AT, Giovannucci EL. Trends in prescription drug use among adults in the united states from 1999–2012. Obstet Gynecol Surv. 2015;314:1818–30.

    Google Scholar 

  2. Qato MD, Wilder J, Gillet V, Alexander GC. Changes in prescription and over-the-counter medication and dietary supplement use among older adults in the united states, 2005 vs 2011. Pharmacoepidemiol Drug Saf. 2016;176:473–82.

    Google Scholar 

  3. Han K, Jeng EE, Hess GT, Morgens DW, Li A, Bassik MC. Synergistic drug combinations for cancer identified in a CRISPR screen for pairwise genetic interactions. Nat Biotechnol. 2017;35:463–74.

    Article  Google Scholar 

  4. Tatonetti NP, Ye PP, Daneshjou R, Altman RB. Datadriven prediction of drug effects and interactions. Sci Transl Med. 2012;4:125ra31.

    Article  Google Scholar 

  5. Pazdernik T. Lippincott’s illustrated reviews: pharmacology. Med Sci Sports Exerc. 2009;41:1531.

    Article  Google Scholar 

  6. Prueksaritanont T, Chu X, Gibson C, Cui D, Yee KL, Ballard J, Cabalu T, Honchman J. Drug-drug interaction studies: regulatory guidance and an industry perspective. AAPS J. 2013;15:629–45.

    Article  Google Scholar 

  7. Pushpakom S, Iorio F, Eyers PA, Escott KJ, Hopper S, Wells A, Doig A, Guilliams T, Latimer J, Mcnamee C. Drug repurposing: progress, challenges and recommendations. Nat Rev Drug Discov. 2019;18:41–58.

    Article  Google Scholar 

  8. Jiang HJ, Huang YA, You ZH. Predicting drug-disease associations via using gaussian interaction profile and kernel-based autoencoder. Biomed Res Int. 2019;2019:1–1.

    Google Scholar 

  9. Li ZC, Huang QX, Chen X, Wang Y, Li JL, **e Y, Dai D, Zou XY. Identification of drug-disease associations using information of molecular structures and clinical symptoms via deep convolutional neural network. Front Chem. 2020;7:924.

    Article  Google Scholar 

  10. Yu Z, Huang F, Zhao X, **ao W, Zhang W. Predicting drug-disease associations through layer attention graph convolutional network. Brief Bioinform. 2020;22:1–1.

    Google Scholar 

  11. Monteiro N, Ribeiro B, Arrais J. Drug-target interaction prediction: end-to-end deep learning approach. IEEE-ACM Trans Comput Biol Bioinform. 2020;18:2364–74.

    Article  Google Scholar 

  12. Lian MJ, Wang XJ, Du WL. Integrated multi-similarity fusion and heterogeneous graph inference for drugtarget interaction prediction. Neurocomputing. 2022;500:1–2.

    Article  Google Scholar 

  13. Xu L, Ru XQ, Song R. Application of machine learning for drug-target Interaction prediction. Front Genet. 2021;12: 680117.

    Article  Google Scholar 

  14. Munir A, Elahi S, Masood N. Clustering based drug-drug interaction networks for possible repositioning of drugs against EGFR mutations: clustering based DDI networks for EGFR mutations. Comput Biol Chem. 2018;75:24–31.

    Article  Google Scholar 

  15. Ryu JY, Kim HU, Lee SY. Deep learning improves prediction of drug-drug and drug-food interactions. Proc Natl Acad Sci U S A. 2018;115:E4304–11.

    Article  Google Scholar 

  16. Deng YF, Yang Q, Xu XR, Liu SC, Zhang ZF, Zhu SF, Zhang W. META-DDIE: predicting drug-drug interaction events with few-shot learning. Brief Bioinform. 2022;23:1–8.

    Article  Google Scholar 

  17. Vilar S, Uriarte E, Santana L. Similarity-based modeling in large-scale prediction of drug-drug interactions. PNat Protoc. 2014;9:2147–63.

    Article  Google Scholar 

  18. Yan C, Duan G, Zhang Y, Wu FX, Wang J. Predicting drug-drug interactions based on integrated similarity and semi-supervised learning. IEEE-ACM Trans Comput Biol Bioinform. 2020;9:168–79.

    Google Scholar 

  19. Lee G, Park C, Ahn J. Novel deep learning model for more accurate prediction of drug-drug interaction effects. BMC Bioinformatics. 2019;20:415.

    Article  Google Scholar 

  20. Zhang WA, **g KC, Huang FB, Chen YC, Li BB, Li JB, Gong JA. Sflln: a sparse feature learning ensemble method with linear neighborhood regularization for predicting drug-drug interactions. Inf Sci. 2019;497:189–201.

    Article  Google Scholar 

  21. Zhang YJ, Zheng W, Lin HF, Wang J, Yang ZH, Dumontier M. Drug-drug interaction extraction via hierarchical RNNs on sequence and shortest dependency paths. Bioinformatics. 2018;34:828–35.

    Article  Google Scholar 

  22. Deng Y, Xu X, Qiu Y, **a J, Liu S. Amultimodal deep learning framework for predicting drug-drug interaction events. Bioinformatics. 2020;36:4316–22.

    Article  Google Scholar 

  23. Wang F, Lei XJ, Liao B, Wu FX. Predicting drug-drug interactions by graph convolutional network with multi-kernel. Brief Bioinform. 2021;23:1–11.

    Google Scholar 

  24. Nickel M, Murphy K, Tresp V, Gabrilovich E. A review of relational machine learning for knowledge graphs. Proc IEEE. 2016;104:11–33.

    Article  Google Scholar 

  25. Tiddi I, Schlobach S. Knowledge graphs as tools for explainable machine learning: a survey. Artif Intell. 2022;302:103627.

    Article  MathSciNet  Google Scholar 

  26. Fokoue A, Sadoghi M, Hassanzadeh O, ** Z. Predicting drug-drug interactions through large-scale similarity-based link prediction. In: Proceedings 13th ESWC Conference 774–789 (2016)

  27. Trouillon T, Welbl J, Riedel S, Gaussier R, Bouchard G. Complex embeddings for simple link prediction. In: Proceedings 33rd ICML 2071–2080 (2016)

  28. Karim MR, Cochez M, Jares JB, Uddin M, Beyan O, Decker S. Drug-drug interaction prediction based on knowledge graph embeddings and convolutional-LSTM network. In 10th ACM Conference BCB 113–123 (2019)

  29. Lin X, Quan Z, Wang ZJ, Ma T, Zeng X. KGNN: knowledge graph neural network for drug-drug interaction prediction. In: Proceedings 29th IJCAI-PRICAI-20 2739–2745 (2020)

  30. Niu ZY, Zhong GQ, Hui Y. A review on the attention mechanism of deep learning. Neurocomputing. 2021;452:48–62.

    Article  Google Scholar 

  31. Chen X, Liu X, Wu J. Drug-drug interaction prediction with graph representation learning. In: IEEE International Conference on Bioinformatics and Biomedicine (BIBM) 354–361 (2019)

  32. Nyamabo, AK, Yu, H, Shi, JY. SSI-DDI: substructure-substructure interactions for drug-drug interaction prediction, Brief. Bioinform. 2021; 22:bbab133

    Google Scholar 

  33. Lu J, Yang W, Batra D, Parikh D. Hierarchical question-image coattention for visual question answering. In: Proceedings 34th AAAI Conference, 702–709 (2020)

  34. Wang Y, Min Y, Chen X, Wu J. Multi-view graph contrastive representation learning for drug-drug interaction prediction. In: WWW ’21: The Web Conference 2021, 2921–2933 (2021)

  35. Feng YY, Yu H, Feng YH, Shi JY. Directed graph attention networks for predicting asymmetric drug–drug interactions. Brief Bioinform. 2022;23:bbac151.

    Article  Google Scholar 

  36. Su XR, Hu L, You ZH, Hu PW, Zhao BW. Attention-based knowledge graph representation learning for predicting drug-drug interactions. Brief Bioinform. 2022;23:bbac140.

    Article  Google Scholar 

  37. Wishart DS, Knox C, Guo AC, Shrivastava S, Hassanali M, Stothard P, Zhan C, Woolsey J. Drugbank: a comprehensive resource for in silico drug discovery and exploration. Nucleic Acids Res. 2006;34:D668–72.

    Article  Google Scholar 

  38. Ioannidis VN, Song X, Manchanda S, Li M, Pan X, Zheng D, Ning X, Zeng X, Karypis G (2020) DRKG-Drug Repurposing Knowledge Graph for Covid-19. https://github.com/gnn4dr/DRKG.

  39. Wishart DS, Feunang YD, Guo AC, Lo E, Marcu A, Grant JR, Sajed T, Johnson D, Li C, Sayeeda Z. Drugbank 5.0: a major update to the drugbank database for 2018. Nucleic Acids Res. 2018;46:1074-D1082.

    Article  Google Scholar 

  40. Kanehisa M, Goto S, Furumichi M, Tanabe M, Hirakawa M. KEGG for representation and analysis of molecular networks involving diseases and drugs. Nucleic Acids Res. 2010;38:D355–60.

    Article  Google Scholar 

  41. Li QL, Chen TJ, Wang YL, Bryant SH. Pubchem as a public resource for drug discovery. Drug Discov. 2010;15:1052–7.

    Google Scholar 

  42. Rotmensch M, Halpern Y, Tlimat A, Horng S, Sontag D. Learning a health knowledge graph from electronic medical records. Sci Rep. 2017;7:1–7.

    Article  Google Scholar 

  43. Himmelstein DS, Baranzini SE. Heterogeneous network edge prediction: a data integration approach to prioritize disease-associated genes. PLoS Comput Biol. 2015;11:1004259.

    Article  Google Scholar 

  44. Yu Y, Huang KX, Zhang C, Glass LM, Sun JM, **ao C. SumGNN: multi-typed drug interaction prediction via efficient knowledge graph summarization. Bioinformatics. 2021;37:2988–95.

    Article  Google Scholar 

  45. Wang, X., Wang, R., Shi, C., Song, G., Li, Q.. Multi-component graph convolutional collaborative filtering, In: Proceedings of 34th AAAI Conference on Artificial Intelligence, 6267–6274 (2020)

  46. Hu J, Shen L, Albanie S, Sun G, Wu E. Squeeze-andexcitation networks. IEEE Trans Pattern Anal Mach Intell. 2020;42:2011–23.

    Article  Google Scholar 

  47. Dong, Y., Seltzer, M. L.. Improved bottleneck features using pretrained deep neural networks, in: INTERSPEECH 2011, 12th Annual Conference of the International Speech Communication Association, 244–247 (2011)

Download references

Acknowledgements

The work reported in this paper was partially supported by the National Natural Science Foundation of China project 61963004.

Funding

The funding received from National Natural Science Foundation of China, 61963004, Qingfeng Chen.

Author information

Authors and Affiliations

  1. School of Computer Science and Engineering, South China University of Technology, Guangzhou, 510006, China

    Liyu Huang

  2. School of Computer, Electronics and Information, Guangxi University, Nanning, 530004, China

    Qingfeng Chen & Wei Lan

  3. Department of Computer Science and Information Technology, La Trobe University, Melbourne, 3086, Australia

    Qingfeng Chen

Authors
  1. Liyu Huang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Qingfeng Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Wei Lan
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Qingfeng Chen.

Ethics declarations

Conflict of interest

All authors of this manuscript have directly participated in planning, execution, and/or analysis of this study. And we declare that we do not have any commercial or associative interest that represents a conflict of interest in connection with the work submitted.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Huang, L., Chen, Q. & Lan, W. Predicting drug–drug interactions based on multi-view and multichannel attention deep learning. Health Inf Sci Syst 11, 50 (2023). https://doi.org/10.1007/s13755-023-00250-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s13755-023-00250-x

Keywords

Search

Navigation