Skip to main content
SpringerLink
Log in

M2GCN: multi-modal graph convolutional network for modeling polypharmacy side effects

  • Published:
Applied Intelligence Aims and scope Submit manuscript

Abstract

Treating patients with complex diseases or co-existing conditions by polypharmacy (i.e., the use of drug combination) is very common. However, due to drug-drug interactions, polypharmacy often results in unpredictable side effects, which may endanger patients’ life. Moreover, since adverse drug reactions are rare, discovering polypharmacy side effects only from sparse drug-drug interactions remains challenging. Thus, it is necessary to explore the knowledge of polypharmacy side effects from the interaction network with complex relationships. In this paper, we propose a novel multi-modal graph convolutional neural network (M2GCN) for link prediction in multi-modal networks which consist of protein-protein interactions, drug-protein interactions and drug-drug interactions. Specifically, we first propose a propagation strategy to perform graph aggregations on each subgraph. Then we leverage consistency regularization to align the consistency across different subgraphs. Finally, referring to the DistMult method, we use the embeddings obtained above to calculate the probability of side effects between drugs. Experimental results on benchmark dataset show that our method significantly outperforms the compared network embedding models.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

Notes

  1. https://github.com/farkguidao/M2GCN

References

  1. Xu H, Sang S, Lu H (2020) Tri-graph information propagation for polypharmacy side effect prediction. arXiv:2001.10516

  2. Tatonetti N P, Patrick P Y, Daneshjou R, Altman R B (2012) Data-driven prediction of drug effects and interactions. Science Translational Medicine, 125ra31–125ra31

  3. Bansal M, Yang J, Karan C, Menden M P, Costello J C, Tang H, Xiao G, Li Y, Allen J, Zhong R et al (2014) A community computational challenge to predict the activity of pairs of compounds. Nat Biotechnol, 1213–1222

  4. Bowes J, Brown A J, Hamon J, Jarolimek W, Sridhar A, Waldron G, Whitebread S et al (2012) Reducing safety-related drug attrition: the use of in vitro pharmacological profiling. Nature Reviews Drug Discovery, 909–922

  5. Cheng Y, Gong Y, Liu Y, Song B, Zou Q (2021) Molecular design in drug discovery: a comprehensive review of deep generative models. Brief Bioinform

  6. Ibrahim M M (2006) Ras inhibition in hypertension. J Hum Hypertens 20(2):101–108

    Article  Google Scholar 

  7. Sun M, Zhao S, Gilvary C, Elemento O, Zhou J, Wang F (2020) Graph convolutional networks for computational drug development and discovery. Briefings in Bioinformatics

  8. Song B, Li F, Liu Y, Zeng X (2021) Deep learning methods for biomedical named entity recognition: a survey and qualitative comparison. Brief Bioinform

  9. Wishart D S, Feunang Y D, Guo A C, Lo E J, Marcu A, Grant J R et al (2018) Drugbank 5.0: a major update to the drugbank database for 2018. Nucleic Acids Research, D1074–D1082

  10. Szklarczyk D, Santos A, Von Mering C, Jensen L J, Bork P, Kuhn M (2016) Stitch 5: augmenting protein–chemical interaction networks with tissue and affinity data. Nucleic Acids Research, D380–D384

  11. Zitnik M, Agrawal M, Leskovec J (2018) Modeling polypharmacy side effects with graph convolutional networks. Bioinformatics, i457–i466

  12. Kipf T N, Welling M (2017) Semi-supervised classification with graph convolutional networks. In: International conference on learning representations (ICLR)

  13. Veličković P, Cucurull G, Casanova A, Romero A, Liò P, Bengio Y (2018) Graph attention networks. In: International conference on learning representations

  14. Feng W, Zhang J, Dong Y, Han Y, Luan H, Xu Q et al (2020) Graph random neural network for semi-supervised learning on graphs. In: NeurIPS’20

  15. Zhu Z, Fan X, Chu X, Bi J (2020) Hgcn: a heterogeneous graph convolutional network-based deep learning model toward collective classification. In: Proceedings of the 26th ACM SIGKDD international conference on knowledge discovery & data mining, pp 1161–1171

  16. Fu X, Zhang J, Meng Z, King I (2020) Magnn: metapath aggregated graph neural network for heterogeneous graph embedding. In: Proceedings of the web conference 2020, pp 2331–2341

  17. Wang X, Ji H, Shi C, Wang B, Ye Y et al (2019) Heterogeneous graph attention network. In: The World wide web conference, pp 2022–2032

  18. Dong Y, Chawla N V, Swami A (2017) metapath2vec: scalable representation learning for heterogeneous networks. In: Proceedings of the 23rd ACM SIGKDD international conference on knowledge discovery and data mining, pp 135–144

  19. Shi C, Hu B, Zhao W X, Philip S Y (2018) Heterogeneous information network embedding for recommendation. IEEE Trans Knowl Data Eng

  20. Wang X, Zhang Y, Shi C (2019) Hyperbolic heterogeneous information network embedding. In: Proceedings of the AAAI conference on artificial intelligence, pp 5337–5344

  21. Yang Y, Guan Z, Li J, Zhao W, Cui J, Wang Q (2021) Interpretable and efficient heterogeneous graph convolutional network. IEEE Trans Knowl Data Eng

  22. Zhao L, Akoglu L (2019) Pairnorm: tackling oversmoothing in gnns. In: International conference on learning representations

  23. Li G, Muller M, Thabet A, Ghanem B (2019) Deepgcns: can gcns go as deep as cnns?. In: Proceedings of the IEEE/CVF international conference on computer vision, pp 9267–9276

  24. Ryall K A, Tan A C (2015) Systems biology approaches for advancing the discovery of effective drug combinations. Journal of Cheminformatics, 1–15

  25. Loewe S (1953) The problem of synergism and antagonism of combined drugs. Arzneimittelforschung, 285–290

  26. Lewis R, Guha R, Korcsmaros T, Bender A (2015) Synergy maps: exploring compound combinations using network-based visualization. Journal of Cheminformatics, 1–11

  27. Takeda T, Hao M, Cheng T, Bryant S H, Wang Y (2017) Predicting drug–drug interactions through drug structural similarities and interaction networks incorporating pharmacokinetics and pharmacodynamics knowledge. Journal of Cheminformatics, 1–9

  28. Sun Y, Sheng Z, Ma C, Tang K, Zhu R, Wu Z et al (2015) Combining genomic and network characteristics for extended capability in predicting synergistic drugs for cancer. Nature Communications, 1–10

  29. Zitnik M, Zupan B (2016) Collective pairwise classification for multi-way analysis of disease and drug data. In: Biocomputing 2016: Proceedings Of The Pacific Symposium. World Scientific, pp 81–92

  30. Chen X, Ren B, Chen M, Wang Q, Zhang L, Yan G (2016) Nllss: predicting synergistic drug combinations based on semi-supervised learning. PLoS Computational Biology, e1004975

  31. Shi J-Y, Li J-X, Gao K, Lei P, Yiu S-M (2017) Predicting combinative drug pairs towards realistic screening via integrating heterogeneous features. BMC Bioinformatics, 1–9

  32. Lin X, Quan Z, Wang Z-J, Ma T, Zeng X (2020) Kgnn: knowledge graph neural network for drug-drug interaction prediction. In: IJCAI, pp 2739–2745

  33. Yu Z, Huang F, Zhao X, Xiao W, Zhang W (2021) Predicting drug–disease associations through layer attention graph convolutional network. Brief Bioinform, bbaa243

  34. Lee C Y, Chen Y-P P (2021) Prediction of drug adverse events using deep learning in pharmaceutical discovery. Briefings in Bioinformatics, 1884–1901

  35. Kastrin A, Ferk P, Leskošek B (2018) Predicting potential drug-drug interactions on topological and semantic similarity features using statistical learning. PloS One, e0196865

  36. Bang S, Ho Jhee J, Shin H (2021) Polypharmacy side effect prediction with enhanced interpretability based on graph feature attention network. Bioinformatics

  37. Huang K, Xiao C, Hoang T, Glass L, Sun J (2020) Caster: predicting drug interactions with chemical substructure representation. In: Proceedings of the AAAI conference on artificial intelligence, pp 702–709

  38. Fu T, Xiao C, Qian C, Glass L M, Sun J (2021) Probabilistic and dynamic molecule-disease interaction modeling for drug discovery. In: Proceedings of the 27th ACM SIGKDD conference on knowledge discovery & data mining, pp 404–414

  39. Defferrard M, Bresson X, Vandergheynst P (2016) Convolutional neural networks on graphs with fast localized spectral filtering. Advances in Neural Information Processing Systems, 3844–3852

  40. Hamilton W L, Ying R, Leskovec J (2017) Inductive representation learning on large graphs. In: Proceedings of the 31st international conference on neural information processing systems, pp 1025–1035

  41. Hu Z, Dong Y, Wang K, Sun Y (2020) Heterogeneous graph transformer. In: Proceedings of the web conference 2020, pp 2704–2710

  42. Chen J, Huang F, Peng J (2021) Msgcn: multi-subgraph based heterogeneous graph convolution network embedding. Appl Sci

  43. Chen J, Zhang A (2020) Hgmf: heterogeneous graph-based fusion for multimodal data with incompleteness. In: Proceedings of the 26th ACM SIGKDD international conference on knowledge discovery & data mining, pp 1295–1305

  44. Wang P, Agarwal K, Ham C, Choudhury S, Reddy C K (2021) Self-supervised learning of contextual embeddings for link prediction in heterogeneous networks. In: Proceedings of the web conference 2021, pp 2946–2957

  45. Chen Y, Ma T, Yang X, Wang J, Song B, Zeng X (2021) Muffin: multi-scale feature fusion for drug–drug interaction prediction. Bioinformatics

  46. Dai Y, Guo C, Guo W, Eickhoff C (2021) Drug–drug interaction prediction with wasserstein adversarial autoencoder-based knowledge graph embeddings. Briefings in Bioinformatics, bbaa256

  47. Zeng X, Tu X, Liu Y, Fu X, Su Y (2022) Toward better drug discovery with knowledge graph. Current opinion in structural biology

  48. Mikolov T, Sutskever I, Chen K, Corrado G S, Dean J (2013) Distributed representations of words and phrases and their compositionality. In: Advances in neural information processing systems, pp 3111–3119

  49. Liu Q, Long C, Zhang J, Xu M, Lv P (2021) Triatne: tripartite adversarial training for network embeddings. IEEE Transactions on Cybernetics

  50. Li Q, Wu X-M, Liu H, Zhang X, Guan Z (2019) Label efficient semi-supervised learning via graph filtering. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp 9582–9591

  51. Miao X, Gürel N M, Zhang W, Han Z, Li B, Min W et al (2019) Degnn: characterizing and improving graph neural networks with graph decomposition. arXiv:1910.04499

  52. Wu F, Souza A, Zhang T, Fifty C, Yu T, Weinberger K (2019) Simplifying graph convolutional networks. In: International conference on machine learning. PMLR, pp 6861–6871

  53. Yang B, Yih W-, He X, Gao J, Deng L (2014) Embedding entities and relations for learning and inference in knowledge bases. arXiv:1412.6575

  54. Kuhn M, Letunic I, Jensen L J, Bork P (2016) The sider database of drugs and side effects. Nucleic Acids Research

  55. Menche J, Sharma A, Kitsak M, Ghiassian S D, Vidal M, Loscalzo J, Barabási A-L (2015) Uncovering disease-disease relationships through the incomplete interactome. Science

  56. Chatr-Aryamontri A, Breitkreutz B-J, Oughtred R, Boucher L, Heinicke S, Chen D et al (2015) The biogrid interaction database: 2015 update. Nucleic acids research

  57. Szklarczyk D, Morris J H, Cook H, Kuhn M, Wyder S, Simonovic M et al (2016) The string database in 2017: quality-controlled protein–protein association networks, made broadly accessible. Nucleic Acids Research

  58. Rolland T, Taşan M, Charloteaux B, Pevzner S J, Zhong Q, Sahni N et al (2014) A proteome-scale map of the human interactome network. Cell

  59. Perozzi B, Al-Rfou R, Skiena S (2014) Deepwalk: online learning of social representations. In: Proceedings of the 20th ACM SIGKDD international conference on knowledge discovery and data mining, pp 701–710

  60. Grover A, Leskovec J (2016) node2vec: scalable feature learning for networks. In: Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp 855–864

Download references

Acknowledgments

This work was supported in part by the National Natural Science Foundation of China under Grant (61906174, 62036010, 61903341, 61972362), in part by the China Postdoctoral Science Foundation under Grant 2020M672275, in part by the Department of Science and Technology of Henan Province under Grant (222102210248, 201100312000), in part by the Henan Province Natural Science Foundation under Grant (212300410291, 202300410378).

Author information

Authors and Affiliations

  1. School of Computer and Artificial Intelligence, Zhengzhou University, No.100 Science Avenue, Zhengzhou, 450001, China

    Qidong Liu, Enguang Yao, Chaoyue Liu, Yafei Li & Mingliang Xu

  2. School of Computer Science and Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore, 639798, Singapore

    Xin Zhou

Authors
  1. Qidong Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Enguang Yao
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Chaoyue Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Xin Zhou
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Yafei Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Mingliang Xu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Mingliang Xu.

Additional information

Publisher’s note

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

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Liu, Q., Yao, E., Liu, C. et al. M2GCN: multi-modal graph convolutional network for modeling polypharmacy side effects. Appl Intell 53, 6814–6825 (2023). https://doi.org/10.1007/s10489-022-03839-z

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10489-022-03839-z

Keywords

Search

Navigation