Abstract
Purpose
Drug repositioning, a strategy that repurposes already-approved drugs for novel therapeutic applications, provides a faster and more cost-effective alternative to traditional drug discovery. Network-based models have been adopted by many computational methodologies, especially those that use graph neural networks to predict drug-disease associations. However, these techniques frequently overlook the quality of the input network, which is a critical factor for achieving accurate predictions.
Methods
We present a novel network-based framework for drug repositioning, named DRAW+, which incorporates noise filtering and feature extraction using graph neural networks and attention mechanisms. The proposed model first constructs a heterogeneous network that integrates the drug-disease association network with the similarity networks of drugs and diseases, which are upgraded through reduced-rank singular value decomposition. Next, a subgraph surrounding the targeted drug-disease node pair is extracted, allowing the model to focus on local structures. Graph neural networks are then applied to extract structural representation, followed by attention walking to capture key features of the subgraph. Finally, a multi-layer perceptron classifies the subgraph as positive or negative, which indicates the presence of the link between the target node pair.
Results
Experimental validation across three benchmark datasets showed that DRAW+ outperformed seven state-of-the-art methods, achieving the highest average AUROC and AUPRC, 0.963 and 0.564, respectively. Moreover, DRAW+ demonstrated its robustness by achieving the best performance across two additional datasets, further confirming its generalizability and effectiveness in diverse settings.
Conclusions
The proposed network-based computational approach, DRAW+, demonstrates exceptional accuracy and robustness, confirming its effectiveness in drug repositioning tasks.
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Data availability
The source code and datasets are available at https://ads.yonsei.ac.kr/DRAW+.
Change history
01 February 2025
In the article’s title, “Draw+” should be corrected to “DRAW+” and The second heading of the article is “Materials and Methods,” but it is labeled with an incorrect word (“Matelhods”) this has been corrected now.
References
Li J, Zheng S, Chen B, Butte AJ, Swamidass SJ, Lu Z. A survey of current trends in computational drug repositioning. Brief Bioinform. 2015;17(1):2–12. https://doi.org/10.1093/bib/bbv020.
Paul SM, Mytelka DS, Dunwiddie CT, Persinger CC, Munos BH, Lindborg SR, Schacht AL. How to improve R&D productivity: the pharmaceutical industry’s grand challenge. Nat Rev Drug Discov. 2010;9(3):203–14. https://doi.org/10.1038/nrd3078.
Hurle MR, Yang L, Xie Q, Rajpal DK, Sanseau P, Agarwal P. Computational drug repositioning: from data to therapeutics. Clin Pharmacol Ther. 2013;93(4):335–41. https://doi.org/10.1038/clpt.2013.1.
Baker NC, Ekins S, Williams AJ, Tropsha A. A bibliometric review of drug repurposing. Drug Discov Today. 2018;23(3):661–72. https://doi.org/10.1016/j.drudis.2018.01.018.
Kim Y, Jung Y-S, Park J-H, Kim S-J, Cho Y-R. Drug-disease association prediction using heterogeneous networks for computational drug repositioning. Biomolecules. 2022;12(10):1497. https://doi.org/10.3390/biom12101497.
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(1):41–58. https://doi.org/10.1038/nrd.2018.168.
Chan HS, Shan H, Dahoun T, Vogel H, Yuan S. Advancing drug discovery via artificial intelligence. Trends Pharmacol Sci. 2019;40(8):592–604. https://doi.org/10.1016/j.tips.2019.06.004.
Astrup A, Rössner S, Van Gaal L, Rissanen A, Niskanen L, Al Hakim M, Madsen J, Rasmussen MF, Lean ME. Effects of liraglutide in the treatment of obesity: a randomised, double-blind, placebo-controlled study. Lancet. 2009;374(9701):1606–16. https://doi.org/10.1016/S0140-6736(09)61375-1.
Yang R, Fu Y, Zhang Q, Zhang L. GCNGAT: drug-disease association prediction based on graph convolution neural network and graph attention network. Artif Intell Med. 2024;150: 102805. https://doi.org/10.1016/j.artmed.2024.102805.
Wang Y, Song J, Dai Q, Duan X. Hierarchical negative sampling based graph contrastive learning approach for drug-disease association prediction. IEEE J Biomed Health Inform. 2024. https://doi.org/10.1109/JBHI.2024.3360437.
Kim Y, Park J-H, Cho Y-R. Network-based approaches for disease-gene association prediction using protein–protein interaction networks. Int J Mol Sci. 2022;23(13):7411. https://doi.org/10.3390/ijms23137411.
Luo H, Li M, Yang M, Wu F-X, Li Y, Wang J. Biomedical data and computational models for drug repositioning: a comprehensive review. Brief Bioinform. 2021;22(2):1604–19. https://doi.org/10.1093/bib/bbz176.
Zeng X, Zhu S, Liu X, Zhou Y, Nussinov R, Cheng F. deepDR: a network-based deep learning approach to in silico drug repositioning. Bioinformatics. 2019;35(24):5191–8. https://doi.org/10.1093/bioinformatics/btz418.
Kim Y, Cho Y-R. Predicting drug-gene-disease associations by tensor decomposition for network-based computational drug repositioning. Biomedicines. 2023;11(7):1998. https://doi.org/10.3390/biomedicines11071998.
Li Y, Yang Y, Tong Z, Wang Y, Mi Q, Bai M, Liang G, Li B, Shu K. A comparative benchmarking and evaluation framework for heterogeneous network-based drug repositioning methods. Brief Bioinform. 2024;25(3):172. https://doi.org/10.1093/bib/bbae172.
Meng Y, Wang Y, Xu J, Lu C, Tang X, Peng T, Zhang B, Tian G, Yang J. Drug repositioning based on weighted local information augmented graph neural network. Brief Bioinform. 2024;25(1):431. https://doi.org/10.1093/bib/bbad431.
Wu Z, Pan S, Chen F, Long G, Zhang C, Philip SY. A comprehensive survey on graph neural networks. IEEE Trans Neural Netw Learn Syst. 2020;32(1):4–24. https://doi.org/10.1109/TNNLS.2020.2978386.
Kipf TN, Welling M. Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 [Preprint]. 2016. Available from: arXiv:1609.02907.
Veličković P, Cucurull G, Casanova A, Romero A, Lio P, Bengio Y. Graph attention networks. arXiv:1710.10903 [Preprint]. 2017. Available from: arXiv:1710.10903
Yu Z, Huang F, Zhao X, Xiao W, Zhang W. Predicting drug-disease associations through layer attention graph convolutional network. Brief Bioinform. 2021;22(4):243. https://doi.org/10.1093/bib/bbaa243.
Meng Y, Lu C, Jin M, Xu J, Zeng X, Yang J. A weighted bilinear neural collaborative filtering approach for drug repositioning. Brief Bioinform. 2022;23(2):581. https://doi.org/10.1093/bib/bbab581.
Jin W, Ma Y, Liu X, Tang X, Wang S, Tang J. Graph structure learning for robust graph neural networks. In: Proceedings of the 26th ACM SIGKDD international conference on knowledge discovery & data mining, 2020. p. 66–74. https://doi.org/10.1145/3394486.3403049.
Steinbeck C, Han Y, Kuhn S, Horlacher O, Luttmann E, Willighagen E. The chemistry development kit (CDK): an open-source java library for chemo-and bioinformatics. J Chem Inf Comput Sci. 2003;43(2):493–500. https://doi.org/10.1021/ci025584y.
Van Driel MA, Bruggeman J, Vriend G, Brunner HG, Leunissen JA. A text-mining analysis of the human phenome. Eur J Hum Genet. 2006;14(5):535–42. https://doi.org/10.1038/sj.ejhg.5201585.
Park J-H, Cho Y-R. Computational drug repositioning with attention walking. Sci Rep. 2024;14(1):10072. https://doi.org/10.1038/s41598-024-60756-6.
Gottlieb A, Stein GY, Ruppin E, Sharan R. PREDICT: a method for inferring novel drug indications with application to personalized medicine. Mol Syst Biol. 2011;7(1):496. https://doi.org/10.1038/msb.2011.26.
Luo H, Wang J, Li M, Luo J, Peng X, Wu F-X, Pan Y. Drug repositioning based on comprehensive similarity measures and bi-random walk algorithm. Bioinformatics. 2016;32(17):2664–71. https://doi.org/10.1093/bioinformatics/btw228.
Liang X, Zhang P, Yan L, Fu Y, Peng F, Qu L, Shao M, Chen Y, Chen Z. LRSSL: predict and interpret drug-disease associations based on data integration using sparse subspace learning. Bioinformatics. 2017;33(8):1187–96. https://doi.org/10.1093/bioinformatics/btw770.
Knox C, Wilson M, Klinger CM, Franklin M, Oler E, Wilson A, Pon A, Cox J, Chin NE, Strawbridge SA (2024) Drugbank 6.0: the drugbank knowledgebase for 2024. Nucleic Acids Res. 2024;52(D1):1265–75. https://doi.org/10.1093/nar/gkad976.
Amberger JS, Bocchini CA, Scott AF, Hamosh A. Omim.org: leveraging knowledge across phenotype–gene relationships. Nucleic Acids Res. 2019;47(D1):1038–43. https://doi.org/10.1093/nar/gky1151
Lipscomb CE. Medical subject headings (MESH). Bull Med Libr Assoc. 2000;88(3):265.
Kim S, Chen J, Cheng T, Gindulyte A, He J, He S, Li Q, Shoemaker BA, Thiessen PA, Yu B, Zaslavsky L, Zhang J, Bolton EE. PubChem 2023 update. Nucleic Acids Res. 2022;51(D1):1373–80. https://doi.org/10.1093/nar/gkac956.
Castellanos F, Caufield J, Chan L, Chute C, Cruz-Rojo J, Dahan-Oliel N, Davids J, Dieuleveult M, Souza V, Vries B. The Human Phenotype Ontology in 2024: phenotypes around the world. Nucleic Acids Res. 2023;52(D1):1333–46. https://doi.org/10.1093/nar/gkad1005.
Bahdanau D. Neural machine translation by jointly learning to align and translate. arXiv:1409.0473 [Preprint]. 2014. arXiv:1409.0473.
Zhang M, Chen Y. Link prediction based on graph neural networks. In: Advances in neural information processing systems. vol. 31. 2018.
Luo H, Li M, Wang S, Liu Q, Li Y, Wang J. Computational drug repositioning using low-rank matrix approximation and randomized algorithms. Bioinformatics. 2018;34(11):1904–12. https://doi.org/10.1093/bioinformatics/bty013.
Zhang W, Xu H, Li X, Gao Q, Wang L. DRIMC: an improved drug repositioning approach using bayesian inductive matrix completion. Bioinformatics. 2020;36(9):2839–47. https://doi.org/10.1093/bioinformatics/btaa062.
Cai L, Lu C, Xu J, Meng Y, Wang P, Fu X, Zeng X, Su Y. Drug repositioning based on the heterogeneous information fusion graph convolutional network. Brief Bioinform. 2021;22(6):319. https://doi.org/10.1093/bib/bbab319.
Sun X, Jia X, Lu Z, Tang J, Li M. Drug repositioning with adaptive graph convolutional networks. Bioinformatics. 2024;40(1):748. https://doi.org/10.1093/bioinformatics/btad748.
Jia X, Sun X, Wang K, Li M. DRGCL: drug repositioning via semantic-enriched graph contrastive learning. IEEE J Biomed Health Inform. 2024. https://doi.org/10.1109/JBHI.2024.3372527.
Davis AP, Wiegers TC, Johnson RJ, Sciaky D, Wiegers J, Mattingly CJ. Comparative toxicogenomics database (CTD): update 2023. Nucleic Acids Res. 2023;51(D1):1257–62. https://doi.org/10.1093/nar/gkac833.
Acknowledgements
This research was supported by the National Research Foundation of Korea (NRF) grant funded by the Ministry of Science and ICT (2021R1A2C1011946), Basic Science Research Program through the NRF funded by the Ministry of Education (RS-2024-00405984), and Regional Innovation Strategy (RIS) through the NRF funded by the Ministry of Education (2022RIS-005) in 2024.
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Park, JH., Cho, YR. DRAW+: network-based computational drug repositioning with attention walking and noise filtering. Health Inf Sci Syst 13, 14 (2025). https://doi.org/10.1007/s13755-024-00326-2
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DOI: https://doi.org/10.1007/s13755-024-00326-2