AI Chat Paper
Note: Please note that the following content is generated by AMiner AI. SciOpen does not take any responsibility related to this content.
{{lang === 'zh_CN' ? '文章概述' : 'Summary'}}
{{lang === 'en_US' ? '中' : 'Eng'}}
Chat more with AI
Powered by AMiner. Clicking this button will take you away from SciOpen.
Home Tsinghua Science and Technology Article
PDF (1.9 MB)
Cite
EndNote(RIS) BibTeX
Collect
Submit Manuscript AI Chat Paper
Show Outline
Outline
Show full outline
Hide outline
Outline
Show full outline
Hide outline
Open Access

Synergistic Multi-Drug Combination Prediction Based on Heterogeneous Network Representation Learning with Contrastive Learning

School of Computer Science and Engineering, Southeast University, Nanjing 211189, China
Nanjing FiberHome Tiandi Co., Ltd., Nanjing 211161, China
Show Author Information

Abstract

The combination of multiple drugs is a significant therapeutic strategy that can enhance treatment effectiveness and reduce medication side effects. However, identifying effective synergistic drug combinations in a vast search space remains challenging. Current methods for predicting synergistic drug combinations primarily rely on calculating drug similarity based on the drug heterogeneous network or drug information, enabling the prediction of pairwise synergistic drug combinations. However, these methods not only fail to fully study the rich information in drug heterogeneous networks, but also can only predict pairwise drug combinations. To address these limitations, we present a novel Synergistic Multi-drug Combination prediction method of Western medicine based on Heterogeneous Network representation learning with Contrastive Learning, called SMC-HNCL. Specifically, two drug features are learnt from different perspectives using the drug heterogeneous network and anatomical therapeutic chemical (ATC) codes, and fused by attention mechanism. Furthermore, a group representation method based on multi-head self-attention is employed to learn representations of drug combinations, innovatively realizing the prediction of synergistic multi-drug combinations. Experimental results demonstrate that SMC-HNCL outperforms the state-of-the-art baseline methods in predicting synergistic drug pairs on two synergistic drug combination datasets and can also effectively predict synergistic multi-drug combinations.

Keywords

western medicinesynergistic drug combinationheterogeneous networkcontrastive learning

References

[1]

L. Wu, Y. Wen, D. Leng, Q. Zhang, C. Dai, Z. Wang, Z. Liu, B. Yan, Y. Zhang, J. Wang, et al., Machine learning methods, databases and tools for drug combination prediction, Brief Bioinform, vol. 23, no. 1, p. bbab355, 2022.
Crossref Google Scholar
[2]

T. Roemer and C. Boone, Systems-level antimicrobial drug and drug synergy discovery, Nat. Chem. Biol., vol. 9, no. 4, pp. 222–231, 2013.
Crossref Google Scholar
[3]

F. Cheng, I. A. Kovács, and A.-L. Barabási, Network-based prediction of drug combinations, Nat. Commun., vol. 10, no. 1, p. 1197, 2019.
Crossref Google Scholar
[4]
G. Xiong, Z. Yang, J. Yi, N. Wang, L. Wang, H. Zhu, C. Wu, A. Lu, X. Chen, S. Liu, et al., DDInter: An online drug-drug interaction database towards improving clinical decision-making and patient safety, Nucleic Acids Res., vol. 50, no. D1, pp. D1200–D1207, 2022.
Crossref
[5]
G. Shtar, L. Azulay, O. Nizri, L. Rokach, and B. Shapira, CDCDB: A large and continuously updated drug combination database, Sci. Data, vol. 9, p. 263, 2022.
Crossref
[6]

X. M. Zhao, M. Iskar, G. Zeller, M. Kuhn, V. van Noort, and P. Bork, Prediction of drug combinations by integrating molecular and pharmacological data, PLoS Comput. Biol., vol. 7, no. 12, p. e1002323, 2011.
Crossref Google Scholar
[7]

L. Chen, B. Q. Li, M. Y. Zheng, J. Zhang, K. Y. Feng, and Y. D. Cai, Prediction of effective drug combinations by chemical interaction, protein interaction and target enrichment of KEGG pathways, Biomed Res. Int., vol. 2013, p. 723780, 2013.
Crossref Google Scholar
[8]

Y. Sun, Y. Xiong, Q. Xu, and D. Wei, A hadoop-based method to predict potential effective drug combination, Biomed Res. Int., vol. 2014, p. 196858, 2014.
Crossref Google Scholar
[9]
Q. Xu, Y. Xiong, H. Dai, K. M. Kumari, Q. Xu, H. Y. Ou, and D. Q. Wei, PDC-SGB: Prediction of effective drug combinations using a stochastic gradient boosting algorithm, J. Theor. Biol., vol. 417, pp. 1–7, 2017.
Crossref
[10]

Y. Y. Wang, K. J. Xu, J. Song, and X. M. Zhao, Exploring drug combinations in genetic interaction network, BMC Bioinform., vol. 13, no. 7, p. S7, 2012.
Crossref Google Scholar
[11]

J. Zou, P. Ji, Y. L. Zhao, L. L. Li, Y. Q. Wei, Y. Z. Chen, and S. Y. Yang, Neighbor communities in drug combination networks characterize synergistic effect, Mol. Biosyst., vol. 8, no. 12, pp. 3185–3196, 2012.
Crossref Google Scholar
[12]

N. Yin, W. Ma, J. Pei, Q. Ouyang, C. Tang, and L. Lai, Synergistic and antagonistic drug combinations depend on network topology, PLoS One, vol. 9, no. 4, p. e93960, 2014.
Crossref Google Scholar
[13]
X. Chen, B. Ren, M. Chen, Q. Wang, L. Zhang, and G. Yan, NLLSS: Predicting synergistic drug combinations based on semi-supervised learning, PLoS Comput. Biol., vol. 12, no. 7, p. e1004975, 2016.
Crossref
[14]

F. Cheng, I. A. Kovács, and A. L. Barabási, Network-based prediction of drug combinations, Nat. Commun., vol. 10, no. 1, p. 1197, 2019.
Crossref Google Scholar
[15]

P. Ding, R. Yin, J. Luo, and C. K. Kwoh, Ensemble prediction of synergistic drug combinations incorporating biological, chemical, pharmacological, and network knowledge, IEEE J. Biomed. Health Inform., vol. 23, no. 3, pp. 1336–1345, 2019.
Crossref Google Scholar
[16]

P. Ding, C. Shen, Z. Lai, C. Liang, G. Li, and J. Luo, Incorporating multisource knowledge to predict drug synergy based on graph co-regularization, J. Chem. Inf. Model., vol. 2020, pp. 37–64, 2020.
Crossref Google Scholar
[17]

P. Ding, C. Liang, W. Ouyang, G. Li, Q. Xiao, and J. Luo, Inferring synergistic drug combinations based on symmetric meta-path in a novel heterogeneous network, IEEE/ACM Trans. Comput. Biol. Bioinform., vol. 18, no. 4, pp. 1562–1571, 2021.
Crossref Google Scholar
[18]

X. Chen, L. Luo, C. Shen, P. Ding, and J. Luo, An in silico method for predicting drug synergy based on multitask learning, Interdiscip. Sci., vol. 13, no. 2, pp. 299–311, 2021.
Crossref Google Scholar
[19]

L. Yu, M. Xia, and Q. An, A network embedding framework based on integrating multiplex network for drug combination prediction, Brief. Bioinform., vol. 23, no. 1, p. bbab364, 2022.
Crossref Google Scholar
[20]
P. Zhang and S. Tu, MGAE-DC: Predicting the synergistic effects of drug combinations through multi-channel graph autoencoders, PLoS Comput. Biol., vol. 19, no. 3, p. e1010951, 2023.
Crossref
[21]

T. C. Chou, Theoretical basis, experimental design, and computerized simulation of synergism and antagonism in drug combination studies, Pharmacol. Rev., vol. 58, no. 3, pp. 621–681, 2006.
Crossref Google Scholar
[22]
J. Chen, L. Wu, K. Liu, Y. Xu, S. He, and X. Bo, EDST: A decision stump based ensemble algorithm for synergistic drug combination prediction, BMC Bioinform., vol. 24, no. 1, p. 325, 2023.
Crossref
[23]

X. Wang, D. Bo, C. Shi, S. Fan, Y. Ye, and P. S. Yu, A survey on heterogeneous graph embedding: Methods, techniques, applications and sources, IEEE Trans. Big Data, vol. 9, no. 2, pp. 415–436, 2023.
Crossref Google Scholar
[24]
A. Grover and L. Jure, Node2vec: Scalable feature learning for networks, in Proc. 22nd ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining (KDD '16), New York, NY, USA, 2016, pp. 855–864.
Crossref
[25]
Y. Dong, N. V. Chawla, and S. Ananthram, Metapath2vec: Scalable representation learning for heterogeneous networks, in Proc. 23rd ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining, New York, NY, USA, 2017, pp. 135–144.
Crossref
[26]

C. Shi, B. Hu, W. X. Zhao, and P. S. Yu, Heterogeneous information network embedding for recommendation, IEEE Trans. Knowl. Data Eng., vol. 31, no. 2, pp. 357–370, 2019.
Crossref Google Scholar
[27]
C. Zhang, J. Yin, and X. Zhu, MetaGraph2Vec: Complex semantic path augmented heterogeneous network embedding, in Advances in Knowledge Discovery and Data Mining, D. Phung, V. Tseng, G. Webb, B. Ho, M. Ganji, and L. Rashidi, eds. Cham, Switzerland: Springer, vol. 10938, pp. 196–208, 2018.
Crossref
[28]
H. X. Chen, H. Z. Yin, W. Q. Wang, H. Wang, Q. V. H. Nguyen, and X. Li, PME: Projected metric embedding on heterogeneous networks for link prediction, in Proc. 24th ACM SIGKDD Int. Conf. on Knowledge Discovery & Data Mining, New York, NY, USA, 2018, pp. 1177–1186.
Crossref
[29]
H. Gui, J. Liu, F. Tao, M. Jiang, B. Norick, and J. Han, Large-scale embedding learning in heterogeneous event data, in Proc. IEEE 16th Int. Conf. Data Mining (ICDM), Barcelona, Spain, 2016, pp. 907–912.
Crossref
[30]
C. X. Zhang, D. J. Song, C. Huang, A. Swami, N. V. Chawla, Heterogeneous graph neural network, in Proc. 25th ACM SIGKDD Int. Conf. on Knowledge Discovery & Data Mining, New York, NY, USA, pp. 793–803, 2019.
Crossref
[31]
X. Wang, H. Y. Ji, C. Shi, B. Wang, Y. F. Ye, P. Cui, and P. S. Yu, Heterogeneous graph attention network, in Proc. World Wide Web Conf. 2019, New York, NY, USA, pp. 2022–2032, 2019.
Crossref
[32]
Z. N. Hu, Y. X. Dong, K. S. Wang, and Y. Z. Sun, Heterogeneous graph transformer, in Proc. Web Conference 2020, New York, NY, USA, pp. 2704–2710, 2020.
[33]

S. Yun, M. Jeong, S. Yoo, S. Lee, S. S. Yi, R. Kim, J. Kang, and H. J. Kim, Graph transformer networks: Learning meta-path graphs to improve GNNs, Neural Netw., vol. 153, pp. 104–119, 2022.
Crossref Google Scholar
[34]
J. Y. Guo, L. Du, W. D. Bi, Q. Fu, X. J. Ma, X. Chen, S. Han, D. M. Zhang, Y. Zhang, Homophily-oriented heterogeneous graph rewiring, in Proc. ACM Web Conference 2023 (WWW '23), New York, NY, USA, 2023, pp. 511–522.
Crossref
[35]
Q. Lv, M. Ding, Q. Liu, Y. Chen, W. Feng, S. He, C. Zhou, J. Jiang, Y. Dong, and J. Tang, Are we really making much progress? Revisiting, benchmarking and refining heterogeneous graph neural networks, in Proc. 27th ACM SIGKDD Conf. Knowledge Discovery & Data Mining, Virtual Event, Singapore, 2021, pp. 1150–1160.
Crossref
[36]
P. Velickovic, G. Cucurull, A. Casanova, A. Romero, P. Lio’, and Y. Bengio, Graph attention networks, arXiv:1710.10903, 2017.
[37]
Q. H. Mao, Z. M. Liu, C. H. Liu, and J. L. Sun, HINormer: Representation learning on heterogeneous information networks with graph transformer, in Proc. ACM Web Conf. 2023 (WWW '23), New York, NY, USA, 2023, pp. 599–610.
Crossref
[38]
L. Yu, L. Sun, B. Du, C. Liu, W. Lv, and H. Xiong, Heterogeneous graph representation learning with relation awareness, IEEE Trans. Knowl. Data Eng., p. 1, 2022.
Crossref
[39]
S. Y. Chang, W. Han, J. L. Tang, G. J. Qi, C. R. C. Aggarwal, and T. S. Huang, Heterogeneous network embedding via deep architectures, in Proc. 21th ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining (KDD '15), New York, NY, USA, 2018, pp. 119–128.
[40]
C. X. Zhang, C. Huang, L. Yu, X. L. Zhang, and N. V. Chawla, Camel: Content-aware and meta-path augmented metric learning for author identification, in Proc. 2018 World Wide Web Conf. (WWW'18), Lyon, France, 2018, pp. 709–718.
Crossref
[41]
C. X. Zhang, A. Swami, and N. V. Chawla, SHNE: Representation learning for semantic-associated heterogeneous networks, https://dl.acm.org/doi/pdf/10.1145/3289600.3291001, 2019.
Crossref
[42]
H. Wang, J. Wang, J. Wang, M. Zhao, W. Zhang, F. Zhang, X. Xie, and M. Guo, GraphGAN: Graph representation learning with generative adversarial nets, arXiv:1711.08267, 2018.
Crossref
[43]

T. Lan, C. Wu, C. Yu, and X. Wang, Adversarial network embedding on heterogeneous information networks, J. Phys.: Conf. Ser., vol. 1693, no. 1, p. 012018, 2020.
Crossref Google Scholar
[44]
K. Zhao, T. Bai, B. Wu, B. Wang, Y. J. Zhang, Y. Y. Yang, and J. Y. Nie, Deep adversarial completion for sparse heterogeneous information network embedding, in Proc. Web Conf. 2020 (WWW '20), New York, NY, USA, 2020, pp. 508–518.
Crossref
[45]
C. Wang, S. Zhou, K. Yu, D. Chen, B. Li, Y. Feng, and C. Chen, Collaborative knowledge distillation for heterogeneous information network embedding, in Proc. ACM Web Conf. 2022, Virtual Event, Lyon, France, 2022, pp. 1631–1639.
Crossref
[46]

J. Gasteiger, S. Weißenberger, and S. Günnemann, Diffusion improves graph learning, NeurIPS, vol. 32, pp. 13366–13378, 2019.
Google Scholar
[47]

D. Fogaras, B. Rácz, K. Csalogány, and T. Sarlós, Towards scaling fully personalized PageRank: Algorithms, lower bounds, and experiments, Internet Math., vol. 2, no. 3, pp. 333–358, 2005.
Crossref Google Scholar
[48]
R. Devon Hjelm, A. Fedorov, S. Lavoie-Marchildon, K. Grewal, A. Trischler, and Y. Bengio, Learning deep representations by mutual information estimation and maximization, arXiv preprint arXiv:1808.06670, 2018.
[49]
Gerhard Nahler, Anatomical therapeutic chemical classification system (ATC), https://www.who.int/tools/atc-ddd-toolkit/atc-classification, 2024.
[50]

Z. Sheng, Y. Sun, Z. Yin, K. Tang, and Z. Cao, Advances in computational approaches in identifying synergistic drug combinations, Brief. Bioinform., vol. 2018,19, no. 6, p. 1172, 1182.
Crossref Google Scholar
[51]
S. Zhang, et at, GBERT: Pre-training User representations for Ephemeral Group Recommendation, in Proc. the 31st ACM International Conference on Information & Knowledge Management (CIKM '22 ), New York, USA, 2022, pp. 2631−2639.
Crossref
[52]
A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, L. Kaiser, I. Polosukhin, Attention is All you Need, arXiv preprint arXiv:1706.03762, 2017.
[53]

D. S. Wishart, Y. D. Feunang, A. C. Guo, E. J. Lo, A. Marcu, J. R. Grant, T. Sajed, D. Johnson, C. Li, Z. Sayeeda, et al., DrugBank 5.0: A major update to the DrugBank database for 2018, Nucleic Acids Res., vol. 46, no. D1, pp. D1074–D1082, 2018.
Crossref Google Scholar
[54]
T. U. Consortium UniProt: A worldwide hub of protein knowledge, Nucleic Acids Res., vol. 47, no. D1, pp. D506–D515, 2019.
Crossref
[55]

D. Szklarczyk, A. L. Gable, K. C. Nastou, D. Lyon, R. Kirsch, S. Pyysalo, N. T. Doncheva, M. Legeay, T. Fang, P. Bork, et al., The STRING database in 2021: Customizable protein–protein networks, and functional characterization of user-uploaded gene/measurement sets, Nucleic Acids Res., vol. 49, no. D1, pp. D605–D612, 2021.
Crossref Google Scholar
Tsinghua Science and Technology
Volume 30 Issue 1,
February 2025
Pages 215-233
DOI: 10.26599/TST.2023.9010149
Cite this article:
Xi X, Yuan J, Lu S, et al. Synergistic Multi-Drug Combination Prediction Based on Heterogeneous Network Representation Learning with Contrastive Learning. Tsinghua Science and Technology, 2025, 30(1): 215-233. https://doi.org/10.26599/TST.2023.9010149

126

Views

15

Downloads

0

Crossref

0

Web of Science

0

Scopus

0

CSCD

Altmetrics
Received: 31 August 2023
Revised: 28 October 2023
Accepted: 06 December 2023
Published: 11 September 2024
© The Author(s) 2025.

The articles published in this open access journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).
Return