Graph Neural Networks for Predicting Drug–Target Interactions and Synergistic Therapeutic Combinations
Keywords:
graph neural networks; drug-target interactions; synergistic combinations; systems pharmacology; network medicine; clinical translation; computational governanceAbstract
The identification of drug-target interactions and synergistic therapeutic combinations remains a central challenge in modern drug discovery, particularly as the complexity of biological systems and the cost of experimental screening continue to grow. Graph neural networks have emerged as a powerful framework for representing relational structure in molecular, genomic, and pharmacological data. Unlike traditional machine learning approaches that rely on independent feature vectors, graph-based methods explicitly model interactions among drugs, proteins, pathways, and diseases. This paper provides a system-level analysis of graph neural network architectures for drug-target interaction prediction and synergistic combination discovery. It examines the structural and architectural trade-offs associated with molecular graph encoding, heterogeneous biomedical networks, message passing, and multi-relational learning. The discussion further addresses data infrastructure, standardization, validation, and integration with experimental platforms. Emphasis is placed on deployment in clinical and pharmaceutical settings, including regulatory considerations, explainability, robustness, fairness, and environmental sustainability. The paper also considers policy implications for open data sharing, model governance, and equitable access. By synthesizing technical advances with socio-technical infrastructure concerns, the article offers a comprehensive perspective on the opportunities and systemic constraints that shape the translation of graph neural network models into reliable tools for precision medicine and combination therapy development.
References
1. Kipf, T. N., & Welling, M. (2017). Semi-supervised classification with graph convolutional networks. International Conference on Learning Representations.
2. Veličković, P., Cucurull, G., Casanova, A., Romero, A., Liò, P., & Bengio, Y. (2018). Graph attention networks. International Conference on Learning Representations.
3. Hamilton, W. L., Ying, R., & Leskovec, J. (2017). Inductive representation learning on large graphs. Advances in Neural Information Processing Systems, 30, 1024–1034.
4. Öztürk, H., Özgür, A., & Ozkirimli, E. (2018). DeepDTA: Deep drug-target binding affinity prediction. Bioinformatics, 34(17), i821–i829.
5. Nguyen, T., Le, H., Quinn, T. P., Nguyen, T., Le, T. D., & Venkatesh, S. (2021). GraphDTA: Predicting drug-target binding affinity with graph neural networks. Bioinformatics, 37(8), 1140–1147.
6. Tsubaki, M., Tomii, K., & Sese, J. (2019). Compound-protein interaction prediction with end-to-end learning of neural networks for graphs and sequences. Bioinformatics, 35(2), 309–318.
7. Huang, K., Xiao, C., Glass, L. M., & Sun, J. (2021). MolTrans: Molecular interaction transformer for drug-target interaction prediction. Bioinformatics, 37(6), 830–836.
8. Preuer, K., Lewis, R. P. I., Hochreiter, S., Bender, A., Bulusu, K. C., & Klambauer, G. (2018). DeepSynergy: Predicting anti-cancer drug synergy with deep learning. Bioinformatics, 34(9), 1538–1546.
9. Ianevski, A., He, L., Aittokallio, T., & Tang, J. (2017). SynergyFinder: A web application for analyzing drug combination dose-response matrix data. Bioinformatics, 33(15), 2413–2415.
10. Zitnik, M., Agrawal, M., & Leskovec, J. (2018). Modeling polypharmacy side effects with graph convolutional networks. Bioinformatics, 34(13), i457–i466.
11. Wang, Y., Yao, J., Sui, Y., Jiang, H., Ma, B., Lai, S., ... & Tan, N. (2026). HerbSyner_Finder: a network community-based model for identifying synergistic combinations from herbal medicines and complex systems. Targetome, 2(2).
12. Barabási, A. L., Gulbahce, N., & Loscalzo, J. (2011). Network medicine: A network-based approach to human disease. Nature Reviews Genetics, 12(1), 56–68.
13. Wishart, D. S., Feunang, Y. D., Guo, A. C., Lo, E. J., Marcu, A., Grant, J. R., ... & Wilson, M. (2018). DrugBank 5.0: A major update to the DrugBank database for 2018. Nucleic Acids Research, 46(D1), D1074–D1082.
14. Tang, J., Karhinen, L., Xu, T., Szwajda, A., Yadav, B., Wennerberg, K., & Aittokallio, T. (2014). Target inhibition networks: Predicting selective combinations of druggable targets to block cancer survival pathways. PLoS Computational Biology, 10(9), e1003826.
15. Vamathevan, J., Clark, D., Czodrowski, P., Dunham, I., Ferran, E., Lee, G., ... & Zhao, S. (2019). Applications of machine learning in drug discovery and development. Nature Reviews Drug Discovery, 18(6), 463–477.
16. 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, 21(3), 919–935.
17. Topol, E. J. (2019). High-performance medicine: The convergence of human and artificial intelligence. Nature Medicine, 25(1), 44–56.
18. Mehrabi, N., Morstatter, F., Saxena, N., Lerman, K., & Galstyan, A. (2021). A survey on bias and fairness in machine learning. ACM Computing Surveys, 54(6), 1–35.
19. Strubell, E., Ganesh, A., & McCallum, A. (2019). Energy and policy considerations for deep learning in NLP. Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, 3645–3650.
20. Paul, S. M., Mytelka, D. S., Dunwiddie, C. T., Persinger, C. C., Munos, B. H., Lindborg, S. R., & Schacht, A. L. (2010). How to improve R&D productivity: The pharmaceutical industry's grand challenge. Nature Reviews Drug Discovery, 9(3), 203–214.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2026 Bioinformatics Insights and Analytics
This work is licensed under a Creative Commons Attribution 4.0 International License.
