ARCHIVES
Original Article
AI-Assisted Host–Pathogen Interaction Network Mining Using Contrastive Graph Representation Learning for Druggable Target Discovery
Konam Ramesh1
Assistant Professor, Department of Computer Science and Applications, Kakatiya Government College, Hanumakonda, Telangana, India.
Published Online: January-April 2026
Pages: 86-94
Cite this article
↗ https://www.doi.org/10.59256/indjcst.20260501011References
1. D. E. Gordon et al., “A SARS-CoV-2 protein interaction map reveals targets for drug repurposing,” Nature, vol. 583, pp. 459–468, 2020.
2. T. Rolland et al., “A proteome-scale map of the human interactome network,” Cell, vol. 159, no. 5, pp. 1212–1226, 2014.
3. B. Dyer et al., “Predicting host–pathogen protein interactions using domain–motif networks,” PLoS Computational Biology, vol. 6, no. 6, p.
e1000730, 2010.
4. S. A. Kafkas, M. Pavlopoulos, and P. H. Jensen, “Comparative interactomics of human pathogens,” Bioinformatics, vol. 39, no. 3, 2023.
5. P. Velickovic et al., “Graph Attention Networks,” Proc. ICLR, 2018.
6. Y. Wang et al., “Graph neural networks for biological network analysis: Trends, applications and perspectives,” Briefings in Bioinformatics,
vol. 24, no. 1, pp. 1–17, 2023.
7. W. Hamilton, Z. Ying, and J. Leskovec, “Representation learning on graphs: Methods and applications,” IEEE Data Engineering Bulletin,
vol. 40, no. 3, pp. 52–74, 2017.
8. M. Zitnik, M. Agrawal, and J. Leskovec, “Modeling polypharmacy side effects with graph convolutional networks,” Bioinformatics, vol. 34,
no. 13, pp. i457–i466, 2018.
9. X. Zhou, Y. Liu, and D. Li, “Predicting host–pathogen protein interactions via graph neural networks and attention pooling,” Bioinformatics
Advances, vol. 2, no. 1, 2022.
10. X. Zhang et al., “Cross-species transfer learning for pathogen–host interaction prediction,” BMC Bioinformatics, vol. 25, no. 87, 2024.
11. Y. You et al., “Graph contrastive learning with augmentations,” Advances in Neural Information Processing Systems (NeurIPS), 2020.
12. Z. Qiu et al., “Unsupervised graph representation learning with graph contrastive coding,” IJCAI, 2020.
13. C. Schaefer et al., “HPI-GNN: Graph neural network for host–pathogen protein interaction prediction,” Bioinformatics, vol. 40, no. 2, 2024.
14. A. L. Gonzalez et al., “Integrative machine learning approaches for vaccine antigen discovery,” Trends in Immunology, vol. 44, no. 2, pp.
90–106, 2023.
15. N. Li, S. Saha, and T. M. Murali, “Revealing host–pathogen interactions in malaria using multi-layer network inference,” PLOS Pathogens,
vol. 19, no. 2, p. e1011234, 2023.16. M. Kanehisa and S. Goto, “KEGG: Kyoto Encyclopedia of Genes and Genomes,” Nucleic Acids Research, vol. 28, no. 1, pp. 27–30, 2000.
17. J. Sun, Y. Du, and X. Liu, “PU-learning for bioinformatics: Handling label noise and unlabeled data,” IEEE/ACM Transactions on
Computational Biology and Bioinformatics, vol. 21, no. 1, pp. 177–189, 2024.
18. H. Zhao et al., “Contrastive pretraining for cross-species protein interaction networks,” Nature Computational Science, vol. 5, no. 4, pp.
442–454, 2025.
19. R. Wuchty, “Computational prediction of host–pathogen protein interactions,” Bioinformatics, vol. 27, no. 21, pp. 2925–2932, 2011.
20. J. Chen, H. Peng, and T. Tang, “Explainable graph neural networks for biological interaction interpretation,” IEEE Access, vol. 12, pp.
22341–22358, 2024.
2. T. Rolland et al., “A proteome-scale map of the human interactome network,” Cell, vol. 159, no. 5, pp. 1212–1226, 2014.
3. B. Dyer et al., “Predicting host–pathogen protein interactions using domain–motif networks,” PLoS Computational Biology, vol. 6, no. 6, p.
e1000730, 2010.
4. S. A. Kafkas, M. Pavlopoulos, and P. H. Jensen, “Comparative interactomics of human pathogens,” Bioinformatics, vol. 39, no. 3, 2023.
5. P. Velickovic et al., “Graph Attention Networks,” Proc. ICLR, 2018.
6. Y. Wang et al., “Graph neural networks for biological network analysis: Trends, applications and perspectives,” Briefings in Bioinformatics,
vol. 24, no. 1, pp. 1–17, 2023.
7. W. Hamilton, Z. Ying, and J. Leskovec, “Representation learning on graphs: Methods and applications,” IEEE Data Engineering Bulletin,
vol. 40, no. 3, pp. 52–74, 2017.
8. M. Zitnik, M. Agrawal, and J. Leskovec, “Modeling polypharmacy side effects with graph convolutional networks,” Bioinformatics, vol. 34,
no. 13, pp. i457–i466, 2018.
9. X. Zhou, Y. Liu, and D. Li, “Predicting host–pathogen protein interactions via graph neural networks and attention pooling,” Bioinformatics
Advances, vol. 2, no. 1, 2022.
10. X. Zhang et al., “Cross-species transfer learning for pathogen–host interaction prediction,” BMC Bioinformatics, vol. 25, no. 87, 2024.
11. Y. You et al., “Graph contrastive learning with augmentations,” Advances in Neural Information Processing Systems (NeurIPS), 2020.
12. Z. Qiu et al., “Unsupervised graph representation learning with graph contrastive coding,” IJCAI, 2020.
13. C. Schaefer et al., “HPI-GNN: Graph neural network for host–pathogen protein interaction prediction,” Bioinformatics, vol. 40, no. 2, 2024.
14. A. L. Gonzalez et al., “Integrative machine learning approaches for vaccine antigen discovery,” Trends in Immunology, vol. 44, no. 2, pp.
90–106, 2023.
15. N. Li, S. Saha, and T. M. Murali, “Revealing host–pathogen interactions in malaria using multi-layer network inference,” PLOS Pathogens,
vol. 19, no. 2, p. e1011234, 2023.16. M. Kanehisa and S. Goto, “KEGG: Kyoto Encyclopedia of Genes and Genomes,” Nucleic Acids Research, vol. 28, no. 1, pp. 27–30, 2000.
17. J. Sun, Y. Du, and X. Liu, “PU-learning for bioinformatics: Handling label noise and unlabeled data,” IEEE/ACM Transactions on
Computational Biology and Bioinformatics, vol. 21, no. 1, pp. 177–189, 2024.
18. H. Zhao et al., “Contrastive pretraining for cross-species protein interaction networks,” Nature Computational Science, vol. 5, no. 4, pp.
442–454, 2025.
19. R. Wuchty, “Computational prediction of host–pathogen protein interactions,” Bioinformatics, vol. 27, no. 21, pp. 2925–2932, 2011.
20. J. Chen, H. Peng, and T. Tang, “Explainable graph neural networks for biological interaction interpretation,” IEEE Access, vol. 12, pp.
22341–22358, 2024.
Related Articles
2026
Artificial Intelligence in Learning and Teaching
2026
Admin Assist: An AI – Driven Configuration and Orchestration for Enterprise Application
2026
Enhancing Blood Group Identification using pigeon inspired optimization: An Innovative Approach
2026
Eco-Genius: Power Up Smart, Power Down Waste
2026
Crowd-Sourced Disaster Response and Rescue Assistant
2026