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

References

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.

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

Unveiling Deepfake Detection Using Vision Transformers: A Survey and Experimental Study

Share Article

X
LinkedIn
Facebook
WhatsApp

Or copy link

https://test.indjcst.com/archives/10.59256/indjcst.20260501011

*Instagram doesn't support direct link sharing from web. Copy the link and share it in your Instagram story or post.