GraphBAN: An inductive graph-based approach for enhanced prediction of compound-protein interactions

Hamid Hadipour, Yan Yi Li, Yan Sun, Chutong Deng, Leann Lac, Rebecca Davis, Silvia T. Cardona () and Pingzhao Hu ()
Additional contact information
Hamid Hadipour: University of Manitoba
Yan Yi Li: University of Toronto
Yan Sun: University of Manitoba
Chutong Deng: Western University
Leann Lac: University of Manitoba
Rebecca Davis: University of Manitoba
Silvia T. Cardona: University of Manitoba
Pingzhao Hu: University of Manitoba

Nature Communications, 2025, vol. 16, issue 1, 1-12

Abstract: Abstract Understanding compound-protein interactions is crucial for early drug discovery, offering insights into molecular mechanisms and potential therapeutic effects of compounds. Here, we introduce GraphBAN, a graph-based framework that inductively predicts these interactions using compound and protein feature information. GraphBAN effectively handles inductive link predictions for unseen nodes, providing a robust solution for predicting interactions between entirely unseen compounds and proteins. This capability enables GraphBAN to transcend the constraints of traditional methods that are typically limited to known contexts. GraphBAN employs a knowledge distillation architecture through a teacher-student learning model. The teacher block leverages network structure information, while the student block focuses on node attributes, enhancing learning and prediction accuracy. Additionally, GraphBAN incorporates a domain adaptation module, increasing its effectiveness across different dataset domains. Empirical tests on five benchmark datasets demonstrate that GraphBAN outperforms ten baseline models, while a case study analysis with the Pin1 protein further supports the model’s effectiveness in real world scenarios, making it as a promising tool for early drug discovery.

Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.nature.com/articles/s41467-025-57536-9 Abstract (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:nat:natcom:v:16:y:2025:i:1:d:10.1038_s41467-025-57536-9

Ordering information: This journal article can be ordered from
https://www.nature.com/ncomms/

DOI: 10.1038/s41467-025-57536-9

Access Statistics for this article

Nature Communications is currently edited by Nathalie Le Bot, Enda Bergin and Fiona Gillespie

More articles in Nature Communications from Nature
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().