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

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 studen

SUBMITTER: Hadipour H 

PROVIDER: S-EPMC11920434 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature