Prompt-based multimodal representation learning for drug repurposing.

ABSTRACT: Drug repurposing significantly reduces development costs and shortens research cycles, making it a critical strategy in drug discovery. An emerging class of drug repurposing approaches applies deep learning to structural data. However, these methods often depend on static representations of molecular and protein structures, which may not fully capture the dynamic character of compound-protein interactions. To address these challenges and enhance the accuracy of compound-protein interaction predictions, we introduce an innovative prompt-based multimodal representation learning framework that dynamically encodes task-specific contextual information for drug repurposing. Specifically, the framework includes a dynamic prompt generation module that adaptively creates receptor-specific prompts a

SUBMITTER: Liu J 

PROVIDER: S-EPMC12670639 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature