Prompt-based multimodal representation learning for drug repurposing.

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 and a prompt calibration module for effective multimodal feature integration and optimization. When applied to identifying FDA-approved drug candidates targeting G-protein-coupled receptors, our method achieved a 7.4% improvement in mean absolute error compared with state-of-the-art methods, with up to a 25.1% improvement for specific target-of-interest. By demonstrating potential in repurposing non-opioid treatments without the risk of addiction for safe pain management, our method has the capacity to advance drug discovery and meet a wide range of therapeutic needs.