December 6, 2025Briefings in BioinformaticsOpen Access

Prompt-based multimodal representation learning for drug repurposing

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Authors

JLJinliang LiuUniversity of Technology Sydney UKU KaichengMemorial Sloan Kettering Cancer Center DRDhruv RanaNew York University

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Implication

Innovative approach using deep learning improves mean absolute error in identifying drug candidates targeting G-protein-coupled receptors, highlighting advances in drug repurposing.

Key Points

Mean absolute error improved by 7.4% using novel prompt-based multimodal representation learning.
The method dynamically encodes molecular structures and compound–protein interactions for enhanced predictions.
Drug candidates for G-protein-coupled receptors were identified in the context of safe pain management.
Significant improvements in prediction accuracy can advance drug discovery across therapeutic applications.

Cite This Study

Liu et al. (2025) studied this question.

synapsesocial.com/papers/69337cfbb3f947a0a125a6da https://doi.org/10.1093/bib/bbaf636

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Implication

Innovative approach using deep learning improves mean absolute error in identifying drug candidates targeting G-protein-coupled receptors, highlighting advances in drug repurposing.

Prompt-based multimodal representation learning for drug repurposing

Discussion

Member takes

Implication

Key Points

Cite This Study

Prompt-based multimodal representation learning for drug repurposing

Discussion

Member takes

Implication

Key Points

Cite This Study

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