DrugCLIP: Contrastive Drug-Disease Interaction For Drug Repurposing
This work aims to reduce the time and cost of drug development by enabling more efficient reuse of approved drugs for new diseases, though it appears incremental as it builds on existing contrastive learning approaches in a specific domain.
The paper tackles the problem of automating drug repurposing by addressing challenges like lack of labels and multimodal feature representation, proposing DrugCLIP, a contrastive learning method that learns drug-disease interactions without negative labels, and validates its effectiveness through empirical studies on a curated dataset from clinical trial records.
Bringing a novel drug from the original idea to market typically requires more than ten years and billions of dollars. To alleviate the heavy burden, a natural idea is to reuse the approved drug to treat new diseases. The process is also known as drug repurposing or drug repositioning. Machine learning methods exhibited huge potential in automating drug repurposing. However, it still encounter some challenges, such as lack of labels and multimodal feature representation. To address these issues, we design DrugCLIP, a cutting-edge contrastive learning method, to learn drug and disease's interaction without negative labels. Additionally, we have curated a drug repurposing dataset based on real-world clinical trial records. Thorough empirical studies are conducted to validate the effectiveness of the proposed DrugCLIP method.