DrugCLIP: Contrastive Drug-Disease Interaction For Drug Repurposing
Journal:
arXiv
Published Date:
Jul 2, 2024
Abstract
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.
