Détail du document
Identifiant
oai:arXiv.org:2407.02265
Sujet
Computer Science - Machine Learnin... Quantitative Biology - Biomolecule...Auteur
Lu, Yingzhou Hu, Yaojun Li, ChenhaoCatégorie
Computer Science
Année
2024
Date de référencement
10/07/2024
Mots clés
drugclip drugRésumé
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.
Lu, Yingzhou,Hu, Yaojun,Li, Chenhao, 2024, DrugCLIP: Contrastive Drug-Disease Interaction For Drug Repurposing
