DrugCLIP: Contrastive Drug-Disease Interaction For Drug Repurposing
Fuente:
arXiv
Gespeichert in:
| Hauptverfasser: | , , |
|---|---|
| Format: | Preprint |
| Veröffentlicht: |
2024
|
| Schlagworte: | |
| Online-Zugang: | |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| _version_ | 1866916309664530432 |
|---|---|
| author | Lu, Yingzhou Hu, Yaojun Li, Chenhao |
| author_facet | Lu, Yingzhou Hu, Yaojun Li, Chenhao |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_02265 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | DrugCLIP: Contrastive Drug-Disease Interaction For Drug Repurposing Lu, Yingzhou Hu, Yaojun Li, Chenhao Machine Learning Biomolecules 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. |
| title | DrugCLIP: Contrastive Drug-Disease Interaction For Drug Repurposing |
| topic | Machine Learning Biomolecules |
| url | https://arxiv.org/abs/2407.02265 |