DrugDAGT: a dual-attention graph transformer with contrastive learning improves drug-drug interaction prediction
DrugDAGT provides an effective tool for accurately predicting multiple DDI types by identifying key local chemical structures, offering valuable insights for prescribing medications, and guiding drug development. All data and code of our DrugDAGT can be found at https://github.com/codejiajia/DrugDAGT .
Authors
- Jiangning Song (ORCID: https://orcid.org/0000-0001-8031-9086)
- Quan Zou (ORCID: https://orcid.org/0000-0001-6406-1142)
- Yansu Wang (ORCID: https://orcid.org/0000-0003-1274-4958)
- Mengting Niu (ORCID: https://orcid.org/0000-0001-9175-4649)
- Yaojia Chen (ORCID: https://orcid.org/0000-0003-0204-2725)
- Yijie Ding (ORCID: https://orcid.org/0000-0003-2911-7643)
- Jiacheng Wang (ORCID: https://orcid.org/0009-0005-2981-7924)
Institutions
- University of Electronic Science and Technology of China (CN)
- Shenzhen Polytechnic University (CN)
- Wenzhou Medical University (CN)
- Australian Regenerative Medicine Institute (AU)
- First Affiliated Hospital of Wenzhou Medical University (CN)
- Quzhou University (CN)
- Monash University (AU)
Publication Details
- Journal
- BMC Biology
- Published
- 2024-10-14
- DOI
- https://doi.org/10.1186/s12915-024-02030-9
- Citations
- 25
- Primary Topic
- Computational Drug Discovery Methods
- Type
- article
- Field-Weighted Citation Impact
- 9.04