SMFF-DTA: using a sequential multi-feature fusion method with multiple attention mechanisms to predict drug-target binding affinity
As demonstrated by our extensive studies, SMFF-DTA outperforms the other methods in terms of various metrics, showing its advantages and effectiveness as a drug-target binding affinity predictor.
Authors
- Xingguang Wang
- Hanyu Wang (ORCID: https://orcid.org/0000-0002-2327-3234)
- Wenqian Yu (ORCID: https://orcid.org/0000-0002-5956-5650)
- Zhijun Xia (ORCID: https://orcid.org/0009-0009-0949-5656)
- Xun Wang (ORCID: https://orcid.org/0000-0001-7269-0969)
- T. T. Han (ORCID: https://orcid.org/0000-0001-6487-0281)
- Rui Feng (ORCID: https://orcid.org/0000-0002-0611-9518)
Institutions
- Qingdao Academy of Intelligent Industries (CN)
- Institute of Software (CN)
- China University of Petroleum, East China (CN)
Publication Details
- Journal
- BMC Biology
- Published
- 2025-05-09
- DOI
- https://doi.org/10.1186/s12915-025-02222-x
- Citations
- 4
- Primary Topic
- Computational Drug Discovery Methods
- Type
- article
- Field-Weighted Citation Impact
- 6.02
Funders
- National Natural Science Foundation of China
- Natural Science Foundation of Shandong Province
- National Key Research and Development Program of China