GS-DTA: integrating graph and sequence models for predicting drug-target binding affinity
The results show that GS-DTA achieves good performance in terms of MSE, CI, and r<sup>2</sup><sub>m</sub> on the Davis and KIBA datasets, improving the accuracy of DTA prediction.
Authors
- Zheng Zhu (ORCID: https://orcid.org/0000-0002-2035-0804)
- Chuan‐Le Xiao (ORCID: https://orcid.org/0000-0002-4680-0682)
- Junwei Luo (ORCID: https://orcid.org/0000-0001-6841-9893)
- Jingjing Wei (ORCID: https://orcid.org/0000-0003-2221-6702)
- Zhiyu Xu (ORCID: https://orcid.org/0000-0003-3186-3573)
- Jiquan Shen
Institutions
- Sun Yat-sen University (CN)
- Anyang Institute of Technology (CN)
- Henan Polytechnic University (CN)
Publication Details
- Journal
- BMC Genomics
- Published
- 2025-02-04
- DOI
- https://doi.org/10.1186/s12864-025-11234-4
- Citations
- 9
- Primary Topic
- Computational Drug Discovery Methods
- Type
- article
- Field-Weighted Citation Impact
- 13.54
Funders
- Henan Polytechnic University
- Science and Technology Department of Henan Province