MTAF–DTA: multi-type attention fusion network for drug–target affinity prediction
Abstract Background The development of drug–target binding affinity (DTA) prediction tasks significantly drives the drug discovery process forward. Leveraging the rapid advancement of artificial intelligence, DTA prediction tasks have undergone a transformative shift from wet lab experimentation to...
Saved in:
| Main Authors: | Jinghong Sun, Han Wang, Jia Mi, Jing Wan, Jingyang Gao |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
BMC
2024-12-01
|
| Series: | BMC Bioinformatics |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12859-024-05984-3 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
SMFF-DTA: using a sequential multi-feature fusion method with multiple attention mechanisms to predict drug-target binding affinity
by: Xun Wang, et al.
Published: (2025-05-01) -
GS-DTA: integrating graph and sequence models for predicting drug-target binding affinity
by: Junwei Luo, et al.
Published: (2025-02-01) -
FingerDTA: A Fingerprint-Embedding Framework for Drug-Target Binding Affinity Prediction
by: Xuekai Zhu, et al.
Published: (2023-03-01) -
MDNN-DTA: a multimodal deep neural network for drug-target affinity prediction
by: Xu Gao, et al.
Published: (2025-03-01) -
InceptionDTA: Predicting drug-target binding affinity with biological context features and inception networks
by: Mahmood Kalemati, et al.
Published: (2025-02-01)