208 related articles for article (PubMed ID: 37762445)
1. AMMVF-DTI: A Novel Model Predicting Drug-Target Interactions Based on Attention Mechanism and Multi-View Fusion.
Wang L; Zhou Y; Chen Q
Int J Mol Sci; 2023 Sep; 24(18):. PubMed ID: 37762445
[TBL] [Abstract][Full Text] [Related]
2. MCL-DTI: using drug multimodal information and bi-directional cross-attention learning method for predicting drug-target interaction.
Qian Y; Li X; Wu J; Zhang Q
BMC Bioinformatics; 2023 Aug; 24(1):323. PubMed ID: 37633938
[TBL] [Abstract][Full Text] [Related]
3. Drug-target interaction predictions with multi-view similarity network fusion strategy and deep interactive attention mechanism.
Song W; Xu L; Han C; Tian Z; Zou Q
Bioinformatics; 2024 Jun; 40(6):. PubMed ID: 38837345
[TBL] [Abstract][Full Text] [Related]
4. Drug-Target Interaction Prediction Using Multi-Head Self-Attention and Graph Attention Network.
Cheng Z; Yan C; Wu FX; Wang J
IEEE/ACM Trans Comput Biol Bioinform; 2022; 19(4):2208-2218. PubMed ID: 33956632
[TBL] [Abstract][Full Text] [Related]
5. DTI-HETA: prediction of drug-target interactions based on GCN and GAT on heterogeneous graph.
Shao K; Zhang Y; Wen Y; Zhang Z; He S; Bo X
Brief Bioinform; 2022 May; 23(3):. PubMed ID: 35380622
[TBL] [Abstract][Full Text] [Related]
6. DTI-HeNE: a novel method for drug-target interaction prediction based on heterogeneous network embedding.
Yue Y; He S
BMC Bioinformatics; 2021 Sep; 22(1):418. PubMed ID: 34479477
[TBL] [Abstract][Full Text] [Related]
7. MIFAM-DTI: a drug-target interactions predicting model based on multi-source information fusion and attention mechanism.
Li J; Sun L; Liu L; Li Z
Front Genet; 2024; 15():1381997. PubMed ID: 38770418
[TBL] [Abstract][Full Text] [Related]
8. Efficient machine learning model for predicting drug-target interactions with case study for Covid-19.
El-Behery H; Attia AF; El-Feshawy N; Torkey H
Comput Biol Chem; 2021 Aug; 93():107536. PubMed ID: 34271420
[TBL] [Abstract][Full Text] [Related]
9. MHADTI: predicting drug-target interactions via multiview heterogeneous information network embedding with hierarchical attention mechanisms.
Tian Z; Peng X; Fang H; Zhang W; Dai Q; Ye Y
Brief Bioinform; 2022 Nov; 23(6):. PubMed ID: 36242566
[TBL] [Abstract][Full Text] [Related]
10. Semi-supervised heterogeneous graph contrastive learning for drug-target interaction prediction.
Yao K; Wang X; Li W; Zhu H; Jiang Y; Li Y; Tian T; Yang Z; Liu Q; Liu Q
Comput Biol Med; 2023 Sep; 163():107199. PubMed ID: 37421738
[TBL] [Abstract][Full Text] [Related]
11. MSI-DTI: predicting drug-target interaction based on multi-source information and multi-head self-attention.
Zhao W; Yu Y; Liu G; Liang Y; Xu D; Feng X; Guan R
Brief Bioinform; 2024 Mar; 25(3):. PubMed ID: 38762789
[TBL] [Abstract][Full Text] [Related]
12. Drug-target interaction predication via multi-channel graph neural networks.
Li Y; Qiao G; Wang K; Wang G
Brief Bioinform; 2022 Jan; 23(1):. PubMed ID: 34661237
[TBL] [Abstract][Full Text] [Related]
13. GraphormerDTI: A graph transformer-based approach for drug-target interaction prediction.
Gao M; Zhang D; Chen Y; Zhang Y; Wang Z; Wang X; Li S; Guo Y; Webb GI; Nguyen ATN; May L; Song J
Comput Biol Med; 2024 May; 173():108339. PubMed ID: 38547658
[TBL] [Abstract][Full Text] [Related]
14. MHTAN-DTI: Metapath-based hierarchical transformer and attention network for drug-target interaction prediction.
Zhang R; Wang Z; Wang X; Meng Z; Cui W
Brief Bioinform; 2023 Mar; 24(2):. PubMed ID: 36892155
[TBL] [Abstract][Full Text] [Related]
15. A heterogeneous network-based method with attentive meta-path extraction for predicting drug-target interactions.
Wang H; Huang F; Xiong Z; Zhang W
Brief Bioinform; 2022 Jul; 23(4):. PubMed ID: 35641162
[TBL] [Abstract][Full Text] [Related]
16. GCHN-DTI: Predicting drug-target interactions by graph convolution on heterogeneous networks.
Wang W; Liang S; Yu M; Liu D; Zhang H; Wang X; Zhou Y
Methods; 2022 Oct; 206():101-107. PubMed ID: 36058415
[TBL] [Abstract][Full Text] [Related]
17. MFA-DTI: Drug-target interaction prediction based on multi-feature fusion adopted framework.
Chen S; Li M; Semenov I
Methods; 2024 Apr; 224():79-92. PubMed ID: 38430967
[TBL] [Abstract][Full Text] [Related]
18. Hierarchical multimodal self-attention-based graph neural network for DTI prediction.
Bian J; Lu H; Dong G; Wang G
Brief Bioinform; 2024 May; 25(4):. PubMed ID: 38920341
[TBL] [Abstract][Full Text] [Related]
19. DeepMGT-DTI: Transformer network incorporating multilayer graph information for Drug-Target interaction prediction.
Zhang P; Wei Z; Che C; Jin B
Comput Biol Med; 2022 Mar; 142():105214. PubMed ID: 35030496
[TBL] [Abstract][Full Text] [Related]
20. iNGNN-DTI: prediction of drug-target interaction with interpretable nested graph neural network and pretrained molecule models.
Sun Y; Li YY; Leung CK; Hu P
Bioinformatics; 2024 Mar; 40(3):. PubMed ID: 38449285
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]