Multi-layer graph attention neural networks for accurate drug-target interaction mapping.
Journal
Scientific reports
ISSN: 2045-2322
Titre abrégé: Sci Rep
Pays: England
ID NLM: 101563288
Informations de publication
Date de publication:
30 10 2024
30 10 2024
Historique:
received:
02
02
2024
accepted:
08
10
2024
medline:
31
10
2024
pubmed:
31
10
2024
entrez:
31
10
2024
Statut:
epublish
Résumé
In the crucial process of drug discovery and repurposing, precise prediction of drug-target interactions (DTIs) is paramount. This study introduces a novel DTI prediction approach-Multi-Layer Graph Attention Neural Network (MLGANN), through a groundbreaking computational framework that effectively harnesses multi-source information to enhance prediction accuracy. MLGANN not only strides forward in constructing a multi-layer DTI network by capturing both direct interactions between drugs and targets as well as their multi-level information but also amalgamates Graph Convolutional Networks (GCN) with a self-attention mechanism to comprehensively integrate diverse data sources. This method exhibited significant performance surpassing existing approaches in comparative experiments, underscoring its immense potential in elevating the efficiency and accuracy of DTI predictions. More importantly, this study accentuates the significance of considering multi-source data information and network heterogeneity in the drug discovery process, offering new perspectives and tools for future pharmaceutical research.
Identifiants
pubmed: 39478027
doi: 10.1038/s41598-024-75742-1
pii: 10.1038/s41598-024-75742-1
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
26119Informations de copyright
© 2024. The Author(s).
Références
Nosengo, N. et al. Can you teach old drugs new tricks? Nature 534, 314–316 (2016).
doi: 10.1038/534314a
pubmed: 27306171
Ezzat, A., Zhao, P., Wu, M., Li, X.-L. & Kwoh, C.-K. Drug-target interaction prediction with graph regularized matrix factorization. IEEE/ACM Trans. Comput. Biol. Bioinform. 14, 646–656 (2016).
doi: 10.1109/TCBB.2016.2530062
pubmed: 26890921
Ding, Y., Tang, J. & Guo, F. Identification of drug-target interactions via dual Laplacian regularized least squares with multiple kernel fusion. Knowl.-Based Syst. 204, 106254 (2020).
doi: 10.1016/j.knosys.2020.106254
An, Q. & Yu, L. A heterogeneous network embedding framework for predicting similarity-based drug-target interactions. Brief. Bioinform. 22, bbab275 (2021).
doi: 10.1093/bib/bbab275
pubmed: 34373895
Gao, K. Y. et al. Interpretable drug target prediction using deep neural representation. In IJCAI Vol. 2018, 3371–3377 (2018).
Huang, K., Xiao, C., Glass, L. M. & Sun, J. Moltrans: Molecular interaction transformer for drug-target interaction prediction. Bioinformatics 37, 830–836 (2021).
doi: 10.1093/bioinformatics/btaa880
pubmed: 33070179
Chu, Y. et al. DTI-MLCD: Predicting drug-target interactions using multi-label learning with community detection method. Brief. Bioinform. 22, bbaa205 (2021).
doi: 10.1093/bib/bbaa205
pubmed: 32964234
Liu, B., Pliakos, K., Vens, C. & Tsoumakas, G. Drug-target interaction prediction via an ensemble of weighted nearest neighbors with interaction recovery. Appl. Intell. 52, 1–23 (2022).
Pliakos, K., Vens, C. & Tsoumakas, G. Predicting drug-target interactions with multi-label classification and label partitioning. IEEE/ACM Trans. Comput. Biol. Bioinform. 18, 1596–1607 (2019).
doi: 10.1109/TCBB.2019.2951378
Olayan, R. S., Ashoor, H. & Bajic, V. B. DDR: Efficient computational method to predict drug-target interactions using graph mining and machine learning approaches. Bioinformatics 34, 1164–1173 (2018).
doi: 10.1093/bioinformatics/btx731
pubmed: 29186331
Zhang, Z.-C. et al. A graph regularized generalized matrix factorization model for predicting links in biomedical bipartite networks. Bioinformatics 36, 3474–3481 (2020).
doi: 10.1093/bioinformatics/btaa157
pubmed: 32145009
Luo, Y. et al. A network integration approach for drug-target interaction prediction and computational drug repositioning from heterogeneous information. Nat. Commun. 8, 573 (2017).
doi: 10.1038/s41467-017-00680-8
pubmed: 28924171
pmcid: 5603535
Wan, F., Hong, L., Xiao, A., Jiang, T. & Zeng, J. NeoDTI: Neural integration of neighbor information from a heterogeneous network for discovering new drug-target interactions. Bioinformatics 35, 104–111 (2019).
doi: 10.1093/bioinformatics/bty543
pubmed: 30561548
Li, Y., Qiao, G., Gao, X. & Wang, G. Supervised graph co-contrastive learning for drug-target interaction prediction. Bioinformatics 38, 2847–2854 (2022).
doi: 10.1093/bioinformatics/btac164
pubmed: 35561181
Li, J., Wang, J., Lv, H., Zhang, Z. & Wang, Z. IMCHGAN: Inductive matrix completion with heterogeneous graph attention networks for drug-target interactions prediction. IEEE/ACM Trans. Comput. Biol. Bioinform. 19, 655–665 (2021).
doi: 10.1109/TCBB.2021.3088614
Adasme, M. F., Parisi, D., Sveshnikova, A. & Schroeder, M. Structure-based drug repositioning: Potential and limits. In Seminars in cancer biology Vol. 68 192–198 (Elsevier, 2021).
Mohamed, S. K., Nounu, A. & Nováček, V. Drug target discovery using knowledge graph embeddings. In Proceedings of the 34th ACM/SIGAPP Symposium on Applied Computing, 11–18 (2019).
Trouillon, T., Welbl, J., Riedel, S., Gaussier, É. & Bouchard, G. Complex embeddings for simple link prediction. In International Conference on Machine Learning, 2071–2080 (PMLR, 2016).
Ye, Q. et al. A unified drug-target interaction prediction framework based on knowledge graph and recommendation system. Nat. Commun. 12, 6775 (2021).
doi: 10.1038/s41467-021-27137-3
pubmed: 34811351
pmcid: 8635420
Li, M., Cai, X., Xu, S. & Ji, H. Metapath-aggregated heterogeneous graph neural network for drug-target interaction prediction. Brief. Bioinform. 24, bbac578 (2023).
doi: 10.1093/bib/bbac578
pubmed: 36592060
Fan, Y. et al. SGCLDGA: Unveiling drug-gene associations through simple graph contrastive learning. Brief. Bioinform. 25, bbae231 (2024).
doi: 10.1093/bib/bbae231
pubmed: 38754409
pmcid: 11097980
Cao, J. et al. NGCN: Drug-target interaction prediction by integrating information and feature learning from heterogeneous network. J. Cell Mol. Med. 28, e18224 (2024).
doi: 10.1111/jcmm.18224
pubmed: 38509739
pmcid: 10955156
Dehghan, A., Razzaghi, P., Abbasi, K. & Gharaghani, S. TripletMultiDTI: Multimodal representation learning in drug-target interaction prediction with triplet loss function. Expert Syst. Appl. 232, 120754 (2023).
doi: 10.1016/j.eswa.2023.120754
Rafiei, F. et al. DeepTraSynergy: Drug combinations using multimodal deep learning with transformers. Bioinformatics 39, btad438 (2023).
doi: 10.1093/bioinformatics/btad438
pubmed: 37467066
pmcid: 10397534
Gharizadeh, A., Abbasi, K., Ghareyazi, A., Mofrad, M. R. & Rabiee, H. R. HGTDR: Advancing drug repurposing with heterogeneous graph transformers. Preprint at arXiv:2405.08031 (2024).
Zeng, X. et al. Target identification among known drugs by deep learning from heterogeneous networks. Chem. Sci. 11, 1775–1797 (2020).
doi: 10.1039/C9SC04336E
pubmed: 34123272
pmcid: 8150105
Bordes, A., Usunier, N., Garcia-Duran, A., Weston, J. & Yakhnenko, O. Translating embeddings for modeling multi-relational data. Adv. Neural Inf. Processi. Syst. 26 (2013).