TY - GEN
T1 - Deep Learning Integration with Phenotypic Similarities and Heterogeneous Networks for Drug-Target Interaction Prediction
AU - Wang, Yongtian
AU - Li, Li
AU - Shen, Yewei
AU - Zhang, Yizhuo
AU - Zhang, Yuhe
AU - Shang, Xuequn
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - In the field of drug discovery, the accurate prediction of drug-target interactions (DTIs) is a critical yet challenging task, hindered by the intricate dynamics of biological systems and molecular interplay. To address this, we propose the DTI-VGAE model, a novel deep learning framework that integrates variational graph autoencoders (VGAE) with a multi-layer perceptron (MLP) for robust DTI prediction. Our approach focuses on three key aspects: learning distinct representations of drugs and proteins from heterogeneous networks, constructing Drug-Protein Pair (DPP) networks to capture the complex interactions, and employing MLP for the final prediction of DTIs. This comprehensive methodology not only enhances the accuracy of DTI predictions but also ensures greater reliability and stability. Validated through extensive 5-fold cross-validation, the DTI-VGAE model consistently outperforms existing methods, achieving superior average AUROC, AUPR scores, and accuracy. The DTI-VGAE model's innovative integration of VGAE and MLP offers a significant advancement in the computational approach to drug discovery, paving the way for more efficient and precise drug development processes.
AB - In the field of drug discovery, the accurate prediction of drug-target interactions (DTIs) is a critical yet challenging task, hindered by the intricate dynamics of biological systems and molecular interplay. To address this, we propose the DTI-VGAE model, a novel deep learning framework that integrates variational graph autoencoders (VGAE) with a multi-layer perceptron (MLP) for robust DTI prediction. Our approach focuses on three key aspects: learning distinct representations of drugs and proteins from heterogeneous networks, constructing Drug-Protein Pair (DPP) networks to capture the complex interactions, and employing MLP for the final prediction of DTIs. This comprehensive methodology not only enhances the accuracy of DTI predictions but also ensures greater reliability and stability. Validated through extensive 5-fold cross-validation, the DTI-VGAE model consistently outperforms existing methods, achieving superior average AUROC, AUPR scores, and accuracy. The DTI-VGAE model's innovative integration of VGAE and MLP offers a significant advancement in the computational approach to drug discovery, paving the way for more efficient and precise drug development processes.
KW - Deep Learning
KW - Drug Discovery
KW - Drug-Target Interaction
KW - Multi-Layer Perceptron
KW - Variational Graph Autoencoders
UR - https://www.scopus.com/pages/publications/85184905705
U2 - 10.1109/BIBM58861.2023.10385907
DO - 10.1109/BIBM58861.2023.10385907
M3 - 会议稿件
AN - SCOPUS:85184905705
T3 - Proceedings - 2023 2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023
SP - 2945
EP - 2951
BT - Proceedings - 2023 2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023
A2 - Jiang, Xingpeng
A2 - Wang, Haiying
A2 - Alhajj, Reda
A2 - Hu, Xiaohua
A2 - Engel, Felix
A2 - Mahmud, Mufti
A2 - Pisanti, Nadia
A2 - Cui, Xuefeng
A2 - Song, Hong
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023
Y2 - 5 December 2023 through 8 December 2023
ER -