Drug-Target Interaction Prediction Using Multi-Head Self-Attention and Graph Attention Network
Published 2021 View Full Article
- Home
- Publications
- Publication Search
- Publication Details
Title
Drug-Target Interaction Prediction Using Multi-Head Self-Attention and Graph Attention Network
Authors
Keywords
-
Journal
IEEE-ACM Transactions on Computational Biology and Bioinformatics
Volume 19, Issue 4, Pages 2208-2218
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Online
2021-05-07
DOI
10.1109/tcbb.2021.3077905
References
Ask authors/readers for more resources
Related references
Note: Only part of the references are listed.- Improved protein structure prediction using potentials from deep learning
- (2020) Andrew W. Senior et al. NATURE
- Learning to Predict Drug Target Interaction From Missing Not at Random Labels
- (2019) Chen Lin et al. IEEE TRANSACTIONS ON NANOBIOSCIENCE
- DeepConv-DTI: Prediction of drug-target interactions via deep learning with convolution on protein sequences
- (2019) Ingoo Lee et al. PLoS Computational Biology
- Advances in protein structure prediction and design
- (2019) Brian Kuhlman et al. NATURE REVIEWS MOLECULAR CELL BIOLOGY
- Compound-protein Interaction Prediction with End-to-end Learning of Neural Networks for Graphs and Sequences
- (2018) Masashi Tsubaki et al. BIOINFORMATICS
- OUP accepted manuscript
- (2018) BIOINFORMATICS
- Identification of drug-target interaction by a random walk with restart method on an interactome network
- (2018) Ingoo Lee et al. BMC BIOINFORMATICS
- Computational prediction of drug–target interactions using chemogenomic approaches: an empirical survey
- (2018) Ali Ezzat et al. BRIEFINGS IN BIOINFORMATICS
- DrugE-Rank: improving drug–target interaction prediction of new candidate drugs or targets by ensemble learning to rank
- (2016) Qingjun Yuan et al. BIOINFORMATICS
- CGBVS-DNN: Prediction of Compound-protein Interactions Based on Deep Learning
- (2016) Masatoshi Hamanaka et al. Molecular Informatics
- Improving compound–protein interaction prediction by building up highly credible negative samples
- (2015) Hui Liu et al. BIOINFORMATICS
- Predicting drug-target interactions using restricted Boltzmann machines
- (2013) Yuhao Wang et al. BIOINFORMATICS
- Similarity-based machine learning methods for predicting drug–target interactions: a brief review
- (2013) Hao Ding et al. BRIEFINGS IN BIOINFORMATICS
- Predicting Drug-Target Interactions for New Drug Compounds Using a Weighted Nearest Neighbor Profile
- (2013) Twan van Laarhoven et al. PLoS One
- Prediction of chemical–protein interactions: multitarget-QSAR versus computational chemogenomic methods
- (2012) Feixiong Cheng et al. Molecular BioSystems
- Gaussian interaction profile kernels for predicting drug–target interaction
- (2011) Twan van Laarhoven et al. BIOINFORMATICS
- Semi-supervised drug-protein interaction prediction from heterogeneous biological spaces
- (2010) Zheng Xia et al. BMC Systems Biology
- Supervised prediction of drug–target interactions using bipartite local models
- (2009) Kevin Bleakley et al. BIOINFORMATICS
Discover Peeref hubs
Discuss science. Find collaborators. Network.
Join a conversationBecome a Peeref-certified reviewer
The Peeref Institute provides free reviewer training that teaches the core competencies of the academic peer review process.
Get Started