… We propose a learning-based method based on feature representation learning and deep
… Drug targets are special molecules that can bind to drugs and produce effects in cells, the …

… ) is a powerful deep representation learning method for network data, … representation
learning-based framework, named EEG-DTI, to identify the interactions between drug and target. To …

… multilayer network representation learning method for drug-target … learn the feature vectors
of drugs and targets. The feature vectors of the drug and the target are put into the drug-target …

… In our joint representation learning framework, we take the … drugs, targets, side effects and
diseases as constraints, and map the sequence representations of drugs and targets to the …

… side effects of drugs. We propose a network representation learning method based on matrix
factorisation to learn low-dimensional vector representations of drug and protein nodes. On …

… Consequently, we constructed a large-scale graph representation learning network to
learn the features of each node, as shown in Figure 2. In which Figure 2A is the drug-target …

… learning methods are also widely used in feature mapping [3], classification task [4] and disease
prediction [5]. Moreover, differentiable representation learning … Representation Learning …

… Then, we introduce the representation learning for drugs and targets, respectively (Sections
2.2∼2.3). Finally, we discuss the binding affinity prediction with DeepGS (Section 2.4). …

… drugs interact with targets that is beneficial for predictive accuracy. In this paper, we propose
a novel hierarchical graph representation learning … hierarchical graph learning architecture …

… of downstream task related to molecular representation learning for testing: molecular
property prediction, drug metabolism prediction, drug–protein binding prediction and antiviral …