# GraphMS:Drug Target Prediction Using Graph Representation Learning with Substructures
###### Abstract  
The prediction of drug-target interactions is always a key task in the field of drug redirection. However, traditional methods of predicting drug-target interactions are either mediocre or rely heavily on data stacking. In this work, we proposed our model named GraphMS. We merged heterogeneous graph information and obtained effective node information and substructure information based on mutual information in graph embeddings. We then learned high quality representations for downstream tasks, and proposed an end-to-end auto-encoder model to complete the task of link prediction. Experimental results show that our method outperforms several state-of-the-art models. The model can achieve the area under the receiver operating characteristics (AUROC) curve of 0.959 and area under the precise recall curve (AUPR) of 0.847. We found that the mutual information between the substructure and graph-level representations contributes most to the mutual information index in a relatively sparse network. And the mutual information between the node-level and graph-level representations contributes most in a relatively dense network. graph embedding; link prediction; mutual information; subgraph +
Footnote †: journal: Medicallow-dimensional embedding [6]. Such method includes the random walk method [7] and Graph Convolutional Network (GCN) model [8]. Ordinary graph embedding tends to make the entire input graph smoother. The substructural information about the part of the input graph will be ignored. However, there have been few studies on preserving substructure information in GRL. A substructure is a set of subgraphs represented by a subset of vertices and edges, which is often able to express the unique semantics and fundamental expressions of the graph. More precisely, neighbor nodes in the graph (such as first-order neighbor nodes) are usually trained to obtain similar embedding representations [9]. However, nodes that are far apart in the graph have no similar representations, even if they are similar in structure. Preserving substructure information could effectively prevent such a situation from occurring. Substructure in graph is generally used to solve three types of problems. One type can be utilized to accelerate large-scale graph training. Cluster-GCN [10] is an example of this. The core idea of Cluster-GCN is to apply the clustering algorithm to divide the large graph into multiple clusters. The division follows the principle of fewer connections between clusters and more connections within clusters. The simple method effectively reduces the consumption of memory and computing resources. At the same time, good prediction accuracy can be achieved. One type can be used for self-supervised learning. SUBG-CON [11] exploits the strong correlation between the central graph node and its sampled subgraphs to capture regional structure information. SUBG-CON is a self-supervised representation learning method based on subgraph contrast. The other type can be applied on denoising in a network, such as a graph. For instance, there are only three nodes around a node. The substructure embedding will select the most representative neighbor node, which can eliminate unnecessary confusion of neighbor nodes. Mutual Information (MI) is a measure of quantifying the relationship between two random variables. Inspired by the MI-based learning algorithms [12; 13], we combine substructure embedding with mutual information, ie, adversarial learning, and apply it to DTI prediction to obtain more accurate embedding. In order to make the embedding pay more attention to the contribution of the substructure in the graph, the representation of the substructure should be highly relevant to the graph-level representation. That is, maximizing the correlation between the graph-level and substructure representations helps to retain substructure information. To a certain extent, the application of substructures can improve the embedding effect of the sparse graph network.