# GraphMS:Drug Target Prediction Using Graph Representation Learning with Substructures
## 4 Experiments and Results
### Comparative Experiment  
In the chart, 1:10 means that the ratio of positive samples to negative samples was 1:10. 1:all means that all unknown drug-target interaction pairs were considered. Specially, the ratio between positive and negative samples was around 1:500. The whole network is sparse against the network with the ratio of 1:10. Single-view means that we only use the drug-structure-similarity network and protein-sequence-similarity network. Multi-view means that we use all networks mentioned above. The results are shown in Tables 3 and 4. In particular, the numbers reported for the NeoDTI method differ from our work with the same dataset. On the one hand, we have reproduced NeoDTI without evaluation strategies. Experiments under various conditions have been carried out more than 5 times. The average level of our reproduced results is lower than the results reported in NeoDTI. NeoDTI followed the evaluation strategies by removing DTIs with similar drugs or similar proteins and so on while we didn't follow the evaluation strategies. Also our comparison procedure extends to the other methods. We conduct comparative experiments without evaluation strategies in this paper. The calculation strategy improves the performance of  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Edge Type** & **Number** \\ \hline Drug-Protein & 1923 \\ Drug-Disease & 199,214 \\ Protein-Disease & 1,596,745 \\ Drug-Drug & 10,036 \\ Protein-Protein & 7363 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Statistics of edges.  
NeoDTI to a certain extent, which should be the main reason for the difference in results from our work. On the other hand, we did not use the drug-side-effect network in the comparative experiment, while the network was used in NeoDTI.  
The AUPR and AUROC metrics were used to evaluate the performance of the above prediction methods. Among the existing methods, LightGCN showed the best performance. Our model improved AUROC by nearly 3% and AUPR by nearly 5% against NeoDTI. In highly skewed datasets, AUPR is usually more informative than AUROC. Since drug discovery is usually a problem like finding a needle in a haystack, the high AUPR score also truly proves the superior performance of GraphMS, compared to other methods.  
The multi-view model is better than the single-view model experimental results. This is because the multi-view model integrates more feature information of drug targets. Higher quality features are extracted through the framework to provide good results for subsequent reconstruction of the matrix. The graph convolutional neural network indicates that the feature prediction result of the node vector extracted by learning is higher than that of the ordinary auto-encoder. This also proves from the side that the graph convolutional neural network has stronger feature expression ability for non-Euclidean data. Compared with the GAT network index, LightGCN has little change, which proves that the graph embedding makes the input graph smoother. The indicators of our model perform well. On the one hand, the addition of mutual information allows the model to consider the strong correlation between the graph-level representation and its subgraph representation. On the other hand, the subgraph embedding can eliminate certain noise in the network.