  Drug-target relationships may now be predicted computationally using
bioinformatics data, which is a valuable tool for understanding pharmacological
effects, enhancing drug development efficiency, and advancing related research.
A number of structure-based, ligand-based and network-based approaches have now
emerged. Furthermore, the integration of graph attention networks with
intricate drug target studies is an application area of growing interest. In
our work, we formulate a model called MKDTI by extracting kernel information
from various layer embeddings of a graph attention network. This combination
improves the prediction ability with respect to novel drug-target
relationships. We first build a drug-target heterogeneous network using
heterogeneous data of drugs and targets, and then use a self-enhanced
multi-head graph attention network to extract potential features in each layer.
Next, we utilize embeddings of each layer to computationally extract kernel
matrices and fuse multiple kernel matrices. Finally, we use a Dual Laplacian
Regularized Least Squares framework to forecast novel drug-target entity
connections. This prediction can be facilitated by integrating the kernel
matrix associated with the drug-target. We measured our model's efficacy using
AUPR and AUC. Compared to the benchmark algorithms, our model outperforms them
in the prediction outcomes. In addition, we conducted an experiment on kernel
selection. The results show that the multi-kernel fusion approach combined with
the kernel matrix generated by the graph attention network provides
complementary insights into the model. The fusion of this information helps to
enhance the accuracy of the predictions.
