HGNA-HTI: Heterogeneous graph neural network with attention mechanism for prediction of herb-target interactions

Herb-target interactions (HTIs) prediction plays a key role in exploring the mechanism of Traditional Chinese Medicine (TCM). There have been many studies on the prediction of drug-target interactions. However, these methods cannot be directly applied to TCM, which has the characteristics of multiple-component, multiple-target and multiple-pathway. Although network-based methods began to be used to study the prediction of HTIs, the aggregate information from the high-order neighborhood on the heterogeneous herb-target network is underutilized. Therefore, this paper proposes a Heterogeneous Graph Neural Network with Attention Mechanism for Prediction of Herb-Target Interactions (HGNA-HTI). Specifically, based on the heterogeneous herb-target graph, HGNA-HTI uses attention mechanism to give high attention values to important nodes and edges based on different types of meta relations, and applies message passing process to incorporate information from different types of links. Moreover, HGNA-HTI aggregates the above information to extract the semantic information and high-level structure of the heterogeneous herb-target graph to obtain the final feature representation to enhance the ability for prediction of HTIs. The experiment results on two datasets, show that the HGNA-HTI model is better than state-of-the-art approaches.