Comparative Analysis of Attention-based Models for Drug-Target Interaction Prediction

Identifying drug-target interactions is a crucial step in drug discovery and drug repurposing. To efficiently predict these interactions, various computational tools have been developed. Recently, machine learning, especially deep learning, has gained significant attention for predicting drug-target interactions. Deep learning models extract features of both drugs and targets, capturing hidden relationships between them through deep neural networks. In this study, we evaluate the performance of several attention-based models in predicting interactions using four publicly available benchmark datasets. Their performance is assessed with three metrics: the area under the receiver operating characteristic curve, the area under the precision-recall curve, and the F1-score. Experimental results demonstrate that attention-based methods generally exhibit high predictive performance and possess distinct advantages under various conditions.