A Heterogeneous Ranking Contrastive Learning Method for Drug-Target Interaction Prediction

Recently, with the in-depth study of biological network structures, methods such as graph neural networks and graph contrastive learning have attracted significant attention and demonstrated notable advantages in DTI prediction. Nevertheless, it remains a challenging task that predicting new DTIs by a small number of known data in heterogeneous biological networks. Graph contrastive learning as an effective method to solute this issue has been proposed recently. Although contrastive learning have shown significant advantages, the objective function heavily relies on unbiased positive and negative samples. Inspired by this issue, a heterogeneous ranking contrastive learning method (HRCL-DTI) for DTI prediction is proposed. Specifically, multiple graph encoders are employed to capture the topological relationships in heterogeneous biological networks. After that, the prediction scores are calculated in the ranking module of HRCL-DTI to select reliable positive and negative samples for the heterogeneous contrastive learning module, aiming to enhance the consistency of DTI representations. Experimental results demonstrate that HRCL-DTI outperforms existing stateof-the-art baselines on multiple datasets and it possesses strong generalization ability and practical effectiveness.