Enhancing Cross-scale Feature Mutual Information via Heterogeneous Graph Contrastive Learning for Drug-Target Binding Affinity Prediction.

Predicting drug-target binding affinity is a crucial step in drug discovery, with the aim of estimating the strength of interactions between unknown drug-target pairs. In recent years, with the advancement of graph neural networks and self-supervised learning, numerous novel approaches have been proposed by researchers. However, these methods still exhibit the following drawbacks: 1) They only consider drug-target interaction information when learning relationship features, resulting in low information density and an inability to obtain high-quality representations. 2) The integration of cross-scale features is rigid, with features across different scales showing no correlation, failing to reflect the consistency of the same entity's features across different perspectives. 3) They employ outdated Graph Contrastive Learning (GCL) strategies, which have been proven to yield highly biased estimates. Therefore, to address these challenges, we propose a method that Enhances cross-scale feature Mutual information via Heterogeneous Graph Contrastive Learning for predicting Drug-Target binding Affinity (EMHGCL-DTA). The proposed model reconstructs the relationship network on the macro scale, incorporating similarity information while preserving drug-target interaction data. Furthermore, to establish a connection between macro-scale and micro-scale features, our similarity information is derived directly from the molecular structures of drugs and targets. In addition, we introduce a pertinent unbiased feature augmentation strategy to enhance the representations of drugs and targets. The novelty and effectiveness of the EMHGCL-DTA model are validated on two datasets, where it outperforms state-of-the-art methods.