HSGCL-DTA: Hybrid-scale Graph Contrastive Learning based Drug-Target Binding Affinity Prediction

Drug-target binding affinity (DTA) is a critical criterion for drug screening. Accurate affinity prediction will significantly cut the cost of new drug development and accelerate the drug discovery process. However, most existing approaches frequently utilize sequence or structure information without incorporating any additional information. At the same time, they encode drugs and targets separately, ignoring the important existing drug-target relationships. In this study, we propose a novel DTA prediction approach, named HSGCL-DTA, which is based on hybrid-scale graph contrastive learning. To completely capture the global information and discriminative properties of the heterogeneous graphs, HSGCL-DTA divides the drug-target affinity graph into two subgraphs with stronger and weaker affinities respectively, and the node embeddings of the two subgraphs are obtained based on node-graph level contrastive learning. Afterwards, graph convolutional network (GCN) is used to encode the molecular graph of drugs and targets, and the node embeddings in the strong affinity subgraph are fused with molecule graph embeddings to fully utilize the distinct information in two different views. Another node-node level contrastive learning is performed between the affinity graph and molecular graphs, thereby filtering out task-independent noise that only appears in one graph. The final drug-target embeddings are put into a multilayer perceptron (MLP) for affinity prediction. Experiments on two widely-used datasets have shown that HSGCL-DTA achieves better prediction performance and generalization than the state-of-the-art DTA prediction methods.