Hierarchical graph representation learning for the prediction of drug-target binding affinity

Computationally predicting drug-target binding affinity (DTA) has attracted increasing attention due to its benefit for accelerating drug discovery. Currently, numerous deep learning-based prediction models have been proposed, often with a biencoder architecture that commonly focuses on how to extract expressive representations for drugs and targets but overlooks modeling explicit drug-target interactions. However, known DTA can provide underlying knowledge about how the drugs interact with targets that is beneficial for predictive accuracy. In this paper, we propose a novel hierarchical graph representation learning model for DTA prediction, named HGRL-DTA. The main contribution of our model is to establish a hierarchical graph learning architecture to integrate the coarse- and fine-level information from an affinity graph and drug/target molecule graphs, respectively, in a well-designed coarse-to-fine manner. In addition, we design a similarity-based representation inference method to infer coarse-level information when it is unavailable for new drugs or targets under the cold start scenario. Comprehensive experimental results under four scenarios across two benchmark datasets indicate that HGRL-DTA outperforms the state-of-the-art models in almost all cases.