Graph neural network approaches for drug-target interactions

Developing new drugs remains prohibitively expensive, time-consuming, and often involves safety issues. Accurate prediction of drug-target interactions (DTIs) can guide the drug discovery process and thus facilitate drug development. Non -Euclidian data such as drug-like molecule structures, key pocket residue structures, and protein interaction networks can be represented effectively using graphs. Therefore, the emerging graph neural network has been rapidly applied to predict DTIs, and proved effective in finding repositioning drugs and accelerating drug discovery. In this review, we provide a brief overview of deep neural networks used in DTI models. Then, we summarize the database required for DTI prediction, followed by a comprehensive introduction of applications of graph neural networks for DTI prediction. We also highlight current challenges and future directions to guide the further development of this field.

Automated summary

This article provides an overview of the application of deep neural networks and graph neural networks in drug-target interaction (DTI) prediction. The use of graph neural networks has proven effective in predicting DTIs, finding repositioning drugs, and accelerating drug discovery. The article also highlights current challenges and future directions for further development in this field.