Predicting drug–target binding affinity with graph neural networks

Abstract Background The development of new drugs is costly, time consuming, and often accompanied with safety issues. Drug repurposing can avoid the expensive and lengthy process of drug development by finding new uses for already approved drugs. In order to repurpose drugs effectively, it is useful to know which proteins are targeted by which drugs. Computational models that estimate the interaction strength of new drug–target pairs have the potential to expedite drug repurposing. Several models have been proposed for this task. However, these models represent the drugs as strings, which are not a natural way to represent molecules. Methods We propose a new model called GraphDTA that represents drugs as graphs and uses graph neural networks to predict drug–target affinity. We test 4 graph neural network variants, including GCN, GAT, GIN, and a combined GAT-GCN architecture, for the task of drug–affinity prediction. We benchmark the performance of these models on the Davis and Kiba datasets. Results We show that graph neural networks not only predict drug–target affinity better than non-deep learning models, but also outperform competing deep learning methods. Of note, the GIN method performs consistently well for two separate benchmark datasets and for two key performance metrics. In a post-hoc analysis of our model, we find that a graph neural network can learn the importance of known molecular descriptors without any prior knowledge. We also examine the model’s performance and find that a handful of drugs contribute disproportionately to the total prediction error. Conclusions Our results confirm that deep learning models are appropriate for drug–target binding affinity prediction, and that representing drugs as graphs can lead to further improvements. Although we focus on drug–target affinity prediction, our GraphDTA model is a generic solution for any collaborating filtering or recommendation problem where either data input can be represented as a graph.