GraphDTA: prediction of drug–target binding affinity using graph convolutional networks

While the development of new drugs is costly, time consuming, and often accompanied with safety issues, drug repurposing, where old drugs with established safety are used for medical conditions other than originally developed, is an attractive alternative. Then, how the old drugs work on new targets becomes a crucial part of drug repurposing and gains much of interest. Several statistical and machine learning models have been proposed to estimate drug–target binding affinity and deep learning approaches have been shown to be among state-of-the-art methods. However, drugs and targets in these models were commonly represented in 1D strings, regardless the fact that molecules are by nature formed by the chemical bonding of atoms. In this work, we propose GraphDTA to capture the structural information of drugs, possibly enhancing the predictive power of the affinity. In particular, unlike competing methods, drugs are represented as graphs and graph convolutional networks are used to learn drug–target binding affinity. We trial our method on two benchmark drug–target binding affinity datasets and compare the performance with state-of-the-art models in the field. The results show that our proposed method can not only predict the affinity better than non-deep learning models, but also outperform competing deep learning approaches. This demonstrates the practical advantages of graph-based representation for molecules in providing accurate prediction of drug–target binding affinity. The application may also include any recommendation systems where either or both of the user- and product-like sides can be represented in graphs.