AttentionDTA: prediction of drug–target binding affinity using attention model

Abstract

In bioinformatics, machine learning-based prediction of drug-target interaction (DTI) plays an important role in virtual screening of drug discovery. DTI prediction, which have been treated as a binary classification problem, depends on the concentration of two molecules, the interaction between two molecules, and other factors. The degree of affinity between a drug molecule (such as a drug compound) and a target molecule (such as a receptor or protein kinase) reflects how tightly the drug binds to a particular target and is quantified by the measurement which can reflect more detailed and specific information than binary relationship. In this study, we proposed an end-to-end model, named AttentionDTA, based on deep learning, which associates attention mechanism to predict the binding affinity of DTI. The novelty in this work is to use attentional mechanisms to consider which subsequences in a protein are more important for a drug and which subsequences in a drug are more important for a protein when predicting its affinity. So that the representational ability of the model is stronger. The model uses one-dimensional Convolution Neural Networks (1D-CNNs) to extract the abstract information of drug and protein, and makes the drug and protein representations mutually adapt through the attention mechanisms. We evaluate our model on two established drug-target affinity benchmark datasets, Davis and KIBA. The model outperforms DeepDTA, a state-of-the-art deep learning method for drug–target binding affinity prediction, with better Mean Squared Error (MSE), Concordance Index (CI), $r_{m}^{2}$ , and Area Under Precision Recall Curve (AUPR). Our results show that the attention-based model can effectively extract effective representations by calculating the weight of the representation between the drug and the protein. Finally, we visualize the attention weight. It proves our model can obtain the information of binding sites.