NG-DTA: Drug-target affinity prediction with n-gram molecular graphs

Abstract

Drug-target affinity (DTA) prediction is crucial to speed up drug development. The advance in deep learning allows accurate DTA prediction. However, most deep learning methods treat protein as a 1D string which is not informative to models compared to a graph representation. In this paper, we present a deep-learning-based DTA prediction method called N-gram Graph DTA (NG-DTA) that takes molecular graphs of drugs and n-gram molecular sub-graphs of proteins as inputs which are then processed by graph neural networks (GNNs). Without using any prediction tool for protein structure, NG-DTA performs better than other methods on two datasets in terms of concordance index (CI) and mean square error (MSE) (CI: 0.905, MSE: 0.196 for the Davis dataset; CI: 0.904, MSE: 0.120 for Kiba dataset). Our results showed that using n-gram molecular sub-graphs of proteins as input improves deep learning models' performance in DTA prediction.