Fusion-Based Deep Learning Architecture for Detecting Drug-Target Binding Affinity Using Target and Drug Sequence and Structure

Accurately predicting drug-target binding affinity plays a vital role in accelerating drug discovery. Many computational approaches have been proposed due to costly and time-consuming of wet laboratory experiments. In the input representation, most methods only focus on the target sequence properties or target structure properties while ignore the overall contribution. Therefore, we develop a novel fusion protocol based on multiscale convolutional neural networks and graph neural networks, named CGraphDTA, to predict drug-target binding affinity using target sequence and structure. Unlike existing methods, CGraphDTA is the first model constructed with target sequence and structure as input. Concretely, the multiscale convolutional neural networks are utilized to extract target and drug presentation from sequence, graph neural networks are employed to extract graph presentation from target and drug molecular structure. We compare CGraphDTA with the state-of-the-art methods, the results show that our model outperforms the current methods on the test sets. Furthermore, we conduct ablation studies, biological interpretation examination and drug selectivity evaluation, all results suggest that CGraphDTA is a useful tool to predict drug-target binding affinity and accelerate drug discovery.