A Deep Learning Drug-Target Binding Affinity Prediction Based on Compound Microstructure and Its Application in COVID-19 Drug Screening

Drug target relationship (DTR) prediction is a rapidly evolving area of research in computational drug discovery. Despite recent advances in computational solutions that have overcome the challenges of in vitro and in vivo experiments, most computational methods still focus on binary classification. They ignore the importance of binding affinity, which correctly distinguishes between on-targets and off-targets. In this study, we propose a deep learning model based on the microstructure of compounds and proteins to predict drug-target binding affinity (DTA), which utilizes topological structure information of drug molecules and sequence semantic information of proteins. In this model, graph attention network (GAT) is used to capture the deep features of the compound molecular graph, and bidirectional long short-term memory (BiLSTM) network is used to extract the protein sequence features, and the pharmacological context of DTA is obtained by combining the two. The results show that the proposed model has achieved superior performance in both correctly predicting the value of interaction strength and correctly discriminating the ranking of binding strength compared to the state-of-the-art baselines. A case study experiment on COVID-19 confirms that the proposed DTA model can be used as an effective pre-screening tool in drug discovery.