BiTGNN: prediction of drug-target interactions based on bidirectional transformer and graph neural network on heterogeneous graph
Keywords: DTI prediction, bidirectional transformer, graph attention network, graph convolutional neural network
Abstract: Drug-target interaction (DTI) is a widely explored topic in the field of bioinformatics and plays a pivotal role in drug discovery. However, the traditional bio-experimental process of drug-target interaction identification requires a large investment of time and labor. To address this challenge, graph neural network (GNN) approaches in deep learning are becoming a prominent trend in the field of DTI research, which is characterized by multimodal processing of data, feature learning and interpretability in DTI. Nevertheless, some methods are still limited by homogeneous graphs and single features. To address the problems we mechanistically analyze graph convolutional neural networks (GCN) and graph attentional neural networks (GAT) in order to propose a new model for drug-target interaction prediction based on graph neural networks named BiTGNN (bidirectional transformer and graph neural network). The method first establishes drug-target pairs through the pseudo-position specificity scoring matrix (PsePSSM) and drug fingerprint data, and constructs a heterogeneous network by utilizing the relationship between the drug and the target. Then, the computational extraction of drug and target attributes is performed using GCN and GAT for the purpose of model information flow extension and graph information enhancement. We collect interaction data using the proposed Bi-directional transformer (Bi-transformer) architecture, in which we design a bi-directional cross-attention mechanism for calculating the effects of drug-target interactions for realistic biological interaction simulations. Finally, a feed-forward neural network is used to obtain the feature matrices of the drug and the target, and DTI prediction is performed by fusing the two feature matrices. The Enzyme, Ion Channel (IC) , G Protein-coupled Receptor (GPCR) , and Nuclear Receptor (NR) datasets are used in the experiments, and compared with several existing mainstream models, our model outperforms the others in Area Under the Curve (AUC), Area Under the Precision-Recall Curve (AUPR) , Accuracy and Specificity metrics.
Primary Area: applications to physical sciences (physics, chemistry, biology, etc.)
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Submission Number: 2454
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