DeepMCL-DTI: predicting drug-target interactions using multi-channel deep learning with attention mechanism

Drug-target interaction (DTI) prediction is crucial for drug discovery. Deep learning has been extensively utilized to reduce costs and expedite this process. However, most existing methods employ a two-channel architecture that separately constructs feature extraction networks for the drug and the target. These approaches fail to fully leverage the original input data and are unable to completely learn the features from it. In this study, we propose DeepMCL-DTI, an attention-based multi-channel deep learning model with four feature extraction channels: Graph Sample and Aggregate and convolutional neural network for drug features, and ProtBert and bidirectional convolutional long short-term memory for protein features. An interact-attention module models drug-target interactions across both spatial and channel dimensions. Extensive experiments conducted on the DrugBank and Davis datasets demonstrate that DeepMCL-DTI outperforms state-of-the-art methods. A case study on the angiotensin-converting enzyme 2 receptor further confirms its effectiveness as a pre-screening tool for drug discovery.