BN-DTI: A deep learning based sequence feature incorporating method for predicting drug-target interaction

The prediction of drug-target interaction (DTI) is a momentous task in modern medicine. Using efficient computational methods to predict the relationship between drugs and targets can effectively shorten the drug development cycle, reduce the blindness of new drug development, and provide theoretical guidance for biochemical experiments. It also plays a crucial role in drug repurposing, optimizing drug treatment plans, and drug resistance. Due to the vast number of molecules and proteins in the real world, individually screening them is impractical and costly. Therefore, utilizing deep learning to learn the features of proteins and molecules and enable the prediction of drug-target interaction (DTI) has gradually become mainstream. However, most studies extract features using the entire molecular structure and target, which introduces a significant amount of noise and overlooks the complex interactions within crucial components. To address these issues, we propose an end-to-end prediction model called BN-DTI for drug-target interaction. In this model, we extract numerous fragments from the amino acid sequences of drugs and proteins. Then, we employ CNN for feature extraction and utilize a multi-head cross-attention mechanism to learn the interactions between drugs and targets. Finally, the input is fed into the prediction module for prediction. Our model achieved significant results on the Human dataset, C.elegans dataset, and Davis dataset, with AUC values of 0.988, 0.992, and 0.911, respectively. The results show that BN-DTI requires lower training costs and provides new ideas for drug discovery and other fields.