Multi-scale Feature Fusion Neural Network for Accurate Prediction of Drug-Target Interactions

Identification of drug-target interactions (DTI) is crucial in drug discovery and repositioning. However, identifying DTI is a costly and time-consuming process that involves conducting biological experiments with a vast array of potential compounds. To accelerate this process, computational methods have been developed, and with the growth of available datasets, deep learning methods have been widely applied in this field. Despite the emergence of numerous sequence-based deep learning models for DTI prediction, several limitations endure. These encompass inadequate feature extraction from protein targets using amino acid sequences, a deficiency in effective fusion mechanisms for drug and target features, and a prevalent inclination among many methods to solely treat DTI as a binary classification problem, thereby overlooking the crucial aspect of predicting binding affinity that signifies the strength of drug-target interactions. To address these concerns, we developed a multi-scale feature fusion neural network (MSF-DTI), which leverages the potential semantic information of amino acid sequences at multiple scales, enriches the feature representation of proteins, and fuses drug and target features using a designed feature fusion module for predicting drug-target interactions. According to experimental results, MSF-DTI outperforms other state-of-the-art methods in both DTI classification and binding affinity prediction tasks.