MultiscaleDTA: A multiscale-based method with a self-attention mechanism for drug-target binding affinity prediction

The task of predicting drug-target affinity (DTA) plays an increasingly important role in the early stage of in silico drug discovery and development. Currently, a variety of machine learning-based methods have been presented for DTA prediction and achieved outstanding performance, which is beneficial for speeding up the development of new drugs. However, most convolutional neural networks (CNNs) based methods ignore the significance of information from CNN layers with different scales for DTA prediction. In addition, each feature provides different contributions to the final task. Therefore, in this study, we propose a novel end-to-end deep learning-based framework, MultiscaleDTA, to predict drug-target binding affinity. MultiscaleDTA incorporates multi-scale CNNs and a self-attention mechanism to capture multi-scale and comprehensive features for characterizing the intrinsic properties of drugs and targets. Extensive experimental results on both regression and binary classification tasks demonstrate that MultiscaleDTA achieves competitive performance compared to state-of-the-art methods.