MHAN-DTA: A Multiscale Hybrid Attention Network for Drug-Target Affinity Prediction

Drug-target affinity prediction is a key challenge in the drug discovery process. Recent advances have demonstrated the great potential of deep learning in predicting affinities; however, existing approaches learn the representation of drug-target complex insufficiently, leading to suboptimal performance. Here, we propose a Multiscale Hybrid Attention Network for the Drug-Target Affinity prediction, named MHAN-DTA, which aims to address the problem of insufficient feature mining thereby improving the prediction performance. To empower the model with global perception ability, a pocket-oriented feature aggregation and extraction module is developed based on self-attention mechanisms, together with a hierarchical strategy applied to the target proteins. We further introduce a cross-modal fusion module and a cross-entity interaction module for mining the multiscale intra-cellular and inter-cellular features within the binding sites. Comprehensive evaluations on four benchmark test sets, including an internal and three external benchmark datasets, demonstrate that the proposed approach achieves superior and robust performance. Our code is publicly available at https://github.com/anxiangbiye1231/MHAN-DTA