HierL2GDTA: Hierarchical Interaction Modeling with Local and Global Features for Drug–Target Affinity Prediction

Accurate prediction of drug–target binding affinity is essential for drug discovery. However, existing methods face limitations in capturing local and global molecular features and in effectively integrating protein and ligand representations for interaction modeling. Here, we present HierL2GDTA, a sequence-based hierarchical framework for predicting binding affinity. Pretrained protein and molecular representations are processed by multi-scale convolutions to extract local features, followed by a bidirectional gated recurrent unit and dilated convolutions to capture broader protein and ligand context, respectively. A modified Transformer encoder integrates global protein context and ligand features through sequential cross-attention to model intramolecular and intermolecular dependencies. HierL2GDTA outperformed the evaluated baseline methods on both BindingDB and Davis. Further evaluations demonstrated the effectiveness of the model components and favorable predictive performance across selected kinase families. Attention visualization highlighted molecular substructures associated with protein binding, while retrospective screening ranked known inhibitors of disease-associated targets among the leading candidates. These results indicate that HierL2GDTA is an effective deep learning method for binding affinity prediction, with potential application for target-based drug screening.