Adaptive Feature Selection With Hierarchical Learning for Drug-Target Interaction Prediction

Accurate prediction of drug-target interactions (DTIs) is essential for drug discovery and repurposing. Although deep learning has driven substantial progress, critical limitations remain: a singular focus on intermolecular associations results in suboptimal representation learning, and the failure to leverage key features during interactions constrains further performance gains. Here, we propose ASHL-DTI, a novel framework that integrates hierarchical learning with adaptive feature selection to significantly boost both feature quality and model generalizability. Specifically, the hierarchical learning component captures multi-level intramolecular associations to learn more discriminative representations. Simultaneously, we incorporate an adaptive Top-k selection mechanism to retain the most predictive features, facilitating effective interaction between drugs and targets. Experimental results across multiple public benchmark datasets demonstrate that ASHL-DTI achieves superior performance compared with state-of-the-art approaches. Moreover, ASHL-DTI exhibits strong generalization ability in predicting novel drug-target pairs, underscoring its potential in drug discovery. The complete source code of ASHL-DTI is available at https://github.com/Miwkwh/ASHL-DTI.