HiF-DTA: Hierarchical Feature Learning Network for Drug-Target Affinity Prediction
This work addresses the need for accurate computational drug discovery to reduce experimental costs, though it appears incremental as it builds on existing sequence-based deep learning methods.
The paper tackled the problem of predicting drug-target affinity by proposing HiF-DTA, a hierarchical network that extracts global and local features from sequences and models drugs at multiple scales, resulting in improved performance over state-of-the-art baselines on datasets like Davis, KIBA, and Metz.
Accurate prediction of Drug-Target Affinity (DTA) is crucial for reducing experimental costs and accelerating early screening in computational drug discovery. While sequence-based deep learning methods avoid reliance on costly 3D structures, they still overlook simultaneous modeling of global sequence semantic features and local topological structural features within drugs and proteins, and represent drugs as flat sequences without atomic-level, substructural-level, and molecular-level multi-scale features. We propose HiF-DTA, a hierarchical network that adopts a dual-pathway strategy to extract both global sequence semantic and local topological features from drug and protein sequences, and models drugs multi-scale to learn atomic, substructural, and molecular representations fused via a multi-scale bilinear attention module. Experiments on Davis, KIBA, and Metz datasets show HiF-DTA outperforms state-of-the-art baselines, with ablations confirming the importance of global-local extraction and multi-scale fusion.