提出分层网络，同时捕捉药物与靶标序列的全局语义和局部结构特征。
HiF-DTA: Hierarchical Feature Learning Network for Drug-Target Affinity Prediction
- 双路径架构分别提取全局语义与局部拓扑特征
- 在Davis、KIBA、Metz数据集上优于现有模型
- 适合药物-靶点亲和力预测研究者使用
准确预测药物-靶点亲和力（DTA）对降低实验成本、加速早期筛选至关重要。尽管基于序列的深度学习方法避免了对昂贵3D结构的依赖，但仍未能同时建模药物与蛋白质序列中的全局语义特征与局部拓扑结构特征，且将药物表示为扁平序列，缺乏原子级、子结构级与分子级的多尺度特征。我们提出HiF-DTA，一种分层网络，采用双路径策略从药物和蛋白质序列中提取全局语义与局部拓扑特征，并通过多尺度双线性注意力模块融合原子、子结构和分子层级的药物表示。在Davis、KIBA和Metz数据集上的实验表明，HiF-DTA优于当前最优基线模型；消融实验确认了全局-局部特征提取与多尺度融合的重要性。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。