arXiv cs.AI· Huangchao Xu, Baohua Zhang, Qian Liu, Zhong Jin, Yi Du·· 12 小时前AI 评分22
HierDTA：面向药物-靶点亲和力预测的层次结构感知表示学习框架
HierDTA: Hierarchical Structure-Aware Representation Learning for Drug--Target Affinity Prediction
AI 导读
HierDTA 是一个层次结构感知的药物-靶点亲和力（DTA）预测框架，将药物表示为原子-基序异质图并把激酶域结构转为残基交互图，通过交叉注意力整合原子与残基表示。
正文 · 原文
Abstract:Drug--target affinity (DTA) prediction plays an important role in computer-aided drug discovery, where reliable computational models can help prioritize compound--protein pairs before experimental validation. A key challenge in DTA prediction is to effectively mine multi-level molecular information from drug structures while accurately modeling the binding-site environment of target proteins. To address these issues, we introduce HierDTA, a hierarchical structure-aware framework for DTA prediction. \textcolor{black}{HierDTA represents each drug as an atom--motif heterogeneous graph and introduces an augmentation module that propagates functional-motif semantics to the corresponding atoms. For proteins, kinase-domain structures are converted into residue interaction graphs. The resulting atom and residue representations are integrated using cross-attention to capture pair-specific relationships, while molecule fingerprints and pretrained ESM-2 embeddings provide complementary global information. Compared with state-of-the-art methods, HierDTA achieves superior performance on the Davis dataset across all metrics and attains the highest \(r_m^2\) of 0.791 on KIBA. It also exhibits strong generalization under cold-start settings, particularly on KIBA, where MSE is reduced by 21.7\% in the unseen-protein scenario. Ablation study and interpretability analysis supported the contributions of the hierarchical augmentation and interaction components.} Case studies further showed that HierDTA provided early enrichment of candidate bioactive compounds. These results indicate that HierDTA is an effective framework for affinity prediction and target-specific virtual screening.
| Comments: | 20 pages, 7 figures |
| Subjects: | Machine Learning (cs.LG); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2610.27001 [cs.LG] |
| (or arXiv:2610.27001v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2610.27001 arXiv-issued DOI via DataCite |
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| Journal reference: | ACS Omega,2026 |
| Related DOI: | https://doi.org/10.1021/acsomega.6c07956
DOI(s) linking to related resources |
Submission history
From: Baohua Zhang [view email]
[v1]
Mon, 19 Oct 2026 09:17:26 UTC (4,490 KB)
来源：arXiv cs.AI · arxiv.org