发表机构
Beijing University of Technology; China Academy of Chinese Medical Sciences; National University of Singapore; University of New South Wales（北京工业大学; 中国中医科学院; 新加坡国立大学; 新南威尔士大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
ProbeMatchDTI是含IterProbe和BindingProbe的探针驱动框架，用于解决DTI预测中微弱生化模式被抑制的问题，在两个数据集上提升AUC-ROC，且可用于药物发现候选物优化。
AI 中文摘要
药物-靶点相互作用（DTI）预测是AI驱动药物发现中的重要任务。尽管近期的生化表示学习方法提升了DTI预测性能，但它们的被动特征聚合倾向于优先考虑主导分子模式，同时抑制与结合相关的微弱信号，如官能团和残基上下文模式，这限制了对多尺度生化对应关系的建模。为解决该问题，我们提出ProbeMatchDTI，这是一种包含IterProbe和BindingProbe的模式探针驱动框架。IterProbe在细化深度间显式保留上下文状态，并使用可学习探针在每个位置选择这些状态，再进行跨实体匹配，从而保留微弱生化模式并加强官能团、局部基序和分子支架间的关联。BindingProbe随后在局部生化单元和整体配对层面表征跨实体药物-蛋白互补性，联合建模细粒度相互作用和多尺度对应关系，同时保留更微弱的结合相关关联。大量实验表明ProbeMatchDTI具有优越性，其在BindingDB和DrugBank上的AUC-ROC分别提升了2.0%和0.5%。特征层面的模式分析进一步明确了其在跨尺度生化模式匹配中的探针驱动行为。我们还将ProbeMatchDTI的预测与证据引导的下游药物发现工作流关联，证明其在候选物优化和验证规划中的实用性。我们的代码可在该https URL获取。
英文摘要
Drug-target interaction (DTI) prediction is an important task in AI-driven drug discovery. Although recent biochemical representation learning methods have improved DTI prediction, their passive feature aggregation tends to favor dominant molecular patterns while suppressing weak yet binding-relevant signals, such as functional groups and residue-context patterns, limiting the modeling of multi-scale biochemical correspondences. To address this issue, we propose ProbeMatchDTI, a pattern-probe-driven framework comprising IterProbe and BindingProbe. IterProbe explicitly retains contextual states across refinement depths and uses learnable probes to select them at each position before cross-entity matching, thereby preserving weak biochemical patterns and strengthening associations among functional groups, local motifs, and molecular scaffolds. BindingProbe then characterizes cross-entity drug-protein complementarity at local biochemical-unit and whole-pair levels, jointly modeling fine-grained interactions and multi-scale correspondences while preserving weaker binding-relevant associations. Extensive experiments demonstrate the superiority of ProbeMatchDTI, achieving 2.0% and 0.5% higher AUC-ROC on BindingDB and DrugBank, respectively. Feature-level pattern analyses further characterize its probe-driven behavior in cross-scale biochemical pattern matching. We further connect ProbeMatchDTI predictions with an evidence-guided downstream drug-discovery workflow, demonstrating their utility for candidate refinement and validation planning. Our code is available at https://github.com/developer-hq/ProbeMatchDTI
