通过可学习探针捕捉弱信号，提升药物靶点相互作用预测精度
ProbeMatchDTI: Probe-Driven Multi-Scale Biochemical Pattern Matching for Drug-Target Interaction Prediction
- 设计双探针框架，显式保留多尺度生化模式的上下文信息
- 在BindingDB和DrugBank上分别提升2.0%和0.5%的AUC-ROC
- 适合需要精准识别弱结合信号的药物发现研究者
药物-靶点相互作用（DTI）预测是人工智能驱动药物发现中的关键任务。尽管近期生化表征学习方法提升了预测性能，但其被动特征聚合方式倾向于强化主导分子模式，抑制功能基团和残基上下文等弱但与结合相关的信号，限制了多尺度生化对应关系的建模。为此，我们提出ProbeMatchDTI，一个由IterProbe和BindingProbe组成的模式探针驱动框架。IterProbe在迭代优化深度中显式保留上下文状态，并在每个位置使用可学习探针选择状态后进行跨实体匹配，从而保留弱生化模式并增强功能基团、局部基序与分子骨架间的关联。BindingProbe则在局部生化单元与整体配对层次联合建模药物-蛋白互补性，同时保持较弱的结合相关关联。大量实验表明，ProbeMatchDTI表现更优，在BindingDB和DrugBank上分别实现2.0%和0.5%更高的AUC-ROC。特征级模式分析进一步揭示其跨尺度生化模式匹配的探针驱动机制。我们还将预测结果接入证据引导的下游药物发现流程，验证其在候选物筛选与验证规划中的实用性。代码已开源。
原文摘要 · Abstract (English)
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
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