用结构先验提升蛋白表示，实现高效精准的药物靶点相互作用预测
Structure-Aware Contrastive Learning with Fine-Grained Binding Representations for Drug Discovery
- 融合蛋白质结构先验信息，通过双线性注意力与对比学习构建细粒度结合表征
- 在多个数据集上达到最优性能，虚拟筛选中AUROC和BEDROC显著提升
- 结果可解释性强，能对齐已知结合口袋，适合药物发现场景
准确识别药物-靶点相互作用（DTI）仍是计算药理学的核心挑战，序列方法具有可扩展性优势。本文提出一种基于序列的药物-靶点相互作用框架，将结构先验融入蛋白表征，同时保持高通量筛选能力。在多个基准测试中，该模型在Human和BioSNAP数据集上达到当前最佳表现，在BindingDB上也保持竞争力。在虚拟筛选任务中，其在LIT-PCBA上优于先前方法，显著提升AUROC和BEDROC指标。消融实验确认了学习聚合、双线性注意力和对比对齐在增强预测鲁棒性中的关键作用。嵌入可视化显示模型能更好对应已知结合口袋，并揭示出对配体-残基接触的可解释注意力模式。结果验证了该框架在可扩展且结构感知的DTI预测中的有效性。
原文摘要 · Abstract (English)
Accurate identification of drug-target interactions (DTI) remains a central challenge in computational pharmacology, where sequence-based methods offer scalability. This work introduces a sequence-based drug-target interaction framework that integrates structural priors into protein representations while maintaining high-throughput screening capability. Evaluated across multiple benchmarks, the model achieves state-of-the-art performance on Human and BioSNAP datasets and remains competitive on BindingDB. In virtual screening tasks, it surpasses prior methods on LIT-PCBA, yielding substantial gains in AUROC and BEDROC. Ablation studies confirm the critical role of learned aggregation, bilinear attention, and contrastive alignment in enhancing predictive robustness. Embedding visualizations reveal improved spatial correspondence with known binding pockets and highlight interpretable attention patterns over ligand-residue contacts. These results validate the framework's utility for scalable and structure-aware DTI prediction.
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