通过关注蛋白质多层级结构，提升新药与靶点相互作用的预测精度。
Attending on Multilevel Structure of Proteins enables Accurate Prediction of Cold-Start Drug-Target Interactions
- 用分层注意力机制捕捉蛋白质从一级到四级结构与药物的多层次交互。
- 在多个基准数据集上，冷启动场景下性能优于现有方法。
- 适合需要精准预测新药靶点关系的研究者，尤其在缺乏历史数据时。
冷启动药物-靶点相互作用（DTI）预测旨在预测全新药物与蛋白质之间的相互作用。以往方法通常学习药物与蛋白质结构间的可迁移相互作用模式来应对该问题。然而，蛋白质组学研究提示，蛋白质具有多层级结构，且各级结构均影响其相互作用。现有工作通常仅以一级结构表示蛋白质，限制了对涉及高级结构相互作用的捕捉能力。受此启发，我们提出ColdDTI框架，通过关注蛋白质的多层级结构进行冷启动DTI预测。采用分层注意力机制，在局部与全局粒度上挖掘蛋白质（从一级至四级结构）与药物结构间的交互关系；随后利用这些挖掘出的交互信息，融合不同层级的结构表征以实现最终预测。该设计捕捉了生物学上可迁移的先验知识，避免了过度依赖表示学习带来的过拟合风险。在多个基准数据集上的实验表明，ColdDTI在冷启动设置下持续优于先前方法。
原文摘要 · Abstract (English)
Cold-start drug-target interaction (DTI) prediction focuses on interaction between novel drugs and proteins. Previous methods typically learn transferable interaction patterns between structures of drug and proteins to tackle it. However, insight from proteomics suggest that protein have multi-level structures and they all influence the DTI. Existing works usually represent protein with only primary structures, limiting their ability to capture interactions involving higher-level structures. Inspired by this insight, we propose ColdDTI, a framework attending on protein multi-level structure for cold-start DTI prediction. We employ hierarchical attention mechanism to mine interaction between multi-level protein structures (from primary to quaternary) and drug structures at both local and global granularities. Then, we leverage mined interactions to fuse structure representations of different levels for final prediction. Our design captures biologically transferable priors, avoiding the risk of overfitting caused by excessive reliance on representation learning. Experiments on benchmark datasets demonstrate that ColdDTI consistently outperforms previous methods in cold-start settings.
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。