用序列预测药物与蛋白的化学相互作用，无需3D结构
LINKER: Learning Interactions Between Functional Groups and Residues With Chemical Knowledge-Enhanced Reasoning and Explainability
- 仅需蛋白序列和药物分子式，通过功能基团抽象建模
- 在LP-PDBBind上预测准确率接近生化标注真实水平
- 适合无结构数据的药物设计场景，结果可解释性强
精准识别蛋白残基与配体功能基团间的相互作用，对理解分子识别和指导理性药物设计至关重要。现有深度学习方法通常依赖三维结构输入或使用距离接触标签，限制了适用性和生物学相关性。我们提出LINKER，首个基于序列的模型，仅以蛋白序列和配体SMILES为输入，预测残基-功能基团间具有生物定义意义的相互作用类型。该模型采用结构监督注意力机制，通过功能基团驱动的基序提取从3D复合物中生成交互标签。通过将配体结构抽象为功能基团，模型聚焦于化学上有意义的子结构，而非简单空间邻近。关键优势在于推理时仅需序列级输入，可在缺乏结构数据的场景下大规模应用。在LP-PDBBind基准测试中，基于功能基团抽象的结构信息监督使预测结果与真实生化注释高度一致。
原文摘要 · Abstract (English)
Accurate identification of interactions between protein residues and ligand functional groups is essential to understand molecular recognition and guide rational drug design. Existing deep learning approaches for protein-ligand interpretability often rely on 3D structural input or use distance-based contact labels, limiting both their applicability and biological relevance. We introduce LINKER, the first sequence-based model to predict residue-functional group interactions in terms of biologically defined interaction types, using only protein sequences and the ligand SMILES as input. LINKER is trained with structure-supervised attention, where interaction labels are derived from 3D protein-ligand complexes via functional group-based motif extraction. By abstracting ligand structures into functional groups, the model focuses on chemically meaningful substructures while predicting interaction types rather than mere spatial proximity. Crucially, LINKER requires only sequence-level input at inference time, enabling large-scale application in settings where structural data is unavailable. Experiments on the LP-PDBBind benchmark demonstrate that structure-informed supervision over functional group abstractions yields interaction predictions closely aligned with ground-truth biochemical annotations.
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