LINKER：通过化学知识增强的推理与可解释性学习官能团与残基之间的相互作用
LINKER: Learning Interactions Between Functional Groups and Residues With Chemical Knowledge-Enhanced Reasoning and Explainability
AI总结：
针对现有蛋白质-配体相互作用可解释性方法依赖三维结构、适用性受限的问题，提出首个仅需序列输入的LINKER模型，通过结构监督注意力预测生物学定义的相互作用类型，在LP-PDBBind基准上取得与真实标注高度一致的结果。
摘要：
准确识别蛋白质残基与配体官能团之间的相互作用，对于理解分子识别和指导合理药物设计至关重要。现有的蛋白质-配体可解释性深度学习方法通常依赖三维结构输入或使用基于距离的接触标签，这既限制了其适用性，也降低了生物学相关性。我们提出了LINKER，这是首个仅以蛋白质序列和配体SMILES为输入、按生物学定义的相互作用类型预测残基-官能团相互作用的基于序列的模型。LINKER采用结构监督注意力机制训练，其中相互作用标签通过基于官能团的基序提取从三维蛋白质-配体复合物中推导而来。通过将配体结构抽象为官能团，该模型聚焦于具有化学意义的子结构，同时预测相互作用类型而非仅空间邻近性。关键的是，LINKER在推理时仅需序列级输入，可在无结构数据的场景下实现大规模应用。在LP-PDBBind基准上的实验表明，基于官能团抽象的结构信息监督可产生与真实生化标注高度一致的相互作用预测结果。
英文摘要：
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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