摘要：根据分子结构预测嗅觉特性是化学信息学领域中一个长期存在的开放难题。尽管线性模型能够将分子特征与气味描述符关联起来，但在推断新型化学骨架、极端分子量或复杂气味混合物时往往表现不佳。为了解决这一问题，我们推出了 GraphNOSE，一个基于简化分子线性输入规范（SMILES）字符串预测单分子和二元混合物多标签气味描述符的开源图 Transformer 框架。通过在基于 Transformer 的图架构中整合位置编码与结构编码，GraphNOSE 仅用标准图神经网络（GNN）基线六分之一的参数量便取得了优异表现，并且持续超越线性模型、分子语言模型嵌入、分子指纹以及基线 GNN，ROC 曲线下面积（AUROC）平均领先 4.52%（p < 0.01）。在分布外化合物（OOD）数据集上，GraphNOSE 取得了 84% 的 AUROC，超越了目前嗅觉 OOD 任务中的 SOTA GNN 模型（Open-POM：81%，p < 0.001），并从实证角度揭示了线性模型失效的边界条件。最后，我们运用可解释 AI（XAI）方法分析了驱动气味预测的关键亚结构和分子特征，得出了既符合化学直觉又立足于模型表征学习的见解。综合上述结果，GraphNOSE 被确立为一个兼具可扩展性与可解释性的嗅觉预测架构，并对当前感知数据库中缺乏代表性的结构迥异化合物展现出了良好的泛化能力。

Abstract:Predicting olfactory qualities from molecular structure is an open problem in chemoinformatics. Although linear models can link molecular features to odor descriptors, they often fail when extrapolating to novel chemical scaffolds, extreme molecular weights, or complex odor mixtures. To address this, we introduce GraphNOSE, an open-source graph transformer framework that predicts multi-label odor descriptors from simplified molecular-input line-entry system (SMILES) strings for single molecules and binary mixtures. By integrating positional and structural encodings within a transformer-based graph architecture, GraphNOSE achieves strong performance with six times fewer parameters than standard graph neural network (GNN) baseline while consistently outperforming linear models, molecular language model embeddings, molecular fingerprints, and baseline GNNs by an average area under the ROC curve (AUROC) margin of 4.52% (p < 0.01). GraphNOSE achieves an AUROC of 84% on out-of-distribution compounds (OODs). This exceeds the current state-of-the-art GNN for OOD in olfaction (Open-POM: 81%, p < 0.001), and identifies conditions under which linear models empirically fail. Finally, we apply XAI (explainable AI) methods to identify which substructures and molecular features drive odor predictions, yielding insights consistent with chemical intuition and grounded in the model's learned representations. Together, these results establish GraphNOSE as a scalable and interpretable architecture for olfactory prediction that generalizes to structurally distinct compounds underrepresented in current perceptual databases.