用图注意力网络自动学习分子结构特征，提升嗅觉预测准确性
Molecular Odor Prediction Based on Multi-Feature Graph Attention Networks
- 基于图注意力网络建模分子结构，自动提取局部与全局特征
- 无需预定义描述符，显著提升气味预测准确率
- 适合对化学信息学与深度学习交叉应用感兴趣的读者
嗅觉感知在人类及生物体交互中起关键作用，但其机制与影响因素仍不明确。分子结构通过复杂的生化相互作用影响嗅觉体验，准确量化结构-气味关系面临重大挑战。定量结构-气味关系（QSOR）任务旨在预测分子结构与其对应气味之间的关联。为此，我们提出一种基于图注意力网络的方法，用于建模分子结构并捕捉局部与全局特征。与依赖预定义描述符的传统方法不同，本方法利用多种分子特征提取技术，自动学习全面表征，增强模型处理复杂分子信息的能力，提升预测精度。实验表明，该方法在QSOR预测任务中表现优异，为深度学习在化学信息学中的应用提供重要启示。
原文摘要 · Abstract (English)
Olfactory perception plays a critical role in both human and organismal interactions, yet understanding of its underlying mechanisms and influencing factors remain insufficient. Molecular structures influence odor perception through intricate biochemical interactions, and accurately quantifying structure-odor relationships presents significant challenges. The Quantitative Structure-Odor Relationship (QSOR) task, which involves predicting the associations between molecular structures and their corresponding odors, seeks to address these challenges. To this end, we propose a method for QSOR, utilizing Graph Attention Networks to model molecular structures and capture both local and global features. Unlike conventional QSOR approaches reliant on predefined descriptors, our method leverages diverse molecular feature extraction techniques to automatically learn comprehensive representations. This integration enhances the model's capacity to handle complex molecular information, improves prediction accuracy. Our approach demonstrates clear advantages in QSOR prediction tasks, offering valuable insights into the application of deep learning in cheminformatics.
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