Predicting the relationship between a molecule's structure and its odor
remains a difficult, decades-old task. This problem, termed quantitative
structure-odor relationship (QSOR) modeling, is an important challenge in
chemistry, impacting human nutrition, manufacture of synthetic fragrance, the
environment, and sensory neuroscience. We propose the use of graph neural
networks for QSOR, and show they significantly out-perform prior methods on a
novel data set labeled by olfactory experts. Additional analysis shows that the
learned embeddings from graph neural networks capture a meaningful odor space
representation of the underlying relationship between structure and odor, as
demonstrated by strong performance on two challenging transfer learning tasks.
Machine learning has already had a large impact on the senses of sight and
sound. Based on these early results with graph neural networks for molecular
properties, we hope machine learning can eventually do for olfaction what it
has already done for vision and hearing.

本研究利用图神经网络方法，提出了一种基于分子结构与气味之间关系的定量分析模型，并通过实验表明我们的方法在气味分类上较之以往具有更高的性能，同时证明图神经网络的嵌入学习能够提取出有意义的气味空间向量表示，对气味的相关应用具有启示性。