The application of deep learning techniques on aroma-chemicals has resulted in models more accurate than human experts at predicting olfactory qualities. However, public research in this domain has been limited to predicting the qualities of single molecules, whereas in industry applications, perfumers and food scientists are often concerned with blends of many odorants. In this paper, we apply both existing and novel approaches to a dataset we gathered consisting of labeled pairs of molecules. We present a publicly available model capable of generating accurate predictions for the non-linear qualities arising from blends of aroma-chemicals.

翻译：深度学习技术在香气化学物上的应用，已产生比人类专家更准确预测嗅觉特质的模型。然而，该领域的公开研究仅限于预测单一分子的品质，而在工业应用中，调香师和食品科学家通常关注多种气味剂的混合物。本文采用现有及新颖方法，对我们收集的由标注分子对组成的数据集进行处理。我们提出一个公开可用的模型，能够对香气化学物混合物所产生的非线性特质进行准确预测。