###### Abstract  
Understanding the relationship between the chemical structure and physicochemical properties of odor molecules and olfactory perception, i.e., the structure-odor relationship, remains a decades-old, challenging task. However, the differences among the molecular structure graphs of different molecules are subtle and complex, and the molecular feature descriptors are numerous, with complex interactions that cause multiple odor perceptions. In this paper, we propose to decompose the features of the molecular structure graph into feature vectors corresponding to each odor perception descriptor to effectively explore higher-order semantic interactions between odor molecules and odor perception descriptors. We propose an olfactory perception prediction model noted as HGAFMN, which utilizes a hypergraph neural network with the olfactory lateral inhibition-inspired attention mechanism to learn the molecular structure feature from the odor molecular structure graph. Furthermore, existing methods cannot effectively extract interactive features in the large number of molecular feature descriptors, which have complex relations. To solve this problem, we add an attentional factorization mechanism to the deep neural network module and obtain a molecular descriptive feature through the deep feature combination based on the attention mechanism. Our proposed HGAFMN has achieved good results in extensive experiments and will help product design and quality assessment in the food, beverage, and fragrance industries. olfactory perception; quantitative structure-odor relationship; hypergraph neural network; attentional factorization mechanism; lateral inhibition +
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However, this graph structure construction method is less effective for predicting olfactory perception of molecules with similar structures but different odors. This problem can be better solved by considering the \"physicochemical odor space\", i.e., adding molecular feature descriptors that are important for odor prediction. The work [6] combines a simple deep neural network (DNN) and Xception-based convolutional neural network (CNN) to predict odor descriptors using molecular feature descriptors and molecular structure graphs and achieves relatively good results. However, the differences among the structural graphs of different molecules are subtle and complex. For example, single, double, and triple chemical bonds are likely to be identified inaccurately in complex molecules with different odors. In addition, the structural complexity of different molecules varies widely, some are very simple and some are very complex. In the field of olfactory perception, each odor molecule has multiple odor descriptors, and each odor descriptor also corresponds to multiple odor molecules. The olfactory perception prediction problem can be regarded as a multi-label classification problem. Unlike single-label classification, multi-label tasks are more challenging because of two problems: the association of multi-labels with regions in the molecular structure graph, and the correlation between multi-labels. In recent years, graph neural networks have attracted extensive attention in various research fields and achieved remarkable performance. Most of the graph neural network models assume that the research objects are pairwise relationships and cannot model higher-order semantic dependencies. However, in the field of olfactory perception, the relationship between odor molecules and odor descriptors is high-order and goes beyond the pairwise relationship, so the simple graph structure cannot adequately describe this kind of relationship. Hypergraph [7; 8] is more convenient for modeling higher-order data, so we model odor molecule-odor descriptor relationship data as a hypergraph structure that can better encode higher-order data correlations. To the best of our knowledge, there is no work on modeling this relation as a hypergraph structure.