###### Abstract
We decompose the features of molecular structure graphs into feature vectors corresponding to each odor descriptor and associate these vectors with hypergraph neural networks to explore higher-order semantic interactions. Lateral inhibition mechanism of animal olfaction [9; 10] refers to the process in which active neurons inhibit the activity of adjacent neurons by inhibiting connections. In this process, the most active olfactory processing channels will stand out. The lateral inhibition mechanism of animal olfaction plays a very important role in the sharpening and anti-interference ability of animal olfactory recognition [11]. To imitate the process of animal olfaction, we add a lateral inhibition-inspired attention map to the convolution module of the olfactory perception model. We utilize a convolutional neural network with an olfactory lateral inhibition-inspired attention mechanism to extract the features of the molecular structure graph. In more detail, the lateral inhibition mechanism is embedded in the gradient calculation of the convolutional neural network to improve the anti-interference ability of the model. In the backpropagation of the convolutional neural network, lateral inhibition is introduced into the hidden neurons of the same layer. In order to effectively explore the high-order semantic interactions between odor molecules and odor descriptors and improve the anti-interference ability of the model, we propose to extract the features of the molecular structure graph using the convolutional neural network with a lateral inhibition-inspired attention mechanism, and then decompose the features of the molecular structure graph into feature vectors corresponding to each odor descriptor. These vectors are then associated through the hypergraph neural network to learn molecular structure feature vectors from the odor molecular structure graph. We utilize hypergraph neural networks to learn the relationship between molecular structure graphs and olfactory perception, which can further enhance the ability of expression learning to improve the prediction accuracy of olfactory perception. In addition to the odor molecular structure graphs, \"physicochemical odor space\" also plays a very important role in olfactory perception prediction [12; 13]. For this purpose, a large number (tens of thousands) of chemical and physical molecular properties, so called molecular feature descriptors, are calculated using specific software (e.g., PaDEL-descriptor [14]). In order to deal with the resulting high-dimensional properties space, statistical decomposition is usually used to obtain the most important dimensions [15; 16]. A large number of feature descriptors, such as electronic properties and flexibility of odor molecules, and their complex interactions reveal the drawbacks of traditional deep neural networks. Furthermore, not all feature interactions contribute equally to odor classification. To solve these problems, we add an attentional factorization mechanism to the deep neural network, which can effectively learn the weighted interactions between various features and obtain the molecular descriptive features through the deep feature combination based on the attention mechanism. In this paper, we propose an olfactory perception prediction model, noted as HGAFMN, which is a computational framework for classifying and predicting odor molecules. The features of the molecular structure graph are first extracted by the convolutional neural network with a lateral inhibition-inspired attention mechanism, and the features of the molecular structure graph are decomposed into feature vectors corresponding to each odor descriptor. Then these vectors are associated through the hypergraph neural network to learn the molecular structure features from the odor molecular structure graph.