## 2 Related Work  
### Quantitative Structure-Odor Relationship  
In the human cerebral cortex, the interpretation of neural signals produces a representation called \"odor\", which is described semantically by various perceptual descriptors, such as \"floral\", \"sulphhurous\", etc. The construction of olfactory perception prediction models, especially the quantitative structure-odor relationship (QSOR) prediction, has always been one of the difficulties in the field of olfactory research. Early research suggested that human perception of odor is determined by different molecular structures, such as functional groups, molecular size, carbon chain length, and covalent bond type [17], and reached some meaningful conclusions. For example, sulfur atoms in odor molecules are highly correlated with odor descriptors such as \"garlic\" and \"fishy\". Furthermore, the more complex the molecular structure, the stronger the pleasant properties of the smell [18]. However, there are many physicochemical properties of odor molecules, and the relationships between them and olfactory perception are complex nonlinear relationships. Therefore, the development of QSOR is currently slow. With the development of computer technology, QSOR has gradually attracted people's attention and has become a research hotspot. Data-driven olfactory perception prediction methods need to be researched on the basis of olfactory-related chemical or biological information. Input features for these studies include chemical molecular descriptors (CMD), electronic nose signals, gas chromatography (GC), electroencephalogram (EEG), physiological signals, and so on. With the rapid development of machine learning and deep learning, the application of deep neural networks in many fields has attracted great attention. In 2015, the DREAM Olfaction Prediction Challenge was successfully held. A total of 476 odorants (i.e., the well-known DREAM dataset) were provided in the challenge, and the 4884-dimensional chemical molecular feature descriptors (CMDs) of these odorants were used to score 21 odor perception descriptors for individuals and groups. Subsequently, [PERSON] et al. [4] published a related study of the random forest-based prediction model, which won the championship in the DREAM Olfaction Prediction Challenge. It uses random forest as a prediction model to predict the scores of 21 odor perception descriptors and uses the feature selection function of random forest to find the 15 most important features of olfactory perception. [PERSON] et al. [19] applied natural language semantic representation to odor perception descriptors, which more accurately inferred the perceptual ratings of single-molecule odorants on a large set of odor perception descriptors. By treating atoms as nodes and chemical bonds as edges, [PERSON] et al. [5] proposed the GNN-QSOR model, which uses a graph neural network (GNN)-based approach to predict odor perception descriptors for odor molecules. Similarly, [PERSON] et al. [20] also regarded atoms as nodes and chemical bonds as edges and proposed the Odor-GCN model, which is a graph convolutional network model for predicting odor impressions based on molecular structure. [PERSON] et al. [21] proposed a SOR prediction approach based on odor descriptor clustering, and molecular structure features extracted by convolutional neural networks were employed to predict the odor categories. Combining deep neural network (DNN) and convolutional neural network (CNN), ref. [6] used a simple DNN to train molecular feature descriptors and an Xception-based CNN to learn molecular structure graphs to predict odor perception descriptors, which achieved relatively good results. Different from these above works, we imitate the lateral inhibition mechanism of animal olfaction, which plays a very important role in the anti-interference and sharping ability, and integrate the lateral inhibition mechanism into the olfactory perception prediction model.