## 4 Experiments
### Performance Evaluation  
We conduct experiments on the real-world dataset to compare our proposed HGAFMN with state-of-the-art methods. Table 1 summarizes the performance metrics of these models. The best performance is highlighted in bold, while the second best performance is marked with \"+\". Among all models, our proposed HGAFMN achieves the best results in precision, recall, F1-score, and AUROC. Furthermore, we will analyze the shortcomings of these comparative models. DREAM Winner model [4] is a random forest (RF)-based model that relies heavily on the best feature set for the input classifier. If there is a lot of noise in the training data, the RF will be overfitted. In addition, odor data are usually obtained by manual annotation and have a lot of noise, which has some negative effects on the accuracy of RF classification. Furthermore, since there are no standard rules about the number of features that will provide a better-performing model, some key information that is required for accurate prediction may be lost, especially when there are complex correlations among odor molecules. The GNN-QSOR model [5] treats atoms as nodes and chemical bonds as edges to construct an odor molecule as a graph structure. It only extracts the structural features of chemical molecules without using quantitative feature descriptors of odor molecules. A disadvantage of GNN-QSOR is that it is difficult to distinguish molecules that are similar in structure but have different smells. For example, hydrogen cyanide and benzaldehyde have different structures but similar odors, both with a bitter almond. The carvone enantiomer R-(-) carvone and carvone have almost the same structures but completely different odors. The former is spearmint smell, while the latter is caraway smell. The DNN-CNN model [6] greatly improves the prediction accuracy compared to other previous models. This model uses the most naive DNN component to learn molecular descriptive features from molecular feature descriptors, and uses the Xception-based CNN component to learn molecular structural features from molecular structure graphs. However, the feature descriptors of odor molecules are numerous with complex interactions. The model uses a simple DNN to extract molecular descriptive features and does not consider the feature combinations and interactions of these feature descriptors, which will cause omissions in feature extraction. Moreover, the differences between the structure graphs of different molecules are subtle and complex. For example, single, double, and triple bonds in chemical bonds are easily recognized inaccurately in complex molecules, which will lead to inaccurate olfactory perception prediction of odor molecules. In addition, the structural complexity of different molecules varies greatly--some are very simple while \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Models** & **Precision** & **Recall** & **F1** & **AUROC** \\ \hline DREAM & 0.3063 & 0.3110 & 0.3082 & 0.8304 \\ Winner [4] & 0.3614 & 0.3701 & 0.3653 & 0.8618 \({}^{\dagger}\) \\ DNN-CNN [6] & 0.5294 \({}^{\dagger}\) & 0.5307 \({}^{\dagger}\) & 0.5301 \({}^{\dagger}\) & 0.7851 \\ Our Proposal & **0.6552** & **0.7701** & **0.7081** & **0.8656** \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of odor descriptor prediction results with other models. The best values for each metric are in bold, while the second best values for each metric are marked with “+”. others are very complex--so the simple Xception-based CNN used in the DNN-CNN model may ignore some features. Different from these works above, we integrate the lateral inhibition mechanism into the olfactory perception prediction model and use the hypergraph neural network to effectively explore the high-order semantic interaction between odor molecules and odor descriptors. Moreover, the DeepAFM (Deep Attentional Factorization Machine) model can learn the importance of each feature interaction through the attention mechanism and effectively learn the weighted interactions between descriptive features.