# Decoding Structure–Odor Relationship Based on Hypergraph Neural Network and Deep Attentional Factorization Machine

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## Abstract

**:**

## 1. Introduction

- (1)
- We propose a neural network model based on hypergraph neural networks and a deep attentional factorization machine for olfactory perception prediction, solving the complex interactions problem of the large number of molecular feature descriptors which is difficult for the existing methods to deal with.
- (2)
- Imitating the olfactory lateral inhibition of animals, we add a lateral inhibition-inspired attention mechanism to the convolutional neural network of the feature-semantic interaction module of molecule-odor descriptor to improve the prediction accuracy of olfactory perception.
- (3)
- We conduct experiments on real-world datasets. Experimental results show that our proposed HGAFMN achieves good results in olfactory perception prediction.

## 2. Related Work

#### 2.1. Quantitative Structure–Odor Relationship

#### 2.2. Graph Neural Networks-Based Learning

#### 2.3. Lateral Inhibition

## 3. Materials and Methods

#### 3.1. Datasets

#### 3.2. Feature-Semantic Interaction of Molecule-Odor Descriptor through HGNN

**D**and

**B**, respectively, where ${D}_{ii}={\sum}_{\u03f5=1}^{N}{W}_{\u03f5\u03f5}{H}_{i\u03f5}$ and ${B}_{\u03f5\u03f5}={\sum}_{i=1}^{M}{H}_{i\u03f5}$. In this paper, ${W}_{\u03f5\u03f5}$ is uniformly assigned to 1, and hence, $\mathbf{W}$ is an identity matrix.

#### 3.3. Weighted Deep Feature Combination Based on Attentional Factorization Machine

## 4. Experiments

#### 4.1. Experimental Settings

#### 4.1.1. Baselines and Evaluation Metrics

#### 4.1.2. Hyper-Parameter Settings

#### 4.2. Performance Evaluation

#### 4.3. Experiment Analysis

#### 4.3.1. Ablation Study

#### 4.3.2. Performance Comparison w.r.t Layers of HGNNs

#### 4.3.3. Performance Comparison with Respect to Hyper-Parameters

## 5. Conclusions

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Conflicts of Interest

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**Figure 5.**Hypergraph construction processes. (

**a**) The hypergraph structure for odor molecule–odor descriptor modeling. (

**b**) The construction method of the hypergraph.

**Figure 8.**The loss plot, accuracy plot, and AUROC plot of the proposed HGAFMN. (

**a**) loss plot, (

**b**) accuracy plot, (

**c**) AUROC plot.

**Figure 9.**(

**a**) Experimental results of model variants on F1-score; (

**b**) experimental results of model variants on AUROC.

**Figure 10.**(

**a**) Performance comparison with respect to layers of HGNNs on F1-score; (

**b**) performance comparison with respect to layers of HGNNs on embedding size.

**Figure 11.**(

**a**) Performance comparison with respect to dropout rate; (

**b**) performance comparison with respect to embedding size.

**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 “†”.

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**MDPI and ACS Style**

Wang, Y.; Zhao, Q.; Ma, M.; Xu, J.
Decoding Structure–Odor Relationship Based on Hypergraph Neural Network and Deep Attentional Factorization Machine. *Appl. Sci.* **2022**, *12*, 8777.
https://doi.org/10.3390/app12178777

**AMA Style**

Wang Y, Zhao Q, Ma M, Xu J.
Decoding Structure–Odor Relationship Based on Hypergraph Neural Network and Deep Attentional Factorization Machine. *Applied Sciences*. 2022; 12(17):8777.
https://doi.org/10.3390/app12178777

**Chicago/Turabian Style**

Wang, Yu, Qilong Zhao, Mingyuan Ma, and Jin Xu.
2022. "Decoding Structure–Odor Relationship Based on Hypergraph Neural Network and Deep Attentional Factorization Machine" *Applied Sciences* 12, no. 17: 8777.
https://doi.org/10.3390/app12178777