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Olfactory perception prediction model inspired by olfactory lateral inhibition and deep feature combination

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Abstract

Finding the relationship between the chemical structure and physicochemical properties of odor molecules and olfactory perception prediction, i.e. quantitative structure-odor relationship (QSOR), remains a challenging, decades-old task. With the development of deep learning, data-driven methods such as convolutional neural networks or deep neural networks have gradually been used to predict QSOR. However, the differences between the molecular structure of different molecules are subtle and complex, the molecular feature descriptors are numerous and their interactions are complex. In this paper, we propose the Lateral Inhibition-inspired feature pyramid dynamic Convolutional Network, using the feature pyramid network as the backbone network to extract the odor molecular structure features, which can deal with multi-scale changes well. Imitating the lateral inhibition mechanism of animal olfactory, we add the lateral inhibition-inspired attention maps to the dynamic convolution, to improve the prediction accuracy of olfactory perception prediction. Besides, due to a large number of molecular feature descriptors and their complex interactions, we propose to add Attentional Factorization Mechanism to a deep neural network to obtain molecular descriptive features through weighted deep feature combination based on the attention mechanism. Our proposed olfactory perception prediction model noted as LIFMCN has achieved a state-of-the-art result and will help the product design and quality assessment in food, beverage, and fragrance industries.

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Funding

This work was supported in part by the National Key R&D Program of China under Grant 2019YFA0706401, and in part by the National Natural Science Foundation of China under Grant 62172014, Grant 62172015, and Grant 62272009.

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Authors and Affiliations

  1. Key Laboratory of High Confidence Software Technologies of Ministry of Education, School of Computer Science, Peking University, Beijing, 100871, China

    Yu Wang, Mingyuan Ma & Jin Xu

  2. Tencent, Beijing, 100193, China

    Qilong Zhao

Authors
  1. Yu Wang
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  2. Qilong Zhao
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  3. Mingyuan Ma
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  4. Jin Xu
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Correspondence to Yu Wang.

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Wang, Y., Zhao, Q., Ma, M. et al. Olfactory perception prediction model inspired by olfactory lateral inhibition and deep feature combination. Appl Intell 53, 19672–19684 (2023). https://doi.org/10.1007/s10489-023-04517-4

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