JIN Binyan, LI Xiuzhen, SHI Xinyuan, HAN Feiyu, ZHANG Li. Research on multi-category odor model based on machine learning[J]. Chinese Journal of Pesticide Science, 2024, 26(3): 472-481. DOI: 10.16801/j.issn.1008-7303.2024.0038
    Citation: JIN Binyan, LI Xiuzhen, SHI Xinyuan, HAN Feiyu, ZHANG Li. Research on multi-category odor model based on machine learning[J]. Chinese Journal of Pesticide Science, 2024, 26(3): 472-481. DOI: 10.16801/j.issn.1008-7303.2024.0038

    Research on multi-category odor model based on machine learning

    • Olfaction plays a key role in the perception of external chemical signals by organisms, and odor assessment is an important means for humans to understand the olfactory world of organisms. However, the diversity of odor descriptors caused by evaluator subjectivity presents a significant challenge to the prediction of molecular odor attributes using computational methods. Based on single-label data, this study utilizes six machine learning algorithms and soft voting model integration strategies to construct a multi-category odor attribute prediction model for five high-frequency odor categories. The Macro F1 score of the model on the test set and the external test set are all above 0.7, showing good predictive ability and generalization performance. The model also shows some ability to detect counter-intuitive structure-odor relationship, presenting a new possibility for the effective prediction of molecular odor attributes. Simultaneously, this study also predicted the possible odor categories of molecules with mosquito-trapping effect, providing vital clues for elucidating the relationship between mosquito behavior and odor preference.
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