A Fragrance Prediction Model for Molecules Using Rough Set‐based Machine Learning

In this work, a novel machine learning based methodology was developed to predict fragrance from the molecular structure and the effect of the subjects attributes on odour perception. As fragrance is linked to the molecular structure and interactions, topological indices are used to develop a predic...

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Bibliographic Details
Published inChemie ingenieur technik Vol. 95; no. 3; pp. 438 - 446
Main Authors Tiew, Shie Teck, Chew, Yick Eu, Lee, Ho Yan, Chong, Jia Wen, Tan, Raymond R., Aviso, Kathleen B., Chemmangattuvalappil, Nishanth G.
Format Journal Article
LanguageEnglish
Published 01.03.2023
Subjects
Cheminformatics
Fragrance prediction
Molecular structural descriptors
Rough set‐based machine learning
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ISSN0009-286X
1522-2640
DOI10.1002/cite.202200093

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Summary:In this work, a novel machine learning based methodology was developed to predict fragrance from the molecular structure and the effect of the subjects attributes on odour perception. As fragrance is linked to the molecular structure and interactions, topological indices are used to develop a predictive model. Rough set‐based machine learning is used to generate rule‐based models that link the topology of fragrant molecules and dilution to their respective odour characteristics. The results show that the generated models are effective in determining the odour characteristic of molecules. Fragrance is a desirable and often essential attribute in various consumer products. To predict fragrance attributes of molecules, a novel machine learning based methodology has been developed. Rough set‐based machine learning is used to link fragrance to molecular structure. The factors affect the perception of fragrance has also been analysed.
ISSN:0009-286X
1522-2640
DOI:10.1002/cite.202200093