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

被引：2

作者：

Tiew, Shie Teck ^{[1
]}

Chew, Yick Eu ^{[1
]}

Lee, Ho Yan ^{[1
]}

Chong, Jia Wen ^{[1
]}

Tan, Raymond R. ^{[2
]}

Aviso, Kathleen B. ^{[2
]}

Chemmangattuvalappil, Nishanth G. ^{[1
]}

机构：

[1] Univ Nottingham Malaysia, Dept Chem & Environm Engn, Broga Rd, Selangor 43500, Malaysia

[2] De La Salle Univ, Ctr Engn & Sustainable Dev Res, 2401 Taft Ave, Manila 0922, Philippines

来源：

CHEMIE INGENIEUR TECHNIK | 2023年 / 95卷 / 03期

关键词：

Cheminformatics; Fragrance prediction; Molecular structural descriptors; Rough set-based machine learning;

D O I：

10.1002/cite.202200093

中图分类号：

TQ [化学工业];

学科分类号：

0817 ;

摘要：

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.

引用

页码：438 / 446

页数：9

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