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Foods

Title:		Insight into the Structure-Odor Relationship of Molecules: A Computational Study Based on Deep Learning
Author(s):		Bo W; Yu Y; He R; Qin D; Zheng X; Wang Y; Ding B; Liang G;
Address:		"Key Laboratory of Biorheological Science and Technology, Ministry of Education, Bioengineering College, Chongqing University, Chongqing 400044, China"
Journal Title:		Foods
Year:		2022
Volume:		20220709
Issue:		14
Page Number:		-
DOI:		10.3390/foods11142033
ISSN/ISBN:		2304-8158 (Print) 2304-8158 (Electronic) 2304-8158 (Linking)
Abstract:		"Molecules with pleasant odors, unacceptable odors, and even serious toxicity are closely related to human social life. It is impractical to identify the odors of molecules in large quantities (particularly hazardous odors) using experimental methods. Computer-aided methods have currently attracted increasing attention for the prediction of molecular odors. Here, through models based on multilayer perceptron (MLP) and physicochemical descriptors (MLP-Des), MLP and molecular fingerprint, and convolutional neural network (CNN), we conduct the two-class prediction of odor/no odor, fruity/no odor, floral/no odor, and woody/no odor, and the multi-class prediction of fruity/flowery/woody/no odor on our newly refined molecular odor datasets. We show that three kinds of predictors can robustly predict molecular odors. The MLP-Des model not only exhibits the best prediction results (the AUC values are 0.99 and 0.86 for the two- and multi-classification models, respectively) but can also well reflect the characteristics of the structure-odor relationship of molecules. The CNN model takes 2D molecular images as input and can automatically extract the structural features related to molecular odors. The proposed models are of great help for the prediction of molecular odorants, understanding the underlying relationship between chemical structure and odor perception, and the discovery of new odorous and/or hazardous molecules"
Keywords:		convolutional neural network (CNN) hazardous molecules multilayer perceptron (MLP) odor prediction;
Notes:		"PubMed-not-MEDLINEBo, Weichen Yu, Yuandong He, Ran Qin, Dongya Zheng, Xin Wang, Yue Ding, Botian Liang, Guizhao eng 32172196/National Natural Science Foundation of China/ 2107619/National Science Foundation/ 31771975/National Natural Science Foundation of China/ Switzerland 2022/07/28 Foods. 2022 Jul 9; 11(14):2033. doi: 10.3390/foods11142033"