Regression Study of Odorant Chemical Space, Molecular Structural Diversity, and Natural Language Description
Author:
Affiliation:
1. Priority Organization for Innovation and Excellence Laboratory for Data Sciences, Kumamoto University, 2-39-1, Kurokami, Chuo-ku, Kumamoto 860-8555, Japan
2. Emeritus Professors of University of Yamanashi, Takeda 4-4-37, Kofu 400-8510, Japan
Funder
Tateishi Science and Technology Foundation
Publisher
American Chemical Society (ACS)
Link
https://pubs.acs.org/doi/pdf/10.1021/acsomega.4c02268
Reference36 articles.
1. Harada, Y. A Study for Odor Component Exploration with Multi-dimensional Data Analysis of Odor Sensing Spaces. Ph.D. Thesis, Tokyo Institute of Technology, 2016.
2. Characterization of a comprehensive flavor database
3. Predicting human olfactory perception from chemical features of odor molecules
4. Sanchez-Lengeling, B.; Wei, J. N.; Lee, B. K.; Gerkin, R. C.; Aspuru-Guzik, A.; Wiltschko, A. B. Machine learning for scent: Learning generalizable perceptual representations of small molecules. 2019, arXiv:1910.10685 arXiv preprint. https://doi.org/10.48550/arXiv.1910.10685.
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