Prediction of Maximum Absorption Wavelength Using Deep Neural Networks

Autor:	Jinning Shao, Yue Liu, Jiaqi Yan, Ze-Yi Yan, Yangyang Wu, Zhongying Ru, Jia-Yu Liao, Xiaoye Miao, Linghui Qian
Rok vydání:	2022
Předmět:	Machine Learning General Chemical Engineering Solvents Humans Neural Networks Computer General Chemistry Library and Information Sciences Fluorescent Dyes Computer Science Applications
Zdroj:	Journal of Chemical Information and Modeling. 62:1368-1375
ISSN:	1549-960X 1549-9596
Popis:	Fluorescent molecules are important tools in biological detection, and numerous efforts have been made to develop compounds to meet the desired photophysical properties. For example, tuning the wavelength allows an appropriate penetration depth with minimal interference from the autofluorescence/scattering for a better signal-to-noise contrast. However, there are limited guidelines to rationally design or computationally predict the optical properties from first principles, and factors like the solvent effects will make it more complicated. Herein, we established a database (
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a38f8b98055603a26ad65ab959a8556c https://doi.org/10.1021/acs.jcim.1c01449 Zobrazit plný text záznamu