计算机科学
吸收(声学)
人工神经网络
近红外光谱
荧光
生物系统
波长
干扰(通信)
紫外线
材料科学
深度学习
人工智能
光学
光电子学
电信
物理
生物
频道(广播)
复合材料
作者
Jinning Shao,Yue Liu,Jiaqi Yan,Ze‐Yi Yan,Yangyang Wu,Zhongying Ru,Jia‐Yu Liao,Xiaoye Miao,Linghui Qian
标识
DOI:10.1021/acs.jcim.1c01449
摘要
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 (SMFluo1) of 1181 solvated small-molecule fluorophores covering the ultraviolet–visible–near-infrared absorption window and developed new machine learning models based on deep neural networks for accurately predicting photophysical parameters. The optimal system was applied to 120 out-of-sample compounds, and it exhibited remarkable accuracy with a mean relative error of 1.52%. In this new paradigm, a deep learning algorithm is promising to complement conventional theoretical and experimental studies of fluorophores and to greatly accelerate the discovery of new dyes. Due to its simplicity and efficiency, data from newly developed fluorophores can be easily supplemented to this system to further improve the accuracy across various dye families.
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