Efficient Discovery of Visible Light-Activated Azoarene Photoswitches with Long Half-Lives Using Active Search
Efficient Discovery of Visible Light-Activated Azoarene Photoswitches with Long Half-Lives Using Active Search
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使用主动搜索有效发现可见光激活的长半衰期偶氮芳烃光电开关
DOI:
10.1021/acs.jcim.1c00954
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发表时间:
2021
影响因子:
5.6
通讯作者:
Lopez, Steven A.
中科院分区:
文献类型:
--
作者:
Mukadum, Fatemah;Nguyen, Quan;Adrion, Daniel M.;Appleby, Gabriel;Chen, Rui;Dang, Haley;Chang, Remco;Garnett, Roman;Lopez, Steven A.
Photoswitches are molecules that undergo a reversible, structural isomerization after exposure to certain wavelengths of light. The dynamic control offered by molecular photoswitches is favorable for materials chemistry, photopharmacology, and catalysis applications. Ideal photoswitches absorb visible light and have long-lived metastable isomers. We used high-throughput virtual screening to predict the absorption maxima (λmax) of theE-isomer and half-life (t1/2) of theZ-isomer. However, computing the photophysical and kinetic stabilities with density functional theory of each entry of a virtual molecular library containing thousands or millions of molecules is prohibitively time-consuming. We applied active search, a machine-learning technique, to intelligently search a chemical search space of 255 991 photoswitches based on 29 known azoarenes and their derivatives. We iteratively trained the active search algorithm on whether a candidate absorbed visible light (λmax> 450 nm). Active search was found to triple the discovery rate compared to random search. Further, we projected 1962 photoswitches to 2D using the Uniform Manifold Approximation and Projection algorithm and found that λmaxdepends on the core, which is tunable by substituents. We then incorporated a second stage of screening to predict the stabilities of theZ-isomers for the top candidates of each core. We identified four ideal photoswitches that concurrently satisfy the following criteria: λmax> 450 nm andt1/2> 2 h.These candidates had λmaxandt1/2range from 465 to 531 nm and hours to days, respectively.
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影响因子:
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作者:
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DOI:
--
发表时间:
2017
期刊:
International Conference on Machine Learning
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DOI:
--
发表时间:
2012
期刊:
International Conference on Machine Learning
影响因子:
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