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Thermal Half-Lives of Azobenzene Derivatives: Virtual Screening Based on Intersystem Crossing Using a Machine Learning Potential

[Image: see text] Molecular photoswitches are the foundation of light-activated drugs. A key photoswitch is azobenzene, which exhibits trans–cis isomerism in response to light. The thermal half-life of the cis isomer is of crucial importance, since it controls the duration of the light-induced biolo...

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Autores principales: Axelrod, Simon, Shakhnovich, Eugene, Gómez-Bombarelli, Rafael
Formato: Online Artículo Texto
Lenguaje:English
Publicado: American Chemical Society 2023
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9951306/
https://www.ncbi.nlm.nih.gov/pubmed/36844486
http://dx.doi.org/10.1021/acscentsci.2c00897
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author Axelrod, Simon
Shakhnovich, Eugene
Gómez-Bombarelli, Rafael
author_facet Axelrod, Simon
Shakhnovich, Eugene
Gómez-Bombarelli, Rafael
author_sort Axelrod, Simon
collection PubMed
description [Image: see text] Molecular photoswitches are the foundation of light-activated drugs. A key photoswitch is azobenzene, which exhibits trans–cis isomerism in response to light. The thermal half-life of the cis isomer is of crucial importance, since it controls the duration of the light-induced biological effect. Here we introduce a computational tool for predicting the thermal half-lives of azobenzene derivatives. Our automated approach uses a fast and accurate machine learning potential trained on quantum chemistry data. Building on well-established earlier evidence, we argue that thermal isomerization proceeds through rotation mediated by intersystem crossing, and incorporate this mechanism into our automated workflow. We use our approach to predict the thermal half-lives of 19,000 azobenzene derivatives. We explore trends and trade-offs between barriers and absorption wavelengths, and open-source our data and software to accelerate research in photopharmacology.
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spelling pubmed-99513062023-02-25 Thermal Half-Lives of Azobenzene Derivatives: Virtual Screening Based on Intersystem Crossing Using a Machine Learning Potential Axelrod, Simon Shakhnovich, Eugene Gómez-Bombarelli, Rafael ACS Cent Sci [Image: see text] Molecular photoswitches are the foundation of light-activated drugs. A key photoswitch is azobenzene, which exhibits trans–cis isomerism in response to light. The thermal half-life of the cis isomer is of crucial importance, since it controls the duration of the light-induced biological effect. Here we introduce a computational tool for predicting the thermal half-lives of azobenzene derivatives. Our automated approach uses a fast and accurate machine learning potential trained on quantum chemistry data. Building on well-established earlier evidence, we argue that thermal isomerization proceeds through rotation mediated by intersystem crossing, and incorporate this mechanism into our automated workflow. We use our approach to predict the thermal half-lives of 19,000 azobenzene derivatives. We explore trends and trade-offs between barriers and absorption wavelengths, and open-source our data and software to accelerate research in photopharmacology. American Chemical Society 2023-01-25 /pmc/articles/PMC9951306/ /pubmed/36844486 http://dx.doi.org/10.1021/acscentsci.2c00897 Text en © 2023 The Authors. Published by American Chemical Society https://creativecommons.org/licenses/by/4.0/Permits the broadest form of re-use including for commercial purposes, provided that author attribution and integrity are maintained (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Axelrod, Simon
Shakhnovich, Eugene
Gómez-Bombarelli, Rafael
Thermal Half-Lives of Azobenzene Derivatives: Virtual Screening Based on Intersystem Crossing Using a Machine Learning Potential
title Thermal Half-Lives of Azobenzene Derivatives: Virtual Screening Based on Intersystem Crossing Using a Machine Learning Potential
title_full Thermal Half-Lives of Azobenzene Derivatives: Virtual Screening Based on Intersystem Crossing Using a Machine Learning Potential
title_fullStr Thermal Half-Lives of Azobenzene Derivatives: Virtual Screening Based on Intersystem Crossing Using a Machine Learning Potential
title_full_unstemmed Thermal Half-Lives of Azobenzene Derivatives: Virtual Screening Based on Intersystem Crossing Using a Machine Learning Potential
title_short Thermal Half-Lives of Azobenzene Derivatives: Virtual Screening Based on Intersystem Crossing Using a Machine Learning Potential
title_sort thermal half-lives of azobenzene derivatives: virtual screening based on intersystem crossing using a machine learning potential
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9951306/
https://www.ncbi.nlm.nih.gov/pubmed/36844486
http://dx.doi.org/10.1021/acscentsci.2c00897
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