Axelrod Simon, Shakhnovich Eugene, Gómez-Bombarelli Rafael
Department of Chemistry and Chemical Biology, Harvard University, Cambridge, Massachusetts02138, United States.
Department of Materials Science and Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts02139, United States.
ACS Cent Sci. 2023 Jan 25;9(2):166-176. doi: 10.1021/acscentsci.2c00897. eCollection 2023 Feb 22.
Molecular photoswitches are the foundation of light-activated drugs. A key photoswitch is azobenzene, which exhibits - isomerism in response to light. The thermal half-life of the 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.
分子光开关是光激活药物的基础。一种关键的光开关是偶氮苯，它会根据光的照射呈现顺反异构现象。反式异构体的热半衰期至关重要，因为它控制着光诱导生物效应的持续时间。在此，我们介绍一种用于预测偶氮苯衍生物热半衰期的计算工具。我们的自动化方法使用了基于量子化学数据训练的快速且准确的机器学习势函数。基于先前确凿的证据，我们认为热异构化是通过系间窜越介导的旋转过程进行的，并将这一机制纳入我们的自动化工作流程中。我们运用该方法预测了19000种偶氮苯衍生物的热半衰期。我们探究了能垒与吸收波长之间的趋势和权衡关系，并将我们的数据和软件开源，以加速光药理学的研究。
