论文标题
偶氮苯衍生物的热半衰期：使用机器学习潜力基于间间穿越的虚拟筛选
Thermal half-lives of azobenzene derivatives: virtual screening based on intersystem crossing using a machine learning potential
论文作者
论文摘要
分子照片开关是光激活药物的基础。关键的照片开关是偶氮苯，它表现出对光线的反式cis异构主义。顺式异构体的热半衰期至关重要，因为它控制着光诱导的生物学效应的持续时间。在这里，我们介绍了一种计算工具，用于预测偶氮苯衍生物的热半衰期。我们的自动化方法使用了经过量子化学数据训练的快速准确的机器学习潜力。在建立良好的早期证据的基础上，我们认为热异构化是通过Intersystem Crossing介导的旋转来进行的，并将这种机制纳入我们的自动化工作流程。我们使用我们的方法来预测19,000种偶氮苯衍生物的热半衰期。我们探索障碍和吸收波长之间的趋势和权衡，并开源我们的数据和软件以加速光精神病学研究。
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 tradeoffs between barriers and absorption wavelengths, and open-source our data and software to accelerate research in photopharmacology.
