Department of Chemical Engineering, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, Massachusetts, 02139, US.
Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, Massachusetts, 02139, US.
Chemistry. 2023 May 16;29(28):e202300387. doi: 10.1002/chem.202300387. Epub 2023 Apr 4.
Bioorthogonal click chemistry has become an indispensable part of the biochemist's toolbox. Despite the wide variety of applications that have been developed in recent years, only a limited number of bioorthogonal click reactions have been discovered so far, most of them based on (substituted) azides. In this work, we present a computational workflow to discover new candidate reactions with promising kinetic and thermodynamic properties for bioorthogonal click applications. Sampling only around 0.05 % of an overall search space of over 10,000,000 dipolar cycloadditions, we develop a machine learning model able to predict DFT-computed activation and reaction energies within ∼2-3 kcal/mol across the entire space. Applying this model to screen the full search space through iterative rounds of learning, we identify a broad pool of candidate reactions with rich structural diversity, which can be used as a starting point or source of inspiration for future experimental development of both azide-based and non-azide-based bioorthogonal click reactions.
生物正交点击化学已经成为生物化学家工具包中不可或缺的一部分。尽管近年来已经开发出了各种各样的应用，但迄今为止发现的生物正交点击反应数量有限，其中大多数基于（取代）叠氮化物。在这项工作中，我们提出了一种计算工作流程，用于发现具有有前途的动力学和热力学性质的新候选反应，适用于生物正交点击应用。在超过 1000 万的整体搜索空间中，我们仅对大约 0.05%的二极环加成进行采样，开发了一种机器学习模型，能够在整个空间内预测 DFT 计算的活化和反应能，误差约为 2-3 kcal/mol。通过迭代学习回合将该模型应用于全搜索空间筛选，我们确定了一组具有丰富结构多样性的候选反应，它们可用作基于叠氮化物和非叠氮化物的生物正交点击反应的未来实验开发的起点或灵感来源。
