论文标题
机器学习引导的新生物正交点击反应的计算筛选
Machine learning-guided computational screening of new bio-orthogonal click reactions
论文作者
论文摘要
生物正交点击化学已成为生物化学家工具箱中必不可少的一部分。尽管近年来已经开发了各种各样的应用，但到目前为止，已经发现了有限数量的生物正交点击反应，其中大多数基于（取代）叠氮化物。在这项工作中，我们提出了一个计算工作流程，以发现新的候选生物正交点击反应。在整个空间中，在整个空间中，我们开发了一个能够预测〜2-3 kcal/mol内〜2-3 kcal/mol的反应能量的机器学习模型，仅在超过10,000,000个偶极环加成的整体搜索空间中进行抽样约为0.05％。应用此模型通过迭代的学习进行筛选完整的搜索空间，我们确定了具有丰富结构多样性的广泛候选反应，可以用作启发的起点或灵感来源，用于未来基于叠氮化物和基于非氮杂的基于叠氮化物和基于非氮杂的生物 - 正交单击反应。
Bio-orthogonal 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 bio-orthogonal 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 bio-orthogonal click reactions. 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 bio-orthogonal click reactions.
