Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts02142, United States.
College of Chemical and Biological Engineering, Zhejiang University, Hangzhou310027, China.
J Am Chem Soc. 2022 Dec 14;144(49):22599-22610. doi: 10.1021/jacs.2c08997. Epub 2022 Dec 2.
The molecular structures synthesizable by organic chemists dictate the molecular functions they can create. The invention and development of chemical reactions are thus critical for chemists to access new and desirable functional molecules in all disciplines of organic chemistry. This work seeks to expedite the exploration of emerging areas of organic chemistry by devising a machine-learning-guided workflow for reaction discovery. Specifically, this study uses machine learning to predict competent electrochemical reactions. To this end, we first develop a molecular representation that enables the production of general models with limited training data. Next, we employ automated experimentation to test a large number of electrochemical reactions. These reactions are categorized as competent or incompetent mixtures, and a classification model was trained to predict reaction competency. This model is used to screen 38,865 potential reactions in silico, and the predictions are used to identify a number of reactions of synthetic or mechanistic interest, 80% of which are found to be competent. Additionally, we provide the predictions for the 38,865-member set in the hope of accelerating the development of this field. We envision that adopting a workflow such as this could enable the rapid development of many fields of chemistry.
有机化学家可合成的分子结构决定了他们能够创造的分子功能。因此，化学反应的发明和发展对于化学家在有机化学的所有领域获得新的和理想的功能分子至关重要。这项工作旨在通过设计一种机器学习引导的反应发现工作流程来加速有机化学新兴领域的探索。具体来说，本研究使用机器学习来预测有能力的电化学反应。为此，我们首先开发了一种分子表示方法，使我们能够使用有限的训练数据生成通用模型。接下来，我们采用自动化实验来测试大量的电化学反应。这些反应被分类为有能力或无能力的混合物，并训练了一个分类模型来预测反应能力。该模型用于在计算机上筛选 38865 个潜在反应，预测结果用于识别出一些具有合成或机理兴趣的反应，其中 80%的反应被证明是有能力的。此外，我们还提供了 38865 个成员集合的预测，希望以此加速该领域的发展。我们设想，采用这样的工作流程可以使许多化学领域迅速发展。
