Data Science Guided Multiobjective Optimization of a Stereoconvergent Nickel-Catalyzed Reduction of Enol Tosylates to Access Trisubstituted Alkenes
Data Science Guided Multiobjective Optimization of a Stereoconvergent Nickel-Catalyzed Reduction of Enol Tosylates to Access Trisubstituted Alkenes
复制标题
DOI：
10.1021/acscatal.4c00650
复制
发表时间：
2024-03
期刊：
影响因子：
12.9
通讯作者：
Natalie P. Romer;Daniel S Min;Jason Y. Wang;R. Walroth;Kyle A. Mack;Lauren E. Sirois;F. Gosselin;Daniel Zell;A. Doyle;M. Sigman
中科院分区：
文献类型：
--
作者：
Natalie P. Romer;Daniel S Min;Jason Y. Wang;R. Walroth;Kyle A. Mack;Lauren E. Sirois;F. Gosselin;Daniel Zell;A. Doyle;M. Sigman
本文报道了一种以简单的酮为起始原料，分两步立体收敛合成三取代烯烃的方法。关键步骤是镍催化还原相应的烯醇对甲苯磺酸盐，主要依靠单膦配体来指导E-或Z-三取代烯烃产物的立体收敛形成。使用数据科学工作流程完成反应优化，包括单膦训练集设计、统计建模和多目标贝叶斯优化。优化活动显著改善了E-和Z-三取代产品的可获得性，最高可达∼90：10的非对映选择性和90%的产率。在使用训练集设计确定优势配体后，每个目标(E-和Z-异构体形成)只需要25个反应就可以从使用∼+平台的30,000个潜在条件的搜索空间中收敛到改进的反应参数。此外，建立了一个分层的机器学习模型来预测未测试的单膦配体的立体选择性，得到了7.1%的选择性(0.21kcal/mol)的验证平均绝对误差(MAE)。最后，我们提出了一个协同数据科学工作流程，利用训练集设计、统计建模和贝叶斯优化的集成，从而扩大了对立体定义的三取代烯烃的访问。
: Herein we report a method for a stereoconvergent synthesis of trisubstituted alkenes in two steps from simple ketone starting materials. The key step is a nickel-catalyzed reduction of the corresponding enol tosylates that predominantly relies on a monophosphine ligand to direct the stereoconvergent formation of either the E - or Z -trisubstituted alkene products. Reaction optimization was accomplished using a data science workflow including monophosphine training set design, statistical modeling, and multiobjective Bayesian optimization. The optimization campaign significantly improved access to both the E - and Z -trisubstituted products in up to ∼ 90:10 diastereoselectivity and >90% yield. After identifying superior ligands using training set design, only 25 reactions were required for each objective ( E - and Z -isomer formation) to converge on improved reaction parameters from a search space of ∼ 30,000 potential conditions using the EDBO+ platform. Additionally, a hierarchical machine learning model was developed to predict the stereoselectivity of untested monophosphine ligands to achieve a validation mean absolute error (MAE) of 7.1% selectivity (0.21 kcal/mol). Ultimately, we present a synergistic data science workflow leveraging the integration of training set design, statistical modeling, and Bayesian optimization, thereby expanding access to stereodefined trisubstituted alkenes.