Leveraging Regio- and Stereoselective C(sp3)-H Functionalization of Silyl Ethers to Train a Logistic Regression Classification Model for Predicting Site-Selectivity Bias

The C-H functionalization of silyl ethers via carbene-induced C-H insertion represents an efficient synthetic disconnection strategy. In this work, site-and stereoselective C(sp(3))-H functionalization at alpha, gamma, delta, and even more distal positions to the siloxy group has been achieved using donor/ acceptor carbene intermediates. By exploiting the predilections of Rh-2(R-TCPTAD)(4) and Rh-2(S-2-Cl-5-BrTPCP)(4) catalysts to target either more electronically activated or more spatially accessible C-H sites, respectively, divergent desired products can be formed with good diastereocontrol and enantiocontrol. Notably, the reaction can also be extended to enable desymmetrization of meso silyl ethers. Leveraging the broad substrate scope examined in this study, we have trained a machine learning classification model using logistic regression to predict the major C-H functionalization site based on intrinsic substrate reactivity and catalyst propensity for overriding it. This model enables prediction of the major product when applying these C-H functionalization methods to a new substrate of interest. Applying this model broadly, we have demonstrated its utility for guiding late-stage functionalization in complex settings and developed an intuitive visualization tool to assist synthetic chemists in such endeavors.

Automated summary

In this work, C-H functionalization of silyl ethers via carbene-induced C-H insertion has been achieved, providing an efficient synthetic disconnection strategy. The use of different catalyst preferences has enabled site- and stereoselective functionalization at different positions relative to the siloxy group. Additionally, a machine learning model has been developed to predict the major product, aiding in the application of these methods to new substrates.