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

Abstract

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 α, γ, δ, 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.

Hybrid Machine Learning Approach to Predict the Site Selectivity of Iridium-Catalyzed Arene Borylation

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Data-Driven Multi-Objective Optimization Tactics for Catalytic Asymmetric Reactions Using Bisphosphine Ligands

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Mechanism to model: a physical organic chemistry approach to reaction prediction

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