Cyclization and cyclopolymerization of silicon-containing dienes ; Functionalization of carbosilane dendrimers

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Chemistry, 1997.Vita.Includes bibliographical references (leaves 288-294).by Shane William Krska.Ph.D

Copper-Catalyzed Benzylic C–H Coupling with Alcohols via Radical Relay

Cross coupling reactions enable rapid convergent synthesis of diverse molecules and provide the foundation for modern chemical synthesis. The most widely used methods employ sp2-hybridized coupling partners, such as aryl halides or related pre-functionalized substrates. Here, we demonstrate copper-catalyzed oxidative cross-coupling of benzylic C–H bonds with alcohols to afford benzyl ethers, enabled by mechanistic insights that led to a novel reductant-based strategy for in situ regeneration of the active copper catalyst. The reactions employ the C–H substrate as the limiting reagent and exhibit broad scope with respect to both substrate partners. This approach to direct site-selective functionalization of sp3 C–H bonds provides the basis for efficient three-dimensional diversification of organic molecules and should find widespread utility in organic synthesis, particularly for medicinal chemistry applications

A Ligand Exchange Process for the Diversification of Palladium Oxidative Addition Complexes

Palladium oxidative addition complexes (OACs) have recently emerged as useful tools to enable challenging bond connections. However, each OAC can only be formed with one dative ligand at a time. As no one ligand is optimal for every cross-coupling reaction, we herein disclose a ligand exchange protocol for the preparation of a series of OACs bearing a variety of ancillary ligands from one common complex. These complexes were further applied to cross-coupling transformations

Roadmap to Pharmaceutically Relevant Reactivity Models Leveraging High-Throughput Experimentation

The merger of High-Throughput Experimentation (HTE) and data science presents an opportunity to both accelerate and inspire innovations in synthetic chemistry. Similarly, developments in machine learning (ML) have enabled the distillation of large and complex data sets into predictive models capable of generalizing patterns in the data. However, efforts to merge HTE with ML remain constrained by a few reported datasets with limited structural diversity and corresponding trained models that do not extrapolate well to substrates beyond the training set. Herein, we detail the first ML models for Pd-catalyzed C–N couplings using pharmaceutically relevant structurally diverse large data sets (~ 5000 unique products) generated using nanomole scale compatible chemistry. Careful consideration is given to both the diversity of the data set and accurate model predictions for substrates bearing features beyond those present in the training set. The structural diversity in the data set is enabled by leveraging the Merck & Co., Inc Building Block Collection with an initial focus on C–N coupling using secondary amines. The large dataset enables the systematic evaluation of model performance using five different data-splitting strategies. These five splits are carefully designed to evaluate the model’s ability to extrapolate beyond the substrates in the training set. The accuracy of classification models built with a lens toward application to medicinal chemistry campaigns exceeded the baseline precision-recall by 25-67% depending on the splitting strategy. These results would manifest as significant enrichment of successful C–N couplings using the hits recommended by the models. In addition, the accuracy of the best models for each of the five splits ranges between 70-87% suggesting excellent overall predictivity of the models even for completely unseen substrates