Hydroamination of alkenes catalyzed by transition-metal complexes is an atom-economical method for the synthesis of amines, but reactions of unactivated alkenes remain inefficient. Additions of N–H bonds to such alkenes catalyzed by iridium, gold, and lanthanide catalysts are known, but they have required a large excess of the alkene. New mechanisms for such processes involving metals rarely used previously for hydroamination could enable these reactions to occur with greater efficiency. We report ruthenium-catalyzed intermolecular hydroaminations of a variety of unactivated terminal alkenes without the need for an excess of alkene and with 2-aminopyridine as an ammonia surrogate to give the Markovnikov addition product. Ruthenium complexes have rarely been used for hydroaminations and have not previously catalyzed such reactions with unactivated alkenes. Identification of the catalyst resting state, kinetic measurements, deuterium labeling studies, and DFT computations were conducted and, together, strongly suggest that this process occurs by a new mechanism for hydroamination occurring by oxidative amination in concert with reduction of the resulting imine.

A general procedure for the asymmetric synthesis of highly substituted 1,2-amino alcohols in high yield and diastereoselectivity is described that uses organometallic additions of a wide range of nucleophiles to tertbutylsulfinimines as the key step. The addition of organolithium reagents to these imines follows a modified Davis model. The diastereoselectivity for this reaction depends significantly on both the nucleophile and electrophile. These highly substituted 1,2-amino alcohols are used to synthesize stereochemically diverse and structurally novel, polysubstituted 2,2′-methylene(bisoxazoline) ligands in high yields.

A catalyst selection method for the optimization of an asymmetric, vinylogous Mukaiyama aldol reaction is described. A large library of commercially available and synthetically accessible copper-bis(oxazoline) catalysts was constructed in silico. Conformer-dependent, grid-based descriptors were calculated for each catalyst, defining a chemical feature space suitable for machine learning. Selection of a diverse subset of catalyst space produced an initial training set of 26 novel bis(oxazoline) ligands which were synthesized and tested for stereoselectivity in the copper-catalyzed, vinylogous Mukaiyama aldol reaction for five substrate combinations. One ligand in the training set provided 88% average enantiomeric excess, exceeding the performance of catalysts identified through an initial optimization campaign. Supervised and semi-supervised catalyst selection methods, including quantitative structure-selectivity relationship modelling, nearest neighbors analysis, and a focused analogue clustering strategy, were employed to identify an additional 12 novel bis(oxazoline) ligands. The selected ligands outperformed the initial training set hit in four out of five product classes, and in some cases demonstrated excellent enantiocontrol exceeding 95% ee. The effectiveness of the unsupervised training set selection process is discussed, and the expediency of the nearest neighbor and focused analogue approaches are contrasted with the supervised quantitative structure-selectivity relationship modelling approach.

A catalyst selection method for the optimization of an asymmetric, vinylogous Mukaiyama aldol reaction is described. A large library of commercially available and synthetically accessible copper-bis(oxazoline) catalysts was constructed in silico. Conformer-dependent, grid-based descriptors were calculated for each catalyst, defining a chemical feature space suitable for machine learning. Selection of a diverse subset of catalyst space produced an initial training set of 26 novel bis(oxazoline) ligands which were synthesized and tested for stereoselectivity in the copper-catalyzed, vinylogous Mukaiyama aldol reaction for five substrate combinations. One ligand in the training set provided 88% average enantiomeric excess, exceeding the performance of catalysts identified through an initial optimization campaign. Supervised and semi-supervised catalyst selection methods, including quantitative structure-selectivity relationship modelling, nearest neighbors analysis, and a focused analogue clustering strategy, were employed to identify an additional 12 novel bis(oxazoline) ligands. The selected ligands outperformed the initial training set hit in four out of five product classes, and in some cases demonstrated excellent enantiocontrol exceeding 95% ee. The effectiveness of the unsupervised training set selection process is discussed, and the expediency of the nearest neighbor and focused analogue approaches are contrasted with the supervised quantitative structure-selectivity relationship modelling approach.