ABSTRACT
Density functional theory (DFT) is a powerful tool to model transition state (TS) energies to predict selectivity in chemical synthesis. However, a successful multistep synthesis campaign must navigate energetically narrow differences in pathways that create some limits to rapid and unambiguous application of DFT to these problems. While powerful data science techniques may provide a complementary approach to overcome this problem, doing so with the relatively small data sets that are widespread in organic synthesis presents a significant challenge. Herein, we show that a small data set can be labeled with features from DFT TS calculations to train a feed-forward neural network for predicting enantioselectivity of a Negishi cross-coupling reaction with P-chiral hindered phosphines. This approach to modeling enantioselectivity is compared with conventional approaches, including exclusive use of DFT energies and data science approaches, using features from ligands or ground states with neural network architectures.
ABSTRACT
Catalytic asymmetric remote conjugate borylation is challenging as the control of regioselectivity is not trivial, the electrophilicity of remote sites is extenuated, and the remote asymmetric induction away from the carbonyl group is difficult. Herein, catalytic asymmetric conjugate 1,6-, 1,8- and 1,10-borylation was developed with excellent regioselectivity, which delivered α-chiral boronates in moderate to high yields with high enantioselectivity. The produced chiral boronate smoothly underwent oxidation, cross-coupling, and one-carbon homologation to give synthetically versatile chiral compounds in moderate yields with excellent stereoretention. Furthermore, a stereomechanistic analysis was conducted using DFT calculations, which provides insights into the origins of the regioselectivity. Finally, the present 1,6-borylation was successfully applied in an efficient one-pot asymmetric synthesis of (-)-7,8-dihydrokavain.
