RESUMO
Herein we report the first total synthesis of the indole diterpenoid natural product shearilicine by an 11-step sequence via a generalizable precursor to the highly oxidized subclass of indole diterpenoids. A native chiral auxiliary strategy was employed to access the target molecule in an enantiospecific fashion. The formation of the key carbazole substructure was achieved through a mild intramolecular Heck cyclization, wherein a computational study revealed noncovalent substrate-ligand and ligand-ligand interactions that promoted migratory insertion.
RESUMO
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.