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.
RESUMO
Highly substituted pyridine scaffolds are found in many biologically active natural products and therapeutics. Accordingly, numerous complementary de novo approaches to obtain differentially substituted pyridines have been disclosed. This article delineates the evolution of the synthetic strategies designed to assemble the demanding tetrasubstituted pyridine core present in the limonoid alkaloids isolated from Xylocarpus granatum, including xylogranatopyridine B, granatumine A and related congeners. In addition, NMR calculations suggested structural misassignment of several limonoid alkaloids, and predicted their C3-epimers as the correct structures, which was further validated unequivocally through chemical synthesis. The materials produced in this study were evaluated for cytotoxicity, anti-oxidant effects, anti-inflammatory action, PTP1B and Nlrp3 inflammasome inhibition, which led to compelling anti-inflammatory activity and anti-oxidant effects being discovered.
