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Feed-Forward Neural Network for Predicting Enantioselectivity of the Asymmetric Negishi Reaction

2023; American Chemical Society; Volume: 9; Issue: 9 Linguagem: Inglês

10.1021/acscentsci.3c00512

2374-7951

Abbigayle E. Cuomo, Sebastian Ibarraran, Sanil Sreekumar, Haote Li, Jungmin Eun, Jan Paul Menzel, Pengpeng Zhang, Frédéric G. Buono, Jinhua J. Song, Robert H. Crabtree, Víctor S. Batista, Timothy R. Newhouse,

Surface Chemistry and Catalysis

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.