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

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.

SUBMITTER: Cuomo AE 

PROVIDER: S-EPMC10540279 | biostudies-literature | 2023 Sep

REPOSITORIES: biostudies-literature