Reaction Profile Forecasting by Artificial Data Generation for Wittig-Type Geminal Bromofluoroolefination

Machine learning (ML) is emerging as a valuable tool in organic synthesis for reaction design and prediction. In recent studies, the ML approach for reaction development using big data with many features provided the best reaction conditions for optimal yields and stereoselectivities. However, the preparation of large data sets is often challenging, especially for nonspecialists such as experimental scientists. In this study, we developed simple ML models for predicting reaction profiles of our geminal bromofluoroolefination with a minimal data set containing only readily accessible features, including ¹³C NMR chemical shifts of the reacting sites and Verloop's Sterimol values. Notably, the model's efficiency was significantly enhanced through an underutilized tabular augmentation method. By fitting the sparse data points to proper sigmoidal curves, we generated augmented data sets that improved the predicting ability of the feed-forward neural network (FNN). Furthermore, the combination of this augmentation technique with a conditional tabular generative adversarial network (CTGAN) synergistically refined the model's performance. Our achievement highlights the utility of tailored augmentation strategies as a potential solution for the limitations posed by small experimental data sets in ML-driven reaction development.