Organic Synthesis

A Graph-Convolution Neural Network Model for the Prediction of Chemical Reactivity

We present a supervised learning approach to predict the products of organic reactions given their reactants, reagents, and solvent(s). The prediction task is factored into two stages comparable to manual expert approaches: considering possible sites of reactivity and evaluating their relative likelihoods. By training on hundreds of thousands of reaction precedents covering a broad range of reaction types from the patent literature, the neural model makes informed predictions of chemical reactivity. The model predicts the major product correctly over 85% of the time requiring around 100 ms per example, a significantly higher accuracy than achieved by previous machine learning approaches, and performs on par with expert chemists with years of formal training. We gain additional insight into predictions via the design of the neural model, revealing an understanding of chemistry qualitatively consistent with manual approaches.

Using Carbon Dioxide as a Building Block in Continuous Flow Synthesis

Carbon dioxide (CO2) is an attractive building block for organic synthesis that is environmentally friendly. Continuous flow technologies have enabled C−O and C−C bond forming reactions with CO2 that previously were either low-yielding or impossible in batch to afford value-added chemicals. This review describes recent advances in continuous flow as an enabling strategy in utilizing CO2 as a C1 building block in chemical synthesis.