Conventional, time-consuming, and expensive approaches have been proposed to be improved using machine learning techniques, such as LabMate.ML ( ). Also, Aspuru-Guzik et al. effectively implemented automated chemical design utilising data-driven approaches ( ) by using graph convolutional neural networks, especially specialised for molecular analysis. Building on graph convolution, Kearnes et al. ( ) proposed one-shot learning as a tool to support drug discovery. The example shows how ML applied in chemical reaction optimisation can overcome the restrictions of conventional approaches for optimising chemical reactions.