Structure-based AI tool can predict wide range of very different reactions
Molecular properties, enantioselectivities, yields and relative conversion can all be modelled by the same program
New software that can predict a wide range of reaction outcomes without being specifically trained on similar ones has been created. The machine-learning platform, which uses structure-based molecular representations instead of big reaction-based datasets, could find diverse applications in organic chemistry.
Although machine-learning methods have been widely used to predict the molecular properties and biological activities of target molecules, their application in predicting reaction outcomes has been limited because current models usually can’t be transferred to different problems. Instead, complex parameterisation is required for each individual case to achieve good results. Researchers in Germany are now reporting a general approach that overcomes this limitation.