AI enantioselectivity predictor set to power computational catalyst screening

By Ellis Crawford2021-05-03T12:56:00+01:00
Workflow involves mapping a reaction representation onto the activation energy of the stereocontrolling step
Scientists in Switzerland have developed a machine learning method that can determine the enantioselectivities of reactions catalysed by complex organocatalysts. Key to the strong performance of this machine learning technique is a clever trick to avoid time-consuming calculations, enabled by an informed choice of molecular descriptors, reaction representations and feature engineering.
Source: © Raimon Fabregat/LCMD, EPFL
The model could be used to quickly identify selective propargylation organocatalysts
Developing new catalysts is essential for ensuring quicker, more selective and more reliable reactions. ‘Experimentally, large-scale screenings remain expensive in terms of personnel resources, time and equipment needs,’ explains Simone Gallarati, from the Swiss Federal Institute of Technology Lausanne (EPFL), who is one of the lead researchers behind the study. ‘From a computational perspective, running computations on hundreds of catalytic systems is still a burdensome job, and achieving accurate predictions of enantioselectivity with standard methods is an incredibly difficult task.’ This is due to traditional computational methods needing to determine the transition states that lead to different enantiomers.

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