Reaction-based machine learning representations for predicting the enantioselectivity of organocatalysts
Simone Gallarati, Raimón Fabregat, Rubén Laplaza, Sinjini Bhattacharjee, Matthew D. Wodrich, Clémence Corminbœuf
Abstract
were achieved in predictions of the activation energy with respect to DFT computations. By virtue of its design, this strategy is generalisable to other ML models, to experimental data and to any catalytic asymmetric reaction, enabling the rapid screening of structurally diverse organocatalysts from available structural information.
Topics & Concepts
Artificial intelligenceOrganocatalysisComputer scienceEnantioselective synthesisChemistryCombinatorial chemistryMachine learningOrganic chemistryCatalysisMachine Learning in Materials ScienceComputational Drug Discovery MethodsCatalysis and Oxidation Reactions