Virtual screening of a combinatorial library of enantioselective catalysts with chirality codes and counterpropagation neural networks

Conformation-independent chirality codes, radial distribution function (RDF) codes, and indicator variables are implemented to represent 1914 catalysts in a combinatorial library which was tested by Riant and co-workers for the asymmetric-hydrogen transfer to acetophenone. The catalysts which combine a metallic center with a chiral ligand have been evaluated in terms of both enantiomeric excess and yield. A counterpropagation neural network (CPG NN) was trained with a small fraction of the library to predict the performance of catalysts, and applied to the virtual screening of the remaining library. Selection of < 20.8% of the virtual library with the highest predicted performance enables to identify up to 85.5% of the best catalysts. The approach illustrates a chemoinformatic method to assist the optimization of resources for the screening of enantioselective catalysts.