Random Forest‐Based Prediction of Enantioselectivity in Thiol Addition to Imines Catalyzed by Chiral Phosphoric Acids

Abstract This paper, for the first time, reports calculating molecular descripts from the combinations of catalysts and substrates, for enantioselectivity predictions in chiral phosphoric acid–catalyzed thiol addition to N ‐acylimines. Ten molecular descriptors, together with random forest (RF) algorithm, were used to establish the quantitative structure–selectivity relationship (QSSR) model (RF Model I), yielding correlation coefficient of R =0.982, root mean square ( rms ) error of 0.604 kJ/mol (for 800 enantioselectivity ΔΔG from 32 catalysts in the training set), R =0.922, rms =1.075 kJ/mol (for 275 enantioselectivity ΔΔG from 11 new catalysts in the test set). The RF Model I was proved to be more accurate in enantioselectivity predictions, compared with the QSSR models reported in the literature and with the RF Model II and RF Model III in this work. Moreover, the RF Model I was based on simple Dragon descriptors and capable of extrapolative prediction for new catalysts. Therefore, it is reasonable deriving molecular descriptors from the combinations of catalysts and substrates (thiols and imines). This work provides a new method for the predictions of enantioselectivity ΔΔG .