As a final validation of the workflow, the prediction of outof-sample combinations of substrates and enzyme variants were explored. Compared to established procedures to predict biocatalyst selectivity, the statistical models presented herein possessed the unique ability to evaluate substrates that were not included in model training. We therefore used both IFD and aMD statistical models to predict the performance of various GluER-T36A mutants with two new substrates: 5a and 6a (Table ). These were selected to incorporate substrate characteristics that were not represented in the training set, including an alkyl substituted example (5a), and a different cyclization mode (6a, 7-exo). For the aMD model, 5a and 6a conformers were collected and combined with existing enzyme trajectories, and for IFD the relevant [EL] poses were generated. Descriptors for these ensembles were automatically extracted. Using these descriptors, the enantioselectivities of 5a and 6a with each GluER-T36A variant were predicted using the statistical models from Figure (Table ). Gratifyingly, experimental evaluation of these combinations revealed the IFD model successfully predicted the enantioselectivity of 50% of the reactions within one MAE (0.29 kcal/mol). Within two MAE (0.58 kcal/mol), the IFD a Enantioselectivities of reactions with 5a and 6a to form 5b and 6b, predicted from the IFD and aMD models. The range of predicted selectivities from IFD was derived from the mean absolute error (MAE) of 0.29 kcal/mol. The range for the aMD predictions was derived from an MAE of 0.19 kcal/mol. b Predicted from aMD Model 2, Fig. . model successfully predicted 71% of enantioselectivities. The aMD model successfully predicted 57% of reaction enantioselectivities within one MAE (0.19 kcal/mol) and 64% within two MAE (0.38 kcal/mol). These results demonstrated the ability of these statistical models to predict quantitative enantioselectivity data of unseen substrates and enzyme combinations, which has been a historic limitation in other ML, rational design, or computational enzyme investigation