Upon acquisition of the enzyme and ligand conformational ensembles with either IFD or aMD, quantitative chemical descriptors were computed, automatically extracted, and curated for the ligands as well as for individual residues in the active site (Fig. ). These descriptors included electronic (e.g., natural bond orbital (NBO) charges), steric (e.g., sterimol values), and dynamic descriptors, which describe topographical properties of a collection of conformers (e.g., dynamic surface area, (DSA)). Describing the active site by its individual residues in this manner reveals which residues have the most influence on reaction enantioselectivity. These descriptors were regressed against the experimentally collected dataset (70:30 split of training:test set data points) using a forward-stepwise multivariate linear regression (MLR) algorithm, which resulted in thousands of candidate models for each conformational search platform (Fig. ). From these candidate models, we identified a representative highperforming IFD statistical model (Fig.