We selected GluER-T36A as a biocatalytic framework to study as it has recently been utilized in a range of non-native enantioselective reactions with potential applications in the chemical industry. Specifically, GluER-T36A is a selective catalyst for the photoenzymatic cyclization of many -chloroamides (Fig. ). However, a number of substrates required alternative enzymes to achieve high enantioselectivity in the initial report. Although effective in this instance, the general a, The workflow was developed using the enantioselective photoenzymatic cyclization of ɑ-chloroamide substrates facilitated by GluER-T36A. b, Substrates 1a-4a were subjected to GluER-T36A or mutant to generate a training set for model development. c, Two complementary approaches were developed to generate enzyme conformers from a GluER-T36A crystal structure (PDB ID: 6MYW) after introducing the desired mutation in silico. d, Enzyme features were quantified using a residue and site-based approach. Dynamic parameters were measured by overlaying a residue conformational ensemble, encapsulating it in a fictitious surface, and measuring the resulting surface area and volume. Site-specific descriptors were generated by measuring the length, width, and backbone angles of each residue conformer, and the fluctuations of these measurements. Ligands were subjected to both a geometric analysis and DFT calculations to acquire electronic descriptors, including the NBO charges of atoms indicated by a yellow sphere. e, Descriptors for each enzyme/ligand were regressed against the experimentally determined selectivites, resulting in statistical models that were validated using leave-one-out and k-fold crossvalidation. f, Select statistical models were interrogated for mechanistic insight and used to predict the selectivity of untested enzyme and substrate combinations.