practice of shifting enzyme frameworks can lead to unexpected results (i.e., enantiodivergent transformations or unexpected products), and introduce biosynthetic challenges including re-optimization of expression and reaction conditions. An alternative tactic to improve underperforming substrates would be to engineer the GluER-T36A active site to each substrate; 21 however, site-saturation mutagenesis (SSM) of even five active-site residues would require 100 individual variants to be constructed, expressed, and evaluated in the laboratory. Additionally, reaction improvements in a SSM library rarely transfer to new substrates, necessitating evaluation of existing or new mutant libraries to optimize for the best performing variant. By relying on a small, representative training set that encompasses a range of reaction outputs, we hypothesized that robust statistical models relating function to structural features could be developed to predict the performance of new substrates or substrate/mutant combinations. Importantly, this method would draw from all training set data, including mutants that did not enhance selectivity. In this context, we designed a focused training set with diversity in both substrate characteristics and enzyme mutations. The transformation of substrates 1a-4a (Fig. ) encompass three different cyclization modes (1a : 5-endo, 2a, 3a: 5-exo, and 4a: 6-exo), varying electronic properties (as in 2a and 3a), and alkene substitution pattern (1a vs 2a-4a). We also identified five residues within the GluER-T36A active site for mutation: W66, Y177, Q232, F269, and Y343 (Fig. ) and subjected each site to site-directed mutagenesis to introduce residues W, F, D, L, or A, as these mutations would sample a wide range of active site properties. Substrates 1a-4a were subjected to reaction with each expressible mutant, resulting in a total of 50 datapoints to use in model training and selection. A full table of substrate/enzyme combinations and the resultant e.e.'s are included in Table .