Thus, we were motivated to develop a strategy to concurrently optimize biocatalytic reactions and gain mechanistic insight, with an emphasis on simultaneous exploration of sequence (enzyme mutants) and chemical (substrate scope) space. Specifically, we envisioned a tool that would quantitatively relate the molecular features of enzymes and substrates to observables like enantioselectivity using statistical models. This would require the design and acquisition of empirical results for a matrix of both substrate variants and enzyme mutants, and computational characterization of the reaction components. The empirical data would be regressed against computed molecular features, resulting in statistical models that would provide predictive power and molecular-level insights into the origin of substrate and enzyme performance. Although ideologically similar to quantitative structure/sequence activity relationships, our attention to molecular conformational ensembles, simultaneous parameterization of both enzyme and substrate, use of multivariate linear regression models, and implementation of higher-level descriptors would result in models with greater interpretability and generalizability. We designed a workflow to accommodate several common types of empirical data, including enantioselectivity, conversion, regioselectivity, or substrate specificity. Notably, this process would not require large data sets and would be adoptable by laboratories with a range of experimental and computational capabilities. Herein, we demonstrate the viability of this general strategy in the context of non-native enantioselective photoenzymatic radical cyclization reactions catalyzed by 'ene'-reductase variants from Gluconobacter oxydans (GluER, Fig. ). This strategy successfully guided GluER substrate scope expansion to reduce experimental screening efforts, and the statistical models rationalized the observed and predicted selectivities of previously untested substrates. Unlike current technologies for in silico protein engineering, which require additional datasets for each new substrate, our method accounts for both substrate and enzyme features, allowing for knowledge transfer to predict the performance of new substrate and mutant combinations.