To expand the limited space of NCB candidates, we assembled a library of 132 NCBs, over 85% of which have not (to our knowledge) been previously evaluated. Each molecule contains the redox active 1,4-dihydropyridine ring and is uniquely identified by the combination of R1, R2, and R3 substituents (Figure ). Each substituent possibility was combinatorically added to the core 1,4dihydropyridine moiety. Substituents were selected based on previously evaluated NCBs to improve the likelihood that the resultant molecules will maintain some semblance of those that have been experimentally validated. However, we also included two additional R2 substituents which have not appeared in the literature to help examine substituent effects within the library, 4 and 6 in Figure . These substituents were added due to their electron-withdrawing capabilities, which we hypothesize will improve cofactor stability and allow further examination of the electronic effect of R2 substituents on stability while maintaining chemical similarity to precedented mimics. To refer to specific molecules we will adopt a three-character naming convention detailed in Figure , where the first, second and third characters correspond to the R1 (A-K), R2 (1-6), and R3 (a, b) substituent identities, respectively. For example, all NCBs with an octyl substituent at the R1 position are named D**, where the asterisks represent wildcard characters that could be any R2 and R3 substituent, respectively. Similarly, all NCBs which have a methyl group as the R3 substituent are named **b. In addition to the reduced form of each NCB, our automated workflow also generates the oxidized and decomposed species to study substituent effects on stability or redox activity. Generation of this expansive library was automated using an in-house developed Python script that is publicly available at and can be openly used to further expand this library with different substituents. More details about the generation of our NCB library can be found in section 1 the supplemental information. After SMILES (text-based) representations of each NCB in our library were generated, we followed an in-house automated protocol to perform conformational sampling and obtain optimized geometries at the DFT level of theory. This in-house script can be found at . Our conformational sampling procedure, using CREST to generate conformers and CENSO then CREST clustering to refine the ensemble, was used to isolate ten conformers of each NCB which were used for DFT optimizations and the curation of electronic-structure derived descriptors. A description of all calculated descriptors can be found in section 6 of the supplemental information. Principal component analysis (PCA) was applied to visualize the variations within our dataset of NCBs featurized by calculated atomic descriptors (Figure ). The first two principal components account for 71.6% of the total variance in the data, providing a sufficient representation of the diversity in our NCB library. Most of the experimental structures occupy a localized region of the chemical space, alluding to an improvement in molecular diversity. This robust library of structures allows more research into various substituent effects, opening the door for tuning specific properties of NCBs, such as reduction potential and stability in buffer.