To ensure that the biaryl aldehydes 10 selected for evaluation are chemically diverse, we employed a data-driven substrate selection strategy (Figure ). This was comprised of an initial generation of a data-rich virtual library of arylboronic acids 13 followed by a machine learningbased selection strategy. The virtual library was first curated by computationally assessing all commercially available and in-house aryl boronic acids (a total of 5,136 boronic acids) (Step 1). To accomplish this, the geometry of each substrate was minimized and then 1,357 computationally inexpensive 2D and 3D Quantitative Structure Activity Relationship (QSAR) parameters -simple molecular descriptors routinely used in medicinal chemistry -were calculated for each boronic acid (Step 2, see SI for details). With this computational data set, the boronic acids were analyzed based on their similarity in descriptor space by performing principal component analysis (PCA) to reduce the dimensionality of the computed descriptors. A small, representative set was then selected for biaryl aldehyde synthesis by employing K-means clustering. This machine learning algorithm is used to group or "cluster" structures based on structural similarity (Step 3). Through this process, nine boronic acids (described below) were identified from distinct clusters, procured, and subjected to a Suzuki cross-coupling with aryl bromide 14 to produce the initial library of biaryl aldehydes 10a -10k (Step 4). We note that the selection strategy depicted in Figure resulted in a small, highly diverse library of candidates. Intuitively, this is inferred from a simple inspection of the structures and substitution patterns of 10a -10k. The library features both electron rich and electron poor arenes (e.g., 10a and 10d, respectively), substrates with extended p surface area (10f, 10g, and 10h), and a multitude of substitution patterns (e.g., 10c, 10e, and 10k). Moreover, the chemical diversity resulting from our workflow can be visualized (each member plotted as a blue point) using a Uniform Manifold Approximation and Projection (UMAP) visualization plot (see plot depicted in step 3). With UMAP, similar boronic acids are positioned close to one another in their descriptor space as demonstrated by their grouping in "islands". The compounds that were selected from our workflow (plotted with yellow circles) are well spread across the representation, reflecting the statistical diversity of our library. It should be noted that the UMAP axes enable visualization of the variance in the two plotted dimensions, similar to a principal component representation. We next turned our attention to the evaluation of assemblies 12, which were readily formed from the reaction of biaryl aldehydes 10 with Zn(OTf)2, dipicolylamine, and an enantioenriched g-stereogenic alcohol (either 11a or 11b, Figure ). The resultant assemblies 12a and 12b were assessed via CD spectroscopy (subscript a and b denote the use of alcohols 11a and 11b, respectively). In nearly all cases, we observed a common Cotton effect at 267 nm (see representative spectra, Figure ). On the basis of previous reports, it was concluded that this feature arose from exciton coupled circular dichroism (ECCD) of the helical pyridine chromophore (depicted in blue). The intensity of the CD signal varied substantially depending on the structure of 12, with signals of up to 22.7 mdeg observed for 12da and near-zero signals for assemblies 12bb, 12gb, and 12hb (Figure ). The diversity of responses is likely a result of the intentional incorporation of disparate In order to correlate CD intensity to the structural features of 12, we next employed a two-step statistical modelling protocol (Figure & B). This was accomplished by computing a range of physical organic descriptors to reflect the physical properties of 12a -12k. These were computed using a simplified chemical structure 15, which captured the essential structural elements anticipated to influence the CD response to the chiral alcohols. The descriptors included sterimol values (steric measurements), global electronic terms such as HOMO/LUMO energies and polarizability, as well as local electronic terms reflected by NBO charges on various atoms (see SI for full parameter list). Using a forward stepwise linear regression algorithm to correlate experimental CD intensities to computed parameters, a fourterm statistical model was found (Figure ). This model included one parameter to account for the alcohol 11a/11b and three derived from the biaryl pyridine motif. The alcohol parameter (classifier) was a simple unitless value (1 for 11a, -1 for 11b), used to assign which alcohol was used in the assembly. The remaining three parameters, NBOipso, DEFMO, and ortho B5 (a sterimol value), were derived from 15 (Figure ). While the parameters NBOipso and DEFMO had a relatively even distribution of values across 15a -15k (see SI), the B5 (Sterimol parameter reflecting size) for the ortho substituent had only three discrete values. Assemblies stemming from biaryl aldehydes with H at both A-ring ortho positions (12a -12i) all had an ortho B5 of 1.1Å and only 12j and 12k had larger measurements. Therefore, the parameter ortho B5 can be considered a classification term that reflects whether 12 has an ortho substituent.