Building Block-Centric Approach to DNA-Encoded Library Design

Abstract

DNA-encoded library technology grants access to nearly infinite opportunities to explore the chemical structure space for drug discovery. Successful navigation depends on the design and synthesis of libraries with appropriate physicochemical properties (PCPs) and structural diversity while aligning with practical considerations. To this end, we analyze combinatorial library design constraints including the number of chemistry cycles, bond construction strategies, and building block (BB) class selection in pursuit of ideal library designs. We compare two-cycle library designs (amino acid + carboxylic acid, primary amine + carboxylic acid) in the context of PCPs and chemical space coverage, given different BB selection strategies and constraints. We find that broad availability of amines and acids is essential for enabling the widest exploration of chemical space. Surprisingly, cost is not a driving factor, and virtually, the same chemical space can be explored with "budget" BBs.

Figure 1

Property-focused analysis of DEL synthesis strategies. (A) Synthesis of DEL1 proceeds on an amine-functionalized photocleavable linker. Fmoc–AA coupling occurs in the first chemistry cycle, followed by carboxylic acid coupling in the second step. Compounds photocleave at the indicated bond (UV) as primary amides. Primary amine and secondary amine containing Fmoc–AAs are depicted along with their calculated properties. The constant library scaffold is indicated (red). (B) DEL2 synthesis starts from an aldehyde-functionalized photocleavable linker. Primary amines are coupled by reductive amination in the first chemistry cycle, followed by carboxylic acid coupling in the second step. Compounds photocleave at the indicated bond (UV) as secondary amides. An example compound is illustrated along with its calculated properties according to RO5 criteria.

Figure 2

UMAP analysis of Fmoc–amino acid, primary amine, and carboxylic acid BBs from Enamine as truncates. Density plots arranged by chemical similarity are compared for (A) Fmoc–amino acid, (B) primary amine, and (C) carboxylic acid truncates. Grayscale intensity denotes the probability density of points. BB truncate counts in each pool are indicated in the top right of each plot. (D) Example BBs from Fmoc–AA, primary amine, and carboxylic acid sets are depicted for specific regions of UMAP space [colored points/rectangular outlines in (A–C)], and their truncates are indicated (bold). Fmoc–AAs cover a limited portion of the UMAP space relative to primary amines or carboxylic acids.

Figure 3

Availability of BB truncates following cost filtering. UMAP analyses are shown for three different cost thresholds (≤$100, ≤$250, and ≤$500 per 250 mg) for (A) Fmoc–AAs, (B) primary amines, and (C) carboxylic acids. Grayscale intensity denotes the probability density of points. BB truncate counts in each pool are indicated on the top right of each plot. Primary amines and carboxylic acids cover more diverse chemical space compared to Fmoc–amino acids at all cost points, especially at the lowest cost cutoff.

Figure 4

Availability of BB truncates following MW filtering. UMAP analyses are shown for three different MW thresholds (≤200, ≤250, and ≤300 Da) for (A) Fmoc–AAs, (B) primary amines, and (C) carboxylic acids. Grayscale intensity denotes the probability density. BB truncate counts in each pool are indicated on the top right of each plot. Filtering at these MW thresholds does not substantially alter the accessibility of chemical diversity for any of the BB sets. These regions (color coded) are shown to aid in comparison of identical UMAP regions between BB sets and MW filters.

Figure 5

Impact of different BB selection strategies on chemical space sampling. Density plots of primary amine truncates arranged by chemical similarity are overlaid with BB selections (colored points) generated by random, diversity, and uniform methods. Grayscale intensity denotes the probability density of points. Random selection is biased toward dense UMAP regions. Diversity selection improves the sampling of sparsely populated regions but remains biased toward densely populated regions. Uniform selection maximizes the coverage of the UMAP space.

Figure 6

PCP and diversity summary statistics for three two-cycle libraries (condensation of 192 primary amines with 192 carboxylic acids) with different cost constraints. BBs are filtered by cost (≤$100, ≤$250, and ≤500 per 250 mg) and then randomly selected until reaching the intended split size (192). Libraries are enumerated, and their properties are compared. The PCPs of enumerated products including (A) MW, (B) n-octanol–water partition coefficient (x Log P), (C) total polar surface area, and (D) HBD count are calculated and plotted as density plots. Black dashed lines indicate RO5 thresholds. The complete similarity matrix of all BBs within a given library is calculated, from which nn-Tanimoto scores for (E) the primary amine BBs and (F) the carboxylic acid BBs are determined and plotted.