DNA-encoded
chemical libraries (DELs) provide a high-throughput
and cost-effective route for screening billions of unique molecules
for binding affinity for diverse protein targets. Identifying candidate
compounds from these libraries involves affinity selection, DNA sequencing,
and measuring enrichment in a sample pool of DNA barcodes. Successful
detection of potent binders is affected by many factors, including
selection parameters, chemical yields, library amplification, sequencing
depth, sequencing errors, library sizes, and the chosen enrichment
metric. To date, there has not been a clear consensus about how enrichment
from DEL selections should be measured or reported. We propose a normalized z-score enrichment metric using a binomial distribution
model that satisfies important criteria that are relevant for analysis
of DEL selection data. The introduced metric is robust with respect
to library diversity and sampling and allows for quantitative comparisons
of enrichment of n-synthons from parallel DEL selections.
These features enable a comparative enrichment analysis strategy that can
provide valuable information about hit compounds in early stage drug
discovery.