DNA-Encoded Chemical Library Screening with Target Titration Analysis: DELTA

DNA-encoded
chemical libraries (DELs) enable the highly efficient
screening of billions of small molecules for binding to a target of
interest and provide valuable training data for machine learning models
for virtual screening. However, DEL screening data are notoriously
noisy due in large part to significant variance in the synthetic yield
of library members. Here, we show an analysis from a split-sample
DEL screening strategy against Bruton’s tyrosine kinase (BTK),
which includes a panel of affinity selections against the target at
varying concentrations and a probabilistic model to estimate the binding
affinity and relative input concentrations of library members. We
compared model predictions to SPR measurements of resynthesized DNA-conjugated
compounds and found that this methodology yielded an improved ranking
of library members by binding affinity compared to enrichment metrics.
Additionally, the method successfully recovered a library member with
a potent binding affinity that would not have been detected in our
standard DEL selection.