Development and Validation of an Automated DNA-Encoded Library Screening Data Analysis Platform: PB-DEL Autoscreening Analysis (PB-DELASA)

Tools
available for analyzing next-generation sequencing (NGS)
data produced from DNA-encoded library (DEL) screening campaigns are
often constrained to customized methods developed internally by individual
institutes, which usually generate data specifically focusing on protein–ligand
interactions and based on distinguished criteria of compound recommendation.
Existing approaches do not consider sequencing depth, sequencing error,
and quality control when identifying candidate compounds. The analysis
processes and criteria of compound recommendation for off-DNA synthesis
and confirmation are highly time-consuming and subjective, significantly
hindering the application of DEL screening in novel drug discovery.
Here, to address these challenges, we developed an integral, accurate,
and automated analysis workflow containing the tractability of the
building blocks and DNA tags in split-and-pool cycles, 2D and 3D plots,
and an enriched compound list, which was constructed based on computational
analysis, artificial intelligence, and the experiential knowledge
of medicinal chemists. This automated and standardized workflow was
further validated through a showcase screening campaign on a novel
antitumor target of CDK9. Novel hit compounds with high potency and
selectivity were identified efficiently with minimal synthesis effort.
The source code is available at https://github.com/kelly1210/PB-DELASA.