ArticleACS combinatorial science2017
Poisson Statistics of Combinatorial Library Sampling Predict False Discovery Rates of Screening.
Article in ACS combinatorial science, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
16 citing papers in PubMed.
- Automated DNA-Encoded Library Synthesis and Activity-Based Screening at the Attomole Scale.Journal of medicinal chemistry · 2026Article
- Solid-phase DNA-encoded library synthesis: a master builder's instructions.Nature protocols · 2026Review
- Evaluation of multiplexed liquid glycan Array (LiGA) for serological detection of glycan-binding antibodies.Glycobiology · 2025Article
- Hydrogel-Bead Particles Enable Activity-Based Identification of Nucleic Acid Aptamer Enzyme Inhibitors.Analytical chemistry · 2025Article
- Activity-Based DNA-Encoded Library Screening for Selective Inhibitors of Eukaryotic Translation.ACS central science · 2024Article
- Evolution of chemistry and selection technology for DNA-encoded library.Acta pharmaceutica Sinica. B · 2024Review
- Dose-Response Activity-Based DNA-Encoded Library Screening.ACS medicinal chemistry letters · 2023Article
- Translating the Genome into Drugs.Accounts of chemical research · 2023Article
- Study of an RNA-Focused DNA-Encoded Library Informs Design of a Degrader of a r(CUG) Repeat Expansion.Journal of the American Chemical Society · 2022Article
- DNA-encoded library versus RNA-encoded library selection enables design of an oncogenic noncoding RNA inhibitor.Proceedings of the National Academy of Sciences of the United States of America · 2022Article
- Engineering Proteins Containing Noncanonical Amino Acids on the Yeast Surface.Methods in molecular biology (Clifton, N.J.) · 2022Article
- Antibacterial Discovery via Phenotypic DNA-Encoded Library Screening.ACS chemical biology · 2021Article
- DNA-Encoded Chemical Libraries: A Comprehensive Review with Succesful Stories and Future Challenges.ACS pharmacology & translational science · 2021Review
- High-throughput screening by droplet microfluidics: perspective into key challenges and future prospects.Lab on a chip · 2020Article
- Off-DNA DNA-Encoded Library Affinity Screening.ACS combinatorial science · 2020Article
- Activity-Based DNA-Encoded Library Screening.ACS combinatorial science · 2019Article
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
Microfluidic droplet-based screening of DNA-encoded one-bead-one-compound combinatorial libraries is a miniaturized, potentially widely distributable approach to small molecule discovery. In these screens, a microfluidic circuit distributes library beads into droplets of activity assay reagent, photochemically cleaves the compound from the bead, then incubates and sorts the droplets based on assay result for subsequent DNA sequencing-based hit compound structure elucidation. Pilot experimental studies revealed that Poisson statistics describe nearly all aspects of such screens, prompting the development of simulations to understand system behavior. Monte Carlo screening simulation data showed that increasing mean library sampling (ε), mean droplet occupancy, or library hit rate all increase the false discovery rate (FDR). Compounds identified as hits on k > 1 beads (the replicate k class) were much more likely to be authentic hits than singletons (k = 1), in agreement with previous findings. Here, we explain this observation by deriving an equation for authenticity, which reduces to the product of a library sampling bias term (exponential in k) and a sampling saturation term (exponential in ε) setting a threshold that the k-dependent bias must overcome. The equation thus quantitatively describes why each hit structure's FDR is based on its k class, and further predicts the feasibility of intentionally populating droplets with multiple library beads, assaying the micromixtures for function, and identifying the active members by statistical deconvolution.
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Registered trials
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