Articlenpj drug discovery2025
Evaluation of DNA encoded library and machine learning model combinations for hit discovery.
Article in npj drug discovery, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 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
9 citing papers in PubMed.
- Contemporary design of small-molecule kinase modulators: orthosteric, allosteric and induced-proximity strategies.Nature reviews. Drug discovery · 2026Review
- Assessing the Generalizability of Machine Learning and Physics-Based Methods with DNA-Encoded Libraries.bioRxiv : the preprint server for biology · 2026Article
- Black-box data: a new paradigm for biomedicine in the AI era.Chemical science · 2026Review
- Deciphering DEL pocket patterns through contrastive learning.Nature communications · 2026Article
- From Serendipity to Strategy: Rationalizing Molecular Glue Discovery and Proximity-Induced Pharmacology through Chemical Biology.Journal of the American Chemical Society · 2026Review
- From randomness to recognition: modeling the evolution of DNA sequence information during enrichment for binding.Bioinformatics advances · 2026Article
- RNA-Focused DNA-Encoded Library Construction, Screening, and Integration of Docking Identify Bioactive Ligands of Pathogenic r(GbioRxiv : the preprint server for biology · 2025Article
- Enabling Open Machine Learning of Deoxyribonucleic Acid-Encoded Library Selections to Accelerate the Discovery of Small Molecule Protein Binders.Journal of medicinal chemistry · 2025Article
- Undersampling techniques for large datasets.bioRxiv : the preprint server for biology · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
14 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
DNA-Encoded Library (DEL) technology allows the screening of millions to billions of compounds in a pooled fashion, which is faster and cheaper than traditional approaches. The massive amounts of DEL binder and not-binder data enable Machine Learning (ML) model development and virtual screening of readily accessible, drug-like libraries in an ultra-high-throughput fashion. Here, we report a comparative assessment of DEL + ML pipeline for hit discovery using three DELs and five ML models (fifteen DEL + ML combinations). Each ML model was used to identify orthosteric binders of two therapeutic targets, Casein kinase 1α/δ (CK1α/δ). Overall, 10% and 94% of the predicted binders and not-binders were confirmed in biophysical assays, including two nanomolar binders (187 and 69.6 nM). Our study provides insights into the DEL + ML paradigm for hit discovery: the importance of chemical diversity in training data and ML model generalizability over accuracy. We publicly shared our results for further use and similar developments.
Identifiers
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.