ArticleJournal of medicinal chemistry2025
Enabling Open Machine Learning of Deoxyribonucleic Acid-Encoded Library Selections to Accelerate the Discovery of Small Molecule Protein Binders.
Article in Journal of medicinal chemistry, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
5 citing papers in PubMed.
- Linkerability of Protein Ligands: Insights From Cocrystal Structures and Implications for DNA-Encoded Libraries.Molecular informatics · 2026Article
- 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
- Undersampling techniques for large datasets.bioRxiv : the preprint server for biology · 2025Article
- CACHE Challenge #2: Targeting the RNA Site of the SARS-CoV-2 Helicase Nsp13.Journal of chemical information and modeling · 2025Article
Corrections and comments
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Authors and funding
32 authors.
Funding
Abstract
Machine learning (ML) is increasingly used in DNA-encoded library (DEL) screening for ligand discovery, but its success depends on access to suitable data sets, which are often proprietary and costly. To overcome this, we present the first fully open, automated DEL-ML framework using public DEL data sets and chemical fingerprints to enable reproducible, accessible drug discovery. Our workflow─from model training to virtual screening and compound selection─requires no human intervention. As a proof of concept, we identified binders for WDR91 by training ML models on the HitGen OpenDEL library (3B molecules) and screening the Enamine REAL Space library (37B molecules), yielding 50 candidates. Experimental testing confirmed seven novel binders with dissociation constants between 2.7-21 μM. Our open-source approach matches the performance of proprietary methods, demonstrating that public DEL data can support robust ML-driven ligand discovery and fostering transparency and broader community participation in drug development.
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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.