Amanda W. Dombrowski，Florent Samain

ACS Medicinal Chemistry Letters

DOI: 10.1021/acsmedchemlett.6c00047

Abstract

Over the past 30 years, the field of DNA-encoded libraries (DELs) has become a mature and robust technology platform for the identification of ligands against relevant biological protein targets. Most of the major innovative pharmaceutical companies have integrated DEL platforms into their drug discovery workflows. Indeed, DELs have significantly impacted drug discovery efforts in the last 10 years with the identification of ligands that have progressed into clinical trials for various disease indications. One could assume that there are likely even more DEL-derived ligands that have reached the clinic, but candidate stories do not necessarily mention the hit generation methods utilized. (1−3) The fundamental concept of DELs emerged in a theoretical paper from Lerner and Brenner in 1992. (4) DELs are defined as collections of small molecules that are covalently attached to unique DNA tags, serving as amplifiable identification barcodes. Encoding procedures allow the generation and screening of combinatorial libraries of high diversity and unprecedented size. Preferential binding molecules identified by high throughput DNA sequencing of libraries after affinity capture procedures are typically resynthesized and tested to characterize their binding properties. (5) Success of DEL-based drug discovery screening campaigns is affected by various factors, including quality and diversity of the libraries, screening protocols, protein selection conditions, downstream validation and data analysis methods. Advances in the field have expanded DEL applications beyond traditional synthesis and screening procedures. Recently, the incorporation of artificial intelligence (AI) and machine learning (ML) techniques in DEL workflows has been accelerating, driving significant advancements and extending the potential of technology. (6,7) This ACS Medicinal Chemistry Letters Collection features recent success stories that highlight the value and impact of DEL technology in drug discovery. The following report, A Novel Small Molecule Allosteric Inhibitor of IL-17A from a DNA-Encoded Library, demonstrates the ability of DEL to identify small molecules that directly modulate the IL-17 pathway by inhibiting IL-17A with their cognate receptors, proving that small molecules can mimic the action of macromolecular biologics to disrupt high affinity protein–protein interactions. DEL macrocycle-like libraries have also proven effective for the recognition of larger protein surfaces (DNA-Encoded Macrocyclic Peptide Libraries Enable the Discovery of a Neutral MDM2–p53 Inhibitor). Beyond traditional inhibitor identification, DEL technology has become a pivotal approach for the development of targeted protein degradation (TPD) therapeutics. The report, Discovery of Small-Molecule Ligands for the E3 Ligase STUB1/CHIP from a DNA-Encoded Library Screen, from AstraZeneca scientists, shows that DEL can be effectively used to identify small molecule ligands for STIP1 homology and U-box containing protein 1 (STUB1), an E3 ligase that contains protein–protein interaction (PPI) sites, where previously only peptide binding ligands have been discovered. DEL hits can also serve as tools to provide structural basis for further hit-to-lead progression, thus accelerating medicinal chemistry lead optimization activities (Structural and Molecular Insight into the PWWP1 Domain of NSD2 from the Discovery of Novel Binders Via DNA-Encoded Library Screening; Optimization of a Novel DEL Hit That Binds in the Cbl-b SH2 Domain and Blocks Substrate Binding; Chemical Space Profiling of SARS-CoV-2 PLpro Using DNA-Encoded Focused Libraries). By leveraging the broad chemical diversity offered by DEL techniques, researchers have been able to identify robust covalent inhibitors that specifically interact with cysteine residues. This targeted approach helps improve both the selectivity and efficacy of potential therapeutic agents (Identification and Evaluation of Reversible Covalent Binders to Cys55 of Bfl-1 from a DNA-Encoded Chemical Library Screen). Recent literature indicates that DEL designs have become more strategic, influenced by progress in synthetic methodologies, expanded chemical diversity, and enhanced access to structural biology data. The perspective, Design of DNA Encoded Libraries for Medicinal Chemistry, provides a comprehensive analysis of DEL derived hits that enabled medicinal chemistry programs from publications between 2020 and 2025. The development of computational tools has offered new opportunities to rationalize DEL designs and DEL data set analysis. The integration of DEL technology with computational approaches, such as machine learning, continues to unleash the potential of the technology (Highly Selective Novel Heme Oxygenase-1 Hits Found by DNA-Encoded Library Machine Learning beyond the DEL Chemical Space; Evaluating the Diversity and Target Addressability of DELs using Scaffold Analysis and Machine Learning). This Collection features 10 publications, which highlight the rapid growth in many areas of the DEL field. We thank the authors for their work, which we hope will inform and inspire our readers to expand upon that work. This article references 7 other publications. This article has not yet been cited by other publications.
 

Summary

This editorial introduces a special collection of 10 publications highlighting the rapid evolution and maturation of DNA-encoded library (DEL) technology over the past 30 years. DELs, which consist of small molecules covalently attached to unique DNA barcodes for identification, have transitioned from a theoretical concept (first proposed by Lerner and Brenner in 1992) to a robust, industry-standard platform for drug discovery. Major pharmaceutical companies have now integrated DEL platforms into their workflows, with several DEL-derived ligands progressing into clinical trials over the last decade. The editorial emphasizes that recent advances—including AI/ML integration, novel library designs, and expanded applications beyond traditional inhibitor discovery—are significantly extending the potential of this technology.

Highlights

1. Diverse Therapeutic Applications

• IL-17A Inhibition: DEL technology successfully identified small molecule allosteric inhibitors that disrupt high-affinity protein-protein interactions (PPIs), demonstrating that small molecules can mimic macromolecular biologics.
• Macrocyclic Libraries: DEL macrocycle-like libraries enabled the discovery of neutral MDM2-p53 inhibitors, effective for recognizing larger protein surfaces.
• Targeted Protein Degradation (TPD): DEL screening identified small molecule ligands for STUB1/CHIP (an E3 ligase containing PPI sites), where previously only peptide binders were known.

2. Structural and Mechanistic Insights

• DEL hits provide structural foundations for hit-to-lead optimization, accelerating medicinal chemistry efforts. Examples include:
• Novel binders to the PWWP1 domain of NSD2
• Inhibitors targeting the Cbl-b SH2 domain
• Chemical space profiling of SARS-CoV-2 PLpro

3. Covalent Inhibitor Discovery

• Strategic DEL designs enable the identification of reversible covalent binders targeting specific cysteine residues (e.g., Cys55 of Bfl-1), improving selectivity and efficacy.

4. Integration of Artificial Intelligence and Machine Learning

• AI/ML techniques are increasingly incorporated into DEL workflows for:
• Rationalizing library designs
• Analyzing DEL datasets
• Discovering hits beyond traditional DEL chemical space (e.g., highly selective heme oxygenase-1 inhibitors)
• Scaffold analysis and target addressability prediction

5. Strategic Library Design Evolution

• Recent DEL designs have become more sophisticated, influenced by:
• Progress in synthetic methodologies
• Expanded chemical diversity through validated on-DNA chemical reactions
• Enhanced access to structural biology data

Conclusion

The editorial concludes that DEL technology has become a mature, robust, and indispensable platform in modern drug discovery. The featured collection demonstrates the technology's versatility across multiple applications—from traditional inhibitor identification to targeted protein degradation and covalent drug discovery. The integration of computational approaches, particularly machine learning, continues to unlock new potential and extend DEL capabilities beyond its original scope. The editors (Amanda W. Dombrowski and Florent Samain) express gratitude to the contributing authors and anticipate that these works will inform and inspire readers to further expand the boundaries of DEL technology in pharmaceutical research.