Yuichi Onda ,  Yurika Ochi ,  Toshihiro Araki ,  Miho Kageoka ,  Shuzo Takeda ,  Kazunori Yamada ,  Takehiko Ueda ,  Ken Ohno ,  Minoru Tanaka ,  Daiki Sakai ,  Miki Hasegawa ,  Yoshihito Tanaka  bioRxiv - Synthetic Biology DOI: 10.1101/2025.11.26.690606 Abstract Targeted protein degradation (TPD), including proteolysis targeting chimeras (PROTACs) and molecular glue degraders (MGDs), is a promising therapeutic approach. However, systematic discovery of such small molecules remains a major challenge. Here, we present PhenoDEL, a novel phenotypic DNA-encoded library (DEL) screening platform that integrates one-bead one-compound DEL (OBOC-DEL) with the Beacon® optofluidic system for high-throughput, single-cell analysis. By co-culturing individual OBOC-DEL beads and engineered reporter cells in nanoliter-scale chambers, PhenoDEL enables direct observation of compound-induced protein degradation at single-cell resolution. We demonstrate this approach by identifying compounds that induce degradation of FKBP12F36V-EGFP fusion proteins in PC-3 cells. The workflow allows precise linkage between compound identity and cellular phenotype via DNA barcoding and next-generation sequencing. PhenoDEL overcomes limitations of conventional screening methods, offering high sensitivity, spatial control, and scalability. This platform holds significant potential for mechanism-driven drug discovery, including identification of novel PROTACs and MGDs. Summary This preprint introduces PhenoDEL, a novel phenotypic screening platform that integrates One-Bead One-Compound DNA-Encoded Library (OBOC-DEL) technology with the Beacon® optofluidic system for high-throughput, single-cell analysis of targeted protein degradation (TPD). The platform enables direct observation of compound-induced protein degradation by co-culturing individual OBOC-DEL beads with engineered reporter cells in nanoliter-scale chambers (NanoPens). Upon UV-A irradiation, compounds are photoreleased from beads and diffuse to the cell, causing degradation of a FKBP12F36V-EGFP fusion protein. Beads associated with cells showing EGFP fluorescence loss are recovered, and their DNA barcodes are sequenced to identify active compounds. The authors optimized compound release kinetics (200 ms UV exposure), retention conditions (halting CO₂ flow increases concentration 5-fold), and imaging protocols. In a proof-of-concept screen, PhenoDEL successfully identified PROTAC molecules with >99% cell viability, demonstrating its capacity for mechanism-driven discovery of protein degraders including PROTACs and molecular glues. Highlights Single-cell resolution screening: PhenoDEL achieves 1:1 pairing of individual OBOC-DEL beads and cells in 0.75 nL NanoPen chambers, enabling direct linkage between compound identity and cellular phenotype via DNA barcoding. Real-time quality control: The platform excludes dead or damaged cells by monitoring EGFP fluorescence before and after compound release, reducing false positives and improving data reliability compared to pooled screening methods. Optimized compound delivery: UV-A irradiation (390 nm, 200 ms) cleaves photolabile linkers to achieve biologically relevant concentrations (10-90 µM) while stopping CO₂ flow enhances compound retention within chambers. High throughput: Up to four OptoSelect® chips (3,500 nanopens/chip) can run simultaneously, enabling >10,000 samples per run; scalable to 80,000 compounds using 20k-nanopen chips. Validated proof-of-concept: Engineered PC-3 cells expressing FKBP12F36V-EGFP showed robust degradation of the fusion protein within 6 hours of PROTAC FKBP Degrader-3 exposure, with minimal cytotoxicity and stable EGFP baseline (>5,000 fluorescence units). Versatile applicability: The system is compatible with various cell types (suspension, adherent, primary cells, organoids) and reporter systems, positioning it for personalized medicine and comprehensive functional genomics. Conclusion PhenoDEL represents a significant advancement in DNA-encoded library screening by overcoming limitations of conventional affinity-based and droplet-based methods. The integration of OBOC-DEL with Beacon's optofluidic technology enables high-resolution, activity-based screening at the single-cell level, providing spatial control, real-time phenotypic tracking, and direct genotype-phenotype correlation. The platform's ability to precisely modulate compound release, maintain cell viability, and automatically filter out low-quality data points establishes a robust framework for discovering novel PROTACs, molecular glue degraders, and other proximity-inducing molecules. With demonstrated scalability and compatibility across diverse biological models, PhenoDEL holds substantial potential for next-generation drug discovery, particularly in targeting previously undruggable proteins through event-driven pharmacology.

Li Zhou , Yong Ju , Zhijuan Cao , Sheng Cai , Jiayuan Su , Jianzhong Lu  Journal of Pharmaceutical Analysis DOI: 10.1016/j.jpha.2025.101498 Highlight Through screening 31 DNA-encoded chemical libraries, totaling 4.4 billion molecules, we identified a novel class of selective CDK2 inhibitors. The drug-likeness of C172 at the cellular level was evaluated, such as in vitro enzymatic and cellular assays, mechanistic studies on protein degradation, ADME characterization, single-dose pharmacokinetics in rats and metabolite identification.  

Yiwei Zhang, Yuqiu Lan, Rufeng Fan, Lei Feng, Guoliang Wang, Xinyuan Wu, Lulu Wen, Zhiqiang Duan, Yueyue Xia, Xudong Wang, Lingrui Zhang, Lu Zhou, Minjia Tan, Cangsong Liao, Xiaojie Lu  Journal of the American Chemical Society DOI: 10.1021/jacs.5c14634 Abstract DNA-encoded libraries (DELs) have emerged as an effective and efficient selection strategy for lead compound discovery in academia and industry over the past few decades. Despite recent advancements in this field, DEL remains limited by sensitive DNA-based constructs, particularly with low selection success rates resulting from the random selection of targets. Here, we describe a chemoenzymatic on-DNA reaction for DEL syntheses and develop a chemoproteomic-guided DEL selection platform. This platform, termed FF tags-biocatDEL, integrates DEL technology, chemoenzymatic synthesis, and fully functionalized (FF) chemical tags to match DELs with selection targets, even with limited information about ligandable hotspots. Using two diazirine-based FF indole probes, we comprehensively surveyed binding partners in cells and identified phosphoglycerate dehydrogenase (PHGDH) as a potential target for DEL selection. DEL01 and DEL02 were designed, synthesized, and selected against PHGDH, leading to the identification of a novel enzyme-active compound with an IC50 value of 2.5 μM. Our strategy, utilizing FF tags-biocatDEL, establishes a generalizable workflow for rapid target hunting and ligand discovery. It provides an effective method for precisely matching DELs with potential targets, demonstrating its significant potential as a complementary approach to drug discovery based on DELs. Summary This study presents a novel FF tags-biocatDEL platform that integrates chemoenzymatic synthesis, chemoproteomics, and DNA-encoded library (DEL) technology to overcome the low success rates of traditional DEL selection. The researchers developed a DNA-compatible decarboxylative aldol reaction using the PLP-dependent enzyme ApUstD to generate indole scaffolds bearing amine and carboxyl functional groups. Through chemoproteomic profiling with diazirine-based fully functionalized (FF) indole probes, they identified phosphoglycerate dehydrogenase (PHGDH) as a high-priority target from 2,208 enriched proteins. Two focused DELs were constructed: DEL01 (281,158 members via 2-cycle synthesis) and DEL02 (1.35 million members derived from a lactone fragment). Affinity selection against PHGDH yielded L5, a novel indole-based inhibitor with an IC₅₀ of 2.5 μM that acts via an allosteric mechanism. This strategy demonstrates that chemoproteomic guidance significantly enhances DEL selection efficiency and expands the chemical space for challenging targets. Highlights Innovative Chemoenzymatic Reaction: The first application of ApUstD on DNA substrates, achieving quantitative conversion (up to 100%) under mild aqueous conditions to generate complex indole scaffolds with γ-hydroxy-α-amino acid structures. Chemoproteomic-Guided Target Identification: Diazirine-based FF indole probes enabled unbiased profiling of 2,208 ligandable proteins, with PHGDH emerging as a clinically relevant target (ranked 111th) for cancer and neurodegenerative diseases. Potent Allosteric Inhibitor Discovery: L5, derived from a lactone byproduct (L3) scaffold, showed sub-micromolar potency (IC₅₀ = 2.5 μM) and allosteric inhibition independent of NAD⁺ concentration, representing a new chemotype for PHGDH. Scaffold Optimization via DEL Iteration: Initial hits (L1, L2) showed modest activity, but leveraging a side-product scaffold (L3) to build DEL02 (1.35M compounds) enabled a 20-fold activity improvement over the parent fragment. Technical Milestones: Successfully synthesized large-scale DELs using biocatalysis, validated target engagement via photo-crosslinking and thermal shift assays, and established a generalizable workflow combining fragment-based DELs with proteome-wide targeting data. Conclusion The FF tags-biocatDEL platform successfully bridges biocatalysis, chemoproteomics, and DEL technology to create a highly efficient, target-directed drug discovery workflow. By using chemoproteomic data to rationally select PHGDH and focused DELs to optimize a biocatalytically derived indole scaffold, the team discovered L5, a novel, compact PHGDH inhibitor with promising activity. This approach significantly outperforms random target selection and expands the accessible chemical space for traditionally challenging enzymes. While the platform currently leverages biocatalysis primarily for scaffold generation, future expansion to multiple DEL synthesis steps could further enhance diversity. Additionally, the affinity-based selection may identify non-functional binders that could be repurposed as PROTACs or other modalities. Overall, this strategy offers a robust complement to conventional DEL methods and holds substantial promise for accelerating lead discovery against emerging therapeutic targets.

Wenyi Zhang, Yuxing Wang, Rui Zhan, Runtong Qian, Qi Hu, Jing Huang bioRxiv - Biophysics DOI: 10.1101/2025.06.12.659183 Abstract DNA-encoded libraries (DELs) facilitate high-throughput screening of trillions of molecules against protein targets through split-pool synthesis and DNA tagging. Despite their potential, only a few DEL-derived compounds have advanced to clinical trials or reached the market. A better understanding of the defining characteristics of target proteins, particularly those with binding pockets suitable for DEL screening, is critical to improving success rates. However, existing approaches remain limited in assessing pocket flexibility and functional similarity. Here, we present ErePOC, a pocket representation model based on contrastive learning with ESM-2 embeddings to address these challenges. ErePOC captures both structural and functional features of binding pockets, enabling identification of shared characteristics among DEL targets. By integrating analyses of low-dimensional physicochemical properties and high-dimensional ErePOC embeddings, we provide a comprehensive view of DEL target space. With 98% precision in downstream classification tasks, ErePOC demonstrates high performance in pocket representation, which is then applied to predict human proteins suitable for DEL screening, with enrichment uncovered across 18 functional categories. This work establishes a new framework for enhancing DEL-based drug discovery through more effective target selection and pocket similarity analysis. Summary This study introduces ErePOC, a novel pocket representation model that employs contrastive learning with ESM-2 embeddings to decode the defining characteristics of protein binding pockets amenable to DNA-encoded library (DEL) screening. Despite DEL technology's capacity to screen trillions of compounds, clinical translation remains limited due to poor understanding of target druggability. The researchers analyzed 128 successful DEL targets and compared them to 326,416 general ligand pockets (BioLiP2) and 340 FDA-approved drug pockets, revealing that DEL pockets are uniquely larger (28.1 vs 16.1 residues), more hydrophobic, and enriched in specific amino acids (Met, Tyr, Trp, Phe, Leu). ErePOC was trained to map pockets to a 256-dimensional latent space aligned with ligand chemical similarity, achieving 98% precision in functional classification. Applied to 23,391 AlphaFold2-predicted human proteins, the model identified 2,739 DEL-compatible targets with pockets showing >0.8 cosine similarity to known DEL pockets. Enrichment analysis revealed 18 functional categories, particularly oxidoreductases, transferases, and multifunctional enzymes. In silico docking of 2.8 million virtual DEL compounds against 14 selected targets confirmed that ErePOC-enriched proteins exhibit significantly better predicted binding affinities than neutral controls. This work establishes a computational framework for rational DEL target selection beyond traditional structural similarity metrics. Highlights Distinct DEL Pocket Signature: DEL-binding pockets are 1.3× larger (3,301 ų volume), more hydrophobic (50.7% hydrophobic interactions vs 32.5% in natural pockets), and enriched in flexible aromatic/hydrophobic residues (Met, Tyr, Trp, Phe) compared to regular ligand and FDA-approved drug pockets. ErePOC Model Innovation: A contrastive learning framework that aligns pocket representations with ligand Morgan fingerprints via KL divergence loss, generating function-aware embeddings that capture physicochemical and evolutionary features beyond 3D geometry, robust to pocket flexibility. Robust Zero-Shot Performance: Achieves superior classification of 7 ligand-binding pocket types (~43,000 pockets) with 98.5–98.9% accuracy; maintains strong performance even for pocket classes excluded from training, demonstrating powerful generalization. Large-Scale Human Proteome Screening: Identified 2,739 unique human proteins with DEL-compatible pockets from AlphaFold2 structures, with significant enrichment in transferases (17.9%), hydrolases (11.6%), and oxidoreductases (9.4%), plus novel classes like RNA-binding proteins and chromatin regulators. Experimental Validation via Docking: In silico screening of 2.8M DEL-like molecules against ErePOC-selected targets showed statistically significant better binding affinity (mean Z-score –2.18 vs –1.07) and higher enrichment for DEL-enriched vs DEL-neutral protein families. Case Study Insights: The regulatory protein MAT2B exhibits higher DEL compatibility (cosine similarity 0.93, docking –8.8 kcal/mol) than its catalytic paralog MAT2A (0.66, –5.3 kcal/mol), demonstrating ErePOC's ability to resolve subtle family-level differences in druggability. Conclusion ErePOC provides a transformative approach to DEL target selection by learning high-dimensional, function-aware representations of binding pockets that transcend traditional structural alignment limitations. The model successfully deciphers a unique DEL pocket pattern—characterized by larger size, enhanced hydrophobicity, and specific amino acid biases—and leverages this to predict over 2,700 human proteins likely amenable to DEL screening across 18 enriched functional categories. By capturing physicochemical relationships rather than relying solely on geometric similarity, ErePOC addresses the critical challenge of pocket flexibility and low structural overlap among functionally related sites. The significant enrichment of oxidoreductases, transferases, and multifunctional enzymes validates known DEL success stories while expanding the targetable space to include chromatin regulators and RNA-binding proteins. In silico validation confirms that ErePOC-selected targets bind DEL-like molecules more favorably, supporting its practical utility. This framework not only enhances DEL efficiency but also offers broad applicability for virtual screening, molecule generation, and protein design, particularly when integrated with advanced structure prediction tools like AlphaFold3.

Mengke You, Hong Liu, Chunpu Li JACS Au  DOI: 10.1021/jacsau.5c01134 Abstract Covalent modification of therapeutic targets has emerged as a powerful platform for creating clinical drugs and chemical probes. Covalent drugs have evolved from serendipitous discoveries to rationally designed therapeutics, driven by advances in electrophile-first screening technologies. This perspective takes stock of alternative technologies currently available in laboratories and industry that collectively enable targeted covalent inhibitor development across historically “undruggable” targets. We highlight five such technologies: activity-based protein profiling (ABPP), provides functional proteomic mapping to identify ligandable residues; covalent tethering, exploits dynamic chemistry to capture transient pockets; covalent DNA-encoded libraries, leverages trillion-member libraries for multiresidue targeting; phage/mRNA display, which facilitates evolution of covalent macrocyclic peptides; and sulfur(VI) fluoride exchange (SuFEx), engages residues beyond cysteine. Integration of these approaches with chemoproteomics and artificial intelligence accelerates the discovery of covalent inhibitors with enhanced selectivity and reduced off-target risks. This technological convergence establishes a new paradigm for precision covalent therapeutics, offering innovative solutions to overcome drug resistance and target challenging protein interfaces.

Hossam Nada ,  Shaoren Yuan ,  Farida El Gaamouch ,  Sungwoo Cho ,  Katarzyna Kuncewicz ,  Laura Calvo-Barreiro ,  Moustafa T. Gabr European Journal of Medicinal Chemistry  DOI: 10.1016/j.ejmech.2025.118358 Abstract Triggering receptor expressed on myeloid cells 2 (TREM2) is a key regulator of microglial function, and its loss-of-function variants are linked to Alzheimer’s disease (AD) and neurodegenerative disorders. While TREM2 activation is a promising therapeutic strategy, no small molecule agonists acting via direct TREM2 binding have been reported to date. Here, we describe the discovery of first-in-class, direct small molecule TREM2 agonists identified through DNA-encoded library (DEL) screening. The DEL hit (4a) demonstrated TREM2 binding affinity, as validated by three biophysical screening platforms (TRIC, MST, and SPR), induced Syk phosphorylation, luciferase assay and enhanced microglial phagocytosis. Pre-liminary optimization yielded 4i, which maintained TREM2 engagement with improved selectivity over TREM1 and no cytotoxicity. Molecular dynamics simulations predicted that 4a stabilizes a transient binding pocket on TREM2, indicating the possibility of a novel mechanism for receptor activation. These findings provide the first proof-of-concept for direct pharmacological TREM2 agonism, offering a foundation for developing therapeutics against AD and related disorders.

Roy Aerts, Joris Tavernier, Alan Kerstjens, Mazen Ahmad, Jose Carlos Gómez-Tamayo, Gary Tresadern, Hans De Winter Journal of Cheminformatics DOI: https://doi.org/10.1186/s13321-025-01109-x Abstract Peptide drug development is currently receiving due attention as a modality between small and large molecules. Therapeutic peptides represent an opportunity to achieve high potency, selectivity, and reach intracellular targets. A new era in the development of therapeutic peptides emerged with the arrival of cyclic peptides which avoid the limitations of parenteral administration via achieving sufficient oral bioavailability. However, improving the membrane permeability of cyclic peptides remains one of the principal bottlenecks. Here, we introduce a deep learning regression model of cyclic peptide membrane permeability based on publicly available data. The model starts with a chemical structure and goes beyond the limited vocabulary language models to generalize to monomers beyond the ones in the training dataset. Moreover, we introduce an efficient estimator2generative wrapper to enable using the model in direct molecular optimization of membrane permeability via chemical modification. We name our application C2PO (Cyclic Peptide Permeability Optimizer). Lastly, we demonstrate how a molecule correction tool can be used to limit the presence of unfamiliar chemistry in the generated molecules. Summary This study presents C2PO (Cyclic Peptide Permeability Optimizer), a novel machine learning-driven application that improves the membrane permeability of cyclic peptides through chemical structure modification. The core of C2PO consists of a Graph Transformer deep learning model trained on the CycPeptMPDB dataset (7,451 permeability measurements), achieving state-of-the-art performance (R² = 0.61, Pearson r = 0.78, MAE = 0.37 on test set). Unlike conventional generative models, C2PO employs an estimator2generative approach, using gradient-based optimization based on the HotFlip algorithm to suggest structural modifications. The framework operates in two stages: first, it generates permeability-optimized peptide analogs by mutating side chains while preserving the macrocycle backbone; second, it automatically corrects chemically invalid structures using a dictionary-based correction tool referencing ChEMBL31. A case study on 700 low-permeability cyclic peptides demonstrated that 76.86% of optimization campaigns successfully produced at least one offspring with improved permeability (logPapp > -6.0), with 42.05% of all 13,043 generated molecules crossing this threshold. The system allows flexible user control over modification scope, elemental composition, and optimization parameters, making it a practical tool for medicinal chemists to generate ideas for improving peptide drug candidates. Highlights First-in-class application converting a machine learning model into a generative optimizer specifically for cyclic peptide permeability improvement Estimator2generative paradigm that decouples property estimation from structure generation, enabling broader chemical space exploration beyond training vocabulary State-of-the-art Graph Transformer model (based on GRAPHGPS framework) trained on comprehensive CycPeptMPDB dataset with robust cross-validation performance Automated chemistry correction workflow using a dictionary-based tool to validate and fix chemically unrealistic structures post-optimization, preserving 78% of successful optimizations Demonstrated effectiveness in a large-scale case study: 76.86% success rate for campaigns and 42.05% of offspring molecules achieving high permeability High flexibility allowing user-defined constraints on backbone protection, elemental modifications, molecular size changes (±5 atoms), and optimization parameters Beyond-vocabulary generalization capability to handle monomers not present in training data, overcoming limitations of language model-based approaches Conclusion Generally, cyclic peptides lack adequate membrane permeability to be developed into medicines. We propose C2PO (Cyclic Peptide Permeability Optimizer), an application that improves permeability by modifying the chemical structure of a given cyclic peptide. C2PO is ML-driven, trained on the experimental CycPeptMPDB dataset, and can be categorized in the estimator2generative optimization paradigm. However, ML-based applications that output chemical structures have the tendency of occasionally proposing strange chemistry, attributable to the loss of chemical knowledge, although it is generally considered to be implicitly learned. Therefore, we opted for checking and correcting the outcomes of C2PO using a chemistry library-based autocorrection application in a subsequent step. This contribution provides insights into what one can expect when applying these two applications. Seven hundred permeability optimization campaigns were launched where only peptide side chains were allowed to be modified. In general, we observed optimization for many campaigns, meaning that bad permeability starting points were optimized to structures with estimated permeability above the threshold of -6.0 logPapp. In the chemical correctness check step, we identified that a substantial portion (22.9%) of output structures needed correction. The autocorrection tool modified these, and we tracked how optimized permeability altered upon chemical correction. Various scenarios occurred, but the most important was that for many campaigns, the second step did not counteract the initial permeability optimization. We discussed in detail how to properly use our model and workflow, noting its flexibility for user customization. We focused on providing insights into basic capabilities rather than pursuing optimal performance, while informing about ways to improve both permeability optimization and molecular autocorrection. Finally, we hope to raise general interest in adopting estimator2generative optimizer strategies for chemical problems and deploying chemistry-library-driven applications for post-correcting ML-generated structures.

Ji Young Ryu ,  Dohui Ku ,  Hayeong Shin ,  Yoojin Park ,  Hokyung Kim ,  Jihoon Lee ,  Gil Tae Hwang ,  Minsoo Song ,  Ki Tae Kim Organic Letters DOI: 10.1021/acs.orglett.5c04328 Abstract The development of new on-DNA synthetic methods is crucial for efficient drug discovery using DNA-encoded libraries (DELs). Herein, we present a novel on-DNA approach based on hydrosulfonation of alkynylcarbonyl compounds to access β-sulfonyl-α,β-unsaturated carbonyl scaffolds bearing vinyl and γ-carbonyl sulfone skeletons. The reaction proceeds via a simple mix of alkynylcarbonyl compound and sodium sulfinate at 80 °C, showing moderate to good conversions, Z-geometry preference, and good DNA compatibility, supporting its utility in DEL construction.

Yagong Wang ,  Kehan Zhou ,  Haiqian Zhu ,  Huimin Sun ,  Hongli Cao ,  Lijun Xue ,  Kexin Yang ,  Yun Jin Hu Organic Letters DOI: 10.1021/acs.orglett.5c04222 Abstract We present a modular on-DNA bridging strategy that addresses key reactivity challenges in DNA-encoded library synthesis. By generation of a reactive carbamodithioic acid intermediate from secondary amines, this method enables efficient coupling with epoxides to produce diverse 2-hydroxy dithiocarbamate scaffolds. Gentle and DNA-compatible, it offers a versatile platform for rapid, structurally diverse DEL construction with significant drug discovery potential.

Yi Gan, Yumei Zeng, Haojie Guan, Wenjun Li, Alex Shaginian, Jin Li, Sen Gao, Guansai Liu Bioconjugate Chemistry https://doi.org/10.1021/acs.bioconjchem.5c00455 Abstract Bicylic peptides, with two cyclic substructures, have emerged as a powerful tool for modulating challenging targets such as protein−protein interactions. Meanwhile, DNA-encoded library technology (DELT) provides a powerful platform for hit discovery. The unity of both fields has the potential to identify potent bicyclic ligands for the targets of interest. Therefore, there is a high demand to develop an efficient way to construct bicyclic peptide libraries. Herein, we describe a novel and efficient approach to the synthesis of DNA-encoded bicyclic peptides via a cysteine-promoted cyclization and amide condensation reaction. This strategy proceeds smoothly under mild conditions and can generate a wide range of bicyclic peptides with various peptide sequences and ring sizes in good conversion. The method employs a trifunctional cross-linker, methyl 3,5-bis-(bromomethyl)benzoate, to enable sequential thioether and amide bond formation, overcoming stability issues associated with traditional protecting groups. The chemistry is compatible with diverse functional groups, N-methylation, and both short and long DNA strands, demonstrating its suitability for constructing large-scale DNA-encoded bicyclic peptide libraries. Summary This report presents a robust DNA-compatible methodology for synthesizing bicyclic peptides suitable for DNA-encoded library technology (DELT). The strategy addresses a critical limitation in the field: the instability of StBu-protected cysteine under Fmoc-deprotection conditions, which generates byproducts and compromises library quality. The authors developed a two-step macrocyclization approach using methyl 3,5-bis-(bromomethyl)benzoate as a trifunctional cross-linker. The first cyclization forms a dithioether linkage via reaction with cysteine thiols, while the second cyclization creates an amide bond through ester hydrolysis and intramolecular condensation. This method proceeds under mild aqueous conditions and demonstrates broad substrate scope, enabling synthesis of bicyclic peptides with ring sizes ranging from [2+2] to [7+8] amino acids. The chemistry tolerates various functional groups (indole, guanidine, phenol, thioether, etc.) and backbone N-methylation. Validation studies on 543 di-, tri-, and tetrapeptide building blocks showed 420 achieved ≥50% conversion, confirming feasibility for library production. The method was successfully applied to both short DNA (7 bp) and long DNA (72 bp) with enzymatic ligation, showing no significant DNA damage. Off-DNA synthesis confirmed the structural integrity of the bicyclic products. This approach enables access to billions of diverse bicyclic peptides, bridging the therapeutic gap between small molecules and biologics. Highlights Wide substrate scope: Successfully synthesized bicyclic peptides with diverse amino acid sequences and ring sizes from [2+2] to [7+8] High efficiency: Achieved conversions of 45-88% across various bicyclic peptide structures Mild reaction conditions: Compatible with DNA-encoded library synthesis requirements Two-step macrocyclization: Novel use of trifunctional cross-linker enabling sequential thioether and amide bond formation Functional group tolerance: Compatible with indole, guanidine, phenol, thioether, alcohol, and N-methylated residues DEL compatibility: Validated on long-chain DNA (72 bp) with successful enzymatic ligation and no detectable DNA damage Scalability potential: Demonstrated feasibility for constructing libraries exceeding 1.6 billion compounds using validated di/tri/tetrapeptide building blocks Conclusion In summary, we have developed a novel and efficient methodology to synthesize DNA-encoded bicyclic peptides via two independent macrocyclization reactions. The employment of a trifunctional cross-linker, methyl 3,5-bis-(bromomethyl)benzoate, enabled both cysteine-promoted cyclization and amide cyclization chemistries. A large number of bicyclic peptides with a wide range of structurally diverse backbones and side-chains were constructed with high efficiency. The commercial availability of building blocks (amino acids and peptides) as well as the broad scope exploration demonstrated the feasibility of our protocol for the preparation of a structurally diverse library of bicyclic peptides. Efforts to transform this robust and efficient chemistry to bicyclic peptide DEL construction will be continued in due course.

Maxwell Sigal ,  Markus Egner ,  Chikako Okada ,  Daniel Merk ,  Toru Sengoku ,  Takayuki Katoh ,  Hiroaki Suga Journal of the American Chemical Society  DOI: 10.1021/jacs.5c13803 Abstract α,α-Disubstituted α-amino acids (dαAAs) are important building blocks for peptidomimetics as they are strong inducers of helicity and protect against proteolytic degradation. However, de novo discovery of dαAA-containing peptides with genetically encoded libraries is limited due to their poor incorporation efficiency. Here, we report the optimized ribosomal incorporation of multiple achiral dαAAs into peptide libraries and their application to high-throughput (>1012 members) affinity selection against the nuclear receptor peroxisome proliferator-activated receptor-gamma (PPARγ). This dαAA-based screening methodology discovered potent linear and macrocyclic α-helical peptides with low-to-sub nanomolar binding affinities. Hit peptides were proteolytically stable in serum and cell permeable, allowing for in cellulo antagonism of PPARγ. X-ray crystallography revealed that dαAA-containing peptides bound at the α-helical protein–protein interaction (PPI) interface via an α-helical conformation. This work validates the potential of a dαAA-based, α-helical discovery platform, providing access to new chemical and conformational space to de novo identify novel α-helical peptidomimetics.

Yagong Wang ,  Hangke Ma ,  Ivan Hu ,  Jin Liang ,  Bingxin Chen ,  Lijun Xue ,  Kexin Yang ,  Yun Jin Hu Organic Letters  DOI: 10.1021/acs.orglett.5c04043 Abstract We present a metal-free photochemical strategy for the synthesis of on-DNA β-hydroxy sulfides, which are essential pharmacophores with limited access via existing DNA-encoded library (DEL) methodologies. This innovative hydrothiolation features a broad substrate scope with both thiols and olefins. Its operational simplicity and excellent DNA compatibility enable the efficient construction of diverse DELs, significantly expanding the accessible chemical space for drug discovery and providing a new tool for identifying novel therapeutics. Summary This study introduces a novel metal-free, photoinduced method for synthesizing β-hydroxy sulfides on DNA. The approach leverages visible light to drive the reaction, avoiding the use of metal catalysts that can be harmful to DNA. The protocol is characterized by its operational simplicity, broad substrate scope, and excellent compatibility with DNA, making it highly suitable for constructing diverse DNA-encoded libraries (DELs). This method significantly expands the chemical space accessible for drug discovery and offers a powerful tool for identifying new therapeutic agents. Highlights Metal-Free and Photoinduced: The method is metal-free and utilizes visible light, avoiding conditions harmful to DNA. Broad Substrate Scope: The reaction tolerates a wide range of thiols and olefins, including both aromatic and aliphatic substrates. Operational Simplicity: The protocol is straightforward, requiring only readily available starting materials and mild reaction conditions. DNA Compatibility: The method is highly compatible with DNA, enabling efficient construction of DELs without significant DNA damage. Expands Chemical Space: This approach significantly broadens the chemical space accessible for DEL-based drug discovery. Conclusion The development of a metal-free, photochemical β-hydrothiolation for the on-DNA synthesis of β-hydroxy sulfides represents a significant advancement in the field of DNA-encoded library synthesis. This operationally simple protocol demonstrates excellent functional group tolerance and DNA compatibility, enabling the efficient construction of a diverse DNA-encoded library from readily available thiols and disulfides. The method provides a powerful and versatile tool that significantly expands the accessible chemical space for DEL-based drug discovery, offering a new avenue for identifying novel therapeutic agents.

Wei Zhou, Yan Wang, Shuning Zhang, Chengwei Zhang, Jiacheng Pang, Shaoneng Hou, Jie Li, Ying Yao, An Su, Peixiang Ma, Hongtao Xu, Wei Hou Chemical Science  DOI: 10.1039/d5sc05512a Abstract Chemical reactions compatible with multiple functionalities are essential for rapid, programmable, and automatable synthesis of functional molecules. However, achieving such reactivity poses significant challenges. Here, we developed a novel multi-orthogonal C(sp 2 )-Se bond formation reaction between benzoselenazolones and boronic acids via Ag(I)catalyzed selective selenium(II)-nitrogen exchange. This chemistry is compatible with diverse functionalities, enabling sequential and programmable synthesis. Moreover, it features modular, high-yielding (485 examples, with yields or conversions exceeding 70% in 95% cases), and switchable reaction systems under mild conditions. Its practical utility was exemplified through late-stage functionalization of natural products, peptides modification and ligation, diversified synthesis, sequential click chemistry, protecting group-free syntheses of sequence-defined oligoselenide (nonamer), on-plate nanomole-scale parallel synthesis (200 nmol, 412 selenides), and DNA-encoded library (DEL) synthesis (10 nmol, 92 examples). Notably, a target-based screening identified SA-16 as a potent CAXII inhibitor with an IC 50 value of 72 nM. Furthermore, a machine learning-based model (SeNEx-ML) was established for reaction yield prediction, achieving 80% accuracy in binary classification and 70% balanced accuracy in ternary classification. These results demonstrated that this chemistry serves as a powerful tool to bridge the selenium chemical space with the existing chemical world, offering transformative potential across multidisciplinary fields. Summary This article presents a novel, highly selective, and AI-predictable selenium(II)-nitrogen exchange (SeNEx) chemistry between benzoselenazolones and boronic acids. The Ag(I)-catalyzed reaction forms C(sp²)-Se bonds and is compatible with a wide range of functionalities, making it suitable for programmable and parallel synthesis. The chemistry is modular, high-yielding, and operates under mild conditions. It has been successfully applied in various practical syntheses, including late-stage functionalization of natural products, peptide modifications, and DNA-encoded library synthesis. A machine learning model (SeNEx-ML) was also developed to predict reaction yields with high accuracy. Highlights Development of a novel Se-N exchange chemistry between benzoselenazolones and boronic acids. The reaction is highly selective, modular, and high-yielding, with 95% of 485 examples achieving yields or conversions exceeding 70%. Compatibility with diverse functionalities and orthogonal to other top reactions in medicinal chemistry. Successful application in late-stage functionalization, peptide modification, and DNA-encoded library synthesis. Establishment of a machine learning model (SeNEx-ML) for reaction yield prediction with high accuracy. Conclusion In summary, we have successfully designed and developed an unprecedented highly selective and multi-orthogonal SeNEx chemistry between benzoselenazolones and boronic acids. This chemistry features modular, predictable, robust, and high-yielding characteristics, performed under mild and switchable reaction conditions. It demonstrates exceptional chemo-selectivity and functional group tolerance, enabling orthogonal synthesis with other established reactions. The practical applications in late-stage modification, peptide ligation, and DNA-encoded library synthesis highlight its potential in multidisciplinary fields. The establishment of the SeNEx-ML model further enhances its utility by predicting reaction outcomes accurately. This chemistry serves as a powerful tool to bridge the selenium chemical space with the existing chemical world, offering transformative potential in synthetic chemistry, material science, chemical biology, and medical chemistry.

Edith van der Nol ,  Nils Alexander Haupt ,  Qing Qing Gao ,  Benthe A. M. Smit ,  Martin Andre Hoffmann ,  Martin Engler-Lukajewski ,  Marcus Ludwig ,  Sean McKenna ,  J. Miguel Mata ,  Olivier J. M. Béquignon ,  Gerard van Westen ,  Tiemen J. Wendel ,  Sylvie M. Noordermeer ,  Sebastian Böcker ,  Sebastian Pomplun Nature Communication DOI: 10.1038/s41467-025-65282-1   Abstract Affinity-selection platforms are powerful tools in early drug discovery, but current technologies – most notably DNA-encoded libraries (DELs) – are limited by synthesis complexity and incompatibility with nucleic acid-binding targets. We present a barcode-free self-encoded library (SEL) platform that enables direct screening of over half a million small molecules in a single experiment. SELs combine tandem mass spectrometry with custom software for automated structure annotation, eliminating the need for external tags for the identification of screening hits. We develop efficient, high-diversity synthesis protocols for a broad range of chemical scaffolds and benchmark the platform in affinity selections against carbonic anhydrase IX, identifying multiple nanomolar binders. We further apply SELs to flap endonuclease 1 (FEN1) – a disease related DNA-processing enzyme inaccessible to DELs – and discover potent inhibitors. Taken together, screening barcode-free libraries of this scale all at once represents an important development, enables access to novel target classes, and promises substantial impact on both academic and industrial early drug discovery. Summary This article introduces a barcode-free self-encoded library (SEL) platform for affinity selection-based hit discovery in early drug discovery. The SEL platform leverages tandem mass spectrometry and custom software for automated structure annotation, eliminating the need for external tags. The platform enables the screening of large libraries with diverse scaffolds, identifying high-affinity binders for targets like carbonic anhydrase IX and inhibitors for flap endonuclease 1 (FEN1). This approach overcomes limitations of DNA-encoded libraries (DELs) by avoiding synthesis complexity and incompatibility with nucleic acid-binding targets, offering a streamlined workflow for rapid hit discovery. Highlights 1. A barcode-free self-encoded library (SEL) platform enables screening of over half a million small molecules in a single experiment. 2. SELs combine tandem mass spectrometry with custom software for automated structure annotation, eliminating the need for external tags. 3. Efficient, high-diversity synthesis protocols are developed for a broad range of chemical scaffolds. 4. The platform identifies multiple nanomolar binders for carbonic anhydrase IX and potent inhibitors for flap endonuclease 1 (FEN1). 5. SELs offer advantages in synthesis complexity and scope compared to DNA-encoded libraries (DELs), expanding the accessible chemical space for drug discovery. Conclusion The SEL platform represents a significant advancement in early drug discovery by enabling the screening of large, diverse libraries without the need for external tags. This barcode-free approach overcomes limitations associated with DNA-encoded libraries, such as synthesis complexity and incompatibility with nucleic acid-binding targets. The successful identification of high-affinity binders and inhibitors for challenging targets like carbonic anhydrase IX and flap endonuclease 1 demonstrates the platform's potential for rapid hit discovery. The streamlined synthesis and automated decoding capabilities of SELs make this technology accessible to a broader range of researchers, promising substantial impact on both academic and industrial drug discovery efforts. Future work will focus on further optimizing the platform for even larger libraries and exploring its application to additional challenging targets.

Alice R. Wong ACS Medicinal Chemistry Letters DOI: 10.1021/acsmedchemlett.5c00356 Abstract DNA-encoded libraries (DELs) have revolutionized hit identification in drug discovery by offering an accessible, versatile, and cost-effective alternative to traditional high-throughput screening (HTS). This perspective analyzes the results of recent DEL technology (DELT) screens (2020−2025) to enable medicinal chemistry programs, focusing on case studies where active series were generated from DEL and distills key learnings and design principles for productive library construction. A notable trend is the productivity of 2-cycle DELs, which, despite their smaller size, consistently yield hits and have superior physicochemical properties compared to 3-cycle DELs. The criteria for inclusion are where DEL provided a medicinal chemistry series, defined by off-DNA hit resynthesis, profiling in relevant assay(s), and follow-up SAR optimization. Summary This article provides an in-depth analysis of the design and application of DNA-encoded libraries (DELs) in medicinal chemistry. It examines recent case studies (2020−2025) where DELs have been successfully used to generate active series for drug discovery. Key learnings include the effectiveness of 2-cycle DELs in yielding hits with better physicochemical properties compared to 3-cycle DELs. The article also explores various DEL designs, including linear, branched, and heterocycle-formation designs, and highlights the importance of physicochemical properties in hit identification. The study concludes that while there is no clear correlation between DEL size and productivity, 2-cycle libraries have shown significant promise in generating high-quality hits. Highlights DNA-encoded libraries (DELs) offer a powerful alternative to traditional high-throughput screening (HTS) for hit identification. Recent case studies (2020−2025) demonstrate the effectiveness of DELs in generating active series for medicinal chemistry. 2-cycle DELs, despite their smaller size, consistently yield hits with superior physicochemical properties compared to 3-cycle DELs. Key physicochemical properties, such as molecular weight (MW), topological polar surface area (TPSA), and hydrogen bond donors (HBD), are critical in library design. The article emphasizes the importance of well-established on-DNA chemistry and standard building block classes in generating diverse and high-quality DELs. Conclusion The analysis of recent DEL technology screens highlights the potential of 2-cycle DELs in generating high-quality hits with desirable physicochemical properties. While there is no clear correlation between library size and productivity, 2-cycle libraries have shown significant promise. The study underscores the importance of physicochemical properties in hit identification and the effectiveness of well-established on-DNA chemistry and standard building block classes in library design. Future DEL designs should continue to leverage these principles to accelerate drug discovery.

Susmith Mukund Structural Dynamics DOI: 10.1063/4.0001022 Abstract The methyl thioadenosine phosphorylase (MTAP) gene which is proximal to the CDK2N2A tumor suppressor gene on chromosome locus p23q is frequently deleted in ∼15% of cancer cells. This results in the accumulation of methylthioadenosine (MTA), which competes with S-adenosyl methionine (SAM), the methyl donor for the essential enzyme, protein arginine methyltransferase 5 (PRMT5). PRMT5 is thereby put in a hypomorphic state in these MTAP-deleted cancer cells, presenting an opportunity for its further inhibition with MTA-cooperative inhibitors. DNA- encoded library screening produced hits that cooperatively bound PRMT5:MEP50 and MTA. Optimization of these compounds and structural enablement through both crystallography and cryo-electron microscopy led to the discovery of AMG 193. Crystal structure shows AMG 193 occupying the peptide binding site of the PRMT5 catalytic domain, where the R3 of the peptide substrate binds during the catalytic cycle, and in the vicinity of the co-inhibitor MTA (see figure, PDB id: 9C10). The tricyclic dihydrofuro- naphthyridine warhead mimics the R3 of the substrate with similar interactions, its amino-heterocyclic moiety forming salt bridge with E444 acid and a H-bond with the backbone carbonyl of E435, the furan oxygen in a H-bond with K333. The amino-heterocycle is in a displaced π:π stacking between W579 and F327, as well as van der Waals contact with residue Glu435, and MTA, a key feature of the MTA cooperative nature of the inhibitor. AMG 193 is further stabilized by a H-bond between its amide carbonyl, which is perpendicular to the tricyclic core, and the backbone amide of F580. The morpholine substituent adopts an axial conformation relative to tricyclic warhead. The terminal trifluoro-phenyl is packed in the very hydrophobic distal end of the substrate pocket, F580 in a π:π stack and Y304 in an edge-to-face stack with the phenyl ring of the ligand. The structure further explains why AMG 193 is MTA-cooperative and not synergistic with SAM.

Orville Pemberton, Amanda M Nevins, Thomas E Frederick, Emily Nicholl, Myron Srikumaran, Jun Chen, Alla Korepanova, Vincent Stoll, Andrew Petros, Sujatha Gopalakrishnan, Justin Dietrich, Liliam Rios Cordero, David J Hardee, Teresa I Ng, Chaohong Sun  Structural Dynamics DOI: 10.1063/4.0000814 Abstract The COVID-19 pandemic caused by SARS-CoV-2 has devastated global health, revealing an urgent need for novel therapeutics. The papain-like protease (PLpro) is one of two proteases encoded by SARS-CoV-2, representing an attractive drug target due to its dual roles in viral replication and host immune suppression. We employed a DNA-encoded library (DEL) screen to reveal starting points for our PLpro hit discovery campaign. These efforts led to the identification of compound 1, a diarylmethanol-containing substructure with a unique binding mode that induces PLpro dimerization. Compound 1 demonstrates potent activities in both a biochemical ubiquitin-rhodamine and antiviral HeLa-ACE2 cell assays. An X- ray co-crystal structure of compound 1 bound to PLpro solved to 2.0 Å showed that two molecules of compound 1 glues two monomers of PLpro together via binding to the BL2 groove of one monomer and the Ubl/thumb domain of the other. Several molecular interactions were observed between compound 1 and PLpro including hydrophobic interactions and several hydrogen-bonds across both monomers. The molecular glue-like properties of compound 1 on PLpro were further validated in solution with analytical SEC and protein-detect 2D-NMR. Subsequent rounds of SAR led to compound 2, which has comparable biochemical and antiviral activities and demonstrated the same dimerization mechanism of PLpro as seen in a 1.8 Å X-ray co-crystal structure. In summary, we have identified a new series of PLpro inhibitors with a novel mechanism of SARS-CoV-2 inhibition, providing a promising start for the discovery of antivirals for treating COVID-19.

Katarzyna B. Handing, Mu-Sen Liu, Douglas A. Whittington, Sining Sun, Rebecca Salerno, William D. Mallender, Jon Come, Scott Throner, Andrew Maynard, Patrick McCarren, John P. Maxwell, Serge Gueroussov, Kiera Vassallo, Yingnan Chen, Jannik N. Andersen, Wenhai Zhang Molecular Cancer Therapeutics DOI: 10.1158/1535-7163.targ-25-c104 Abstract MGAT1 is an N-glycosyltransferase essential for the synthesis of N-glycans. Cell surface glycans serve as immune checkpoints, playing a key role in cancer immune evasion. Knockout (KO) of MGAT1 enhances immune recognition and promotes T-cell–mediated killing, with enzymatic activity being necessary for this phenotype. These findings position MGAT1’s catalytic function as an attractive target for cancer therapy. In this study, we report the discovery of novel MGAT1 binders and inhibitors with sub-micromolar potency. Compounds were identified through two independent screening approaches: a UDP-GloTM-based high-throughput screen (HTS) measuring inhibition of MGAT1 enzymatic activity, and a DNA-encoded library (DEL) screen selecting for molecules that reproducibly bind to MGAT1. High-resolution crystal structures reveal detailed interactions between MGAT1 and the compounds, clearly identifying a binding site distinct from the active site. Surface plasmon resonance (SPR) competition assays further demonstrate that these inhibitors bind noncompetitively with respect to the endogenous product, UDP. Together, these results validate allosteric inhibition of MGAT1 as a novel and tractable strategy for impeding MGAT1 activity. Additionally, our findings lay the foundation for future structure-based optimization of MGAT1 inhibitors with potential application in cancer immunotherapy.

Johannes Bingold, Erik Mafenbayer, Wibke Langenkamp, Lisa Liang, Chun Zhang, Malte Mildner, Julia Isabel Bahner, Mohamed Akmal Marzouk, Bettina Böttcher, Ann-Christin Pöppler, Ralf Weberskirch, Andreas Brunschweiger Chemical Science  DOI: 10.1039/d5sc06782k Abstract Chemical diversification of DNA-conjugated substrates is key in DNA-encoded library (DEL) synthesis and other nucleic acid-based technologies. One major challenge to the translation of synthesis methods to DNA-tagged substrates is the lack of solubility of the highly charged DNA oligomer in most organic solvents. A neutral acrylate block copolymer, devoid of any canonical nucleic acid-binding structure, tightly interacted with DNA oligonucleotides in their ammonium form, and solubilized them in nonpolar solvents such as dichloromethane, chloroform and toluene. The ternary DNA–copolymer–ammonium salt interactions led to the formation of aggregates in organic solvents whose size correlated with DNA oligomer length. This method for DNA solubilization was successfully applied to diversify DNA-tagged starting materials by three isocyanide multicomponent reactions (IMCR) with broad scope and excellent yields. The copolymer does not require tailored DNA conjugates and solubilized DNA oligomers of up to 80 nucleotides length. It will likely broaden the toolbox of DEL-compatible synthesis methods well beyond IMCR chemistry and it has application potential in other nucleic acid-based technologies that require a broadened solvent scope for nucleic acid conjugate synthesis. Summary This article presents a novel method for solubilizing DNA oligomers in organic solvents using a neutral acrylate block copolymer. The method leverages ternary interactions between the DNA, the copolymer, and ammonium salts to form aggregates in nonpolar solvents. This approach enables the use of DNA-tagged substrates in a variety of chemical reactions, including isocyanide multicomponent reactions (IMCR), which are typically challenging due to the poor solubility of DNA in organic solvents. The study demonstrates the successful application of this method to diversify DNA-tagged starting materials with excellent yields and broad scope, potentially expanding the range of DEL-compatible synthesis methods. Highlights 1. A neutral acrylate block copolymer solubilizes DNA oligomers in nonpolar organic solvents. 2. Ternary interactions between DNA, copolymer, and ammonium salts form aggregates in organic solvents. 3. The method enables DNA-tagged substrates to undergo isocyanide multicomponent reactions (IMCR) with high yields. 4. DNA oligomers of up to 80 nucleotides length can be solubilized using this approach. 5. The copolymer system is compatible with downstream DEL operations such as enzymatic DNA tag ligation and barcode amplification. Conclusion The study introduces a conceptually novel approach to DNA solubilization in organic solvents using a neutral acrylate block copolymer. This method, termed CECOS (copolymer-mediated encoded chemistry in organic solvents), facilitates the translation of three isocyanide multicomponent reactions to DNA-tagged substrates with excellent yields and broad substrate scope. The copolymer system does not require tailored DNA barcodes or substrates and is compatible with various DNA barcoding strategies. This approach has the potential to significantly expand the range of DEL-compatible synthesis methods and may find applications in other nucleic acid-based technologies requiring a broader solvent scope. Future work will focus on further investigating the hydrophilic–lipophilic balance of the copolymer to improve understanding of the aggregate structure and potentially extend the solvent scope.

Alyssa Grogan , Robin M. Perelli , Seungkirl Ahn , Haoran Jiang , Arun Jyothidasan , Damini Sood , Chongzhao You , David I. Israel , Alex Shaginian , Qiuxia Chen , Jian Liu , Jialu Wang , Jan Steyaert , Alem W. Kahsai , Andrew P. Landstrom , Robert J. Lefkowitz , Howard A. Rockman The Journal of Clinical Investigation  DOI: 10.1172/jci190252 Abstract Orthosteric β-blockers represent the leading pharmacological intervention for managing heart diseases owing to their ability to competitively antagonize β-adrenergic receptors (βARs). However, their use is often limited by the development of adverse effects such as fatigue, hypotension, and reduced exercise capacity, due in part to the nonselective inhibition of multiple βAR subtypes. These challenges are particularly problematic in treating catecholaminergic polymorphic ventricular tachycardia (CPVT), a disease characterized by lethal tachyarrhythmias directly triggered by cardiac β1AR activation. To identify small molecule allosteric modulators of the β1AR that could offer enhanced subtype specificity and robust functional antagonism of β1AR-mediated signaling, we conducted a DNA-encoded small molecule library screen and discovered Compound 11 (C11). C11 selectively potentiates the binding affinity of orthosteric agonists to the β1AR while potently inhibiting downstream signaling following β1AR activation. Moreover, C11 prevents agonist-induced spontaneous contractile activity, Ca2+ release events, and exercise-induced ventricular tachycardia in the CSQ2–/– murine model of CPVT. Collectively, our studies demonstrate that C11 belongs to an emerging class of allosteric modulators termed PAM-antagonists that positively modulate agonist binding but block downstream function. With unique pharmacological properties and selective functional antagonism of β1AR-mediated signaling, C11 represents a promising therapeutic candidate for the treatment of CPVT and other forms of cardiac disease associated with excessive β1AR activation.

Julien Poupart Annual Review of Cancer Biology https://doi.org/10.1146/annurev-cancerbio-071124-011719 Abstract This review describes recent developments in DNA-encoded library (DEL) technology, which has enabled transformative discoveries in cancer research. Successful DEL screening campaigns for cancer-relevant targets are described in detail to highlight the unique advantages of this technology compared to other hit-generation strategies. Moreover, recent developments in screening methods that have helped expand the DEL-addressable target space are described, and their implications for cancer research are emphasized. DEL screening campaigns targeting RNA and transcription factors are discussed, and various cell-based DEL evaluation methods for membrane proteins are compared and put into context. Finally, the use of DEL technology for the discovery of novel bifunctional degraders is presented. Overall, this article provides a comprehensive overview of key DEL discoveries that are expected to be of significant interest to cancer researchers and medicinal chemists working in the field of oncology.

Kangyin Pan , Wentao Meng , Ying Yao , Wanting Bi , Guang Yang , Hongtao Xu Organic Letters  DOI: 10.1021/acs.orglett.5c03695 Abstract The COVID-19 pandemic incited a global health crisis that accelerated the development of antiviral therapeutics. One successful avenue for inhibiting SARS-CoV-2 has been through targeting the main protease (Mpro; 3CLpro), a key enzyme for the viral lifecycle that cleaves at 11 sites in the viral polyprotein pp1a and pp1ab. Although potent inhibitors of Mpro have been discovered, including the FDA-approved drug Paxlovid, the potential emergence of resistant variants requires continued antiviral development efforts. The current methods to characterize binders of Mpro, such as SPR or ITC, are costly and time-consuming. To improve the speed and feasibility of Mpro inhibitor development, we developed a competitive miniaturized fluorescence polarization (FP) binding assay. We repurposed small molecules from a DNA-encoded library screen into FP probes by appending a fluorophore with various linkers. After identifying a probe that exhibited potent Mpro binding (KD ∼43 nM), we optimized buffer conditions, pH, and additives. Assay validation revealed that our competitive fluorescence polarization assay was robust, with a Z′-score of 0.79 and a signal window of 23. This assay can be used as a single-point assay for high-throughput screening or to triage small molecules by generating Ki values for binding. Efforts from this work resulted in an Mpro binding assay that requires minimal protein consumption, low sample volumes, short incubation times (30 min), and operates at room temperature. In conclusion, we developed a robust FP assay that can be used to rapidly characterize Mpro-binding small molecules to support the development of new SARS-CoV-2 antivirals.

Riya, Singh, Aryan Amit, Barsainyan, Abhiraj Pravin, Mengade, Rida, Irfan, Bharath, Ramsundar ChemRxiv DOI: 10.26434/chemrxiv-2025-f11mk Abstract DNA-encoded libraries (DELs) have emerged as a powerful platform for screening ultra-large chemical spaces by leveraging DNA barcodes to tag and track individual small molecules. Recent work has shown that machine learning can enhance DEL based hit discovery by denoising sequencing artifacts and improving binder identification. However, existing tools for DEL modeling remain fragmented, limiting reproducibility and scalability. To address these challenges, we introduce Deepchem-DEL, an open source suite of workflows built on top of the DeepChem ecosystem. Deepchem-DEL integrates (i) a configurable denoising pipeline and (ii) modular Deepchem workflows for enrichment/hit prediction and benchmarking. We evaluated Deepchem-DEL using the KinDEL dataset and reproduced key baselines across diverse model architectures. Our experiments demonstrate that Deepchem-DEL enables reproducible and scalable machine learning workflows for DEL modeling, reducing engineering overhead for hit discovery.

Mackenzie K. Wyllie, Rayhan G. Biswas, Jyoti Vishwakarma, Morgan A. Esler, Joseph A. Rollie, Hideki Aihara, Reuben S. Harris, Daniel A. Harki ACS Pharmacology & Translational Science  DOI: 10.1021/acsptsci.5c00463 Abstract The COVID-19 pandemic incited a global health crisis that accelerated the development of antiviral therapeutics. One successful avenue for inhibiting SARS-CoV-2 has been through targeting the main protease (Mpro; 3CLpro), a key enzyme for the viral lifecycle that cleaves at 11 sites in the viral polyprotein pp1a and pp1ab. Although potent inhibitors of Mpro have been discovered, including the FDA-approved drug Paxlovid, the potential emergence of resistant variants requires continued antiviral development efforts. The current methods to characterize binders of Mpro, such as SPR or ITC, are costly and time-consuming. To improve the speed and feasibility of Mpro inhibitor development, we developed a competitive miniaturized fluorescence polarization (FP) binding assay. We repurposed small molecules from a DNA-encoded library screen into FP probes by appending a fluorophore with various linkers. After identifying a probe that exhibited potent Mpro binding (KD ∼43 nM), we optimized buffer conditions, pH, and additives. Assay validation revealed that our competitive fluorescence polarization assay was robust, with a Z′-score of 0.79 and a signal window of 23. This assay can be used as a single-point assay for high-throughput screening or to triage small molecules by generating Ki values for binding. Efforts from this work resulted in an Mpro binding assay that requires minimal protein consumption, low sample volumes, short incubation times (30 min), and operates at room temperature. In conclusion, we developed a robust FP assay that can be used to rapidly characterize Mpro-binding small molecules to support the development of new SARS-CoV-2 antivirals.

Wei Hou, Shuning Zhang, Wei Yi, Peixiang Ma, Hongtao Xu  Trends in Chemistry https://doi.org/10.1016/j.trechm.2025.08.008 Abstract DNA-encoded libraries (DELs) have emerged as a critical technology for rapid hit identification in drug discovery. The effectiveness of DEL selection fundamentally depends on the chemical diversity present within the library. As DNA-compatible chemistry plays a central role in expanding both the chemical space and molecular diversity of DELs, this area has seen significant advancements in recent years. Additionally, research in DNA-compatible chemistry and bioconjugation chemistry demonstrates a synergistic relationship. This review focuses on the most recent developments in DNA-compatible chemistry and highlights its expanding applications beyond traditional library construction. Current synthetic challenges are examined, and potential directions for future research and development are explored.

James Wellnitz , Shabbir Ahmad , Nabin Bagale , Xuemin Cheng , Jermiah Joseph , Hong Zeng , Albina Bolotokova , Aiping Dong , Shaghayegh Reza , Pegah Ghiabi , Elisa Gibson , Guiping Tu , Xianyang Li , Jian Liu , Dengfeng Dou , Jin Li , Timothy L. Foley , Anthony R. Harris , Jacquelyn L. Klug-McLeod , Jisun Lee , Zsofia Lengyel-Zhand , Justin I. Montgomery , Sylvie Sakata , Jinzhi Zhang , Hongyao Zhu , Dafydd R. Owen , Rachel J. Harding , Aled M. Edwards , Benjamin Haibe-Kains , Levon Halabelian , Alexander Tropsha , Rafael M. Couñago Journal of Medicinal Chemistry   DOI: 10.1021/acs.jmedchem.5c01972   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.

Pratik R. Chheda, Dominic S. Finis, Nicholas Simmons, Zhicai Shi Org. Lett. 2025 https://doi.org/10.1021/acs.orglett.5c03424 Abstract The expansion of chemical diversity in DNA-encoded libraries (DELs) is essential for broadening their potential in drug discovery. Herein, we introduce the extended utility of the Surfactant–DNA (Surf-DNA) workflow that enables on-DNA deoxygenative Csp2–Csp3 cross-coupling of on-DNA halides with activated alcohols via metallaphotoredox catalysis under anhydrous conditions. The developed reaction demonstrates broad substrate scope for both coupling partners, high conversion rates, and proficiency in preserving DNA integrity and allows for the efficient incorporation of alcohol-derived scaffolds, which are an underutilized and widely available class of building blocks.   Summary This study presents a novel method for expanding the chemical diversity in DNA-encoded libraries (DELs) through on-DNA deoxygenative C(sp2)−C(sp3) cross-coupling. The Surfactant−DNA (Surf-DNA) workflow enables this coupling under anhydrous conditions, leveraging metallaphotoredox catalysis. The method shows high conversion rates and broad substrate scope, including primary, secondary, and multifunctional alcohols, as well as various (hetero)aryl halides. The study demonstrates the preservation of DNA integrity and the potential for incorporating a wide range of alcohol-derived scaffolds, significantly broadening the chemical space accessible in DEL synthesis.   Highlights - Introduction of a Surfactant−DNA (Surf-DNA) workflow for on-DNA deoxygenative C(sp2)−C(sp3) cross-coupling. - Utilization of metallaphotoredox catalysis under anhydrous conditions. - Demonstration of broad substrate scope and high conversion rates for both coupling partners. - Preservation of DNA integrity and compatibility with elongated DNA substrates. - Efficient incorporation of alcohol-derived scaffolds, expanding the chemical diversity in DELs.   Conclusion The study successfully developed a robust protocol for on-DNA deoxygenative coupling via the Surf-DNA workflow. This method enables high efficiency, expansive substrate scope, functional group tolerance, and preservation of DNA integrity. By unlocking the potential of a widely available and historically underutilized class of alcohol building blocks, this approach allows for the construction of challenging C(sp2)−C(sp3) bonds and the generation of more structurally and functionally diverse libraries. This significantly broadens the chemical space accessible in DEL synthesis, enhancing their potential in drug discovery.

Tao Chen, Longying Cai, Xiaofei Dong, Lifang Zhang, Xuemin Cheng, Jingsong Qu, Guanyu Yang, Sen Gao, Linfu Luo, Huiyong Ma, Shuai Xia, Guansai Liu, Jin Li, Jianyou Shi, Dengfeng Dou  Bioorganic & Medicinal Chemistry https://doi.org/10.1016/j.bmc.2025.118448 Abstract To better understand how pre-installed covalent warheads affect the ligand discovery in DNA-encoded libraries (DELs), three individual covalent DELs incorporating 7, 32, and 64 cysteine-targeting covalent warheads, respectively, were designed and screened against JAK3 purified protein. The experiments resulted in 6 novel series of covalent inhibitors with drug-like properties, with the most potent compounds achieving picomolar IC50 and good selectivity against a mini panel of kinases. Mass spectrometry studies confirmed their covalent mechanisms of action (MOAs) by targeting JAK3 Cys909. Importantly, the synergistic effect of the binding moiety and warhead was confirmed by comparing the activities with their close analogs, suggesting that these compounds may not be designed by simply installing covalent warheads onto reversible binders. Further analysis revealed that 7 warheads were sufficient for identifying JAK3 covalent ligands. This work deepens our understanding of the design and screening of covalent DELs and demonstrates the power of DEL in the identification of diverse covalent inhibitors.   Summary This study explores the impact of covalent warheads in DNA-encoded libraries (DELs) on ligand discovery by designing and screening three covalent DELs with varying numbers of cysteine-targeting warheads against JAK3. The results identified six novel series of covalent inhibitors with drug-like properties, achieving picomolar IC50 values and good selectivity. Mass spectrometry confirmed the covalent binding to JAK3 Cys909. The study highlights the necessity of covalent warheads for effective screening and demonstrates that a library with 7 warheads was sufficient for hit identification. This work provides insights into optimizing covalent DEL design and screening strategies for discovering potent and selective covalent inhibitors.   Highlights - Covalent DNA-encoded libraries (CoDELs) with 7, 32, and 64 cysteine-targeting warheads were designed and screened against JAK3. - Six novel series of covalent inhibitors with picomolar IC50 values and good selectivity were discovered. - Mass spectrometry confirmed covalent binding to JAK3 Cys909. - The study demonstrated the necessity of covalent warheads for effective screening and identified 7 warheads as sufficient for hit identification. - The findings provide a robust framework for optimizing covalent DEL design and screening strategies.   Conclusion This research demonstrates the power of DNA-encoded library (DEL) technology in discovering novel covalent inhibitors for JAK3. By incorporating covalent warheads into DELs, we identified six series of potent and selective covalent inhibitors with drug-like properties. The study highlights the importance of covalent warheads in achieving effective screening and confirms that a library with 7 distinct warheads is sufficient for identifying JAK3 covalent ligands. The findings provide valuable insights into optimizing covalent DEL design and screening strategies, paving the way for the discovery of novel covalent inhibitors for other challenging targets.  

Rui Jin and Xiaojie Lu https://doi.org/10.1016/j.sbi.2025.103163 Abstract DNA-encoded library (DEL) technology has enabled efficient discovery of both non-covalent and covalent inhibitors. Covalent DELs (CoDELs) incorporating diverse electrophilic warheads have expanded the scope of covalent targeting beyond cysteine to residues like lysine, tyrosine, arginine, and glutamic acid. The integration of CoDEL with activity-based protein profiling (ABPP) has further enabled the identification of potential protein targets for CoDEL screening using residue-selective warheads. Additionally, proteome profiling with fully functionalized tags has guided target identification for focused DELs with privileged structures. This review highlights recent advances in CoDEL technologies for targeting both cysteine and non-cysteine residues and discusses how proteomics facilitates hit discovery through CoDELs and focused DELs.   Summary This review article discusses the recent advancements in DNA-encoded libraries (DELs), particularly focusing on covalent DELs (CoDELs). CoDELs have shown significant potential in discovering covalent inhibitors targeting various amino acid residues, not just cysteine. The article highlights the use of CoDELs in identifying inhibitors for a range of targets, including viral proteins and oncoproteins. It also explores the integration of CoDEL technology with proteomics strategies such as activity-based protein profiling (ABPP) and proteome profiling using fully functionalized tags to guide the construction of focused DELs and improve hit discovery. The review emphasizes the importance of these integrative approaches in expanding the scope of covalent drug design and accelerating the discovery of novel therapeutic agents.   Highlights - Covalent DNA-encoded libraries (CoDELs) have expanded covalent targeting to residues beyond cysteine, including lysine, tyrosine, arginine, and glutamic acid. - Integration of CoDEL with activity-based protein profiling (ABPP) enables the identification of potential protein targets for covalent inhibitors. - Proteome profiling using fully functionalized tags guides the construction of focused DELs with privileged structures, enhancing hit discovery. - Recent studies demonstrate the discovery of covalent inhibitors for viral proteins and oncoproteins using CoDELs, showcasing their translational potential. - The review emphasizes the importance of combining proteomics with CoDEL technology to improve target selection and hit identification.   Conclusion Over the past decade, DNA-encoded library (DEL) technology has undergone rapid development, with significant advancements in the expansion of on-DNA chemical reactions and the construction of diverse libraries. Covalent DELs (CoDELs) have emerged as a powerful tool for discovering covalent inhibitors targeting various amino acid residues. The integration of CoDEL with proteomics strategies such as activity-based protein profiling (ABPP) and proteome profiling using fully functionalized tags has significantly enhanced the efficiency of hit discovery and target identification. As the field progresses, the continued expansion of CoDEL applications and the incorporation of proteomic data to guide library design and target engagement represent promising frontiers for both covalent and non-covalent DELs, potentially leading to the discovery of novel therapeutic agents with improved efficacy and selectivity.

Artur Menzeleev, Sathya Chitturi, Geraint Davies ,Tony Schroeder ,Alpha Lee ChemRxiv  D O I: 10.26434/chemrxiv-2025-8rw8j Abstract DNA-encoded libraries (DELs) are a powerful way to find chemical starting points against challenging biological targets, by rapidly generating billion-scale structure-activity datasets. However, DEL experiment design and interpretation, especially the optimal use of machine learning (ML) to analyse the vast amount of generated data or to screen large external purchasable datasets, remain poorly understood. To address these challenges, we report the development of a digital twin – an in-silico DEL simulator – that models the underlying chemistry and selection processes of typical experiments as a function of key design parameters, including read count, cycles of selection, one-step reaction yield, and library size. We systematically investigate how these design parameters influence downstream ML virtual screening and identify specific regimes where the choice to apply preprocessing steps such as disynthon aggregation can significantly enhance screening performance. In addition, we show that increasing library size can degrade ML-based screening performance. Our simulator provides a statistically principled way to understand and analyse DEL experiments via an interpretable model for DEL data generation.

Marcos E. Milla; Jonathan M. Blevitt*; Steven D. Goldberg;Anthony A. Armstrong;Katherine Y. Blain;Krystal L. Herman;Annie X. Liu;Rosa Luna;Cynthia M. Milligan;Aaron N. Patrick;Ruth A. Steele;Scott D. Bembenek;Paolo Centrella;Matthew A. Clark;John W. Cuozzo;Jeremy S. Disch;Derrick Domingo;Avery Hunt;Christoper D. Hupp;Anthony D. Keefe;Jinquan Luo;Tara Mirzadegan;Marina I. Nelen;Daniel I. Resnicow;Eric A. Sigel;Holly H. Soutter;Dawn M. Troast;Xiaohua XueFang Yi;Ying Zhang;Paul F. Jackson;James P. Edwards;Kevin J. Lumb ACS Med. Chem. Lett. 2025, XXXX, XXX, XXX-XXX https://doi.org/10.1021/acsmedchemlett.5c00502 Abstract A novel series of inhibitors of the interaction of IL-17A with its cognate receptor has been discovered using DNA-encoded library (DEL) technology. The lead compound (JNJ627, Compound 1) of the series occupies the interior interface of the IL-17A homodimer and disables receptor binding. The mechanism of action involves allosteric disruption of the IL-17A quaternary structure to prevent adoption of the receptor-binding conformation, rather than direct orthosteric inhibition at the receptor-binding site. Molecules of this series exhibit remarkably slow on-rate kinetics and potent inhibition of IL-17A signaling in human primary cells.

Suraj Kanoo,∥ Eduardo de Pedro Beato,∥ Tim Schulte, Lara Vogelsang, Luca Torkowski, Felix Waldbach, Philipp Hartmann, Riya Kayal, Karl-Josef Dietz, and Tobias Ritter* J. Am. Chem. Soc. 2025, https://doi.org/10.1021/jacs.5c11842   Abstract C–N cross coupling reactions are widely employed for the construction of carbon–nitrogen bonds. However, control of chemoselectivity in the presence of the amino functionality in oligonucleotides remains a challenge. Here, we report the development of a new ruthenium reagent that enables the chemoselective N-arylation of amine–DNA conjugates with distinct chemoselectivity when compared to conventional palladium-based C–N bond-forming catalysts. The ruthenium reagent activates commercially available haloarenes in situ via η6 π-arene coordination for subsequent SNAr with the amine. The method is compatible with various commercially available haloarenes and aliphatic amines, and the reaction proceeds under mild conditions.

Gavin W. Collie*, Bryony Ackroyd, Catriona Corbishley, Daniel H. O’Donovan, Alex Edwards, Andrea Gohlke, Xiaoxiao Guo, Bethan Howells, Yuliang Li, Andrew Madin, Alexander G. Milbradt, Emma L. Rivers*,Sandeep K. Talapatra, Elizabeth Underwood, and Alice Webb ACS Med. Chem. Lett.  DOI: 10.1021/acsmedchemlett.5c00396   Abstract NSD2 is a key epigenetic regulator and has received considerable attention as a drug target due to its well-documented role in tumorigenesis. We report here a DNA-encoded library screen targeting the PWWP1 domain of NSD2 from which we discovered novel, potent, and selective binders. Furthermore, these compounds were used to develop a novel crystal system, increasing our understanding of the folding of this domain. Together, these results provide a solid molecular and structural basis for the further study of the PWWP1 domain of NSD2 as a cancer drug target.

Tony Georgiev, Francesca Migliorini, Andrea Ciamarone, Marco Mueller, Ilaria Biancofiore, Pinuccia Faviana, Francesco Bartoli, Young Seo Park Kim, Lucrezia Principi, Ettore Gilardoni, Gabriele Bassi, Nicholas Favalli, Emanuele Puca, Dario Neri, Sebastian Oehler & Samuele Cazzamalli  Nature Biomedical Engineering (2025) DOI: 10.1038/s41551-025-01432-6   Abstract Improving the specificity of prostate cancer treatment requires ligands that bind selectively and with ultra-high affinity to tumour-associated targets absent from healthy tissues. Prostatic acid phosphatase has emerged as an alternative target to prostate-specific membrane antigen, as it is expressed in a broader subset of prostate cancers and is not detected in healthy organs such as the salivary glands and kidneys. Here, to discover selective binders to prostatic acid phosphatase, we constructed two DNA-encoded chemical libraries comprising over 6.7 million small molecules based on proline and phenylalanine scaffolds. Screening against the purified human prostatic acid phosphatase yielded OncoACP3, a small organic ligand with picomolar binding affinity. When radiolabelled with lutetium-177, OncoACP3 selectively accumulated in enzyme-expressing tumours with a long residence time (biological half-life greater than 72 h) and a high tumour-to-blood ratio (>148 at 2 h after administration). Lutetium-177-labelled OncoACP3 cured tumours in mice at low, well-tolerated doses. Its conjugation to the cytotoxic agent monomethyl auristatin E facilitated tumour-selective payload deposition, resulting in potent anti-tumour activity. The modular structure of OncoACP3 supports flexible payload delivery for the targeted treatment of metastatic prostate cancer.   Summary Philochem’s research team successfully identified high-affinity ligands targeting prostate-specific membrane antigen (ACP3) using DNA-encoded library (DEL) technology. The study demonstrates a rapid and efficient path from hit identification to preclinical validation, highlighting DEL’s utility in accelerating radioligand therapy development.   Highlights 1. High-Affinity Ligand Discovery - Two phosphonate-focused DELs were screened against ACP3, yielding enriched hits with strong binding motifs. - Optimized compounds achieved sub-nanomolar inhibition (SPR-confirmed) and >100-fold improved affinity versus the original ligand. - Fluorophore-conjugated ligands selectively stained ACP3-expressing prostate cancer cells, confirming target engagement. 2. Therapeutic Efficacy in Preclinical Models - 177Lu-labeled conjugates showed ~70 %ID/g tumor uptake in xenografts with minimal off-target accumulation and slow washout. - Significant tumor regression was observed, outperforming a reference radioligand derived from earlier inhibitors. - Small-molecule drug conjugates (cleavable linker + MMAE payload) also demonstrated potent antitumor activity.   Conclusion Philochem’s work delivers a robust pipeline of ACP3-targeting ligands with translational potential in radioligand therapy and antibody-free drug conjugates. It validates DEL as a key enabling technology for accelerating cancer therapeutic discovery.