Protein-protein interactions (PPIs) sit at the center of cellular biology. They assemble signaling complexes, control gene expression, regulate protein stability, and coordinate nearly every process that keeps cells alive. When those interactions become dysregulated (e.g. through mutation, overexpression, mislocalization, or viral hijacking) they can drive disease. That is why PPIs have long been recognized as an enormous therapeutic opportunity. Yet, for decades, many of the most compelling PPIs were labeled “undruggable.” The underlying reason is structural: a large fraction of PPIs relies on broad, relatively flat contact surfaces rather than deep pockets that small molecules easily occupy. Reviews frames both the challenge and the opportunity: PPI interfaces can be difficult, but they often contain “hotspots” and inducible pockets that can be exploited if you have the right discovery strategy. [1–3]

This is where modern hit-finding approaches matter and why DNA-encoded libraries (DELs) have become increasingly relevant for protein-protein interaction inhibitors and small molecule PPI inhibitors discovery. DELs enable affinity-based screening of extraordinarily large chemical collections, helping identify chemotypes that can engage challenging protein interaction binding sites, including PPI hotspots and allosteric pockets. [12–14]

For organizations working on undruggable targets drug discovery, the question is no longer “Are PPIs druggable?”. It is “Which PPI modality fits the biology, and what screening format best captures that biology?” Increasingly, that includes DEL screening for protein-protein interactions, and in some cases, intracellular or cell-context selections that preserve native conformations, post-translational modifications, cofactors, and competing binding partners. [15–17]

• why PPIs matter biologically and therapeutically,
• what makes PPI interfaces difficult (and what makes them tractable),
• how allosteric PPI modulators and hotspot binders expand the solution space,
• why DEL can be particularly effective on large, shallow interfaces, and
• how an intracellular/cellular DEL workflow can support hit discovery for challenging PPI targets and downstream optimization.

PPIs are not “special cases” – they are the default mechanism by which cells build functional machines. Enzymes often act in complexes; transcription factors recruit co-activators and repressors; kinases dock to scaffolds; ubiquitin ligases recognize substrates via protein interfaces. The clinical relevance is straightforward: if a disease state depends on a pathologic interaction, modulating that interaction can be as direct and causal as inhibiting an enzyme active site.

Many PPIs are transient, forming only in particular signaling states, in specific subcellular compartments, or at defined times in the cell cycle. Others are stable and obligate (e.g., structural complexes). This matters because a screening campaign against a purified fragment can miss (or invent) binding opportunities that do not exist in the cellular state. Reviews of PPI drug discovery emphasize that “what you screen” (constructs, conformations, binding partners) can be as important as “what you screen with.” [1–3]

Cancer provides familiar examples: the p53 tumor suppressor is restrained by the MDM2 protein through a well-defined interaction; small-molecule antagonists of that interaction validated the concept that even high-value PPIs can be addressed with drug-like ligands. [8]
Apoptosis is another canonical area: BCL-2 family proteins control survival by binding BH3 helices; the BCL-2 inhibitor venetoclax (ABT-199) shows how a PPI-like groove can become a tractable small-molecule target with sufficient structural understanding and chemistry. [9]

The phrase “undruggable targets drug discovery” persists because many PPIs do not present the classical features of enzyme active sites. Three structural themes show up repeatedly.

1) Large and adaptive interfaces

Many PPIs bury ~1,500–3,000 Ų of surface area, often spread across discontinuous residues. That looks mismatched to a 300–500 Da molecule. However, the key insight from hotspot research is that binding energy is not evenly distributed. A minority of residues can contribute most of the interaction free energy. [4]

2) Hotspots and “protein interaction binding sites”

Hotspots are clusters of residues where a small chemical footprint can have outsized functional impact. The classic hotspot paper showed that alanine-scanning often reveals a small set of energetically dominant positions. [4]
For drug discovery, this means a large “flat” interface may still contain localized pockets or crevices where a ligand can anchor, especially if the ligand can mimic key hydrophobic/aromatic hotspot contributions.

3) Cryptic pockets and conformational selection

Some PPIs appear pocketless until a ligand (or protein partner) induces or stabilizes a conformation that reveals a pocket. These are often called cryptic binding sites. Reviews and systematic mapping efforts highlight that cryptic pockets can be widespread and can create new entry points for PPI modulation especially for allosteric approaches. [5,6]

Practical implication: many “undruggable” PPIs are not truly undruggable – they are undersampled. Success is often about screening strategies that can find rare chemotypes that bind shallow hotspots, stabilize transient pockets, or engage allosteric sites.

Not all PPIs are equal from a ligandability perspective. If your goal is small molecule PPI inhibitors discovery, it helps to recognize which interaction archetype you are dealing with.

• Transient PPIs (common in signaling) often rely on short linear motifs, phosphorylation-dependent docking, or weak interactions that become strong in complexes. These can be susceptible to inhibitors that block recruitment or disrupt assembly.

• Stable/obligate complexes may be harder to disrupt directly, but can still be modulated allosterically, destabilized indirectly, or addressed via molecular glue/degrader strategies. [1–3]

A frequent “druggable PPI” pattern is an amphipathic helix docking into a groove (e.g., BCL-2 family, MDM2/p53). These are often more pocket-like and have produced multiple clinical-stage ligands. [7–9]

These are the canonical “hard” PPIs: shallow, extended surfaces where no single deep pocket is obvious. Here, discovery frequently relies on:

• finding hotspot anchors (often aromatic/hydrophobic),
• growing or linking fragments, or
• identifying allosteric sites that indirectly modulate the interaction. [1–6]

Allosteric modulation can be a powerful way to avoid the flattest part of the interface. A ligand binds elsewhere, shifts conformational equilibria, and changes the interaction. This is especially attractive when the orthosteric interface lacks pockets or when selectivity is challenging. The PPI field has also matured beyond inhibitors alone: stabilization, induced proximity, and “molecular glue” mechanisms are increasingly recognized as viable therapeutic modes. [7,10,11]

When people search for protein-protein interaction inhibitors, they often mean “blockers.” But many high-value PPI programs now consider multiple modalities, because the biology can favor one mechanism over another.

These bind at the interface and compete with the partner protein. MDM2/p53 antagonists are a flagship example, demonstrating that a small molecule can occupy a key interface pocket and reactivate tumor suppressor signaling. [8]

These bind outside the interface and reduce (or sometimes enhance) binding indirectly. This can be advantageous when:

• the interface is too flat,
• selectivity is difficult at the orthosteric site, or
• you need partial modulation rather than full shutdown. [7]

Stabilization is an underappreciated concept: instead of disrupting a PPI, you strengthen a beneficial interaction or lock a complex in a nonproductive state. A well-known theme in chemical biology is that small molecules can stabilize interactions as part of their mechanism. Reviews highlight this as a distinct and valuable design space. [10]

“Molecular glues” are a related idea: a ligand induces or stabilizes a new protein-protein association. The lenalidomide/cereblon story illustrates a glue-like mechanism where a small molecule alters an E3 ligase’s substrate recognition to drive selective degradation of transcription factors, hereby demonstrating how modulating protein interfaces can rewire cell biology. [11]

Why this matters for screening: a discovery platform that can detect binders to hotspots and allosteric pockets (and that can be run in formats close to physiology) expands your odds of finding workable starting points for challenging PPIs.

The original concept of encoding small molecules with amplifiable DNA information emerged in the early 1990s, linking combinatorial chemistry with genetic-style selection. [12]
Modern DEL platforms routinely enable screening of extremely large chemical spaces, with the practical readout being enrichment of DNA barcodes after binding/selection. Reviews in Nature Reviews Drug Discovery and ACS Pharmacology & Translational Science summarize the evolution of DEL chemistry, selection formats, and downstream translation to off-DNA molecules. [13,14]

For PPIs, scale matters because:

• true hotspot binders can be rare,
• shallow interfaces often reward “lucky” shape complementarity, and
• allosteric pockets can be subtle and hard to predict without empirical screening.

Large, shallow interfaces challenge classical HTS because typical screening collections were historically biased toward enzyme-like pockets. DELs can be designed with PPI-relevant features for example:

• higher 3D character (sp³-rich scaffolds),
• macrocyclic or constrained motifs,
• aromatic-rich hotspot mimics,
• electrophile-free chemotypes for broad compatibility (or purpose-built covalent strategies where appropriate),
• fragment-like elements that can be elaborated.

While DEL hits still require careful validation, the platform is particularly attractive for hit discovery for challenging PPI targets because it increases the odds of sampling unusual geometries that match hotspot landscapes.

One of the strongest conceptual matches between DEL and PPIs is that DEL can yield:

1. direct interface binders (hotspot blockers), and
2. allosteric binders that reshape the interface or partner affinity.

Cryptic pockets and conformational selection provide a structural explanation for why allosteric DEL hits can appear even when an interface looks “flat” in a single structure. [5,6]

Vipergen’s focus on DEL screening against essentially any protein target naturally includes PPIs. For many intracellular PPIs, the biologically relevant state depends on:

• native folding and quality-control pathways,
• post-translational modifications (phosphorylation, acetylation, ubiquitination),
• compartmental localization,
• endogenous binding partners that compete or cooperate.

For these reasons, cell-context screening formats can be compelling complements to purified-protein selections.

Peer-reviewed work has demonstrated multiple strategies for bringing DEL selection closer to cellular biology:

• Cell-based DEL selections: early demonstrations showed that DEL selections can be performed in cell contexts rather than only on purified proteins. [15]
• DEL screening inside living cells: subsequent work advanced the concept further, showing selection/screening formats compatible with intracellular environments. [16]
• Live-cell targeting of endogenous proteins: approaches have also been reported for selections against targets on or in living cells (including endogenous membrane proteins), supporting the broader theme that DEL can move beyond immobilized purified proteins. [17]

These papers do not “solve” every intracellular PPI problem – delivery, nonspecific binding, and assay design remain nontrivial – but they establish an important point: DEL screening for protein-protein interactions can be designed in formats that preserve more native biology than classical in vitro-only workflows.

For PPIs, the binding surface can be:

• transiently formed only in a signaling state,
• reshaped by phosphorylation or ligand binding,
• occluded unless a complex assembles,
• altered by crowding, local concentration, or scaffolding proteins.

Cell-context selection can help capture:

• the correct complex (or complex equilibrium),
• realistic conformational ensembles,
• functional coupling to downstream pathways (if the selection is designed around functional states).

Not every PPI program needs in-cell selection. But for certain classes, the rationale is strong:

1. Complexes that require cofactors or PTMs
   If the interface forms only after phosphorylation (or another modification), screening a static purified construct can be misleading.
2. Targets with conformation-sensitive pockets
   Cryptic pockets may appear only in specific cellular states. [5,6]
3. Competitive interaction networks
   Many PPIs are part of hubs. In-cell formats can bias toward ligands that function in the presence of endogenous competitors, often a major gap between biochemical binding and cellular efficacy.
4. Allosteric modulation as primary strategy
   If you already believe the best route is an allosteric site, preserving native conformational equilibria can help surface those binders.

DNA encoded library PPI screening can be integrated into a modern PPI program – starting from hit finding and moving toward validated chemical matter.

1) Define the PPI hypothesis and intervention strategy

Before screening, clarify what “modulation” means for the biology:

• Do you need an inhibitor (disruptor), stabilizer, or molecular glue-like effect?
• Is orthosteric blocking plausible, or is an allosteric strategy more realistic?
• What functional readout will matter downstream?

This framing reduces wasted cycles and helps interpret DEL enrichments: a binder might not be an inhibitor but could still be a valuable allosteric PPI modulator or a handle for induced proximity strategies. [7,10,11]

2) Choose the selection format: purified complex, reconstituted system, or cellular context

Options often include:

• purified partner proteins (orthosteric hotspot hunting),
• pre-assembled complexes (to bias toward interface pockets present only in the complex),
• competitive selections (add a peptide/protein competitor to enrich higher-affinity or orthosteric binders),
• cellular/intracellular selection formats (to preserve native biology). [15–17]

A useful mental model is the harder the PPI and the more state-dependent the interface, the more valuable a native-like format becomes.

3) Library strategy: match chemical space to PPI reality

For PPIs, “bigger library” is helpful, but “better-shaped library” is often decisive. DEL design considerations commonly include:

• 3D shape and rigidity: helps engage shallow sites and hotspots.
• Aromatic/hydrophobic anchors: frequently match hotspot residue chemistry. [4]
• Macrocycles/constrained motifs: can cover more surface area with fewer rotatable bonds.
• Exit vectors for SAR: ensure hits can be resynthesized and optimized off-DNA.

This is an important bridge between informational and commercial intent: companies searching for PPI drug discovery services are often specifically looking for providers who understand how to tailor libraries and selection conditions for PPI-type binding sites rather than enzyme pockets.

4) Selection execution and decoding

In a typical DEL campaign:

• the library is incubated with the target (or complex),
• non-binders are washed away (with conditions tuned to preserve meaningful interactions),
• binders are recovered,
• DNA tags are PCR amplified and sequenced to identify enriched structures. [13,14]

For PPIs, selection design often includes extra controls:

• counter-selections to remove sticky scaffolds,
• partner-only or target-only controls to map binding specificity,
• competition with peptide motifs to distinguish orthosteric binders.

5) Hit triage: from enriched barcode to real chemistry

DEL data are powerful, but they are not “the answer” on their own. Key steps include:

• resynthesis of top hits off-DNA,
• orthogonal binding assays (SPR, BLI, ITC, NMR, DSF – depending on target),
• functional assays that read out PPI modulation (biochemical and cellular).

This is the critical bridge to hit discovery for challenging PPI targets: what matters is not just binding, but a validated mechanism that can be optimized.

6) Mechanism mapping: orthosteric vs allosteric vs stabilization

For PPIs, you typically want to know:

• Does the ligand compete with the partner?
• Does it bind elsewhere and modulate affinity?
• Does it stabilize a particular complex state?

Examples across the literature highlight that each mechanism can be therapeutically useful. [7,10,11]

7) Medicinal chemistry and expansion

Once a hit series is validated:

• explore SAR around hotspot anchors or allosteric pockets,
• improve potency and selectivity,
• optimize cell permeability (particularly important for intracellular PPIs),
• maintain ligand efficiency while expanding interface coverage.

Many teams want “AI-enabled discovery” not as branding, but as practical leverage:

• prioritize PPIs by tractability signals,
• predict interface structures and possible pockets,
• interpret DEL enrichment patterns at scale.

A concrete example of enabling infrastructure is AlphaFold-level structure prediction, which dramatically expanded access to structural hypotheses for proteins lacking experimental structures. While AlphaFold does not automatically solve PPI pocket discovery, it can accelerate model generation, interface mapping, and hypothesis-driven selection design – especially when paired with experimental validation. [18]

In DEL + PPI workflows, AI/ML is often most valuable in:

• target triage: which PPIs have plausible ligandable hotspots or allosteric pockets?
• selection design: which constructs and conformations should be prioritized?
• data mining: identifying meaningful clusters in DEL enrichments and filtering artifacts.
• SAR acceleration: learning from early hit series to propose analogs for follow-up synthesis.

The modern view of PPIs is pragmatic: many are difficult, but difficulty is not a dead end. Hotspots concentrate binding energy, cryptic pockets create hidden opportunities, and allosteric mechanisms offer alternate routes when the interface is too flat. [1–7]

For discovery teams pursuing undruggable targets drug discovery, DEL offers a compelling engine for small molecule PPI inhibitors discovery and for uncovering allosteric PPI modulators – especially when combined with screening formats designed to preserve relevant biology. Peer-reviewed work now supports cellular and intracellular DEL paradigms, strengthening the case for DNA encoded library PPI screening as a practical route to hit discovery for challenging PPI targets. [13–17]

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7. Lu H., Zhou Q., He J., et al., Recent advances in the development of protein–protein interactions modulators: mechanisms and clinical trials, Sig Transduct Target Ther (2020), 5, 213. DOI: 10.1038/s41392-020-00315-3. https://doi.org/10.1038/s41392-020-00315-3  
8. Vassilev L.T., Vu B.T., Graves B., et al., In vivo activation of the p53 pathway by small-molecule antagonists of MDM2, Science (2004), 303(5659), 844–848. DOI: 10.1126/science.1092472. https://doi.org/10.1126/science.1092472  
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Enzymes remain one of the most important and consistently productive classes of drug targets in modern small-molecule discovery. Across oncology, immunology, infectious disease, neuroscience, inflammation, and metabolic disease, enzymes sit at mechanistically powerful control points: they catalyze pathway-defining reactions, shape signal transduction, regulate epigenetic states, process proteins, repair DNA, and control the concentrations of key metabolites. When those functions become dysregulated, selective chemical modulation can generate direct and therapeutically meaningful biological effects. This is one reason enzymes continue to occupy such a large share of successful drug discovery programs and marketed medicines [1,2].

From a discovery standpoint, enzymes are attractive because they are often intrinsically ligandable. They contain catalytic pockets, substrate-recognition elements, cofactor-binding sites, conformational switches, and, in many cases, allosteric pockets that can be addressed with small molecules. That combination makes them especially well suited to small molecule enzyme inhibitors, whether the goal is direct active-site blockade, allosteric modulation, or covalent engagement of a catalytic or nearby nucleophilic residue [1,3-8]. For this reason, kinase inhibitor discovery, protease inhibitor screening, and broader enzyme inhibitor discovery services remain central parts of early pharmaceutical R&D.

At the same time, enzyme drug discovery is rarely as simple as “find a potent inhibitor and move on.” Many enzyme families have highly conserved active sites. Others are difficult to express, unstable outside their native context, dependent on cofactors or partner proteins, or prone to disconnects between biochemical potency and cellular activity. In practice, the hardest part of an enzyme program is often not proving that the target is druggable, but finding differentiated chemical starting points quickly enough and validating them rigorously enough to justify downstream medicinal chemistry [4,5,9].

That is where DNA-encoded library screening has become especially compelling. DNA-encoded libraries (DELs) enable the pooled screening of extraordinarily large numbers of compounds through affinity selection, opening far broader chemical space than conventional one-compound-per-well formats. For enzyme targets, this breadth is highly valuable. DEL screening for enzymes can help identify new chemotypes for enzyme active site targeting, reveal allosteric binders, and support covalent enzyme inhibitor discovery strategies; all while consuming relatively little target material and allowing a binder-first route to enzyme hit discovery using DNA encoded libraries [10-14].

For organizations evaluating outsourcing enzyme inhibitor discovery, the practical appeal is straightforward: DEL can accelerate early enzyme drug discovery services by generating tractable hit matter before a full medicinal chemistry campaign is built around a target. And when paired with rapid off-DNA resynthesis, orthogonal enzymology, and cellular target-engagement assays, DEL screening for enzyme targets can become a highly efficient engine for discovering decision-ready starting points rather than just large volumes of screening data [10-18].

The idea of “druggability” is often reduced to the presence of a pocket, but enzymes illustrate why the concept is broader than that. A druggable target must bind a small molecule with sufficient affinity and selectivity to produce a useful biological effect, yet enzymes frequently offer several ways to achieve that outcome at once [1]. Their active sites are shaped to recognize specific substrates or cofactors. Their catalytic residues create concentrated interaction points. Their conformational states can expose transient grooves or induce-fit pockets. And their function is often tightly connected to ligand binding, so occupancy can be translated into a measurable phenotypic effect more directly than for many structural or scaffolding proteins [1,6].

The record of approved drugs reinforces this. A major share of known molecular drug targets belong to enzyme families or enzyme-related mechanisms [2]. Kinases, proteases, polymerases, topoisomerases, phosphodiesterases, deacetylases, methyltransferases, and metabolic enzymes have all produced clinically important therapies. That success reflects both biological leverage and tractable chemistry. In other words, enzymes are not just abundant targets; they are targets for which medicinal chemistry has repeatedly demonstrated translational power [2-5].

Kinases are perhaps the clearest example. Protein phosphorylation controls growth, survival, immune signaling, DNA damage responses, metabolism, and cell fate. Unsurprisingly, abnormal kinase activity is implicated in cancer, inflammatory disease, fibrosis, and immune disorders. As a result, kinase inhibitor discovery has produced a deep and sophisticated knowledge base spanning ATP-competitive inhibitors, covalent binders, allosteric ligands, and conformationally selective chemotypes [3]. Proteases provide another archetype. They regulate clotting, apoptosis, extracellular remodeling, antigen processing, protein maturation, and pathogen replication. Protease inhibitor screening has yielded some of the field’s most powerful examples of mechanism-based drug discovery, but also some of its clearest lessons on the need for selectivity and context [4,5].

Importantly, the druggability of enzymes is not limited to orthosteric inhibition. Many enzymes are regulated allosterically, through distal pockets, protein-protein interfaces, lipid interactions, or conformational equilibria that shift catalytic competence. These mechanisms create additional opportunities when direct active-site competition is suboptimal. Allosteric modulation can improve subtype selectivity, avoid direct competition with high intracellular substrate concentrations, and provide access to differentiated pharmacology that is hard to achieve with classic active-site inhibitors [6]. For crowded families such as kinases and proteases, this can be commercially and scientifically decisive.

Covalent inhibition broadens the opportunity further. The modern resurgence of covalent drugs reflects a much more disciplined approach than earlier generations of reactive small molecules. Rather than relying on nonspecific reactivity, contemporary covalent inhibitor discovery uses tuned electrophiles, carefully positioned warheads, and structure-informed residue selection. For enzyme targets with catalytic serines, cysteines, or other suitably nucleophilic residues, covalent mechanisms can improve residence time, potency, and durability of target suppression [7,8]. This is particularly relevant to enzyme programs because catalysis often depends on exactly the kind of residue geometry that covalent chemistry can exploit.

Taken together, these features explain why enzymes remain such attractive drug targets. They are biologically central, chemically tractable, and mechanistically rich. But that same mechanistic richness also means discovery strategies need to be chosen carefully.

Although “enzyme” is a broad category, several target classes dominate practical small-molecule discovery.

Kinases remain one of the most important. The ATP-binding pocket offers a clear foothold for small molecules, but selectivity across the kinome is often difficult because the catalytic machinery is conserved. That challenge has pushed the field toward more nuanced strategies, including type II kinase inhibitors, allosteric binders, and covalent kinase inhibitors directed toward nonconserved residues [3,7,8]. For DEL screening for kinase targets, this means the value is not merely in identifying any ATP-site binder, but in surfacing chemotypes that offer credible selectivity and optimization headroom.

Proteases are similarly important but pose different challenges. Their active sites can be highly ligandable, yet peptide-mimetic chemistry can lead to poor permeability, off-target protease inhibition, or metabolic liabilities. Some protease families also rely on substrate-recognition motifs that are broad enough to create selectivity problems across related enzymes [4,5]. DEL screening for protease targets can therefore be especially valuable when it identifies nonclassical chemotypes, non-peptidic scaffolds, or covalent warhead placements that would be difficult to intuit from standard substrate-based design.

Beyond kinases and proteases, metabolic enzymes, epigenetic enzymes, deubiquitinases, helicases, polymerases, phosphatases, and oxidoreductases are all relevant enzyme classes for early discovery. In many of these families, the same central questions recur: is the best path an active-site inhibitor, an allosteric modulator, or a covalent mechanism? Is purified protein a good proxy for the relevant cellular state? Is selectivity the main technical hurdle, or is it cell penetration and target engagement? These are the kinds of questions that should shape how DEL screening for enzymes is designed from the outset.

The phrase “enzymes are highly druggable” is true, but it can be misleading if it suggests low risk. In reality, enzyme inhibitor discovery fails for recurring reasons.

The first is selectivity. Many enzymes belong to large homologous families, and their active sites are built around conserved catalytic machinery. Kinases all bind ATP. Serine proteases share common catalytic logic. Metalloproteases often organize ligands around related metal-dependent motifs. A compound that looks excellent in a single biochemical assay can therefore prove unusable when challenged across close homologues, anti-targets, or broader selectivity panels [3-5]. This is especially relevant for commercial enzyme inhibitor discovery services, because early false confidence around selectivity can waste months of medicinal chemistry.

The second is the gap between biochemical inhibition and cellular activity. A molecule can inhibit purified enzyme strongly yet fail in cells because it is poorly permeable, rapidly effluxed, unstable, sequestered, or unable to compete with native substrate concentrations. Conversely, a compound with only modest biochemical potency can perform better than expected if it reaches the target efficiently or maintains long residence time [9,17,18]. For enzyme programs, the question is never just “does it inhibit?” but “does it engage the biologically relevant target state in a useful context?”

Residence time complicates the picture further. Classical IC₅₀ or K_d values are informative, but they do not always capture how long a ligand remains bound under dynamic biological conditions. For enzymes operating in fluctuating substrate pools or signaling cascades, slower off-rates can improve pharmacological durability and, in some cases, functional selectivity [9]. That means early hit discovery should ideally generate multiple mechanistic hypotheses rather than optimizing around a single potency metric too early.

Assayability is another major issue. Some enzymes are straightforward to evaluate functionally. Others require unstable substrates, unusual cofactors, multi-component assemblies, membranes, or specific post-translational states. Some are better understood through conformational or binding readouts than through simple turnover assays. In those settings, a classical high-throughput functional screen may be expensive, artifact-prone, or simply too narrow in the chemistry it explores. DEL screening offers a useful alternative because it can begin with binder discovery and then move into biochemical and cellular confirmation once enriched chemotypes are in hand [10-16].

These realities are why enzyme hit discovery using DNA encoded libraries has become attractive. The value is not only scale; it is flexibility in how discovery is staged.

DNA-encoded libraries consist of small molecules linked to DNA tags that encode their synthetic identity. Because each compound carries its own barcode, very large libraries can be pooled, screened together, and decoded by sequencing after selection. This architecture fundamentally changes the front end of hit discovery. Rather than testing thousands or tens of thousands of individually arrayed molecules, DEL enables the exploration of millions to billions of compounds in compact, pooled workflows [10-13].

For enzymes, that scale matters because it makes it easier to search for rare but informative chemotypes. A modest screening deck may be enough to find a familiar active-site inhibitor for a well-studied kinase. It is much less likely to reveal new binding modes, differentiated selectivity escape routes, or unusual allosteric series. DEL breadth increases the odds of finding those less obvious opportunities, which is one reason DNA encoded library screening services are increasingly relevant to early-stage enzyme programs [10-14].

Another strength is that DEL is, at its core, a binding-based technology. For enzyme drug discovery, this is often an advantage rather than a limitation. Binding can be discovered before the perfect functional assay exists, before the best substrate pair is optimized, or before the team decides whether orthosteric, allosteric, or covalent inhibition is the right objective. In that sense, DEL screening for enzymes is often best understood as a front-end chemical space exploration tool that feeds a richer validation cascade [10-16].

This is particularly powerful for enzyme active site targeting. Enzyme pockets often reward modular optimization around a recognition core, and DEL library design can exploit that logic by combining privileged motifs with diverse appendages or warheads. The field’s early landmark studies showed that DEL could yield genuine enzyme-active chemotypes. Clark and colleagues demonstrated that DNA-encoded small-molecule libraries could identify inhibitors of Aurora A kinase and p38 MAP kinase, helping establish DEL as a practical route to enzyme hit discovery rather than just a theoretical screening format [15].

Since then, DEL has advanced far beyond generic affinity selections. Reviews collectively show how DNA-encoded library technology has matured in chemistry, selection design, and post-selection strategy [10-14]. These advances matter directly for enzymes because not every enzyme target behaves well in the same selection setup. Some are soluble and stable. Others require more nuanced formats or more careful off-DNA follow-up. The most productive DEL campaigns are therefore not generic – they are deliberately matched to the biology of the target.

Off-DNA strategies also strengthen the platform. Hackler and colleagues showed how off-DNA DNA-encoded library affinity screening can help interrogate compounds in a context that is closer to classical assay formats, reducing ambiguity around the effect of the DNA tag itself [16]. For enzyme programs, that can be especially useful when transitioning from enrichment data to genuine inhibitor confirmation.

One reason early enzyme programs stall is that the recombinant protein used in screening is not a faithful representation of the biologically relevant target state. The active conformation may depend on a cofactor, membrane association, binding partner, cellular redox state, or post-translational modification that is difficult to preserve in vitro. Even when purified protein is available, biochemical activity does not guarantee that a hit will engage the target in cells [17,18].

This is where more physiologically relevant DEL strategies become interesting. In-cell DEL concepts aim to move hit finding closer to the environment where the target operates [25]. More broadly, cell-relevant follow-up assays such as CETSA can test whether a compound physically engages its target in cells rather than only inhibiting a purified enzyme in a tube [17,18]. Martinez Molina and colleagues first showed that the cellular thermal shift assay can monitor drug-target engagement in cells and tissues, and Jafari and colleagues later described the method in practical detail [17,18]. For enzyme inhibitor discovery, that matters because it helps bridge one of the biggest early questions: not just “does the compound bind?” but “does it bind under intracellular conditions that matter?”

Scientifically, the combination of DEL screening and early cellular target engagement is compelling. DEL can search broad chemical space quickly, then off-DNA compounds can be triaged using biochemical inhibition, orthogonal binding assays, and cell-based target engagement or pathway readouts. That workflow is particularly attractive for challenging enzymes, for membrane-associated proteins, and for targets where purified protein is not the most informative representation of the druggable state.

For a company like Vipergen, which emphasizes both Binder Trap Enrichment® and Cellular Binder Trap Enrichment® in its platform positioning, this creates a strong content angle: DEL screening does not need to stop at purified proteins, and physiologically relevant screening contexts can help prioritize enzyme binders that have a better chance of surviving downstream attrition [23-25].

After a DEL screen, enriched compounds or chemotypes need to be resynthesized off-DNA and tested in orthogonal assays. The first question is whether the compound truly binds the target without the DNA tag. The second is whether that binding translates into meaningful modulation of enzyme function. The third is whether the mechanism is orthosteric, allosteric, or covalent. And the fourth is whether the compound has early signs of tractability in terms of selectivity, cellular engagement, and chemistry follow-up [10-18].

This sequence matters because enzyme binders are not automatically useful inhibitors. A compound may bind in the active site without blocking catalysis under physiological substrate conditions. It may bind a nonproductive conformation. It may show good enzymology but poor cellular exposure. Or it may be a very valuable allosteric ligand that would be missed if the wrong functional assay is used too early. The practical goal in early enzyme drug discovery is therefore not only to confirm activity, but to clarify mechanism and optimization potential.

That is why the most credible enzyme inhibitor discovery services usually combine DEL screening with rapid off-DNA resynthesis and focused medicinal chemistry follow-up. Once series are confirmed, biochemical potency, counterscreens, anti-target profiling, and early cellular assays can be layered into separate attractive starting points. In a commercial setting, the quality of this transition from sequencing output to decision-ready compounds often matters more than the raw number of enriched hits.

Kinases and proteases make especially useful case studies because they represent two high-value enzyme classes with distinct discovery constraints.

For kinases, DEL screening can help in at least three ways. First, it can identify fresh ATP-site chemotypes in cases where known hinge-binding space is crowded. Second, it can support covalent kinase inhibitor discovery when the target offers a suitably positioned residue. Third, it can help surface chemotypes that are worth testing against inactive or allosteric conformations rather than only the canonical active state [3,7,8,15,20]. The early DEL work by Clark et al. on Aurora A and p38 established that kinase-focused DEL screening can produce real enzyme-active matter [15], while later triazine-based covalent DEL work by Li et al. extended the concept into covalent discovery against enzymes such as BTK and JAK3 [20].

Proteases illustrate a somewhat different challenge. Their active sites are often highly tractable, but selectivity and developability can be difficult. Classical peptide-like protease inhibitors may bind well but perform poorly in broader drug-like optimization. This makes protease inhibitor screening a good fit for focused DEL design, where substrate-inspired recognition can be balanced against the need for differentiated, non-peptidic, or covalent chemistry. Dawadi and colleagues showed that a protease-focused DNA-encoded chemical library could yield potent thrombin inhibitors, demonstrating the value of class-biased library design for enzyme discovery [19]. More recently, DEL campaigns against SARS-CoV-2 main protease identified both covalent and non-covalent inhibitor series, reinforcing that even within a single protease target, multiple DEL-enabled discovery routes can be productive [21,22].

DEL screening for enzymes works best when it is not generic. The ideal library, selection conditions, and follow-up assays depend on the enzyme class, the mechanism sought, and the practical bottleneck in the program. For DEL screening for kinase targets, selectivity and differentiation may dominate. For protease inhibitor discovery, the emphasis may be on nonclassical chemistry, covalent logic, or avoiding peptide liabilities. In both cases, the strength of DEL lies in giving the program more chemical options at the point where good options matter most.

For teams considering outsourcing enzyme inhibitor discovery, the right partner is not simply the one with the biggest library. The better question is whether the provider can convert enzyme hit discovery into validated and actionable chemistry.

That usually means five things. First, the partner should understand the biology well enough to choose the correct screening hypothesis: active-site inhibitor, allosteric binder, covalent mechanism, or a staged combination of these. Second, the provider should have a fast path from DEL enrichment to off-DNA confirmation. Third, library design should be connected to the target class rather than purely generic. Fourth, selectivity should be considered early, especially for conserved enzyme families. And fifth, the service should not stop at sequencing output; it should support the progression from hit identification to early lead-quality matter [4-8,10-18].

Vipergen’s high-fidelity DEL technologies, screening in living cells via cBTE,[25] and medicinal chemistry support for off-DNA validation and early series progression [23,24]. Furthermore the cBTE technology allows for multiplex screening against counter targets, which means that selectivity can be dialed into the screening.

Enzymes remain among the most valuable targets in drug discovery because they combine biological centrality with multiple chemically tractable modes of control. Active-site inhibition, allosteric modulation, and covalent targeting all remain highly relevant strategies across kinases, proteases, metabolic enzymes, and many other enzyme classes [1-8].

But the practical challenge in enzyme programs is rarely whether chemistry can bind the target at all. The challenge is finding differentiated series quickly, confirming mechanism rigorously, and prioritizing molecules that stand a realistic chance of working in cells. DNA encoded library screening is especially well suited to that front end of discovery because it enables broad chemical-space exploration while preserving a clear path into biochemical, biophysical, and cellular validation [10-18].

For that reason, DEL screening for enzymes is becoming an increasingly attractive route for enzyme hit discovery using DNA encoded libraries, whether the program is focused on kinase inhibitor discovery, protease inhibitor screening, enzyme active site targeting, or covalent enzyme inhibitors DEL strategies [15,19-22]. And for companies evaluating enzyme inhibitor discovery services, the most valuable DEL partner will be the one that combines screening scale with target-class understanding, rapid off-DNA follow-up, and a practical path from hit to validated inhibitor [23,24].

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[12] Favalli N, Bassi G, Scheuermann J, Neri D. DNA-encoded chemical libraries – achievements and remaining challenges. FEBS Lett (2018), 592(12), 2168-2180. DOI: 10.1002/1873-3468.13068.

[13] Gironda-Martínez A, Donckele EJ, Samain F, Neri D. DNA-encoded chemical libraries: a comprehensive review with successful stories and future challenges. ACS Pharmacol Transl Sci (2021), 4(4), 1265-1279. DOI: 10.1021/acsptsci.1c00118.

[14] Huang Y, Li Y, Li X. Strategies for developing DNA-encoded libraries beyond binding assays. Nat Chem (2022), 14(2), 129-140. DOI: 10.1038/s41557-021-00877-x.

[15] Clark MA, Acharya RA, Arico-Muendel CC, Belyanskaya SL, Benjamin DR, Carlson NR, Centrella PA, Chiu CH, Creaser SP, Cuozzo JW, et al. Design, synthesis and selection of DNA-encoded small-molecule libraries. Nat Chem Biol (2009), 5(9), 647-654. DOI: 10.1038/nchembio.211.

[16] Hackler AL, Fitzpatrick DE, Paegel BM. Off-DNA DNA-Encoded Library Affinity Screening. ACS Comb Sci (2020), 22(1), 25-34. DOI: 10.1021/acscombsci.9b00153.

[17] Martinez Molina D, Jafari R, Ignatushchenko M, Seki T, Larsson EA, Dan C, Sreekumar L, Cao Y, Nordlund P. Monitoring drug target engagement in cells and tissues using the cellular thermal shift assay. Science (2013), 341(6141), 84-87. DOI: 10.1126/science.1233606.

[18] Jafari R, Almqvist H, Axelsson H, Ignatushchenko M, Lundbäck T, Nordlund P, Martinez Molina D. The cellular thermal shift assay for evaluating drug target interactions in cells. Nat Protoc (2014), 9(9), 2100-2122. DOI: 10.1038/nprot.2014.138.

[19] Dawadi S, Simmons N, Miklossy G, Bohren KM, Faver JC, Ucisik MN, Nyshadham P, Yu Z, Matzuk MM. Discovery of potent thrombin inhibitors from a protease-focused DNA-encoded chemical library. Proc Natl Acad Sci U S A (2020), 117(29), 16782-16789. DOI: 10.1073/pnas.2005447117.

[20] Li L, Su M, Lu W, Song H, Liu J, Wen X, Suo Y, Qi J, Luo X, Zhou YB, et al. Triazine-Based Covalent DNA-Encoded Libraries for Discovery of Covalent Inhibitors of Target Proteins. ACS Med Chem Lett (2022), 13(10), 1574-1581. DOI: 10.1021/acsmedchemlett.2c00127.

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Structural proteins sit at the heart of cancer cell biology. They build the cytoskeleton (microtubules, actin filaments, intermediate filaments), organize the mitotic spindle, shape membranes, and coordinate transport, polarity, invasion in cancer, and survival. Because these systems are essential for proliferation and metastasis, they have also produced some of the most durable drug classes in oncology, especially microtubule-targeting agents used across solid tumors and hematological malignancies [1], [2].

At the same time, “structural protein drug discovery” has historically come with tradeoffs: narrow therapeutic windows, complex cellular context, and targets that are dynamic, polymeric, and highly conserved. These challenges are why many cytoskeletal drug targets still rely on legacy chemotypes or phenotypic screening hits, rather than modern, binding-driven campaigns. Yet the last decade has brought a major shift in what is possible: improved structural biology (cryo-EM of polymeric complexes), better in-cell target engagement assays, and DNA-encoded library (DEL) methods that can generate binders for hard-to-drug proteins, including in living cells.

This article maps the opportunity landscape for cytoskeleton drug targets (with emphasis on microtubule ligand discovery and antimitotic programs), outlines why structural proteins are “binding-driven targets” despite their reputation, and explains how DNA encoded library oncology screening (including in-cell approaches) can complement (and de-risk) traditional phenotypic screening.

Structural proteins are often described as “scaffolding,” but in cancer biology they are better understood as adaptive machines. They remodel in seconds, respond to force, and integrate signaling and metabolism. That dynamism is precisely what makes them attractive, albeit difficult drug targets.

The cytoskeleton is not one system; it is a coordinated network:

• Microtubules: polarized polymers of α/β-tubulin that form tracks for kinesin/dynein transport, organize organelles, and build the mitotic spindle.
• Actin filaments: rapidly assembling networks that generate force for migration, cytokinesis, and membrane remodeling.
• Intermediate filaments: stress-bearing polymers (e.g., vimentin, keratins) that provide mechanical resilience and influence signaling states.

Crosstalk between actin and microtubules is central to migration and division and provides multiple intervention points beyond “direct tubulin binders” [3]. Natural products have historically been a key source of cytoskeletal probes and drugs, highlighting that these proteins are chemically addressable even when they appear “flat” in simplified models [4].

Cytoskeletal proteins become most targetable in oncology when one connects them to vulnerabilities:

• Mitosis and chromosomal instability: spindle assembly, microtubule dynamics, and checkpoint timing are sensitive to small changes in polymer behavior – one reason antimitotic drug discovery continues to evolve [1], [2].
• Metastasis and invasion: actin remodeling, adhesion turnover, and intermediate filament switching (e.g., vimentin upregulation) are connected to Epithelial–Mesenchymal Transition (EMT)-like programs and aggressive phenotypes [3], [7].
• Stress tolerance: intermediate filaments buffer mechanical stress and influence organelle positioning; their dysregulation links to human disease and cancer biology [6].

The key takeaway for drug hunters: structural proteins are essential, but their essentiality is conditional; cell type, state, microenvironment, and genetic background matter. That is both the risk and the opportunity.

When people say, “structural proteins,” they often mean tubulin and actin. In practice, oncology-relevant “structural protein targets” span: polymers (tubulin/actin), polymer regulators, spindle machinery, and cytoskeleton-associated complexes.

Microtubules remain the flagship example of a structural protein drug target class. Multiple binding sites on tubulin enable stabilizers and destabilizers, and clinically validated mechanisms include both mitotic arrest and non-mitotic effects on trafficking, signaling, and tumor vasculature [1], [2].

What’s changing now is not the importance of tubulin, but the precision with which we can define where and how a ligand binds:

• Structural methods have mapped canonical sites (taxane, vinca alkaloids, colchicine, etc. [Scheme 1]) and revealed how distinct ligands tune microtubule dynamics [22]–[24].
• The “tubulin code” – isotypes and post-translational modifications – adds layers of biological selectivity that can be exploited for context-specific targeting and safety [21].

Scheme 1: Structure of different tubulin ligands.

From program-design perspective, this is where microtubule ligand discovery becomes more than “find another tubulin poison.” The frontier is selective modulation: isoform bias, complex-specific binding, or allosteric control of partner proteins.

“Antimitotic drug discovery” is broader than tubulin. Many programs target spindle machinery indirectly:

• Motor proteins (e.g., kinesins such as Eg5/KIF11)
• Microtubule-associated proteins (MAPs)
• Spindle assembly and checkpoint regulators
• Protein–protein interactions (PPIs) within dynamic mitotic complexes

A classic example is the discovery of monastrol, a small molecule that perturbs spindle bipolarity by inhibiting the kinesin Eg5 – an early demonstration that mitotic machinery can yield tractable small-molecule binders even outside tubulin [25].

Actin is deeply implicated in invasion and survival, but direct actin targeting has been limited clinically by toxicity. A major strategy is to target actin regulators (e.g., tropomyosins and nucleation factors), rather than the core polymer [5]. Intermediate filaments are less explored but increasingly recognized as druggable via indirect mechanisms (disrupting assembly, targeting associated kinases, or exploiting cancer-specific dependencies). Their disease links and mechanistic roles are well established [6], and vimentin’s role in cancer phenotypes makes it a frequent point of interest [7].

Structural proteins generate three recurring challenges that directly impact hit discovery and lead optimization.

For essential cellular machines, potency is easy to achieve and hard to use safely. This is one reason microtubule drugs can be effective yet limited by neuropathy or myelosuppression, and why actin-directed approaches can be constrained by cardio-toxicity risks [2], [5].

Practical implications for discovery teams:

• Avoiding “global polymer poisons”: prioritize modulators that bias specific states, complexes, or cell contexts.
• Separating binding from catastrophic function loss: not every binder must be a strong destabilizer; some can be allosteric modulators or complex stabilizers.

Early safety triage: structural proteins demand early counterscreens and cellular selectivity analysis, not late-stage “surprises.”

Structural proteins are dynamic and compartmentalized. The “same” protein can exist in multiple functional forms:

• Soluble monomer/dimer pools vs polymerized filaments
• Distinct complexes with partner proteins
• Isoforms and post-translational modification (PTM)-defined subpopulations (notably for tubulin) [21]

That means a binder found against purified protein may not engage the relevant cellular state. This is where protein binding site identification and in-cell target engagement become central – not optional.

Two widely used families of approaches are:

• CETSA (Cellular Thermal Shift Assay) to measure stabilization of proteins upon ligand binding in cells/tissues [19]
• Thermal proteome profiling (TPP) to assess proteome-wide engagement and downstream effects in living cells [20]

These methods don’t replace functional assays, but they reduce ambiguity early especially when structural proteins generate complex phenotypes.

Structural protein drug discovery increasingly depends on identifying which pocket (or interface) matters biologically.

A misconception is that structural proteins lack binding pockets. In reality, they often offer:

• Interfacial pockets (between subunits or protein partners)
• Conformation-specific cavities (open only in certain states)
• Polymer-specific surfaces (present only in filaments)
• Allosteric sites that tune dynamics rather than block an active site

Tubulin illustrates this best: decades of work have mapped major small-molecule sites and explained how different ligands stabilize or destabilize microtubules [22]–[24]. The emerging frontier is expanding beyond “the usual sites” and leveraging complex-dependent pockets.

A compelling recent study reported a previously unknown tubulin ligand-binding site that only emerges in the context of a tubulin complex with the stathmin family protein RB3 and tubulin tyrosine ligase (TTL) [26]. The authors show that Tumabulin-1 (TM1, Scheme 2), derived from BML284, can bind not only the canonical colchicine site but also a second site located at an interface involving α/β-tubulin plus RB3. Strikingly, two TM1 molecules bind cooperatively in this relatively large pocket, contacting multiple proteins in the complex. The work goes further by designing Tumabulin-2 (TM2, Scheme 2), a derivative engineered to bind selectively to this newly described “Tumabulin site” rather than the colchicine site. Functionally, TM2 behaves as a molecular glue: it strengthens the interaction between RB3 and tubulin, enhances RB3’s tubulin-depolymerizing activity, and supports the idea that “complex-first” pockets can be exploited to create new antimitotic mechanisms. Conceptually, this is a blueprint for future microtubule ligand discovery: instead of only targeting tubulin in isolation, drug discovery can aim at partner-dependent binding sites that may offer more selective biology and new safety/efficacy tradeoffs.

Scheme 2: Structure of TM1 and TM2

Structural proteins are often discovered via phenotypic effects (mitotic arrest, migration block). But that doesn’t mean they are “phenotype-only” targets. They are profoundly binding-driven:

• A small change in microtubule catastrophe frequency can shift mitotic outcomes [1], [2]
• Stabilizing or destabilizing specific actin architectures can alter cancer invasion programs [3], [5]
• Modulating complex formation (molecular glue mechanisms) can create new functional outputs [26]

This is the bridge to DEL: DEL screening is best when binding is the primary signal and when rapid exploration of chemical space is wanted to find rare chemotypes that can engage challenging pockets [11], [12].

Structural proteins are context-dependent; many relevant binding sites are:

• conformation-specific,
• complex-dependent,
• influenced by PTMs/isoforms,
• or only present in living-cell conditions.

In-cell approaches reduce the risk of selecting binders that only recognize an artificial purified state.

DNA-encoded libraries link each small molecule to a DNA barcode so billions of compounds can be pooled, selected for binding, and decoded by sequencing. The foundational concept of encoding chemistry with amplifiable information dates back to early encoded combinatorial chemistry [10], and has since matured into a widely used hit-finding engine [11]. Modern reviews cover selection formats, library design, artifacts, and translation to medicinal chemistry [12], [13].

Several peer-reviewed studies established that DEL selections can be done in living-cell contexts, including on-cell-surface targets and intracellular environments:

• DEL selection within and on living cells using cell-penetrating approaches [14]
• DEL screening inside a living cell using Xenopus laevis oocytes as a cellular “reaction vessel” [15]
• DEL selection against endogenous membrane proteins on live cells, supporting native-context binder discovery without purified target [16]

For structural proteins and cytoskeleton-associated complexes, these approaches are attractive because they better preserve polymer states, native assemblies, and cellular competition effects – key determinants of “real” engagement.

Vipergen has described service and technology approaches that emphasize DEL screening from target binding through hit validation, including screening formats designed for living-cell contexts and difficult targets [17]. Vipergen also offers a “Molecular Glue Direct” DELs-in-cells format intended to discover molecular glues by co-expressing targets and interaction partners (including E3 ligases) and screening in a multiplexed format in living cells [18]. For structural proteins, this matters because complex-dependent pockets (like the Tumabulin site example above) are often the most compelling places to look – but they are also the easiest to miss in simplified assays.

For cytoskeleton drug targets, the hypothesis is usually one of these:

1. Polymer modulation: tune microtubule/actin dynamics without total collapse
2. Complex modulation: target a partner-dependent interface pocket (e.g., tubulin-RB3-like concepts)
3. Spindle machinery inhibition: block a motor protein or mitotic complex assembly
4. Selectivity-by-context: exploit isoforms/PTMs/complexes that differ between tumor and normal tissues [21]

In practical terms: The question is not only asking “can we inhibit this?” but “which binding event produces the oncology-relevant phenotype with acceptable safety?”

• In vitro DEL screening can be ideal when stable purified protein is easily accessible, tight control is wanted and can fold into the relevant conformation.
• In-cell DEL screening becomes especially valuable when:
  • The target is hard to purify
  • The relevant binding site is complex-dependent
  • Post-translational modifications matter,
  • Early evidence of cellular relevance is wanted [14-16].

For cytoskeleton targets, in-cell approaches can also help filter out compounds that bind the purified protein but cannot compete with endogenous binding partners or fail due to localization barriers.

Structural proteins are abundant and sticky; selections must be designed to reduce frequent hitters and nonspecific binders. Typical design principles include:

• Multiple negative selections (beads/DNA controls; off-target protein controls)
• Competition experiments with known ligands (when available)
• Orthogonal selection conditions (salt, detergents compatible with target state, polymer vs monomer conditions)
• Early clustering and “chemical series” prioritization rather than singletons

The point is not to eliminate all noise, it’s to quickly surface chemotypes that behave consistently across selection variants.

DEL delivers hypotheses; medicinal chemistry needs confirmed molecules. Typical validation includes:

• Biophysical binding confirmation (MST, SPR, ITC, DSF)
• Structural follow-up (cryo-EM or X-ray where feasible), particularly important for tubulin sites [22-24]
• Cellular target engagement:
  • CETSA to confirm in-cell binding [19]
  • TPP to assess proteome-wide engagement and potential liabilities [20]

For structural proteins, engagement data is especially useful because phenotypes can be indirect. Binding confirmation quickly identifies wrong mechanism which can then be eliminated.

For oncology, the functional readouts depend on target class:

• Microtubules: polymerization dynamics, spindle formation, mitotic timing, aneuploidy signatures
• Spindle motors: bipolar spindle assembly defects, mitotic arrest phenotypes (monastrol-like patterns) [25]
• Actin systems: migration/invasion assays, adhesion turnover, cytokinesis defects [3], [5]
• Complex-dependent targets: specific biomarker readouts tied to complex stabilization/destabilization (as in molecular glue concepts) [26]

A practical strategy is to start with a binder series, then intentionally explore the “phenotype space” by tuning permeability, residence time, and allosteric bias – rather than pushing raw potency immediately.

Phenotypic screening has historically been productive in cytoskeletal biology – partly because many phenotypes (mitotic arrest, rounded morphology, migration block) are easy to observe. But phenotypic screening also has recurring major bottlenecks and constraints:

• target deconvolution can be slow,
• the same phenotype can come from many targets,
• and off-target toxicity can generate false-positive signals / artifactual activity as “activity.”

Large analyses of discovery strategies have shown that phenotypic approaches have contributed substantially to first-in-class medicines [8], and industry perspectives continue to highlight both opportunity and friction in phenotypic drug discovery [9].

DEL-based discovery offers a complementary angle:

• DEL is binder-first: Chemical matter is the starting point which can be tied to a target hypothesis.
• Phenotypic screens are effect-first: Phenotype is the starting point and search for mechanism is the later work.

For structural proteins, a hybrid approach is often best:

1. use binding-driven DEL screening to generate selective chemotypes for a structural target or complex,
2. then test those chemotypes across phenotypic systems (cell division, migration, survival),
3. while using target engagement tools (CETSA/TPP) to confirm that cellular biology matches the binding hypothesis [19], [20].

This is especially valuable for modern microtubule ligand discovery, where the goal is not “maximum disruption,” but mechanism-shaped modulation that delivers therapeutic windows.

To align with real search behavior and program decisions, here are common scenarios where DEL screening for structural protein targets is often considered:

A team wants microtubule modulation but needs:

• a new binding site,
• less neuropathy risk,
• a differentiated resistance profile,
• or a complex-dependent mechanism.

The Tumabulin site example shows how new tubulin pockets can emerge in complexes and enable novel mechanisms like molecular glue-like stabilization of protein–protein interactions [26].

Phenotypic screens find mitotic arrest hits, but target IDs are unclear. DEL can support a binder-first campaign against a mitotic motor or spindle complex, inspired by precedents like Eg5 inhibition [25].

Instead of actin itself, target actin regulators or cross-talk nodes that influence cytoskeletal architecture [3], [5]. DEL can help identify small-molecule binders for proteins that are hard to screen traditionally.

Structural proteins are no longer “legacy-only” oncology targets. Modern structural biology is exposing new pockets in polymeric complexes [22-24] and even expanding the tubulin site landscape through complex-dependent binding sites and molecular glue concepts [26]. In parallel, target engagement methods like CETSA and TPP reduce ambiguity about what a compound is doing in cells [19], [20], and DEL technologies keep expanding the practical chemical search space for hard-to-drug targets [11-13].

For teams working on structural protein drug discovery, the strategic shift is this: move from “find a toxin” to “find a binder that modulates a defined state or complex in the right cellular context.” That’s where DNA encoded library oncology screening – including in-cell formats – can deliver value: fast hit generation, early selectivity signals, and a clearer line from binding to biology [14]–[18].

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[2] Dumontet C., Jordan M.A., Microtubule-binding agents: a dynamic field of cancer therapeutics, Nat. Rev. Drug Discov. (2010), 9, 790–803. https://doi.org/10.1038/nrd3253 

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Ion channels sit at the center of electrical and chemical signaling in biology. They control excitability in neurons and muscles, shape synaptic transmission, regulate secretion, and tune countless homeostatic processes. Unsurprisingly, ion channel dysfunction is linked to neurological disease, pain, cardiovascular disorders, respiratory disease, and more. Hereby, ion channels remain a proven class of drug targets. Recent advances in cryo-electron microscopy (cryo-EM) have also made it increasingly clear that channels are not static pores: they adopt multiple conformations (resting, activated, inactivated, desensitized) that can present distinct ligand-binding pockets and “druggable” opportunities. [1] 

Yet despite the opportunity, ion channel drug discovery is still widely viewed as challenging. The reason is not a lack of biology, it’s the practical reality of discovering small molecule ion channel binders that are selective, cell-penetrant, and functionally meaningful, while navigating state-dependent binding and assay constraints. Patch clamp electrophysiology remains the gold-standard functional readout [3], but it is time-intensive, and even modern automated patch clamp is more resource-demanding than many early-stage teams can afford to run at true “chemical-space exploration” scale. [4] 

For that reason, today’s best programs use a toolbox approach to ion channel ligand discovery: pairing functional assays (patch clamp or fluorescence flux) with ion channel binding assays, structural biology, and screening strategies that accelerate early hit identification for ion channels. [5] One of those strategies is DNA encoded library (DEL) ion channel screening, which enables pooled, binding-based enrichment of ligands from extremely large libraries. DEL screening is not a replacement for electrophysiology, but it can be a powerful way to generate high-quality chemical starting points that are then validated and optimized using functional assays. [15] 

This article takes a broader view of ion channels as drug targets covering the therapeutic landscape, key scientific constraints, and the main ion channel screening technologies used in discovery while also explaining where DEL screening for ion channel targets can fit (including nonfunctional ion channel ligand discovery approaches) as a complementary engine for ion channel ligand discovery and small molecule ion channel binders.

Ion channels are integral membrane proteins that regulate the movement of ions (Na⁺, K⁺, Ca²⁺, Cl⁻) across membranes. That movement translates into electrical signals, intracellular second-messenger dynamics (especially Ca²⁺), and changes in membrane potential that control downstream physiology. From a translational perspective, channels are “high leverage” targets: modest shifts in gating, conductance, or channel availability can produce meaningful clinical effects – analgesia, antiarrhythmic activity, bronchodilation, anxiolysis, seizure control, and more.

A recent perspective emphasizes that ion channels are a validated but historically underexploited target class, in part because discovery is constrained by selectivity, safety, and assay complexity. [2] Structural biology has strengthened the case for ion channels by revealing conserved and family-specific pockets, lipid interactions, auxiliary subunits, and state-dependent binding sites that can be targeted by diverse chemotypes. [1]

Unlike many soluble enzymes, ion channels cycle between multiple functional states. This is not a nuance, it often is the mechanism. Two practical consequences follow:

1. The same compound can look different in different assays (depending on voltage protocol, agonist conditions, desensitization, or state occupancy).
2. The most relevant ligand-binding pocket may only exist or be most accessible in a particular state.

Modern cryo-EM reviews of voltage-gated channels highlight how drugs and toxins can stabilize different conformations and bind at multiple sites, which helps explain why electrophysiology protocols and assay context matter so much. [1]

There are many ion channel genes and channel-like complexes, but most small-molecule discovery programs cluster around a few major groups.

Voltage-gated channels open and close in response to membrane potential. They are central to excitability in neurons, heart, and muscle. Cryo-EM has enabled direct mapping of drug binding sites in human sodium channels, illustrating how chemically different scaffolds can engage distinct pockets and sometimes distinct numbers of sites on a single channel. [11]

Figure 1: Mechanistic overview of Voltage-gated ion channels (Left) and Ligand-gated ion channels (right).

Drug discovery implications: state dependence, kinetics (on/off rates), subtype selectivity, and tissue distribution often dominate feasibility. For example, sodium channel drug discovery programs frequently aim for use-dependent inhibition in hyperexcitable tissue while minimizing effects in normal physiology.

Ligand-gated channels convert chemical neurotransmitters into fast electrical signals. A major practical challenge is that subunit composition can change pharmacology: the “same” receptor family can exist in many assemblies with distinct binding pockets and functional outcomes.

Recent work isolating native GABAA receptor assemblies and solving structures bound to clinically relevant drugs provides a vivid example of how real-world pharmacology depends on receptor composition and state. [9] 

Transient Receptor Potential (TRP) channels integrate temperature, inflammation, and chemical signals. A recent review describes how TRP channel drug discovery has expanded beyond pain into respiratory, metabolic, and neuropsychiatric diseases as well as oncology-relevant biology, while also underscoring translational pitfalls (e.g., on-target thermoregulation issues in some TRPV1 programs). [8] 

Cystic Fibrosis Transmembrane Conductance Regulator (CFTR) is a clinically transformative case story for ion channel modulation. A study showing high-resolution structural work showed how components of triple-combination therapy bind CFTR and synergistically rescue folding and/or function in the ΔF508 context. [10] 

Takeaway: channels often require mechanism-specific and sometimes multi-ligand strategies. That reality should influence how you design screening cascades and hit-to-lead plans.

Ion channel programs rarely fail because the target is “not relevant.” More often, they fail because connecting binding → functional effect → therapeutic window is harder than expected.

1) State-dependent binding and the “moving target” problem

Channels change conformation during gating. A compound can bind preferentially to one state, leading to strong effects under some protocols and weak effects under others. Structural mapping of Nav1.7 antagonists provides concrete evidence: multiple chemotypes bind different sites and stabilize different states meaning your assay protocol can determine what you see. [11] 

Practical takeaway: define the desired state dependence early (e.g., tonic vs use-dependent inhibition) and ensure the screening cascade measures that property (not just “any inhibition”).

2) Translating binding into functional modulation

A binder is not automatically a blocker or opener. Functional consequence depends on:

• binding site (pore vs allosteric pocket),
• kinetics and residence time,
• state preference,
• membrane access / partitioning,
• and biological system (cell type, auxiliary subunits, lipid composition).

This is why mature discovery programs treat ion channel binding assays and functional assays as complementary rather than competing.

3) Selectivity and safety are inseparable

Channel families can be conserved, and off-target channel activity can create safety concerns. This is one reason automated electrophysiology platforms became prominent in cardiac safety assessment and late-stage profiling. [6] 

4) Assay constraints and throughput limitations

The classic patch clamp paper (Hamill et al.) captures why electrophysiology is so powerful – and implicitly why it has historically limited throughput. [3] Automated patch clamp has improved scale and standardization but remains a more intensive modality than most plate-based assays or pooled binding selections. [4]

The fastest way to build a productive ion channel drug discovery engine is to be explicit about what each assay answers and what it doesn’t.

Manual patch clamp provides:

• precise voltage protocols,
• detailed kinetics,
• mechanistic interpretation (block vs gating shifts),
• and high-confidence data.

It is still the benchmark for many channel decisions. [3] 

Automated patch clamp (APC) boosts throughput and reduces operator variability. Reviews of APC innovation describe how it has become standard in many organizations for lead optimization and safety profiling. [4,6] 

Where APC is strongest:

• confirming functional activity of prioritized hits,
• running SAR efficiently once you have a tractable series,
• safety panels (depending on program needs).

Plate-based assays can screen larger compound sets quickly. A review of high-throughput ion channel screening technologies summarizes the major categories: Binding assays, flux-based assays, fluorescence-based assays, and automated electrophysiology flowed by how they map to different channel classes. [5] 

Common pitfalls: indirect readouts, coupling artifacts, and off-target effects. These assays are extremely useful, but they demand careful controls and orthogonal validation.

Binding assays can feel less fashionable than functional assays, but for channels they remain strategically useful, especially in early phases of drug discovery.

Why binding assays matter in practice:

• triaging functional assay artifacts (is it direct channel binding or pathway interference?)
• mapping competition vs known ligands (where tracers exist)
• supporting selectivity strategies
• and enabling nonfunctional ion channel ligand discovery (binder-first programs)

Importantly, binding assays can be deployed in multiple formats (radioligand displacement, SPA, filtration, and in some cases biophysical approaches when target presentation allows). The best format depends on channel class, available ligands, and how the target is prepared.

There isn’t a single “best” approach to ion channel ligand discovery. Most successful programs mix methods across stages.

1) Classic small-molecule HTS (functional or binding-based)

If you have a robust plate assay or scalable APC, HTS can deliver hits quickly. The challenge is often what happens next: HTS hits can cluster into liabilities (promiscuity, low solubility, poor permeability) and may require significant triage before electrophysiology.

2) Fragment-based discovery (FBDD)

Fragments can be effective if you measure binding reliably and/or have strong structural biology. For channels, feasibility often hinges on target presentation and assay sensitivity.

3) Structure-enabled design (cryo-EM-guided)

Cryo-EM has changed ion channel medicinal chemistry by making binding pockets visible and enabling rational analog selection. Reviews of voltage-gated channels highlight this shift, emphasizing how structures can explain pharmacology and guide optimization. [1] 

Concrete examples include:

• Nav1.7 structures with diverse antagonists and lead compounds [11] 
• Native GABAA receptor assemblies bound to clinically used drugs [9] 
• CFTR modulator binding that clarifies synergy in triple therapy [10] 

4) In silico screening and AI-assisted prioritization

Virtual screening can reduce experimental burden, but outcomes depend on the accuracy of the target model and whether you’re docking into the relevant state. For channels where state dependence dominates, computational work often performs best as a prioritization layer rather than the sole discovery engine.

5) Biologics and alternative modalities (beyond small molecules)

Small molecules dominate ion channel pharmacology because many clinically relevant pockets are in the pore domain or allosteric sites not easily accessed by large biologics. Still, there is an emerging landscape of antibodies targeting ion channels, largely aimed at extracellular epitopes for specific mechanisms. A detailed review discusses the opportunities and technical challenges of ion-channel-targeting antibodies. [12] 

Why mention this on a small-molecule page?

Because it reflects a broader industry trend: teams increasingly pursue multiple modalities in parallel when classic small molecules struggle. Even if your program is small-molecule-led, understanding the modality landscape helps sharpen differentiation and risk planning.

No matter which discovery route you use (HTS, fragments, structure-based, DEL), ion channels bring a practical question to the surface:

Cell-based presentation (native or engineered)

Cell-based systems preserve membrane environment and (often) native assembly. They enable direct functional assays and can support certain target-engagement approaches. The trade-off is complexity: background binding, expression variability, and cell-specific effects can complicate interpretation.

Purified protein in detergent or nanodiscs

Purified channels can enable cleaner biochemical and biophysical work, but detergents may destabilize or bias conformations. Nanodiscs and related membrane mimetics aim to preserve a more native-like lipid environment.

Two recent reviews provide accessible entry points:

• Nanodiscs for the study of membrane proteins (overview of nanodisc platforms and applications) [13] 
• Nanodiscs in membrane protein drug discovery and development (focus on drug discovery applications and workflows) [14] 

Practical point: Most ion channel programs use multiple formats over time: Cell-based for physiological relevance and purified/nanodisc formats for mechanistic binding or structure efforts when feasible.

DEL is best understood as a binder discovery engine: a way to identify ligands by affinity selection from exceptionally large chemical libraries, followed by off-DNA resynthesis and validation. A comprehensive review describes how DEL libraries are built, screened, and analyzed, and why they can deliver rapid access to “early chemical matter.” [15] 

Why DEL can be useful for ion channels

Ion channels can be bottlenecked by functional assay throughput. DEL can help by shifting the earliest step “find starting matter” toward a pooled, binding-based workflow. That enables:

• rapid early hit identification for ion channels across diverse chemotypes,
• efficient exploration of chemical space with low target consumption,
• and nonfunctional ion channel ligand discovery when you need binders first and mechanism later.

This is especially attractive when you have strong genetic/biological rationale but limited chemical starting points.

Live-cell and in-cell selection methods: why they matter for membrane proteins

Historically, DEL selections were most common on purified proteins. However, the field has advanced to include selections in a cellular context:

• A Nature Chemistry paper reported a method enabling DEL selections against endogenous membrane proteins on live cells without overexpression by DNA-tag labeling of the membrane protein. [16] 
• A JACS study reported screening a multimillion-member DEL inside a living cell (Xenopus oocyte-based), demonstrating feasibility of in-cell selections which could potentially lead to the discovery of intracellular ion channel modulators. [17] 

These developments are relevant to ion channels because native membrane context and assembly can strongly influence which ligands bind productively.

“Binding-first” doesn’t mean “function-last”

A healthy ion channel discovery cascade treats DEL as a front end:

1. DEL selection →
2. off-DNA resynthesis →
3. orthogonal confirmation (binding and/or cellular engagement) →
4. functional follow-up (patch clamp/APC and/or plate assays) →
5. iterative SAR.

That flow is consistent with how many organizations allocate resources: run expensive electrophysiology on chemistry that has already “earned” deeper investment.

Below is a pragmatic discovery flow that works whether your entry point is HTS, structure-based design, fragments, or DEL.

Be explicit about:

• blocker vs modulator vs opener,
• tonic vs use-dependent behavior,
• desired kinetics (fast/slow onset, long/short residence),
• key safety concerns and counterscreens.

This prevents the common failure mode of optimizing potency in an assay that does not reflect the desired mechanism.

• Robust plate assay available? HTS can be efficient for initial hit-finding.
• Structural data strong? Structure-guided libraries and rational analog selection may be faster than brute-force screening.
• Electrophysiology bandwidth limited? Consider binder-first strategies (including DEL screening) to generate chemical matter before deep functional investment.

The “right” answer often changes over time as you learn more about the target and the assay landscape.

For ion channels, orthogonal confirmation is not bureaucracy its survival:

• confirm direct target engagement, if possible,
• use at least one independent functional format when feasible (e.g., flux assay + patch clamp),
• counter-screen early against close homologs or liabilities relevant to the indication.

Use patch clamp and APC to answer questions that other assays can’t:

• state dependence,
• gating shifts,
• kinetics/residence,
• protocol-relevant behavior that triggers physiological patterns.

APC reviews emphasize how electrophysiology scale has improved – but also why it still benefits from strong upstream triage. [4,6] 

Vipergen focuses on DNA-encoded library technology, including workflows designed for challenging targets such as membrane proteins and receptors. Vipergen’s technology overview describes three components: YoctoReactor® (DEL creation), Binder Trap Enrichment® (BTE; homogeneous DEL screening), and Cellular Binder Trap Enrichment® (cBTE) for screening DELs inside living cells. [19,20] 

For ion channel programs, that can be most relevant when:

• You want ion channel ligand discovery and small molecule ion channel binders early, before committing heavy electrophysiology resources.
• Your channel is difficult to purify or loses native pharmacology outside the membrane,
• You want a binder-first route to early hit identification for ion channels, feeding your functional cascade.

Vipergen also describes DEL screening services for purified biotinylated integral membrane proteins formulated in nanodiscs or detergent, which can be useful when you have a stable purified channel construct and want controlled binding selections. [21]

Ion channels remain among the most clinically impactful and scientifically demanding drug targets. The combination of state-dependent conformations, membrane biology, and assay constraints means that success usually comes from an integrated strategy:

• Use the right ion channel screening technologies for the question at hand (functional vs binding vs structure).
• Treat ion channel binding assays as a practical way to reduce artifacts and anchor mechanism.
• Deploy electrophysiology (manual or automated) where it delivers decisive information.
• Use pooled binder discovery methods (including DNA encoded library ion channel screening) when early chemical matter is the limiting factor – especially for nonfunctional ion channel ligand discovery and rapid early hit identification for ion channels.

In practice, this integrated mindset is what turns ion channels from a challenge into a manageable, repeatable discovery workflow. 

[1] Huang J, Pan X, Yan N. Structural biology and molecular pharmacology of voltage-gated ion channels. Nat Rev Mol Cell Biol (2024), 25 (11), 904-925. https://doi.org/10.1038/s41580-024-00763-7 

[2] Bagal SK et al. Ion Channels as Therapeutic Targets: A Drug Discovery Perspective. J Med Chem (2013), 56, 3, 593-624. https://pubs.acs.org/doi/10.1021/jm3011433 

[3] Hamill OP et al. Improved patch-clamp techniques for high-resolution current recording from cells and cell-free membrane patches. Pflügers Arch (1981), 391 (2), 85-100. https://link.springer.com/article/10.1007/BF00656997 

[4] Obergrussberger A et al. Automated patch clamp in drug discovery: major breakthroughs and innovation in the last decade. Expert Opin Drug Discov (2021), 16 (1), 1-5. https://doi.org/10.1080/17460441.2020.1791079 

[5] Yu H et al. High throughput screening technologies for ion channels. Acta Pharmacol Sin (2015), 37 (1), 34-43. https://doi.org/10.1038/aps.2015.108 

[6] Bell DC, Fermini B. Use of automated patch clamp in cardiac safety assessment: past, present and future perspectives. J Pharmacol Toxicol Methods (2021), 110, 107072. https://doi.org/10.1016/j.vascn.2021.107072 

[7] Santos R et al. A comprehensive map of molecular drug targets. Nat Rev Drug Discov (2017), 16 (1), 19-34. https://doi.org/10.1038/nrd.2016.230 

[8] Koivisto A-P et al. Advances in TRP channel drug discovery: from target validation to clinical studies. Nat Rev Drug Discov (2022), 21 (1), 41-59. https://doi.org/10.1038/s41573-021-00268-4 

[9] Sun C et al. Cryo-EM structures reveal native GABAA receptor assemblies and pharmacology. Nature (2023), 622, 195-201. https://doi.org/10.1038/s41586-023-06556-w 

[10] Fiedorczuk K, Chen J. Molecular structures reveal synergistic rescue of Δ508 CFTR by Trikafta modulators. Science (2022), 378 (6617), 284-290. https://doi.org/10.1126/science.ade2216 

[11] Wu Q et al. Structural mapping of Nav1.7 antagonists. Nat Commun (2023), 14 (1), 3224. https://doi.org/10.1038/s41467-023-38942-3 

[12] Hutchings CJ et al. Ion channels as therapeutic antibody targets. MAbs (2019), 11 (2), 265-296. https://doi.org/10.1080/19420862.2018.1548232 

[13] Denisov IG et al. Nanodiscs for the study of membrane proteins. Curr Opin Struct Biol (2024), 87, 102844. https://doi.org/10.1016/j.sbi.2024.102844 

[14] Dong Y et al. The application of nanodiscs in membrane protein drug discovery & development and drug delivery. Front Chem (2024), 12, 1444801. https://doi.org/10.3389/fchem.2024.1444801 

[15] Gironda-Martínez A et al. DNA-Encoded Chemical Libraries: A Comprehensive Review with Succesful Stories and Future Challenges. ACS Pharmacol Transl Sci (2021), 4 (4), 1265-1279. https://doi.org/10.1021/acsptsci.1c00118 

[16] Huang Y et al. Selection of DNA-encoded chemical libraries against endogenous membrane proteins on live cells. Nat Chem (2021), 13 (1), 77-88. https://doi.org/10.1038/s41557-020-00605-x 

[17] Petersen LK et al. Screening of DNA-Encoded Small Molecule Libraries inside a Living Cell. J Am Chem Soc (2021), 143, 7, 2751-2756. https://doi.org/10.1021/jacs.0c09213 

[18] Blakskjaer P et al. Fidelity by design: YoctoReactor and binder trap enrichment for small-molecule DNA-encoded libraries and drug discovery. Curr Opin Chem Biol (2015), 26, 62-71. https://doi.org/10.1016/j.cbpa.2015.02.003 

[19] Vipergen website: Technology overview (YoctoReactor®, BTE®, cBTE®). https://www.vipergen.com/technology/ 

[20] Vipergen website: Cellular Binder Trap Enrichment® (cBTE). https://www.vipergen.com/cellular-binder-trap-enrichment/ 

[21] Vipergen website: DEL – Integral Membrane Proteins (nanodiscs/detergent). https://www.vipergen.com/del-integral-membrane-proteins/ 

[22] Vipergen website: Understanding DEL Screening: From Target Binding to Hit Validation. https://www.vipergen.com/understanding-del-screening-from-target-binding-to-hit-validation/

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Receptors sit at the heart of biology’s information flow. They detect extracellular cues (hormones, neurotransmitters, chemokines, growth factors), translate them into intracellular signals, and control everything from metabolism and immune responses to neural function and cell fate. Because receptor activity can be tuned, or redirected with the right ligand, receptors have become some of the most valuable and most intensively pursued drug targets.

Among receptor families, G protein-coupled receptors (GPCRs) are especially prominent: a recent review analyzing the “drugged GPCRome” reports 516 approved drugs targeting GPCRs (~36% of all approved drugs). [17] That scale is exactly why receptor programs are so competitive and why teams increasingly seek technologies that can uncover new chemotypes, new binding sites (including allosteric pockets), and better starting points for medicinal chemistry.

One of the most effective modern approaches for doing this is DNA-encoded library (DEL) screening. DELs link each small molecule to a DNA barcode, enabling massively parallel affinity-driven selections and rapid identification of binders by sequencing. The core concept traces back to early “encoded combinatorial chemistry” work, which since 1990 has matured into an industry workhorse for hit discovery. [1][2]

For receptor programs, DEL screening offers something particularly attractive:

• Receptors are ligand-driven targets, often with multiple conformations and multiple druggable pockets.
• DEL screening can sample very large chemical space efficiently compared with classical plate-based high-throughput screening (HTS). [2]
• DELs can be adapted to challenging receptor formats, including purified membrane receptors in detergent or nanodiscs, and increasingly to live-cell or in-cell selection strategies. [6][7][8][22]

That said, a successful receptor program is rarely “one-tech-only.” Receptor discovery is a pipeline problem: you want the right starting points and the right evidence early – binding, target engagement, selectivity, and functional impact. So, in this article, we’ll take a broader view: how receptors behave as drug targets, how ligands are discovered across multiple discovery paradigms, and where DEL fits best; especially for organizations seeking receptor ligand discovery services, small molecule receptor binders, and a practical receptor drug discovery platform that can handle hard receptor formats.

Receptors can be viewed as “signal processors.” Ligands don’t just turn receptors on or off; they can stabilize specific receptor states, bias signaling toward particular pathways, and change receptor localization or trafficking. This is particularly visible in GPCR pharmacology, where allosteric modulation and state-dependent signaling have become central to modern drug discovery thinking. [9][10]

A helpful way to frame receptor pharmacology (especially for GPCRs) is that ligands can act as:

• Orthosteric ligands that compete with endogenous agonists at the primary binding site.
• Allosteric modulators that bind elsewhere to tune potency/efficacy, improve selectivity, or alter signaling bias.
• Bitopic ligands that engage both orthosteric and allosteric regions (in some receptor systems), often blending affinity with selectivity and unique pharmacology (conceptually aligned with allosteric principles discussed in GPCR allostery reviews). [9][10]

These concepts matter because they directly influence what you screen for and how you interpret hits. A selection or screen that only reports “binding” may miss functional nuance, while a functional assay may miss silent binders that become valuable after optimization or when paired with a second ligand (e.g., Positive Allosteric Modulator [PAM] discovery).

The medical reach of receptors is enormous:

• GPCRs: CNS, cardiometabolic disease, inflammation, respiratory disease, pain relief, pain treatment, and more (with a very large fraction of approved medicines). [17]
• Enzyme-linked receptors (e.g., receptor tyrosine kinases): oncology, immune disease, fibrosis – often targeted with biologics and small molecules.
• Nuclear receptors: endocrine and metabolic disorders, inflammation, oncology; often with small molecules that tune transcriptional programs. [14][15][16]

In other words, receptors remain high-value targets and high-competition targets. That combination increases the premium on discovery strategies that can yield novel chemotypes, allosteric ligands, and selective binders that can be translated into function.

Figure 1: Overview of cell-membrane receptors G protein-coupled receptors (GPCRs) and Enzyme-linked receptors.

Receptor ligand discovery isn’t hard because receptors are “undruggable.” It’s hard because receptors are contextual – their ligandability and pharmacology can shift with receptor state, membrane environment, binding partners, and assay format.

Many receptors exist as ensembles of states whose populations depend on ligand, membrane composition, binding partners, and modifications. For GPCRs, this is the basis for concepts like partial agonism, biased agonism, and ligand-specific receptor conformations – phenomena that are deeply intertwined with allosteric modulation. [9][10]

In practice, this means:

• An assay that traps one receptor state can enrich ligands for that state.
• Ligands can be “real” binders but non-productive for the pathway you care about.
• Allosteric sites can deliver selectivity advantages but hit rates can be low and effects can be subtle. [9][10]

GPCRs and other integral membrane receptors often need stabilizing conditions (detergents, lipids, nanodiscs, stabilizing mutations, binding partners). These conditions can alter which sites are accessible and which conformations dominate. Nanodiscs, for example, are widely used to present membrane proteins in a controlled lipid environment and are broadly recognized as powerful tools in membrane protein biochemistry and biophysics. [8]

From a discovery standpoint, “format sensitivity” shows up as:

• Variable protein stability (aggregation, denaturation, loss of native pharmacology).
• Shifts in orthosteric/allosteric site accessibility.
• Higher background binding from hydrophobic compounds in membrane-like environments.

A binder can be:

• a true receptor ligand with a useful functional profile,
• a binder that fails to engage in cells,
• a binder that hits a non-productive state,
• or a binder that is “real” but not developable (solubility, permeability, off-targets).

A robust receptor strategy accepts this reality: you need both efficient hit-finding and efficient triage. That’s why modern receptor discovery programs increasingly combine hit-finding technologies (HTS, fragments, DEL, computational methods) with early orthogonal validation and functional profiling.

When teams search for receptor ligand discovery services or a receptor drug discovery platform, they’re usually weighing a portfolio of discovery routes, not just one. Different approaches shine at different points in the funnel: some are great at generating starting points, others excel at characterizing mechanisms or accelerating optimization.

HTS tests large numbers of compounds (often hundreds of thousands) using biochemical or cell-based assays. It remains a major pillar of drug discovery because it can be directly functional if the assay is designed that way. [13]

Where HTS excels

• When you have a robust functional assay (e.g., GPCR signaling readout).
• When you want early functional triage.
• When the target behaves well in assay conditions.

Where HTS struggles for receptors

• Assay development for receptors can be slow and artifact-prone (signal window, receptor expression variability, pathway coupling).
• Membrane receptor formats can create background noise or stability issues.
• Chemical space is limited by practical library size and cost compared with pooled approaches. [13]

HTS remains a strong option when the biology is well understood and the assay behaves, but for receptors that are unstable, require specific complexes, or signal in context-dependent ways, HTS can become resource-intensive early in a program.

Fragment screening uses small, low-complexity molecules (“fragments”) to efficiently sample chemical space and then grows or links them into potent ligands. FBDD is now mainstream, with a long pipeline of fragment-derived clinical candidates noted in an influential review. [11]

For GPCRs, fragment screening has historically been constrained by receptor stability in detergents and the sensitivity needed to detect weak fragment binding. Stabilized GPCR constructs (often referred to in the context of “StaR” GPCRs) and biophysical workflows have been used to enable fragment screening against GPCRs. [12]

Where FBDD excels

• When you have strong structural biology and biophysics.
• When you want highly ligand-efficient starting points.
• When you can stabilize the receptor and obtain reliable binding measurements. [11][12]

Where FBDD struggles

• Weak affinities demand sensitive, well-behaved receptor preparations.
• Progress can bottleneck on structural cycles and chemistry.
• Some receptor targets remain difficult to stabilize or measure with adequate throughput.

Structure-guided discovery is increasingly relevant for receptors, especially as structural coverage has expanded across GPCR families and nuclear receptors. In practice, structure-based tools are often most powerful after you have at least one validated chemotype – because they become tools for optimization, selectivity engineering, and hypothesis testing rather than purely for hit generation.

Even when computational approaches are used earlier (virtual screening, pharmacophore models), they typically benefit from experimental anchors, known ligands, binding-site constraints, or assay data that reduces the search space.

Many receptors, especially extracellular or cell-surface receptors are effectively targeted by antibodies, peptides, or engineered proteins. Small molecules still matter profoundly (especially for intracellular receptor domains, GPCRs, and nuclear receptors), but a modern receptor portfolio often includes multiple modalities depending on mechanism, tissue access, and safety constraints. The practical takeaway is that “receptor targeting” is modality-agnostic; what changes is the discovery and validation toolkit.

DEL is a different engine: instead of testing compounds one-by-one, you screen pools of DNA-tagged compounds and identify enriched binders by sequencing. Modern DEL approaches and selection formats are covered in widely cited reviews. [2][3][4]

DEL screening is particularly relevant to receptor teams searching for:

• DNA encoded library receptor screening
• DNA encoded library screening for receptors
• DEL screening for GPCR targets
• small molecule receptor binders
• hit identification for receptor targets
• receptor ligand discovery outsourcing

But DEL screening should be understood in a broader workflow: DEL screening can be a powerful front-end binder discovery tool – especially when paired with strong validation and functional follow-up.

A frequent misconception is that receptor programs can be categorized simply by receptor class (GPCR vs nuclear receptor vs RTK). The more predictive way to plan discovery is by format: purified domain vs full-length receptor, membrane mimetic vs cellular presentation, and the degree to which functional partners must be retained.

GPCR programs often seek orthosteric ligands, allosteric modulators (PAM/NAM), or ligands that bias pathway output. Reviews of GPCR allostery emphasize how allosteric ligands can fine-tune receptor function and often offer selectivity advantages compared with orthosteric ligands. [9][10]

A notable peer-reviewed study demonstrated an allosteric “beta-blocker” for the β2-adrenergic receptor (β2AR) discovered via a DNA-encoded library selection against a purified GPCR. [5] This is a useful proof point: DEL can access GPCR allosteric chemistry when the receptor is presented in a workable format and selection conditions support that binding mode.

GPCRs are among the most format-sensitive targets in drug discovery. Depending on the receptor and hypothesis, teams may choose:

• Purified receptor preparations (often stabilized, with defined partners)
• Detergent-solubilized receptor
• Receptor embedded in nanodiscs (a more native-like lipid environment) [8]
• Live-cell or in-cell strategies when native context is essential for maintaining function or state distributions [6][7]

Even within a “GPCR ligand screening” project, multiple formats are often used sequentially: a binder discovery format (purified receptor or cell-based selection), followed by orthogonal binding assays and cellular function assays.

Nuclear receptors are intracellular transcription factors that regulate gene expression through ligand-dependent recruitment of coactivators and corepressors. Foundational and modern reviews describe the breadth of the nuclear receptor superfamily and the structural basis of ligand-driven receptor regulation. [14][15][16]

For nuclear receptor ligand identification, screening is often feasible with soluble ligand-binding domains or curated receptor complexes. The most common downstream validation steps include:

• Orthogonal binding assays (biophysical or competition binding)
• Coactivator recruitment assays (often energy transfer formats)
• Transactivation reporter assays and gene expression studies

Because nuclear receptors are frequently “ligandable” but biologically nuanced (partial agonism, tissue-selective signaling), the ability to profile functional outcomes early can be as important as potency.

Receptor tyrosine kinases, cytokine receptors, and other signaling receptors may be addressed by small molecules (intracellular domains, allosteric sites, PPIs) or biologics. For small-molecule discovery, the same general principles apply; choose the right target format, use a discovery method compatible with that format, and validate with orthogonal evidence.

For example, even when the extracellular receptor is targeted by biologics, the intracellular kinase domain may be targeted by small molecules making “receptor drug discovery” a spectrum of projects rather than a single category.

Regardless of how you find initial hits (HTS, fragments, DEL, or computational) you need to establish confidence. For receptors, that usually means orthogonal confirmation plus functional profiling. This is also where many programs gain speed: the better your validation plan, the faster you can discard false positives and focus chemistry on the most promising series.

Common receptor binding assays include:

• Biophysical binding (e.g., SPR/BLI) when receptor preparations are compatible and stable
• Competition binding (radioligand or fluorescent ligand), frequently used for GPCR orthosteric sites
• Proximity/energy transfer binding assays (e.g., TR-FRET/HTRF) when labeled reagents are available and the format is well behaved

For GPCRs and fragile membrane proteins, the choice of assay often hinges on receptor stability and presentation format; again highlighting why nanodiscs and stabilized receptors are important enabling technologies. [8][12]

Binding becomes therapeutically meaningful only once you understand functional outcomes. Typical functional assays include:

For GPCRs

• Second messenger assays (cAMP, IP1)
• Calcium flux
• β-arrestin recruitment
• Pathway panels to capture bias (when appropriate)

For nuclear receptors

• Coactivator recruitment assays
• Transactivation reporter assays
• Transcriptomics or targeted gene expression panels

Key point: You can’t run a receptor program effectively unless your hit discovery approach is tightly coupled with a realistic validation plan. This is especially relevant for outsourcing: strong receptor ligand discovery services don’t stop at “hit lists” – they emphasize off-DNA synthesis (when relevant), orthogonal confirmation, and a credible path to functional understanding.

Receptors often fail at screening not because “the library” is wrong, but because the receptor wasn’t presented in a biologically and biophysically meaningful way.

Purified receptors can be used in multiple assay architectures. For pooled selection technologies (including DEL), there are multiple ways to execute selection and capture, and the field has continued to innovate on selection formats and controls. [2][4]

In general:

• Immobilization/capture can provide clean separation but risks perturbing conformation if the receptor is constrained or oriented poorly.
• In-solution approaches can preserve native-like behavior but demand careful handling to control background binding and maintain receptor stability.

For receptors, the best approach is often the one that minimizes artifacts for that specific target while enabling robust counter-selection design.

Nanodiscs are widely used membrane mimetics and are considered powerful for membrane protein studies because they provide control over lipid environment and size while preserving many aspects of native membrane protein behavior. [8]

For receptor ligand discovery, this matters because lipid context can influence:

• pocket accessibility,
• receptor stability,
• active/inactive state distributions,
• and background binding.

Some receptors behave acceptably in detergent, while others show better stability and pharmacology in nanodiscs. Even for the same receptor, different project goals (orthosteric competition vs allosteric site discovery vs state-selective ligand discovery) can shift the optimal format.

Two peer-reviewed examples illustrate how screening can move toward native receptor context:

1. DEL selection against endogenous membrane proteins on live cells: a strategy enabling target-specific DEL selections on live cells for endogenous membrane targets. [6]
2. DEL screening inside a living cell: a demonstration described as the first successful screening of a multimillion-member DEL inside a living cell. [7]

Not every receptor project needs live-cell or in-cell selection, but when target purification disrupts the biology or when native context is essential, these approaches can be strategically valuable. For example, projects involving native receptor complexes, fragile receptors, or receptors whose relevant conformations are stabilized by cellular components may benefit from formats closer to biology.

DEL is best seen as one high-leverage engine in a receptor discovery toolbox – not a replacement for everything else.

• Exploring huge chemical space efficiently relative to plate-based screening. [2]
• Producing multiple series (clusters) that provide options for selectivity and developability.
• Identifying small molecule receptor binders even when hit rates are low (common for allosteric sites).
• Supporting difficult target formats when appropriate selection architectures exist, including advanced selection methodologies and cell-context approaches described in the literature. [3][4][6][7]

• If your key risk is functional mechanism, you may want early functional assays to triage binders quickly.
• If your key advantage is structure-guided optimization, fragment screening or structure-driven workflows may complement DEL series finding. [11][12]
• If your receptor is highly context-dependent, live-cell/in-cell selection or cell-based functional assays may be essential. [6][7]

This is exactly why “fragment vs DEL receptor screening” is often the wrong framing. The real question is: What is the fastest route to validated, optimizable chemotypes for your receptor, given your constraints? Many teams deliberately run two complementary engines in parallel, one that maximizes breadth (DEL or HTS), and one that maximizes interpretability (fragments/structure/biophysics), then converge on the strongest series.

• HTS: functional-first but can be limited by library size and assay complexity; still very powerful if you have a clean GPCR functional assay and robust automation. [13]
• DEL: binding-first with enormous library scale; strong for finding novel binders (including allosteric) but requires deliberate validation and functional triage. [2][3][4]

A practical way to decide is to ask: Is my gating risk “finding any chemotype that binds” or “finding the right functional phenotype”? If binding discovery is the bottleneck (often true for selectivity-driven or allosteric programs), DEL can be a high-return front end. If functional phenotype is the bottleneck (e.g., bias, partial agonism, pathway selectivity), you may prioritize early functional screening or rapidly move DEL hits into functional assays.

• Fragments: superb ligand efficiency; excellent when you have stabilized receptors and a robust structural/biophysical engine; proven for GPCRs with stabilized constructs. [11][12]
• DEL: scale-driven series discovery; can deliver multiple chemotypes fast; especially attractive for hard-to-hit pockets where you need breadth and novelty. [2][4]

Many receptor programs combine both: fragments provide high-quality anchors for rational optimization, while DEL provides breadth and alternative scaffolds that may solve selectivity, permeability, or IP landscape challenges.

• Start with DEL when your main risk is “we need novel chemotypes and multiple starting points quickly,” especially for competitive receptor landscapes.
• Start with FBDD when your main advantage is “we can stabilize the receptor and run structure-guided cycles efficiently.”
• Use HTS when the assay is robust and your program requires functional triage at scale early. [13]

A 2024 Nature study illustrates how receptor conformational biology can be exploited to discover differentiated ligands: O’Brien and colleagues screened an ultra-large (~4.4 billion member) DNA-encoded chemical library against the inactive, naloxone-bound μ-opioid receptor (μOR) while counter-screening against the active, Gi/agonist-bound receptor to “steer” enrichment toward conformation-selective negative allosteric modulators (NAMs). This strategy yielded a single, strongly enriched NAM (compound 368) that enhanced naloxone binding affinity (reported EC50 ~133 nM in radioligand binding) and worked cooperatively with naloxone to potently suppress μOR signaling. Cryo-EM revealed that 368 binds the extracellular vestibule, directly contacting naloxone while stabilizing a distinct inactive extracellular conformation (notably involving TM2/TM7), reshaping orthosteric ligand kinetics in therapeutically favorable ways. In vivo, the NAM combined with low-dose naloxone more effectively reversed morphine- and fentanyl-driven behaviors while reducing withdrawal-like effects compared with conventional high-dose naloxone – highlighting how state-steered DEL screening can uncover receptor modulators with clinically relevant pharmacology. [22]

Whether you run this internally or via receptor ligand discovery outsourcing, the workflow below is a useful “minimum viable” path from receptor target to validated hits.

Clarify:

• Orthosteric vs allosteric intent
• Desired mechanism (agonist, antagonist, inverse agonist, PAM/NAM)
• Selectivity needs (subtype, off-target risk)
• Biological context that must be preserved (membrane lipids, partners, modifications)

This step seems obvious, but it is the most common source of “screening mismatch.” If your therapeutic hypothesis requires an active-state binder or a pathway-biased ligand, you should reflect that early in assay design and target presentation choices (even if your first-stage method is “binding-first”). [9][10]

Options include:

• Functional screening (HTS-like)
• Binding-first approaches (DEL, fragments)
• Combined strategies (e.g., DEL for breadth + functional assays for triage)

DEL and fragment methods have strong review coverage; selection format choice often matters as much as the underlying library. [2][3][4][11]

For membrane receptors, decide:

• detergent vs nanodiscs, stabilized vs wild-type,
• purified vs cell-based vs in-cell.

Nanodisc-based approaches are widely recognized as powerful for membrane proteins and are often chosen specifically to better preserve functional states. [8]

For receptors, counter-screens are not optional if you want clean series:

• matrix/capture controls,
• related receptor subtype controls,
• “sticky” membrane controls for integral membrane formats.

DEL reviews emphasize the importance of selection design and controls for reducing artifacts and improving hit quality. [2][4]

This is where screening becomes drug discovery:

• follow-up chemistry (including off-DNA resynthesis for DEL-derived hits),
• orthogonal receptor binding assays,
• selectivity panels as needed,
• early functional readouts to sort mechanism.

For GPCR targets, allosteric effects can be context-dependent, which is why parallel functional assays (or pathway panels) can be particularly informative once you have confirmed binding. [9][10]

Once you have at least one validated series:

• iterate medicinal chemistry with functional and selectivity feedback,
• apply structural methods where available,
• prioritize developability (solubility, permeability, stability) early.

In practice, this is where multiple hit series payoff: you can choose the series that best balances potency, selectivity, and developability rather than trying to “force” a single series through optimization.

Vipergen focuses on DNA encoded library screening for receptors as part of a broader hit discovery and validation workflow, with specific emphasis on difficult targets and advanced selection formats.

From a receptor perspective, two capabilities are especially relevant:

1. In-solution selection architecture (BTE)
   Vipergen’s Binder Trap Enrichment (BTE) is an emulsion-based method to isolate binding pairs and preserve binding information via ligation. This can be useful when immobilization creates artifacts or when you want selection in solution. [20] Membrane receptor screening formats (detergent or nanodiscs)
   Vipergen describes DEL screening services for purified biotinylated integral membrane proteins formulated in nanodiscs or detergent, a practical option for GPCRs and other membrane receptors where target folding and stability are limiting factors. [18][8]
2. In-cell selection (cBTE) for physiologically relevant target engagement
   Vipergen describes Cellular Binder Trap Enrichment (cBTE) as an approach for DEL screening inside living cells, aligning with the broader scientific direction demonstrated in peer-reviewed literature on intracellular DEL screening. [19][7]

Importantly, the “broader” takeaway is this: Vipergen’s DEL tools are most valuable when used as part of a receptor pipeline that also includes orthogonal receptor binding assays and functional follow-up because receptor success requires binding, engagement, selectivity, and mechanism to line up. [21]

Receptor drug discovery rewards teams that combine biological realism with experimental efficiency. HTS can be functional-first but assay-heavy; fragments can be structure-friendly but format-sensitive; DEL can be scale-dominant but validation-dependent. The strongest receptor programs treat these as complementary tools and design workflows that quickly move from binder discovery to validated target engagement to functional mechanism. [13][11][12]

Within that broader toolkit, DNA-encoded library screening for receptors is a powerful accelerator especially when receptor format is challenging (membrane proteins) or when novelty is essential (allosteric pockets, subtype selectivity). With appropriate target presentation (including nanodiscs where helpful), rigorous orthogonal validation, and early functional profiling, DEL-derived chemotypes can become high-quality starting points for medicinal chemistry and lead discovery. [2][8][9][10]

1. 1. Brenner S, Lerner RA. Encoded combinatorial chemistry. PNAS, 89(12), 5381-5383 (1992). https://doi.org/10.1073/pnas.89.12.5381 
   2. Goodnow RA Jr, Dumelin CE, Keefe AD. DNA-encoded chemistry: enabling the deeper sampling of chemical space. Nature Reviews Drug Discovery, 16, 131-147 (2017). https://doi.org/10.1038/nrd.2016.213 
   3. Gironda-Martínez A, Donckele EJ, Samain F, Neri D. DNA-Encoded Chemical Libraries: A Comprehensive Review with Success Stories and Future Challenges. ACS Pharmacology & Translational Science, 4, 4, 1265-1279 (2021). https://doi.org/10.1021/acsptsci.1c00118
   4. Satz AL, Kuai L, Peng, X. Selections and screenings of DNA-encoded chemical libraries: current state and future perspectives. Bioorganic & Medicinal Chemistry, 39, 127851 (2021). https://doi.org/10.1016/j.bmcl.2021.127851 
   5. Ahn S, et al. Allosteric “beta-blocker” isolated from a DNA-encoded small molecule library. PNAS, 114 (7), 1708-1713 (2017). https://doi.org/10.1073/pnas.1620645114 
   6. Huang Y, et al. Selection of DNA-encoded chemical libraries against endogenous membrane proteins on live cells. Nature Chemistry, 13, 77-88 (2021). https://doi.org/10.1038/s41557-020-00605-x 
   7. Petersen LK, et al. Screening of DNA-Encoded Small Molecule Libraries inside a Living Cell. JACS, 143, 7, 2751-2756 (2021). https://doi.org/10.1021/jacs.0c09213 
   8. Denisov IG, Sligar SG. Nanodiscs in Membrane Biochemistry and Biophysics. Chemical Reviews, 117 (6), 4669-4713 (2017). https://doi.org/10.1021/acs.chemrev.6b00690 
   9. May LT, Leach K, Sexton PM, Christopoulos A. Allosteric modulation of G protein-coupled receptors. Annu Rev Pharmacol Toxicol, 47, 1-51 (2007). https://doi.org/10.1146/annurev.pharmtox.47.120505.105159 
   10. Keov P, et al. Allosteric modulation of G protein-coupled receptors: a pharmacological perspective. Neuropharmacology, 60, 1, 24-35 (2011). https://doi.org/10.1016/j.neuropharm.2010.07.010 
   11. Erlanson DA, et al. Twenty years on: the impact of fragments on drug discovery. Nature Reviews Drug Discovery, 15, 605-619 (2016). https://doi.org/10.1038/nrd.2016.109 
   12. Congreve M, et al. Fragment Screening of Stabilized G-Protein-Coupled Receptors Using Biophysical Methods. Methods in Enzymology, 493, 115-136 (2011). https://doi.org/10.1016/b978-0-12-381274-2.00005-4 
   13. Carnero A. High throughput screening in drug discovery. Clinical and Translational Oncology, 8 (7), 482-490 (2006). https://doi.org/10.1007/s12094-006-0048-2 
   14. Mangelsdorf DJ, Thummel C, Beato M, et al. The nuclear receptor superfamily: the second decade. Cell, 83, 835-839 (1995). https://doi.org/10.1016/0092-8674(95)90199-x 
   15. Huang P, Chandra V, Rastinejad F. Structural Overview of the Nuclear Receptor Superfamily. Annual Review of Physiology, 72, 247-272 (2010). https://doi.org/10.1146/annurev-physiol-021909-135917 
   16. Evans RM, Mangelsdorf DJ. Nuclear Receptors, RXR, and the Big Bang. Cell, 157 (1), 255-266 (2014). https://doi.org/10.1016/j.cell.2014.03.012 
   17. Lorente JS, et al. GPCR drug discovery: new agents, targets and indications. Nature Review Drug Discovery, 24 (6), 458-479 (2025). https://doi.org/10.1038/s41573-025-01139-y 
   18. Vipergen – DEL screening for integral membrane proteins. https://www.vipergen.com/del-integral-membrane-proteins/
   19. Vipergen – Cellular Binder Trap Enrichment (cBTE). https://www.vipergen.com/cellular-binder-trap-enrichment/ 
   20. Vipergen – Binder Trap Enrichment (BTE). https://www.vipergen.com/binder-trap-enrichment-bte/
   21. Vipergen – Drug discovery services overview. https://www.vipergen.com/revolutionizing-research-with-chemical-libraries/  
   22. O’Brien, et al. A µ-opioid receptor modulator that works cooperatively with naloxone. Nature, 631, 686-693 (2024). https://doi.org/10.1038/s41586-024-07587-7 

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Kinases sit at the heart of almost every cellular decision a cell makes — from dividing or dying, to storing energy or burning it. Because of this central role, kinase drug discovery has become one of the most productive areas in modern pharmacology, with more than 70 small-molecule kinase inhibitors now approved worldwide (Cohen 2021, Ayala-Aguilera 2022).

In this article, we’ll unpack what kinases and protein kinases are, the types of kinases you’ll encounter, how they control cell signaling, and why they are such attractive drug targets. We’ll also look at kinase medicinal chemistry, highlight successful clinical inhibitors, and explore how DNA-encoded libraries are reshaping early discovery.

A kinase is an enzyme that transfers a phosphate group (usually from ATP) to another molecule — a process called phosphorylation. When the target is a protein, the enzyme is called a protein kinase. Collectively, protein kinases form one of the largest and most important enzyme superfamilies in biology, with more than 500 members in the human genome (Roskoski 2015).

Phosphorylation acts like a molecular switch: it can change a protein’s activity, localization, stability, or ability to interact with partners. Because of this, kinases regulate a broad range of processes (Ardito 2017, Tales) including:

• Signal transduction pathways
• Cell cycle progression
• Apoptosis (programmed cell death)
• Metabolism and energy homeostasis
• Differentiation and development

Although “kinase” is often used as shorthand for protein kinases, there are many other families, including lipid kinases and carbohydrate kinases, that phosphorylate non-protein substrates.

Cell signaling relies heavily on cascades of phosphorylation. In a typical pathway, a stimulus (like a growth factor) activates a receptor, which then activates a series of protein kinases. Each protein kinase activates the next, amplifying the signal and ensuring a highly tunable response (Ardito 2017).

Key examples include (Pellarin 2025):

• The MAP kinase (MAPK) pathways controlling proliferation and stress responses
• Cyclin-dependent kinases (CDKs) regulating the cell cycle
• Tyrosine kinases (receptor and non-receptor) orchestrating development and immune function

When these networks are perturbed – by mutation, overexpression, or chronic activation – the consequences can be profound: cancer, autoimmune disease, metabolic syndrome, and more.

Despite their diversity, most eukaryotic protein kinases share a conserved bilobal catalytic core (Roskoski 2015):

• An N-terminal lobe (N-lobe) built mainly from β-sheets and the regulatory αC helix
• A C-terminal lobe (C-lobe) dominated by α-helices
• A central ATP-binding pocket between the lobes
• An activation loop (A-loop) and conserved amino acid motifs (VAIK, HRD, DFG) that tune activity

Substrate binding and ATP positioning occur in the active site. Small-molecule inhibitors used in kinase drug discovery typically bind to the ATP pocket and adjacent allosteric regions, mimicking ATP or stabilizing inactive conformations.

Kinases catalyze the transfer of the γ-phosphate from ATP to a hydroxyl group (Ser, Thr, or Tyr in proteins) (Ardito 2017, ScienceDirect):

1. ATP binds in the ATP pocket with the help of the glycine-rich loop.
2. The substrate (for protein kinases, a peptide or protein) is positioned by recognition motifs.
3. Catalytic residues (often Lys, Asp) orient ATP and facilitate the in-line transfer of phosphate.
4. ADP is released, and the phosphorylated product dissociates.

Subtle conformational changes in the activation loop, αC helix, and DFG motif control whether a kinase is active or inactive — features heavily exploited in designing Type I and Type II inhibitors.

Within the human kinome, protein kinases are often grouped into major families such as AGC, CAMK, CMGC (which includes CDKs and MAPKs), TK (tyrosine kinases), TKL (tyrosine kinase-like), and others (Pellarin 2025, Rauch 2011). 

Important subclasses include:

• Serine/threonine kinases – phosphorylate Ser/Thr residues (e.g., protein kinase A (PKA), protein kinase B/AKT, protein kinase C).
• Tyrosine kinases – act on Tyr residues (e.g., EGFR, SRC, ABL).
• Cyclin-dependent kinases (CDKs) – drive progression through cell cycle phases.

When people ask about the types of kinases, they are usually referring to substrate specificity and domain architecture:

• Receptor tyrosine kinases (RTKs) – membrane receptors with extracellular ligand-binding domains and cytoplasmic tyrosine kinase domains (e.g., EGFR, VEGFR).
• Non-receptor tyrosine kinases – cytoplasmic kinases such as SRC, JAK, BTK that relay signals from receptors (Tomuleasa 2024). 
• Serine/threonine kinases – a broad group including PKA, PKC, AKT, and many MAPKs.
• Atypical kinases – structurally divergent but catalytically related (e.g., PI3K-related kinases, RIO kinases) (Science Direct)

Beyond protein kinases, other types of kinases include:

• Lipid kinases – such as PI3Ks, generating phosphoinositide second messengers.
• Carbohydrate kinases – e.g., hexokinases and pyruvate dehydrogenase kinases (PDKs), central to metabolism (Jeoung 2015).

Lipid kinases regulate membrane signaling and vesicle trafficking; dysregulation of PI3K or related kinases is common in cancer and immune disease.

Carbohydrate kinases like PDKs modulate glucose oxidation and are implicated in diabetes and metabolic syndrome (Le 2019, Jeoung 2015). 

Other notable families include:

• CK1/CK2 (casein kinases) with roles in circadian rhythm, DNA repair, and lipid metabolism
• PIM kinases, which integrate growth factor signaling with metabolism
• ADCK/aarF-domain kinases, emerging regulators of mitochondrial bioenergetics (Jeoung 2015)

A tightly choreographed sequence of CDK activation ensures orderly passage through G1, S, G2, and M phases of the cell cycle. Mis-timed or unrestrained protein kinase activity here is a classic route to oncogenic transformation (Pellarin 2025, Ardito 2017).

Kinases also:

• Control apoptosis, e.g., via JNK, p38 MAPKs, and AKT.
• Integrate metabolic cues, with kinases like AMPK, mTOR, and PDKs sensing cellular energy status and nutrient availability.

In essence, kinases act as logic gates for cellular decision-making, integrating myriad inputs into coherent physiological outputs.

Because protein kinases sit at critical regulatory nodes, their mutations or dysregulation are heavily represented in disease:

• Cancer – Oncogenic RTKs (EGFR, HER2, ALK), BCR-ABL fusion kinase in CML, mutant BRAF in melanoma, and many more (Cohen 2021, Tomuleasa 2024).
• Metabolic disorders – Stress-activated protein kinases (SAPKs) and other signaling pathways contribute to obesity, fatty liver, diabetes, and cardiovascular complications (Nikolic 2020). 
• Neurological and inflammatory diseases – Kinase pathways regulate synaptic plasticity, neuroinflammation, and immune cell activation.

This broad pathogenic footprint is exactly why kinase drug discovery has been so productive — and why new targets keep emerging.

A typical kinase drug discovery program starts with target validation (Cohen 2021, Stephenson 2023):

1. Genetic evidence (mutations, amplifications, knock-down or CRISPR studies).
2. Disease association (pathway analysis, expression patterns).
3. Druggability assessment (structural knowledge of the ATP site and pockets). 

Researchers then build a toolkit of:

• Biochemical assays (enzyme activity, ATP competition, radiometric or fluorescence-based readouts)
• Cellular assays (phospho-biomarkers, functional phenotypes)
• Selectivity panels profiling compounds across broad protein kinase and lipid kinase panels (Ayala-Aguilera 2022, Stephenson 2023).

Once hits are identified, kinase medicinal chemistry takes center stage. Medicinal chemists work to (Wang 2024, Li 2023):

• Improve potency (tighter binding to the kinase active site)
• Tune selectivity against other kinases to reduce off-target toxicity
• Optimize ADME properties (solubility, permeability, metabolic stability)
• Address resistance mutations, especially in oncology settings 

Common strategies in kinase medicinal chemistry include (Cohen 2021, Ayala-Aguilera 2022):

• Designing ATP-competitive scaffolds (Type I inhibitors)
• Targeting inactive conformations and allosteric pockets (Type II and allosteric inhibitors)
• Employing covalent warheads to form irreversible bonds with nucleophilic residues
• Exploring macrocycles and fragment-based approaches to better exploit the 3D shape of the ATP site 

Approved small-molecule kinase inhibitors now span numerous indications (Wang 2024, Cohen 2021):

• Imatinib (BCR-ABL) – paradigm-shifting therapy for chronic myeloid leukemia.
• EGFR, ALK, and ROS1 inhibitors – precision medicines for subsets of lung cancer.
• BTK inhibitors – transforming treatment of several B-cell malignancies.

Reviews summarizing the medicinal chemistry of FDA-approved kinase inhibitors highlight recurring pharmacophores, hinge-binding motifs, and strategies to balance selectivity and safety (Wang 2024, Shinymol 2025).

DNA-encoded libraries (DELs) or DNA-encoded chemical libraries (DECLs) are massive collections (often billions) of small molecules, each covalently linked to a unique DNA barcode that records its synthetic history (Wikipedia, Gironda-Martínez 2021, Favali 2018).

Key features:

• DNA tags encode each compound and enable PCR-based amplification and sequencing.
• Libraries are screened in a single tube against a protein target (e.g., a protein kinase domain).
• After selection and washing, bound compounds are decoded by sequencing their DNA tags.

DEL technology elegantly merges combinatorial chemistry with molecular biology and has become a powerful engine for early-stage kinase drug discovery, enabling rapid hit identification against challenging targets (Kunig 2018, Gironda-Martínez 2021). 

One notable application is the YoctoReactor platform, which uses DNA junctions to bring building blocks into nanoscopic proximity during synthesis. This approach has been used to generate DELs that yield novel inhibitors for kinases such as p38α MAP kinase. 

In a 2016 study, Petersen and co-workers combined pharmacophore models derived from YoctoReactor DNA-encoded libraries with structure-based design to identify potent p38α MAP kinase inhibitors, illustrating how DEL-derived data can directly inform kinase medicinal chemistry campaigns (Petersen 2014).

More broadly, DELs are now:

• Used across many kinase families (tyrosine and serine/threonine kinases, lipid kinases).
• Integrated with AI-driven analysis to prioritize high-value chemotypes (Li 2023, Elgawish 2025).

The field is rapidly evolving:

• Allosteric and pseudokinase targets – expanding beyond the conserved ATP pocket (Rauch 2011, Jacquet 2025). 
• Network-level pharmacology – acknowledging that inhibiting a single kinase in isolation rarely captures the complexity of signaling (Stephenson 2023). 
• AI and machine learning – improving virtual screening, predicting resistance mutations, and guiding multi-parameter optimization in kinase drug discovery (Elgawish 2025, Li 2023). 
• Multitarget and combination therapies – deliberately designing compounds or regimens that modulate several kinases or pathways at once (Cohen 2021).

As our understanding of protein kinases deepens — from canonical catalytic roles to non-catalytic scaffolding functions — the opportunities for new therapies in oncology, immunology, neurology, and metabolic disease will only grow.

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Transcription factors (TFs) sit at the control nodes of oncogenic gene-expression programs—driving proliferation, survival, lineage plasticity, and therapy resistance across tumor types. Decades of genetics and genomics established TFs as recurrent cancer drivers and dependencies, yet their flat, dynamic interfaces long branded them “undruggable.” That view has shifted: systematic catalogs clarified the landscape of ~1,600 human sequence-specific TFs and their motifs, enabling sharper target selection, while advances in structural biology, chemical biology, and modality design produced genuine clinical proofs of concept—from direct allosteric inhibition of HIF-2α (belzutifan) to first-in-human macromolecular MYC inhibitors (OMO-103). Together these milestones mark a transition from aspiration to practice, with TFs now central to precision oncology strategies and combination regimens that aim to rewire malignant transcription at its source.

Transcription factors (TFs) regulate gene programs and usually lack deep pockets; they use broad protein-protein and protein-DNA interfaces and often contain intrinsically disordered regions. Structural biology, chemical biology, and new modalities have shifted the odds: we now have approved drugs that modulate TF output directly or indirectly, credible clinical signals for TF-directed agents, and maturing toolkits to discover and optimize TF binders (Bushweller 2019, Lambert 2018).

Cryo-EM, X-ray, NMR/HDX-MS, AlphaFold2-Multimer, and MD simulations reveal cryptic or allosteric pockets and hot spots on TF complexes (e.g. KIX, BTB, PAS, and nuclear receptors). These methods now routinely guide screen design and hit optimization, including for flat PPI surfaces and disordered regions (Bushweller 2019, Su 2021).

A transcription factor inhibitor can (1) block TF:DNA binding, (2) disrupt TF:cofactor PPI, (3) block TF:TF dimerization, (4) intercalate with DNA to block TF binding, or (5) inhibit subcellular shuttling of TF E(Figure 1A). Alternatively, TFs can be degraded through the proteasome degradation system either using PROTACs (Figure 1B) or molecular glue degraders. Most importantly, degradation already has human proof-of-concept: the IMiDs lenalidomide/pomalidomide recruit CRBN to gdegrade IKZF1/IKZF3 – bona fide TFs – hereby reprogramming myeloma cells. These agents prove that pharmacologic TF degradation is clinically feasible (Krönke 2013).

Figure 1: Different ways for small molecule transcription factor inhibition. A) Traditional ways for small molecule transcription factor inhibition and B) Transcription factor inhibition by PROTACs

Direct clinical and translational examples. HIF-2α (a TF dimer subunit) has an approved small molecule (belzutifan) for VHL-disease RCC (Jonasch 2021), and first-in-human TEAD palmitoylation inhibitors show activity in NF2-mutant tumors (Yap, 2023). A MYC-dominant negative mini-protein (OMO-103) delivered target engagement and early clinical signals (Garralda 2024). PROTACs that degrade STAT3 demonstrate potent clinical activity (Zhou 2019).

• Orthosteric/allosteric small molecules. HIF-2α (belzutifan, approved), TEAD palmitoylation inhibitors (e.g., VT3989, Ph 1), and PPI modulators like BCL6 (79-6; FX1) or CREB (KG-501; 666-15) illustrate tractable TF surfaces and cofactors (Jonasch 2021, Yap 2023, Cerchietti 2011, Cardenas 2016, Best 2004).

• Targeted protein degradation. Besides CRBN-recruiting IMiDs (IKZF1/3), STAT3 PROTACs (e.g., SD-36) exemplify degrader strategies against TFs and co-regulators (Krönke 2013).

• Beyond small molecules. Constrained peptides, mini-proteins, macrocycles, and peptidomimetics expand reach to large PPIs. Omomyc (OMO-103), a myc dominant-negative mini-protein, achieved clinical target engagement and disease stabilization in a subset of patients (Gerralda 2024).

• Indirect TF pathway control. BET bromodomain inhibitors collapse super-enhancer programs (e.g. myc), CDK7/9 inhibitors throttle transcriptional pause-release/elongation, and CBP/p300 HAT inhibitors down-tune coactivator acetylation—all of which modulate TF-driven outputs. The KEAP1–NRF2 PPI inhibitors (e.g., KI-696) illustrate TF activation via PPI blockade, useful for oxidative-stress and inflammatory contexts.

For TF:DNA disruption or TF:PPI modulation, choose a primary assay with an orthogonal biophysics confirm:

• Fluorescence polarization/anisotropy (FP/FA) using labelled DNA (or peptide cofactor) is robust and HTS-friendly; it tolerates inner-filter effects better than many readouts. Confirm hits by SPR/BLI/ITC (Hall 2016).
• AlphaScreen/AlphaLISA proximity assays scale to UH-TS. For example, a miniaturized AlphaScreen found inhibitors of the HMGA2:DNA interaction (Su 2020).
• Reporter assays (luciferase/SEAP) encode pathway-level TF activity; paired with CRISPRi/a or degrader controls, they triage direct vs indirect mechanisms. (Tao 2023)

Assay interferences & counterscreens (critical “hygiene”).
Guard against DNA intercalators/groove binders (run ethidium displacement, calf-thymus DNA counterscreens), colloidal aggregators (add low % detergent, DLS), and redox/fluorescence artifacts (redox scavengers; absorbance/fluorescence scans). Validate with SPR/BLI/ITC and enforce tag-switched controls. Close the loop with in-cell target engagement before chemistry scale-up (Hall 2016).

NanoBRET/NanoBiT target-engagement assays quantify compound binding in living cells; CETSA-MS profiles proteome-wide thermal shifts to reveal on- and off-targets and PD markers. These are particularly valuable for disordered or PPI-rich TFs where classical occupancy assays struggle. 

DEL selections enable billions of compounds to be tested for TF binding at modest cost. Best practices for TFs: include polyanionic competitors (poly(dI–dC), heparin) and high-salt washes to suppress nonspecific capture by the DNA barcode; run tag-flipped negative selections and DNA-only baits; stabilize the biologically relevant TF complex (e.g., TEAD with palmitate, TF+cofactor). Prioritize hits with count-aware statistics (Poisson/Bayesian), then re-synthesize off-DNA for SPR/BLI and cell-based assays (Gironda-Martínez 2021). 

Modern DEL analytics (including uncertainty-aware and 3D-aware ML) help denoise count data and surface chemotypes likely to validate after off-DNA synthesis.

DELs complement medium-throughput transcription factor screening funnels (FP/Alpha/SPR) and can seed degrader ligand discovery (e.g., KEAP1-binding warheads). For practical context on platform variants, see vendor technical notes (e.g., Vipergen’s overview).

TF networks rewire: bypass TFs, enhancer switching, and paralog compensation are common. Track occupancy (ChIP-qPCR/seq on pharmacodynamic loci), PD gene signatures, and circulating tumor DNA for pathway lesions. Combinations are rational: MDM2 antagonists with DNA-damage agents, TEAD inhibitors with FAK/MAPK blockers, STAT3 degraders with JAK inhibitors (Gounder 2023).

While oncology dominates, TF modulation is relevant in autoimmune/inflammatory disease (NF-κB, STATs, AHR), fibrosis (YAP/TAZ-TEAD; SMADs), metabolic/cardio-renal (NR TFs like PPARs/FXR), and neurology/virology (IRFs, NF-κB). Several indirect modulators (BET, CDKs, CBP/p300) already have broad preclinical/clinical footprints across these indications (Filippakopoulos 2010, Bacon 2019).

1. Start biochemical, finish biophysical. Use FP/FA or AlphaScreen for throughput, then SPR/BLI/ITC to confirm direct binding and establish mechanism (DNA vs cofactor vs allosteric) (Hall 2016, Su 2020). 
2. Counterscreens early. DNA intercalation/groove-binding, colloidal aggregation, and redox fluorescence artifacts account for many false positives; bake in detergent, polyanions, and orthogonal readouts (Hall 2016).
3. Go cellular quickly. Move to NanoBRET target engagement and CETSA-MS to verify in-cell binding and explore selectivity (Robers 2015, Savitski 2014).
4. Use DEL for breadth, ML for triage. DEL campaigns with TF-specific safeguards (above) plus uncertainty-aware analytics markedly improve triage to off-DNA and cell follow-up (Gironda-Martínez 2021, Lim 2022).
5. Lean on structure. Where possible, co-crystallize or use cryo-EM/NMR/HDX-MS to map pockets and hot spots, then iterate chemistry against those constraints (Bushweller 2019).

• Undruggable no more: There are approved and clinical stage precedents for TF modulation.
• Effective transcription factor inhibitor screening blends robust biochemical assays, orthogonal biophysics, and in-cell engagement, with DELs and ML providing breadth and precision.
• Expect combinations and biomarker-driven development to be central as programs move from bench to bedside.

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Proteins constitute the foundational framework of biological systems, executing a vast range of essential functions such as enzymatic catalysis, signal transduction, structural support, and intracellular transport. Within the highly crowded cellular environment, their ability to function effectively over distance is enhanced through the formation of intricate interaction networks, termed interactomes. These networks enable coordinated activity among proteins, allowing them to carry out highly specialized and interdependent biological roles (Nada et al., 2024).

Within these interactomes, protein-protein interactions (PPIs) represent the physical contact points between two or more proteins, typically occurring at domain-specific interfaces. These interactions may be transient or stable, and they govern the dynamic regulation of nearly all biological processes (Scott et al., 2016; Shin et al., 2017). Dysregulation of PPIs is implicated in numerous diseases, including cancer, neurodegenerative disorders, and infectious diseases. Historically, such interfaces were viewed as “undruggable” due to their large, hydrophobic, and relatively flat surfaces that lack the traditional pockets suitable for small-molecule binding (Scott et al., 2016).

However, emerging technologies, including DNA-Encoded Libraries (DELs), are shifting this paradigm. DELs enable the ultra-high-throughput screening of vast chemical spaces for potential binders to protein surfaces previously considered inaccessible (Goodnow et al., 2017; Peterson & Liu, 2023). This innovation is driving renewed momentum in the discovery of therapeutic agents.

Unlike traditional drug targets, PPIs often lack well-defined binding pockets, making them difficult to target with conventional small molecules. The interfaces are usually large, flat, and hydrophobic, posing significant challenges for the design of effective protein-protein interaction inhibitors. Moreover, achieving specificity without affecting other PPIs is a considerable hurdle (Shin et al., 2017; Nada et al., 2024).

Strategies to overcome these challenges include:

• Allosteric Inhibition: Targeting sites distal to the PPI interface to induce conformational changes that disrupt the interaction.
• Covalent Inhibition: Designing molecules that form irreversible bonds with specific amino acid residues at the PPI interface (Nada et al., 2024).

PPIs play a critical role in maintaining cellular homeostasis. Disruption or aberrant formation of these interactions can lead to pathological conditions. For instance:

• p53-MDM2 Interaction: MDM2 negatively regulates the tumor suppressor p53. In many cancers, overexpression of MDM2 leads to the inactivation of p53, promoting tumorigenesis (Scott et al., 2016).
• BCL-2 Family Proteins: These proteins regulate apoptosis. Protein-protein interaction inhibitors, targeting the interaction between pro-apoptotic and anti-apoptotic BCL-2 proteins can induce cell death in cancer cells (Kale et al., 2018).
• β-Catenin–TCF Interaction: Dysregulation of this interaction is implicated in colorectal cancer. Targeting this PPI can inhibit Wnt signaling pathways involved in tumor progression (Nada et al., 2024).

An emerging strategy to overcome the challenges of targeting PPIs is targeted protein degradation. This approach involves the use of molecular glues or bifunctional molecules, such as PROTACs (Proteolysis Targeting Chimeras), which recruit E3 ubiquitin ligases to tag the target protein for degradation by the proteasome (Cromm & Crews, 2017; Schreiber, 2024; Tomlinsson et al., 2025).

Notable examples include:

• Lenalidomide: Functions as a molecular glue by recruiting cereblon E3 ligase to degrade transcription factors IKZF1 and IKZF3 in multiple myeloma.
• ARV-110: A PROTAC that targets the androgen receptor for degradation, currently in clinical trials for prostate cancer.

Breakthroughs in medicinal chemistry and structural biology are redefining what’s possible in PPI targeting. Several success stories demonstrate that with the right tools, protein-protein binding can be modulated therapeutically.

Key examples include:

• Venetoclax (Venclexta) and Navitoclax: Bcl-2 inhibitors that restore apoptosis in leukemia cells by disrupting critical PPIs (Kale et al., 2018).
• Idasanutlin: Protein-protein interaction inhibitor of the MDM2-p53 proteins to reactivate tumor suppressor pathways (Scott et al., 2016).
• Maraviroc: An HIV entry inhibitor affecting the CCR5–gp120 interaction.
• Tirofiban: Inhibits the glycoprotein IIb/IIIa receptor, preventing platelet aggregation in cardiovascular diseases (Nada et al., 2024).

DNA-Encoded Libraries (DELs) consist of vast collections of small molecules; each tagged with a unique DNA sequence that serves as a barcode. This technology allows for the simultaneous screening of billions of compounds against a target protein in a single experiment (Goodnow et al., 2017; Favalli et al., 2018; Peterson & Liu, 2023). DELs enable high-throughput, cost-effective identification of novel binders for challenging targets like PPIs.

Advancements in DEL technology have facilitated:

• Rapid Screening: Accelerated identification of lead compounds with high affinity and specificity.
• DELs in Cells: Screening directly inside living cells, providing small-molecule ligands that bind to target proteins in a native cellular environment, enhancing the physiological relevance of the screening process (Petersen et al., 2021).
• Structure-Activity Relationship (SAR) Analysis: Efficient optimization of lead compounds based on binding data.
• Integration with Other Technologies: Combining DELs with techniques like FBDD and computational modeling to enhance drug discovery pipelines (Silvestri et al., 2023).

DELs have been successfully applied in various therapeutic areas:

• Oncology: Identification of protein-protein interaction inhibitors targeting PPIs involved in cancer progression, such as p53-MDM2 and BCL-2 family interactions (Silvestri et al., 2023).
• Neurodegenerative Diseases: Discovery of compounds that modulate PPIs implicated in protein aggregation and neuronal dysfunction (Peterson & Liu, 2023).
• Infectious Diseases: Development of agents that disrupt PPIs essential for pathogen survival and replication (Favalli et al., 2018).

Vipergen offers industry-leading DNA-Encoded Library (DEL) technology designed to accelerate early drug discovery, particularly for challenging targets such as protein-protein interactions (PPIs). At the core of Vipergen’s innovation is the YoctoReactor® platform, which enables the on-DNA synthesis of high-fidelity small-molecule libraries and the efficient identification of potent, selective binders against biologically relevant targets (Blakskjær et al., 2015).

What sets Vipergen apart is its unique ability to conduct DEL screening in living cells, a significant advancement over traditional in vitro assays. Screening in a physiologically relevant environment ensures that identified binders not only engage their target but also retain activity under native cellular conditions. This in-cell screening capability improves the likelihood that hits will translate effectively to functional cellular outcomes, reducing attrition in later stages of drug development. Due to the multiplexing capability of Vipergen’s technology, it can be used for direct discovery of PPI inhibitors. 

By combining robust chemistry, scalable technology, and physiologically meaningful screening conditions, Vipergen’s DEL approach enhances hit quality and therapeutic relevance. These strengths position the company as a key partner for biotech and pharmaceutical companies pursuing next-generation therapeutics, making Vipergen’s Drug Discovery Services particularly suited for identifying hits against PPI targets previously considered undruggable.

Protein-protein interactions represent a vast and largely untapped landscape for therapeutic intervention. Advances like DELs and targeted degradation have transformed early drug discovery approaches. Vipergen is among the leaders making these advances accessible and practical. Learn more at https://www.vipergen.com/services/.

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• Peterson, A. A., & Liu, D. R. (2023). Small-molecule discovery through DNA-encoded libraries. Nature Reviews Drug Discovery, 22, 699–722. https://doi.org/10.1038/s41573-023-00713-6
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