The target of the new study is VAV1, a protein found mainly in immune cells, where it functions as a signaling hub that helps activate T cells and other components of the immune system. When VAV1 signaling runs normally, it supports a healthy immune response. When it goes awry, however, abnormal VAV1 activity can contribute to serious conditions, including T-cell lymphomas and chronic inflammatory disorders. Traditional drugs that inhibit VAV1 would suppress only one of its many functions, leaving the rest of the protein free to cause trouble. Targeted protein degradation, by contrast, removes the entire protein from the cell, which could deliver a more complete therapeutic effect. The challenge has been finding small molecules capable of dragging VAV1 to the cell’s protein-recycling system in the first place.

That challenge is what the Baylor-led team set out to solve. Their strategy, described in a paper published in Nature Communications, began with an unbiased search rather than a targeted one. The researchers screened a library of molecules using high-throughput proteomics, a technology capable of measuring the abundance of thousands of proteins simultaneously. Instead of looking only for a predicted effect on VAV1, they let the data reveal which compounds changed the cellular protein landscape in interesting ways. This unbiased analysis surfaced a series of compounds, including one designated NGT-201-12, that caused VAV1 levels to drop while affecting relatively few other proteins, a selectivity profile that is critical for any future drug candidate.

Follow-up experiments confirmed that the compounds were working through the cell’s natural protein-recycling system and, more specifically, that the degradation depended on cereblon, often abbreviated CRBN. Cereblon is a key component of the CRL4 ubiquitin ligase complex, a molecular machine that tags proteins for destruction by the proteasome. Molecular glues that engage cereblon effectively trick the ligase into recognizing proteins it would normally ignore, gluing the target onto the degradation machinery. Cereblon-recruiting glues have already proven their worth in the clinic through drugs such as lenalidomide, but expanding this approach to new targets like VAV1 has been slow, in large part because scientists rarely understand exactly how a glue molecule brings its target and cereblon together.

Understanding that three-way interaction is the central bottleneck in molecular glue research, and it is where artificial intelligence entered the project. The team developed a computational workflow called GluePlex, which combines AI-driven protein-structure prediction tools with physics-based modeling to predict how VAV1, cereblon, and a molecular glue assemble into a ternary complex. Crucially, GluePlex made its predictions without any experimental structure of the complex to guide it. The model identified a specific region of VAV1, known as the SH3-2 domain, as essential for degradation, and it pinpointed the exact contact point: a small surface loop on VAV1 that serves as a degradation signal, or degron. Laboratory tests subsequently confirmed the prediction, showing that the glue exploits this loop to flag VAV1 for destruction.

The predicted contact point is notable because it does not resemble the degradation signals commonly associated with cereblon-targeting molecular glues. Until recently, most known cereblon-dependent glues were thought to require a structural feature called a G-loop on their target proteins. The Baylor study, together with an independent report from a separate research team that reached a similar conclusion about VAV1 using different experiment-based methods, indicates that cereblon can also recognize an entirely different structure. That finding broadens the range of proteins that may be reachable by cereblon-recruiting degraders in the future, expanding the theoretical target space of this drug modality well beyond what was previously assumed.

Perhaps the most consequential aspect of the work is what it says about the role of AI in early drug discovery. In most medicinal chemistry projects, structural biology comes late: chemists optimize compounds first, and only later do crystallographers or cryo-electron microscopists reveal how the molecules actually work. Here, the computational workflow arrived at a hard-to-anticipate contact point on its own, before any experimental structure existed, and the prediction was subsequently validated in the lab. That means AI-based structural modeling can guide chemists at the very beginning of a project, steering compound optimization toward the interactions that matter, well before expensive experiments reveal the mechanism. A separate research team’s independent confirmation of the VAV1 mechanism strengthens confidence that this predictive approach can be trusted.

With the mechanism in hand, the team turned to medicinal chemistry to improve their lead compounds. They introduced chlorine atoms into the molecular scaffolds, a modification that reduced the molecules’ flexibility and increased degradation efficiency. Rigidifying the compounds appears to have stabilized the three-part complex of VAV1, cereblon, and glue, which is the species that must form for the proteasome to destroy the target. One of the resulting compounds, NGT-201-18, showed substantially improved potency and produced a stronger protein complex required for degradation, demonstrating how even modest chemical refinements can translate structural insight into tangible gains in drug-like performance.

The improved compound was then put to the test in a biologically relevant setting: primary human T cells, the very cells in which VAV1 performs its immune-signaling role. NGT-201-18 successfully reduced VAV1 levels in these cells and suppressed T-cell activation, showing that the degrader could meaningfully alter immune-cell signaling outside of simplified laboratory models. Although considerably more research is needed before such compounds could be considered for patients, the results support the idea that VAV1 degradation could become a viable strategy for treating autoimmune and inflammatory diseases, and potentially T-cell lymphomas as well. The work offers a concrete proof of concept that a signaling protein long considered difficult to drug can be removed from immune cells by a small molecule.

The study also carries a cautionary lesson about the complexity of targeted protein degradation. While VAV1 was the primary target, comprehensive proteomic profiling revealed that some of the compounds also degraded another protein, LIMD1. Such off-target activity is not necessarily disqualifying, but it underscores the need to evaluate both intended and unintended protein targets throughout drug development, particularly for glues whose selectivity depends on subtle features of protein surfaces rather than well-defined binding pockets. Overall, the researchers say, the work introduces a series of VAV1-targeting molecular glues and, just as importantly, demonstrates how artificial intelligence, structural modeling, and proteomics can work together at the earliest stage of a project, when no experimental structure is yet available, to accelerate drug discovery. If the approach generalizes, it may help pave the way toward new treatments for immune-related diseases and cancers that currently lack effective targeted therapies.

Subject of Research: AI-guided discovery of molecular glue degraders targeting the immune signaling protein VAV1

Article Title: AI structure prediction speeds the discovery of ‘molecular glues’ to treat disease

Article References: AI structure prediction speeds the discovery of ‘molecular glues’ to treat disease. (n.d.). Original publication

Image Credits: AI Generated

DOI: Not provided

Keywords: molecular glues, VAV1, targeted protein degradation, cereblon, artificial intelligence, proteomics, drug discovery, T cells, autoimmune disease, blood cancers, GluePlex, medicinal chemistry

The original call for papers read like a catalogue of the field’s hottest frontiers: targeted protein degradation, molecular glues, covalent inhibitors, antibody-drug conjugates, macrocycles, nucleoside therapeutics, immuno-oncology agents, natural-product discovery, multicomponent reactions, advanced imaging and screening technologies, and artificial intelligence. That breadth is significant in itself. A decade ago, many of these approaches were considered speculative or confined to a handful of specialist laboratories. Today they constitute a mainstream toolkit, and the 58 accepted articles show researchers across medicinal chemistry, chemical biology, and pharmacology routinely combining them rather than working in silos. The result, according to the editorial framing the collection, is a discovery enterprise that is faster, more efficient, and increasingly sophisticated in how it approaches molecular design.

A central theme running through the issue is the development of efficient and diversity-oriented synthetic strategies for bioactive molecules. The published articles describe asymmetric and visible-light-promoted reactions, multicomponent and one-pot syntheses, iron-catalyzed functionalization, nanocatalytic and sonochemical methods, skeletal editing, total synthesis, and optimized synthetic routes. Each of these techniques addresses a persistent bottleneck in drug discovery: the speed and reliability with which chemists can actually make the molecules that computational and biological studies suggest might work. Visible-light photocatalysis, for example, allows bond formations under mild conditions that were previously difficult or impossible, while skeletal editing permits late-stage modifications of molecular frameworks that would once have required rebuilding a candidate from scratch. Multicomponent reactions compress multi-step sequences into single operations, dramatically shortening the path from idea to testable compound.

Crucially, the synthetic advances described in the collection are not presented as ends in themselves. The resulting compounds, which include diverse indoles, indolizines, heterocycles, molecular hybrids, peptides, and natural-product analogues with enhanced structural and stereochemical complexity, were investigated as anticancer, antimicrobial, antitubercular, antiviral, anti-inflammatory, antidiabetic, antiseizure, and neuroprotective agents. This direct linkage between methodology and biological application is what distinguishes the current wave of synthetic innovation from earlier eras in which method development and drug hunting often proceeded on separate tracks. When a new catalytic reaction can be evaluated within weeks against disease-relevant cell models, the feedback loop between chemistry and pharmacology tightens, and the odds that an interesting molecule becomes a therapeutic candidate improve accordingly.

Another striking pattern in the collection is the continuing shift beyond the traditional one drug-one target paradigm that dominated pharmaceutical research for much of the past half-century. Among the highlighted examples are dual inhibitors targeting BTK/FLT3, COX-2/5-LOX, and FAAH/sEH, enzyme pairs relevant to cancer and inflammation, alongside multifunctional agents designed for Alzheimer’s disease and reviews of xanthone hybrids and pyrazolopyrimidine-based dual inhibitors. Multi-target design acknowledges that complex diseases rarely hinge on a single protein, and that modulating several nodes of a pathological network simultaneously can be more effective than maximal blockade of one. The approach demands a different kind of medicinal chemistry, one in which selectivity is engineered across multiple binding sites rather than maximized against a single target, and the articles in the issue show that scaffold design and mechanistic understanding are being integrated to meet exactly that challenge.

The issue also surveys work on established and emerging molecular targets, including FAK, HDAC, ERα, PI3Kδ, p38 MAPK, EZH2, DprE1, histamine H1 and H2 receptors, SFRP1, PDE4B, and nitric oxide synthase. This list spans kinases, epigenetic enzymes, nuclear receptors, phosphodiesterases, and bacterial cell-wall biosynthesis machinery, reflecting the wide biological terrain on which modern medicinal chemistry now operates. A dedicated review on PROTAC technology underscores the prominence of targeted protein degradation, one of the most promising new therapeutic modalities of the past decade. Rather than inhibiting a protein’s activity, degraders recruit cellular disposal machinery to eliminate the disease-causing protein altogether, an approach that can succeed against targets long considered undruggable by conventional small molecules. Its inclusion alongside classical target families illustrates how new modalities are being folded into, rather than replacing, the existing discovery apparatus.

Natural products and biomolecule-inspired scaffolds remain a major focus of the collection, and the evidence assembled suggests that nature is far from exhausted as a source of molecular diversity. Studies on phorbazole D, menominin A, polyprenylated acylphloroglucinols, oleanolic and alepterolic acids, Eucommiae folium, µ-conotoxins, honey-bee antimicrobial peptides, and marine cyclopeptides demonstrate the remarkable chemical inventiveness of the natural world, from terrestrial plants to venomous cone snails and social insects. What has changed is the technology brought to bear on these molecules. Total synthesis, analogue generation, chemical ligation, mass spectrometry, network pharmacology, and cell-based screening are overcoming longstanding challenges in natural-product discovery and optimization, problems of supply, structural complexity, and limited optimization potential that historically kept many natural products out of the clinic despite compelling biological activity.

The synergy between computation and experimentation emerges as perhaps the defining feature of the modern discovery pipeline. Molecular docking, molecular dynamics simulations, pharmacophore modeling, network pharmacology, and integrated in silico-in vitro workflows now support compound prioritization and mechanistic studies across the collection. These are not decorative additions; they determine which of millions of conceivable molecules get synthesized and tested, effectively allocating scarce laboratory resources. AI-assisted analysis, label-free cell-based screening, high-resolution LC-Orbitrap mass spectrometry, and zebrafish disease models further illustrate advances in compound characterization, phenotypic screening, and translational validation. Phenotypic screening in whole organisms such as zebrafish is particularly notable, because it allows compounds to be evaluated for efficacy and toxicity in a living system before the costly transition to mammalian models, catching failures earlier and more cheaply than traditional pipelines allow.

The collective message of the 58 articles is that new technologies in drug discovery are not defined by any single platform or methodology, but by the integration of innovative chemistry, emerging therapeutic modalities, computational prediction, advanced screening technologies, and rigorous biological validation. This multidisciplinary convergence is expanding druggable chemical space, the universe of molecules that can realistically be made, characterized, and developed into medicines, while accelerating therapeutic discovery and enabling increasingly sophisticated approaches to drug design. For decades, the pharmaceutical industry has grappled with declining productivity per research dollar, and collections like this one suggest a credible path forward: rather than betting on any single breakthrough, the field is stacking multiple incremental advantages in synthesis, target biology, computation, and screening into compounding gains across the entire pipeline.

In closing the special issue, Shi thanks the authors, reviewers, and the editorial team of Molecular Diversity, and expresses the hope that the collection will stimulate new collaborations, inspire continued technological innovation, and contribute to making drug discovery faster, more efficient, and more successful while preserving the molecular and mechanistic diversity that underpins transformative medicines. That emphasis on diversity is more than rhetorical. History shows that transformative drugs often emerge from unexpected chemical territory, and the deliberate cultivation of varied scaffolds, modalities, and screening strategies is the best insurance against the field narrowing prematurely around fashionable targets. If the technologies surveyed here continue to mature and interconnect, the coming decade of drug discovery may look markedly different from the last, with molecules designed, synthesized, and validated at a pace and precision that earlier generations of researchers could scarcely have imagined.

Subject of Research: Emerging technologies and multidisciplinary approaches in drug discovery

Article Title: New technologies in drug discovery

Article References: Shi, T. (2026). New technologies in drug discovery. Molecular Diversity. https://doi.org/10.1007/s11030-026-11711-2

Image Credits: AI Generated

DOI: 10.1007/s11030-026-11711-2

Keywords: drug discovery, medicinal chemistry, targeted protein degradation, PROTAC, molecular glues, natural products, artificial intelligence, multicomponent reactions, phenotypic screening, dual inhibitors, chemical biology, Molecular Diversity