The team synthesized DNA sequences encoding proteins developed via NISE and expressed them in Escherichia coli, achieving robust protein yields. Initial biophysical characterization through size-exclusion chromatography affirmed that all four NISE-designed proteins existed predominantly as monomers, a critical feature ensuring the stability and functional integrity of the binders. Exploiting the intrinsic fluorescence properties of exatecan, the researchers conducted fluorescence polarization assays to quantify the binding affinities, obtaining dissociation constants (K_d) spanning from an impressive 0.12 µM to 17 µM. Notably, three designs manifested K_d values below 10 µM, significantly outperforming human serum albumin (HSA), a known non-specific binder with a considerably weaker K_d of 43 µM.

Among the array of NISE constructs, the exatecan-protein interaction construct (EPIC) emerged as the highest-affinity binder, exhibiting approximately 360-fold tighter association with exatecan compared to HSA. Structural analysis revealed that EPIC’s binding site was distinctively characterized by an optimized balance: it contained the fewest polar residues relative to other designs but conserved key interactions such as a buried glutamine residue positioned near the lactone ring of the ligand and an aspartic acid residue engaging the exatecan amine group. This intricate design resulted in a more extensive burial of the ligand’s apolar surface area, an attribute closely correlated with enhanced binding affinity.

For broader context, the investigators also explored proteins designed through the earlier COMBS methodology. Sixteen COMBS-designed proteins were expressed, yet only three demonstrated measurable binding to exatecan with K_d values of 8 µM, 12 µM, and 44 µM respectively. Among these, aggregation tendencies diminished their practical utility, and detailed analysis suggested that while COMBS could generate binders, their affinities were generally weaker than those produced by NISE. Comparative metrics such as ligand root-mean-square deviation (r.m.s.d.), backbone Cα r.m.s.d., and ligand predicted local-distance difference test (pLDDT) scores underscored how NISE optimized both protein folding and ligand interaction in a synergistic fashion.

Crucially, the stark contrast in binding efficacy between NISE and COMBS designs could not be chalked up to the use of residue-fluctuation-aware algorithms (RFAA) for filtering, as both approaches incorporated this quality control measure. Instead, the definitive advantage of NISE stemmed from its capacity to dynamically remodel protein backbones and binding pockets, crafting sequences better attuned to both structural stability and ligand complementarity. Case in point, EPIC’s binding site harbored a dramatically altered architecture compared with the baseline COMBS model, eschewing numerous original hydrogen-bond-forming residues in favor of a refined ensemble that judiciously balanced hydrophobic packing with targeted polar interactions.

Thermostability assays further attested to EPIC’s resilience under physiologically relevant conditions, bolstering its potential utility in therapeutic or diagnostic contexts. Intriguingly, the specificity profile of EPIC aligned tightly with chemical and steric resemblance among camptothecin-based drugs. Binding affinity titrations revealed a gradient of weakening interactions from exatecan to structurally related molecules such as FL118, belotecan, and camptothecin, establishing a clear structure-activity relationship revolving around the ligand’s unique fluoro-phenyl ring and amine functionalities.

The analysis suggested that molecular features contributing most substantially to binding free energy changes were hydrophobic contributions from desolvated fluoro and methyl groups, exceeding the influence of polar amine engagement. On the other hand, EPIC displayed no detectable affinity for bulky prodrug derivatives like irinotecan, nor for off-target ligands from unrelated drug classes such as the anticoagulant apixaban or the steroid dexamethasone. This intrinsic selectivity emerged despite the absence of explicit negative design constraints against off-target interactions, indicating that NISE inherently fosters specificity by optimizing for ligand compatibility during design iterations.

Remarkably, the NISE design trajectory reflected these precise binding preferences in silico, as ligand pLDDT values progressively increased when EPIC was co-folded with exatecan, yet remained low for off-target binders. This correlation between computational confidence metrics and experimental binding underscored the power of the integrative approach to predict and fine-tune protein-ligand interactions from first principles, without resorting to prior structural data or extensive screening.

This study spotlights the transformative potential of marrying deep learning with biophysical principles and synthetic biology, effectively enabling researchers to sculpt protein architectures from scratch tailored to challenging small molecule targets. The remarkable performance of EPIC exemplifies how iterative optimization frameworks can surmount longstanding hurdles in de novo binder design, which traditionally relied on laborious laboratory evolution or fragment-based screening techniques.

Looking ahead, the success of NISE could catalyze a new era in drug development where bespoke protein binders serve as versatile agents for targeted drug delivery, molecular sensing, or even in the modulation of pharmacokinetics. Beyond oncology, such an approach could be readily adapted to diverse therapeutic areas and emerging pharmaceutical modalities, drastically accelerating the pace of innovation.

Moreover, this method’s reliance on computational infrastructure, combined with validation through synthetic biology techniques, offers a scalable pipeline ideal for rapidly prototyping protein-ligand pairs against novel pharmacophores. While further studies will be necessary to explore in vivo stability, immunogenicity, and therapeutic indices, the foundational work presented here constitutes a compelling proof of concept with wide-reaching implications.

In sum, this research marks a pivotal advance in protein engineering, demonstrating that zero-shot design models empowered by neural iterative selection-expansion can generate high-affinity, highly specific drug-binding proteins. The confluence of cutting-edge machine learning, structural bioinformatics, and experimental biochemistry heralds a paradigm shift, enabling the rational creation of precision therapeutics that were previously beyond reach.

Subject of Research: Protein engineering and drug discovery; computational design of high-affinity drug-binding proteins.

Article Title: Zero-shot design of drug-binding proteins via neural iterative selection−expansion.

Article References:
Fry, B., Slaw, K. & Polizzi, N.F. Zero-shot design of drug-binding proteins via neural iterative selection−expansion. Nature (2026). https://doi.org/10.1038/s41586-026-10670-w

Image Credits: AI Generated

DOI: https://doi.org/10.1038/s41586-026-10670-w

This novel machine learning framework sidesteps the traditional reliance on expensive and computationally demanding physics-based chemical simulations. While these classical methods provide detailed reaction insights, their scalability is constrained, especially when tasked with evaluating thousands of potential molecular candidates. Researchers, spearheaded by Simone Gallarati, a joint postdoctoral investigator affiliated with the University of Utah and UCLA, endeavored to craft a statistical model capable of predicting reaction outcomes with remarkable accuracy, yet at a fraction of conventional costs. The core ambition was to build a “smart” system that could tackle complex chemical reactions without necessitating an impractically large dataset.

Integral to the challenge of drug molecule design is the phenomenon of chirality—the “handedness” of molecules. These mirror-image forms, though structurally similar, can possess starkly different biological activities. In pharmaceutical chemistry, synthesizing the therapeutically beneficial enantiomer while minimizing production of its potentially harmful counterpart is paramount. This demand has driven the exploration of asymmetric catalysis, where catalysts are engineered to preferentially produce one enantiomer over the other. However, screening the vast landscape of catalysts, ligands, and substrates to achieve optimal enantioselectivity is a daunting task that magnifies the need for predictive computational tools.

The research team’s novel system represents a high-throughput computational filter that converts the molecular components of reactions into quantifiable numerical data amenable to machine learning analysis. This innovation allows for the rapid, cost-effective screening of tens of thousands of chemical structures. Remarkably, their model demonstrated the ability to make reliable predictions with limited input data, significantly reducing the laborious trial-and-error experimentation traditionally required in laboratories. Such efficiency not only saves time and resources but also accelerates the pace at which promising drug candidates progress through development pipelines.

Matthew Sigman, a coauthor and chemistry professor at the University of Utah, underscores a persistent challenge within the AI-driven chemistry domain: the scarcity of extensive, high-quality datasets. Unlike broad AI applications that thrive on massive data pools, experimental chemistry often faces prohibitive costs and lengthy timelines associated with acquiring detailed reaction data. This scarcity makes training robust predictive models difficult. The breakthrough in this study lies in the system’s ability to construct effective models from sparse datasets and, impressively, extrapolate predictive power to chemical reactions unencountered during training, thus expanding the utility and applicability of the tool.

The focus of this work lies in asymmetric cross-coupling reactions—meticulous chemical processes crucial for constructing complex molecular frameworks in pharmaceutical agents. These reactions enable the union of two carbon-based fragments through a metal-catalyzed mechanism, which, with the aid of specific ligands, determines the three-dimensional orientation and stereochemical outcome of the product molecule. In practice, traditional experimentation without strategic guidance often yields a racemic mixture—equal amounts of left- and right-handed enantiomers. The researchers’ system, however, optimizes conditions to achieve striking enantioselectivity, potentially delivering 95% of the desired enantiomer in contrast to an unimproved 50/50 distribution.

Training the model entailed assimilating data from four key academic studies that explored nickel-catalyzed asymmetric cross-coupling reactions with a variety of ligands. The integrity and diversity of these data sets formed the backbone of the model’s learning phase. To rigorously test its predictive prowess, the research team challenged the algorithm to forecast outcomes for hypothetical reactions featuring compounds outside the essential training set. These progressively difficult tests evaluated the model’s capacity for generalization, revealing robust prediction accuracy even when confronted with uncharacterized chemical environments.

The validation phase of this computational endeavor was conducted in the laboratory of Abigail Doyle at UCLA, with doctoral candidate Erin Bucci undertaking a pivotal role in experimental testing. Bucci highlights the enormous practical impact of integrating this machine learning tool in a laboratory setting. By reducing the number of reactions from dozens to a mere handful, the tool directly mitigates the consumption of costly reagents and the labor required for chemical synthesis, leading to substantial cost savings and a more efficient research cycle.

Beyond the specific reaction systems tested, the authors articulate a broader vision for the applicability of their approach. This predictive framework, adaptable in principle to diverse catalytic systems and reaction types, opens doors to deeper mechanistic understanding and more informed rational design strategies within chemistry as a whole. Abigail Doyle notes that this approach is far from a mysterious “black box” and instead offers chemists nuanced insights that can inspire novel hypotheses and experimental pursuits.

From an industrial perspective, the implications of this work are profound. The pharmaceutical sector, perpetually driven to accelerate timeframes from discovery to clinical trials, stands to benefit immensely from tools capable of optimizing chemical syntheses for proprietary molecules not previously documented. Matthew Sigman emphasizes the strategic value in streamlining reaction development and cost management, elements that can decisively influence whether promising compounds successfully advance in the drug development pipeline.

This innovative work was orchestrated through collaboration among leading academic scientists, supported by major funding bodies including the Swiss National Science Foundation, the U.S. National Science Foundation, and the National Institutes of Health. The successful integration of computational chemistry, machine learning, and experimental validation embodies a compelling model for future interdisciplinary endeavors aimed at transforming the landscape of medicinal chemistry and pharmaceutical innovation.

In sum, this pioneering advancement in transferable enantioselectivity modeling surmounts long-standing limitations posed by data scarcity and computational expense. By enabling accurate, generalizable reaction predictions with minimal input, it ushers in a new era where artificial intelligence and chemistry synergize to expedite drug discovery—offering tangible hope for swifter development of safe, effective therapies that can improve human health on a global scale.

Subject of Research: Not applicable

Article Title: Transferable enantioselectivity models from sparse data.

News Publication Date: 11-Feb-2026

Web References:
https://www.nature.com/articles/s41586-026-10239-7

References:
Gallarati, S. et al., Transferable enantioselectivity models from sparse data. Nature (2026). https://doi.org/10.1038/s41586-026-10239-7

Image Credits:
Madeline Ruos/UCLA

Keywords

Drug discovery, Drug development, Drug candidates, Bioactive compounds, Drug targets, Medicinal chemistry, Biochemical engineering, Computational chemistry, Organic reactions, Organic compounds, Asymmetric catalysis, Organic synthesis

The study’s focus is on OGG1, an enzyme crucial for repairing damaged DNA, which is fundamental for maintaining cellular health. Inhibitory molecules that can bind to OGG1 may lead to breakthrough treatments for inflammatory diseases and other serious health conditions. The research team, comprising experts from renowned institutions including Karolinska Institutet and Stockholm University, utilized advanced computer modeling to design a multitude of molecules intended to interact with the enzyme. By synthesizing over a hundred unique compounds, researchers have initiated a revolutionary form of drug design that leverages computational power to streamline the discovery process.

This innovative approach was successfully employed to not only find but experimentally confirm compounds that inhibit the action of OGG1, showcasing promising anti-inflammatory effects. The process of designing these molecules was described by Jens Carlsson, one of the key authors, as akin to completing a jigsaw puzzle. Starting with fragments – tiny molecules capable of binding to the enzyme – researchers methodically built upon these initial pieces, gradually enhancing and refining them into viable drug candidates. This fragment-based drug design method presents a marked departure from traditional aggressive screening techniques, which often prove time-consuming and financially prohibitive.

Employing commercial molecular libraries provided the initial resources for the research, with computational programs designed to sift through billions of readily accessible molecules. Harnessing the capability of supercomputers, the team meticulously analyzed binding affinities to the OGG1 enzyme. Remarkably, this search yielded functional molecules that exhibited significant inhibition of the enzyme’s activity. This success bolstered the researchers’ confidence, leading them to explore the potential of expanding their inquiry beyond commercially available substances.

The new computational tool developed by PhD student Andreas Luttens unlocked the possibility of exploring a staggering number of synthetic molecules. This system provided the researchers with the unprecedented ability to generate a database of highly diverse molecular candidates, significantly broadening the scope of their search. Enabling the examination of a staggering ten sextillion molecules reveals the groundbreaking nature of this research; it illustrates the emerging intersection of computational chemistry and practical medicine.

As the researchers detailed their findings, they noted that while the power of computation presents new opportunities, the reality of producing these engineered molecules remains a challenge. The ability to theoretically design potent inhibitors does not guarantee that these substances can be synthesized or developed into front-line treatments. Consequently, there is an urgent need for advancements in synthetic methods and collaborative frameworks among medicinal chemists and computational biologists to ensure that drug candidates transition from computer models into real-world applications.

The implications of this study reverberate across the pharmaceutical industry, suggesting that drug discovery could soon be transformed by integrating sophisticated algorithms with traditional laboratory work. The potential for this technology to speed up the drug development timeline while simultaneously reducing costs may reshape therapeutic strategies for various diseases. As scientists aim to model disease states through computational simulations, this technological breakthrough may facilitate the development of drugs that have previously taken years to identify and produce.

Moving forward, it is clear that interdisciplinary collaboration will be pivotal to maximizing the efficacy of these techniques. As computational methods evolve and deepen our understanding of molecular interactions, researchers who can effectively combine computational insights with empirical findings will drive the future of drug discovery. The synergy between computational power and medicinal chemistry could signal the dawn of a new era in pharmacology, where the rapid synthesis of innovative anti-inflammatory drugs may soon become routine.

As expectations for pharmaceutical solutions continue to rise, the necessity for robust, efficient, and scalable drug discovery methodologies remains paramount. This study lays important groundwork for future research in molecular design, emphasizing the need for continuing advances in both algorithmic approaches and practical applications. Moving forward, it will be crucial to investigate how these promising inhibitors can be effectively tested and brought into clinical settings.

Through the lens of this transformative research, we witness the promise of computational models not merely as theoretical constructs but as foundational tools for optimizing the process of drug discovery. As the world stands on the brink of a scientific revolution in medicine, it is exciting to envision the future landscape where computational chemistry and experimental research converge to create novel treatments that improve the quality and longevity of human life.

By exploring new methods of research that evolve with technology, scientists will be poised to address the complexities of disease with unprecedented speed and precision. The ongoing integration of computational strategies in drug discovery heralds a future in which we harness the full potential of innovation to create profound impacts on health outcomes globally.

Subject of Research: Drug discovery, computational chemistry
Article Title: Harnessing Computational Power to Discover Anti-Inflammatory Drugs
News Publication Date: February 18, 2025
Web References: Nature Communications
References: Luttens, A., Vo, D.D., Scaletti, E.R. et al. Virtual fragment screening for DNA repair inhibitors in vast chemical space. Nat Commun 16, 1741 (2025). DOI: 10.1038/s41467-025-56893-9
Image Credits: Andreas Luttens

Keywords
Computational modeling
Protein analysis
Antiinflammatory drugs
Drug design
Algorithms
Drug candidates
Enzymes
Protein design
Enzyme inhibitors