At the heart of this innovation lies the asymmetric bimetallic synergistic copper-catalyzed propargyl substitution (CuAPS), an elegant transformation where the activation of both the propargyl electrophile and α-cyano ester nucleophile is finely orchestrated. This is mediated by a chiral Cu(I)–BOX complex catalyst, which not only recognizes the subtle electronic landscapes of both substrates but also transiently assembles into a binuclear complex of quasi-C₂ symmetry. This dynamic entity, combining a Cu-propynyl intermediate on one side and a stabilized deprotonated α-cyano ester on the other, facilitates an exquisitely stereoselective carbon–carbon bond formation. The conformational locking enabled by H···π stacking interactions underpins the high enantio- and diastereoselectivity achieved, culminating in yields of cyclic imidates with enantioselectivities as high as 94%.

This breakthrough addresses a longstanding challenge in asymmetric synthesis: the comprehensive activation and stereocontrol over both partners in propargyl substitution reactions. Historically, the understanding of catalytic activation modes has been fragmented, limiting the scope and efficiency of constructing chiral propargyl skeletons. Moreover, chiral cyclic imides—particularly those accessed as N-unprotected variants—have remained elusive targets due to synthetic limitations. The approach presented here revolutionizes this field by harnessing a transiently formed, dual-copper catalytic system that effectively lowers activation barriers and unlocks previously inaccessible chemical space.

One of the most striking facets of this method is its broad substrate scope and robustness under mild reaction conditions. The system tolerates diverse propargyl carbonates and α-cyano esters bearing a variety of functional groups, enabling the rapid assembly of structurally diverse chiral cyclic imidates. This versatility not only expands the utility of chiral imidates but also opens myriad avenues for late-stage functionalization and derivatization, positioning these scaffolds as versatile building blocks in synthetic and medicinal chemistry.

The potential of the synthesized cyclic imine esters extends well beyond their immediate formation. Their rich functional group profile, including reactive N–H and alkynyl groups, permits facile post-synthetic modification. Using strategies such as N-alkylation and the Sonogashira cross-coupling reaction, researchers can swiftly access a library of chiral imidate derivatives with tailored properties. This modularity is particularly valuable in drug discovery, where rapid generation of analogues is critical for structure-activity relationship studies and optimization of pharmacological profiles.

In a compelling demonstration of the biomedical promise embedded in these new compounds, several derivatives exhibited remarkable antiviral activity against the feline calicivirus (FCV) infection model. The antiviral efficacy surpassed that of nitrozonide, a benchmark positive control drug, highlighting these chiral imidates not only as chemical curiosities but also as potential therapeutic leads. This intersection of synthetic innovation and biological relevance underscores the translational impact of the research and fuels optimism for future drug development endeavors centered on these novel scaffolds.

The mechanistic insights gained through this work are equally transformative. The transient binuclear copper complex and its quasi-C₂ symmetric nature elucidate how the cooperative interplay between two distinct copper catalytic centers can be tactically exploited to direct stereochemical outcomes with precision. Such mechanistic clarity paves the way for the rational design of next-generation bimetallic catalysts tailored for other challenging asymmetric transformations, signaling a paradigm shift in catalyst development.

From a practical perspective, the catalytic efficiency demonstrated—manifested in high substrate-to-catalyst ratios (S/C up to 2000)—and mild reaction milieu suggest scalability and potential industrial applicability. The synthetic accessibility and operational simplicity may transform how chiral cyclic imidates are produced at scale, benefiting sectors ranging from pharmaceutical synthesis to agrochemicals and materials science.

This research also embodies the spirit of international collaboration and support, having been facilitated by foundational grants provided by the Fundamental Research Funds for the Central Universities and Shanghai Jiao Tong University’s Research Start-up Fund. The choice to publish open access in CCS Chemistry, a premier platform established by the Chinese Chemical Society to disseminate cutting-edge chemical research globally, ensures that these scientific advancements are readily accessible to the worldwide community, accelerating innovation.

The Chinese Chemical Society, established in 1932 and now comprising over 120,000 members worldwide, continues to foster the growth and development of chemistry in China and beyond. Its flagship publication CCS Chemistry serves as a beacon for seminal discoveries like this, bridging frontiers in fundamental chemistry and its applications with open sharing of knowledge.

Ultimately, this study represents a hallmark achievement in asymmetric catalysis, with the dual copper-catalyzed tandem reaction epitomizing synergy at the molecular level to unlock new chemical transformations. The confluence of mechanistic insight, synthetic scope, catalytic performance, and biological activity exemplifies the multidisciplinary impact of modern chemistry, affirming its pivotal role in addressing both scientific and societal challenges.

As researchers delve deeper into the subtleties of bimetallic synergy and continue optimizing related catalytic systems, the horizon promises even more unprecedented reactions, new classes of chiral molecules, and potential breakthroughs in drug discovery. The chiral N-unprotected cyclic imidates synthesized through this elegant methodology are poised to become indispensable tools, laying the foundation for future innovations in chemical synthesis and therapeutic development.

Subject of Research: Not applicable

Article Title: Bimetallic Copper-Catalyzed Asymmetric Propargylic Substitution: Synthesis of Chiral N-Unprotected Imidates, Mechanistic Study, and Antiviral Activity

News Publication Date: 5-Mar-2026

Web References:
– CCS Chemistry Journal: https://www.chinesechemsoc.org/journal/ccschem
– Chinese Chemical Society: https://www.chinesechemsoc.org/

References:
DOI: 10.31635/ccschem.026.202507061

Image Credits: CCS Chemistry

Keywords

Catalysis, Asymmetric synthesis, Copper catalysis, Propargyl substitution, Chiral imidates, Binuclear catalysis, Tandem reaction, Enantioselectivity, Pinner reaction, Antiviral compounds, Medicinal chemistry, Chiral ligand design

Conventional predictive models have long been hampered by a variety of limitations. Many existing machine learning approaches successfully address stereoselectivity in reactions with a single prochiral site, yet struggle to extend their applicability to more complex scenarios involving multiple prochiral sites. Furthermore, traditional methods are often bounded by a dependency on predefined descriptors, restricting their versatility in practical applications. ChemAHNet seeks to transcend these limitations through an innovative architecture grounded in the reaction mechanisms pertinent to olefin hydrogenation.

At the core of ChemAHNet’s design are three structure-aware modules, meticulously engineered to capture the intricate details of catalyst-olefin interactions. By employing these modules, ChemAHNet achieves a level of prediction accuracy that is both remarkable and necessary for modern organic synthesis. The framework not only forecasts the absolute configurations of major enantiomers with unprecedented precision but also facilitates a deeper understanding of the underlying molecular dynamics that govern these transformations.

One of the most exciting features of ChemAHNet is its capability to delineate the free energy landscape of asymmetric hydrogenation through computed values of ∆∆G‡. This parameter encapsulates the energy changes associated with various transition states within the reaction pathway. By quantifying these interactions, the model generates insights that inform practitioners about the most favorable pathways for achieving high stereoselectivity. This information is particularly useful in streamlining the optimization of reaction conditions for desired outcomes.

The implications of ChemAHNet extend well beyond the realm of olefin hydrogenation. With its foundation on simplified molecular-input line-entry system (SMILES) representations, the model stands as a robust tool that can adapt to multiple asymmetric catalytic reactions. This flexibility can facilitate accelerated development and optimization when exploring new catalytic systems, thereby aiding researchers who may be investigating various reaction architectures across diverse chemical spaces.

ChemAHNet opens new frontiers not just in predictive capabilities but also in the strategic design of catalysts. By leveraging machine learning, chemists can uncover relationships between molecular structures and their catalytic performance that were previously challenging to discern. This aligns with the broader movement in science towards integrative approaches combining artificial intelligence with traditional chemistry, ultimately bridging the gap between computation and empirical experimentation.

The advent of ChemAHNet reinforces the potential of deep learning to address complex challenges in catalysis. With models trained on vast datasets, researchers can harness the capabilities of ChemAHNet to accelerate the development of new methodologies, potentially leading to breakthroughs in asymmetric synthesis. The ability to produce compounds with specific stereochemistry is intrinsically valuable not only in pharmaceuticals but also in materials science and agrochemicals, where chirality can dictate functionality.

As the scientific community continues to explore the convergence of chemistry and artificial intelligence, interpretations of data through such models will likely catalyze further advancements in our understanding of molecular interactions. Moreover, the deployment of ChemAHNet illustrates a case study on how machine learning can provide a competitive advantage in molecular design and engineering, encouraging more chemists to embrace computational methodologies in their workflows.

In essence, the creation of ChemAHNet heralds a new era in asymmetric hydrogenation, offering researchers a comprehensive arsenal for predicting outcomes in reactions characterized by complex structures and mechanisms. This is more than just an incremental improvement; it reflects a paradigm shift in how chemists can consider structure-function relationships. By operating independent of strictly defined molecular descriptors, ChemAHNet emphasizes the importance of adaptability and intuition in designing catalytic processes efficiently.

The forward-thinking approach encapsulated in ChemAHNet exemplifies the synergy between machine learning and traditional organic chemistry. With applications beyond olefins, this model stands to redefine how asymmetric transformations are approached and executed. As researchers gradually move towards an era of data-driven innovation, ChemAHNet represents a significant step in facilitating not just predictions but also insight-driven molecular engineering—a clear indication that the future of chemical synthesis will continue to be shaped by the powerful interplay of chemistry and computational technology.

In conclusion, the journey towards a more robust and reliable predicting model for asymmetric hydrogenation has begun with ChemAHNet. As researchers integrate such models into their synthetic methodologies, the potential for discovering novel catalysts and optimizing reaction conditions will undoubtedly expand. The future looks promising as ChemAHNet invites investigation into even broader areas of asymmetric catalysis and encourages ongoing dialogue surrounding the intersection of artificial intelligence and chemistry.

Subject of Research: Asymmetric Hydrogenation of Olefins

Article Title: Chemistry-informed deep learning model for predicting stereoselectivity and absolute configuration in asymmetric hydrogenation.

Article References:

Cheng, L., Shao, PL., Lv, J. et al. Chemistry-informed deep learning model for predicting stereoselectivity and absolute configuration in asymmetric hydrogenation.
Nat Comput Sci (2025). https://doi.org/10.1038/s43588-025-00920-8

Image Credits: AI Generated

DOI: https://doi.org/10.1038/s43588-025-00920-8

Keywords: Asymmetric Hydrogenation, Machine Learning, ChemAHNet, Deep Learning, Stereoselectivity, Catalysis, Organic Synthesis.