Stereoselective nucleophilic additions to α-substituted carbonyl compounds are a crucial area of contemporary research in organic chemistry. Of the various advancements in π-facial selectivity in addition reactions of carbonyl compounds, the (polar) Felkin-Anh model and the chelation model are well recognized for accurately explaining the selectivity of the allylic products. For reactions that involve α-oxy carbonyl groups - known for their broad applications in natural-product synthesis and as effective building blocks in organic synthesis - the stereoselective reaction typically follows the chelation model, favoring syn-selective addition. In contrast to the well-established syn-selective additions of α-oxy carbonyls, anti-selective additions through a non-chelation pathway remain largely unexplored. In this study, we present the anti-selective allylation of α-oxy ketones using allylatranes that feature a highly coordinated group-14-element center. These atranes demonstrate high nucleophilicity and low chelating ability due to their transannular interactions and rigid framework, facilitating anti-selective allylations. A combined experimental and theoretical approach has been used to highlight the unique electronic properties of these atranes. This method is applicable to a wide variety of substrates, producing anti-1,2-diols with a homoallylic moiety in high yield and excellent diastereoselectivity compared to traditional methods.

Recent advances in chiral analytical chemistry have revealed that fermented and natural foods contain substantial amounts of D-amino acids (D-AAs), the mirror-image counterparts of L-amino acids, leading to their recognition as nutraceutical components with potential health relevance. Although clinical evidence provides only limited support for their therapeutic efficacy, commercial expectations have outpaced scientific validation, and recent safety concerns emphasize the need for critical evaluation. In this review, we integrate findings from food chemistry, microbiology, biochemistry, physiology, and clinical research to provide a critical overview of dietary D-AAs. We examine how dietary exposure, microbial metabolism, host clearance capacity, and redox status collectively shape their context-dependent biological effects. We highlight the mechanistic linkage between D-amino acid oxidase (DAAO)-mediated hydrogen peroxide (H2O2) generation and organ-specific vulnerability, thereby clarifying the molecular basis of their “double-edged sword” actions. Within this interdisciplinary framework, we propose that D-AAs function as context-dependent “contronymic” molecules in cellular communication. By distinguishing physiological regulation, experimental modulation, and clinical application, this review aims to support evidence-based nutraceutical strategies and safety assessments that harness the potential benefits of D-AAs while minimizing associated risks.

Objective: This comprehensive in silico computational and theoretical biochemistry/organic chemistry research study covers the inhibition methodologies of 1-BNA, which is an essential template for cancer-suppressing research. By disrupting the minor groove domain binding site of the DNA template 1-BNA dodecamer, three de novo drug candidate molecules via derivatization of anti-epileptic valproic acid (VPA) drug were designed. Methods: Calf thymus DNA (ct-DNA) sequence d(CGCGAATTCGCG)2 dodecamer (PDB ID: 1BNA) was obtained from the Protein Data Bank to study. Molecular docking simulations, the most updated docking programs of AutoDock Vina and PyRx were utilized. Molecular dynamics (MD) simulations and Absorption, distribution, metabolism and excretion (ADME) analyses were employed by Schrödinger’s Desmond software. Results: Thorough in silico results of three new derivatized analogues of VPA showed that chloro-derivatized analogue with its -9.6 kcal/mol docking score and 297.2 µM inhibition constant (IC) has a much better indication in the suppression where isopropyl and fluoro derivatized analogues possess -9.1 kcal/mol docking score with 483.8 µM IC and -8.3 kcal/mol docking score with 896.2 µM IC respectively compared to the results of pristine VPA’s -8.2 kcal/mol docking score with 863.6 µM IC. Along with the detailed ADME results, isopropyl and chloro-derivatized ligands prove their worth of being a new drug candidate. Conclusion: All results proved that the de novo derivatives of VPA illustrate better docking scores (better suppression) along with better ICs and oral absorption percentage values, meaning that the analogues possess better indications compared to pristine VPA toward the suppression of cancerous cell template DNA.

Molecular representation learning (MRL) is critical in computational chemistry and drug discovery, paving the way for efficient molecular properties and biological activity prediction. However, existing sequence-based or graph-based MRL methods emphasize static and intrinsic molecular topological features while ignoring dynamic and interactive chemical knowledge, resulting in insufficient generalization ability. MolR meets this challenge by leveraging the equivalence of molecules participating in chemical reactions in embedding space to assist in learning molecular representations. However, it can suffer from unsatisfactory performance because it lacks reaction center information and the complex relationship between reactions, which provides a deeper understanding of the chemical processes. We propose ReMol, an elaborate chemical reaction knowledge-guided self-supervised molecular image representation learning framework to address this issue. The ReMol framework integrates comprehensive reaction inductive biases, including reaction templates and consistency, and diversity in chemical reactions. Experimental results demonstrate that our framework achieves state-of-the-art results compared with cutting-edge methods on various challenging downstream tasks, such as chemical reaction and molecular property prediction tasks. Overall, our work offers a robust tool for advancing chemistry research, with the potential to make significant contributions to both molecular representation learning and drug discovery.

Democratising real-world drug discovery through agentic AI.

Reliability analysis of photoluminescence measurement data from the intrinsic limitation on the instrument to the practical limitations during the experiments.

Retraction notice to “Ethylenediamine-functionalized ZIF-8 for modification of chitosan-based membrane adsorbents: Batch adsorption and molecular dynamic simulation”, Chemical Engineering Research and Design, Volume 175, November 2021, Pages 131–145

Carbonylation reactions, which rely on the controlled incorporation of carbon monoxide into organic substrates, represent a key tool in both organic and industrial chemistry. Carbonylation offers an efficient route to carbonyl-containing compounds starting from simple and readily available substrates, and it remains a highly active area of research in organometallic chemistry and catalysis. Current efforts focus on developing new catalysts, more efficient and sustainable methodologies, and innovative applications in emerging areas such as green chemistry and asymmetric synthesis. Given the great importance of heterocyclic compounds, the carbonylative approach has become increasingly important for their synthesis. In this review, we summarize and discuss advancements in the synthesis of quinolinone derivatives, a class of benzo-fused nitrogen-containing heterocyclic compounds, via carbonylative approaches.

Borenium ions are tricoordinate boron cations of the type [LBR2]+, where L is a donor ligand. While the inherently reactive nature of these Lewis acidic species has been harnessed to advance bond activation methodologies and catalysis, it has historically limited investigations into the optoelectronic properties of these systems. Borenium ions have the potential to serve as tunable low-energy LUMO materials, but there remains a gap in knowledge concerning the factors that impart stability and the specific optical transitions that mediate their function. This Perspective centralizes the key design principles used to tailor the properties of cyclic borenium ions toward functional luminescent materials applications, with a focus on recent examples from the literature. Design concepts including ligand identity, ring size, heteroatom incorporation, and counteranion selection have been identified as pivotal tools enabling the isolation and discovery of various emissive and stimuli-responsive boron cations. These advancements have resulted in the observation of diverse phenomena, including twisted intramolecular charge transfer (TICT), aggregation-induced emission (AIE), exciton coupling, and thermochromic behavior. The concepts highlighted herein serve as a blueprint for future research in main-group element materials chemistry.

Methionine aminopeptidase (MAP) is useful in chemical biology research for the N-terminal processing of peptides and proteins and in medicine as a potential therapeutic target. These technologies can benefit from a precise understanding of the enzyme's substrate specificity profiled over a wide chemical space, including not just natural substrates, peptides containing N-terminal Met, but also unnatural peptide substrates containing N-terminal Met analogues that are also cleaved by MAP like homopropargylglycine (HPG) and azidohomoalanine (AHA). A few studies have profiled substrate specificity for cleavage of N-terminal Met, but none have systematically done so using N-terminal Met analogues. Therefore, we devised a high-throughput profiling experiment based on mRNA display and next-generation sequencing to probe MAP's substrate specificity using N-terminal HPG. From subgroup analysis of either single residues or two-residue combinations, we could establish the impact of residue identity at various positions downstream from the cleavage site. To validate the selection results, a collection of short peptides was chemically synthesized and assayed for cleavage efficiency, where we observed reasonable agreement with the selection data. Results generally followed previously reported trends using N-terminal Met, the strongest trend being that the second residue (P1' position) had the greatest impact on MAP cleavage efficiency with moderate impacts discerned for residues further downstream, which could be rationalized through modeling the enzyme-substrate interaction.

The rapid adoption of artificial intelligence (AI) and machine learning (ML) in chemistry coincides with increasing structural pressures on academic research, including funding constraints, talent competition, and changing attitudes toward scientific careers. In this Perspective, we argue that this combination of trends may reshape how and by whom chemical knowledge is produced, rather than simply increase research productivity. We discuss recent developments in the automation of experimentation and self-driving labs, ML-based modeling and digital twins, and the use of large language models for literature search, manuscript preparation, and review, and place them against the current financial and social pressures on universities. We outline these trends in the hope of softening the transition for the chemical research community and urging researchers, institutions, and funders to make their research ecosystems more resilient. Finally, we discuss possible shifts in the composition and structure of research groups and in the balance between universities, industry, and government laboratories, raising the central question: who will produce chemical knowledge in the research landscape changed by the wider adoption of AI technologies?

The scalable synthesis of target molecules is essential for advancing applications, such as evaluating biological activity in pharmaceutical candidates and developing functional materials. In the context of medicinal chemistry, both industrial and academic researchers have achieved the scalable synthesis of diverse natural products and their derivatives, vastly boosting the development of pharmaceuticals. Most established scalable syntheses have conventionally relied on chiral pool approaches or stoichiometric asymmetric methodologies, and the use of catalytic asymmetric strategies has remained limited. In this personal account, we outline recent progress in the development and refinement of asymmetric allylboration reactions catalyzed by chiral Brønsted acids, providing important chiral building blocks that were previously difficult to access. We also describe the catalytic scalable synthesis of potent natural products enabled by the efficient preparation of chiral building blocks utilizing our allylboration reactions.

The purpose of the work : assessment of the quality indicators of roach ( Rutilus rutilus caspicus ) to determine its nutritional and biological value. Methods used : analytical research methods in the field of ensuring the quality, nutritional and biological value of products from hydrobionts in international and Russian databases. Physical and chemical research was conducted using standard methods. Instrumental research methods, including mass spectrometry, gas chromatography, and others, were used to determine the biochemical parameters of Caspian roach products. Novelty : modern biochemical indicators of Caspian roach products have been determined, which characterize their biological value and usefulness. Practical significance : collecting up-to-date data on the quality of Caspian roach products is essential for assessing their nutritional and biological value, despite the moratorium on Caspian roach harvesting in the Volga- Caspian region to restore its stocks. The results of these studies will form the basis for subsequent monitoring of the physiological and biochemical state of Caspian roach and assessing the nutritional value of Caspian roach products.

Herein, for monitoring the carboxylesterase (CE) level on studying medicine-food homology samples, an isoflavone-based fluorescent probe, IFone-CE, was developed on the basis of the representative Formonetin and the extended saturated fatty acidic chain as the recognition group. A typical turning-on was established, which remained the modification potential. The probe IFone-CE suggested general advantages including a relatively rapid response, stable optical performance in various working conditions of pH, temperature, and storage. It also exhibited practical linear range, high sensitivity, high selectivity, and low cyto-toxicity. The solution system containing IFone-CE achieved monitoring the biological efficacy of medicine-food homology samples. In the confocal imaging, IFone-CE realized the reduction and promotion effect of the samples in living colorectal cells. This work raised beneficial insights for the effective utilization of resources and medicinal chemistry research.

Three new histamine-derived Schiff base ligands were synthesized and studied: 4-[(E)-{[2-(1H-imidazol-4-yl)ethyl]imino}methyl]-N,N-dimethylaniline (L1), 2-ethoxy-4-[(E)-{[2-(1H-imidazol-4-yl)ethyl]imino}methyl]phenol (L2), and 1-[(E)-{[2-(1H-imidazol-4-yl)ethyl]imino}methyl]-5,8-dihydronaphthalen-2-ol (L3). The ligands were fully characterized by elemental analysis, mass spectrometry, 1H and 1³C NMR spectroscopy, infrared (IR), and UV–visible spectroscopic techniques, confirming their structures and functional groups. The catalytic properties of the in situ Cu(II) complexes were evaluated in the oxidation of catechol, and the kinetic parameters were determined from the Michaelis–Menten plots. The results showed that the structural features of the ligands strongly influenced the catalytic efficiency, as reflected by the variations in Vmax, Km, and kcat values. Furthermore, the antioxidant activity was assessed using the DPPH radical scavenging method. Ligand L1 demonstrated the strongest activity (IC50 = 0.326 µg/L), even higher than ascorbic acid (IC50 = 0.457 µg/L), while L2 and L3 exhibited moderate but significant antioxidant potential. Overall, these findings highlight the dual catalytic and antioxidant properties of histamine-based Schiff bases, supporting their potential applications in coordination chemistry, bioinorganic catalysis, and antioxidant research.

We present a methionine-selective, nonreversible bioconjugation strategy that employs activated allylic bromides under mild, aqueous reaction conditions compatible with various peptides and proteins. Compared with conventional allylic bromides, our method improves conjugate stability and suppresses nonspecific reactivity under the examined reaction conditions. This method enables methionine-preferred labeling of peptides and proteins, and provides proof-of-concept applications in covalent inhibitor design and protein functionalization. As a complementary addition to existing methionine bioconjugation strategies, this chemistry expands the toolkit available for protein modification and chemical biology research.

Electron paramagnetic resonance (EPR) is a technique for studying the microscopic structures by detecting the transitions of the spin magnetic moments of unpaired electrons. Since its discovery by E. K. Zavoisky in 1944, EPR has evolved from a tool for analyzing atomic structures in physics into a core characterization method in the fields of chemistry, biology, and materials science. In surface chemistry, due to its high sensitivity to the local environment, EPR has become a unique technique for elucidating surface-active sites, free radical intermediates, and defect structures. However, for many chemists, EPR testing and analysis, which are based on mathematical and physical principles, is not an easy field to engage in due to its high level of specialization. Some introductory textbooks provide excellent and comprehensive explanations of the basic knowledge, and recent work reports have also demonstrated the continuously developing magnetic resonance spectroscopy methods. Nevertheless, they are not intended to provide a brief and clear overview through a wide range of examples. To bridge the knowledge gap between EPR spectroscopists and chemists unfamiliar with EPR, this work reviews the progress in the application of EPR in surface chemistry, discussing its principles, applications, innovative cases and future challenges. It is hoped that nonprofessionals would gain certain knowledge and technical accumulation from this work, thereby promoting the development of surface chemistry.

Customizing inhibitors for β-sheet mediated protein-protein interactions (PPIs) remains a challenge in medicinal chemistry research. Directly separating β-sheet elements from the PPI interface as inhibitors represents a feasible strategy. Although useful, peptides derived from PPIs typically lose their natural secondary structure, resulting in low affinity. How to construct stable β-sheet inhibitors remains elusive, mainly due to the lack of reliable strategies to guide the design of β-sheet backbones. In this study, we propose an amino acid side-chain engineering strategy (AASE) to design of β-sheet inhibitors. Specifically, this strategy was based on the amino acid pairing (AAP) principle observed in the β-sheets of natural proteins, and further integrates the complementarity of various non-covalent interactions between β-strand side chains. When combined with high-throughput peptide screening technology, above strategy can generate structurally defined β-sheet inhibitors. We validated the physicochemical properties of the β-sheet structures obtained under the aforementioned strategy and prioritized the peptide EH. The β-sheet inhibitor formed by EH exhibits potent biological functions, enabling high-resolution imaging of tumors in the NIR window and guiding tumor resection. In addition, EH can inhibit tumor growth as a PD-1/PD-L1 checkpoint blockade. This strategy provides valuable guidance for the design of PPI inhibitors with β-sheet structures.

Purpose: Computational modelling is central to chemical engineering education, research, and process design, yet sustained access to modelling capabilities in many low-resource institutions remains limited by high licensing costs and dependence on proprietary software ecosystems. This study examines the potential of open-source modelling tools to provide technically robust and institutionally sustainable alternatives, addressing persistent gaps in tool selection, curriculum integration, and long-term adoption. Methodology: A systematic review and synthesis of open-source computational modelling tools across molecular, continuum, and process scales is conducted. Based on this analysis, a decision tree is developed to link modelling objectives and physical-fidelity requirements to appropriate open-source tools. In parallel, a decision-driven institutional adoption framework is proposed to guide phased implementation in resource-constrained chemical engineering environments. Findings: The review shows that mature open-source tools now exist across the full modelling hierarchy, enabling core chemical engineering workflows without reliance on proprietary platforms. The proposed decision tree supports transparent and reproducible software selection, while the adoption framework highlights the central role of infrastructure readiness, skills development, curriculum maturity, and governance in sustaining open-source uptake. Explicit decision points and feedback loops are identified as critical for managing heterogeneous infrastructure and evolving human capacity. Unique contribution to theory, practice and policy: This work delivers an integrated, decision-based approach to open-source modelling adoption in chemical engineering, linking technical capability with institutional capacity building. It provides actionable guidance for educators and institutions seeking equitable and sustainable digital modelling ecosystems, with relevance beyond the Malawian and Sub-Saharan African context.

We present Siam Quantum 2, the next generation of the open-source C program originally published in 2012 for Hartree-Fock electronic structure calculations [T. Khamla and T. Chachiyo, KKU Research Journal (Graduate Studies) 12(3), 8-28 (2012)], developed in Thailand (historically known as Siam) as a platform for quantum chemistry research and education. Over the past decade, Siam Quantum has evolved into a modular toolbox supporting a wide range of quantum modeling capabilities, including density functional theory, second-order Møller-Plesset perturbation theory, analytic energy gradients, molecular geometry optimization, quantum molecular dynamics, and minimum energy crossing point search. The present version introduces an extensible C architecture designed for method development, readability, and performance on multi-CPU environments. Each computational module is accompanied by human-readable Markdown documentation that contains LaTeX-style equations and programming guides, bridging the theoretical formulation and source code and facilitating future computational development. This paper describes the program's design philosophy, theoretical framework, and implementation details. We also highlight studies by various research groups that have made use of Siam Quantum, illustrating how the software can be used for quantum modeling and electronic structure studies.