Публикации

The accumulation of large datasets by the scientific community has surpassed the capacity of traditional processing methods, underscoring the critical need for innovative and efficient algorithms capable of navigating through extensive existing experimental data. Addressing this challenge, our study introduces a machine learning (ML)-powered search engine specifically tailored for analyzing tera-scale high-resolution mass spectrometry (HRMS) data. This engine harnesses a novel isotope-distribution-centric search algorithm augmented by two synergistic ML models, assisting with the discovery of hitherto unknown chemical reactions. This methodology enables the rigorous investigation of existing data, thus providing efficient support for chemical hypotheses while reducing the need for conducting additional experiments. Moreover, we extend this approach with baseline methods for automated reaction hypothesis generation. In its practical validation, our approach successfully identified several reactions, unveiling previously undescribed transformations. Among these, the heterocycle-vinyl coupling process within the Mizoroki-Heck reaction stands out, highlighting the capability of the engine to elucidate complex chemical phenomena.

Catalytic cross-coupling reactions, such as the Mizoroki–Heck reaction, play a crucial role in synthetic chemistry but pose significant environmental and health risks due to the toxicity of reaction components and their mixtures. In this study, we conducted a comprehensive cytotoxicity assessment of individual substances and complex reaction mixtures at different stages of the Mizoroki–Heck reaction. We demonstrate that the cytotoxicity of these mixtures often deviates from predictions on the basis of individual components due to synergistic and antagonistic interactions, with chlorobenzene-containing mixtures mostly exhibiting the lowest toxicity. Furthermore, our findings suggest that noncovalent interactions, including halogen bonding and π-stacking, significantly influence cytotoxicity. Notably, incomplete conversion of the reactants leads to an increase in mixture toxicity, emphasizing the importance of optimizing the reaction conditions. This study underscores the necessity of revising current chemical safety assessment strategies to account for complex molecular interactions in catalytic reactions.

Biofilms are critical for understanding environmental processes, developing biotechnology applications, and progressing in medical treatments of various infections. Nowadays, a key limiting factor for biofilm analysis is the difficulty in obtaining large datasets with fully annotated images. This study introduces a versatile approach for creating synthetic datasets of annotated biofilm images with employing deep generative modeling techniques, including VAEs, GANs, diffusion models, and CycleGAN. Synthetic datasets can significantly improve the training of computer vision models for automated biofilm analysis, as demonstrated with the application of Mask R-CNN detection model. The approach represents a key advance in the field of biofilm research, offering a scalable solution for generating high-quality training data and working with different strains of microorganisms at different stages of formation. Terabyte-scale datasets can be easily generated on personal computers. A web application is provided for the on-demand generation of biofilm images.

High-temperature organic chemistry represents a transformative approach for accessing reaction pathways previously considered unattainable under conventional conditions. This study focuses on a high-temperature synthesis as a powerful method for performing solution-phase organic reactions at temperatures up to 500 °C. Using the isomerization of N-substituted pyrazoles as a model reaction, we demonstrate the ability to overcome activation energy barriers of 50–70 kcal mol−1, achieving product yields up to 50% within reaction times as short as five minutes. The methodology is environmentally friendly, leveraging standard glass capillaries and p-xylene as a solvent. The significance of high-temperature synthesis lies in its simplicity, efficiency, and ability to address the limitations of traditional methods in solution chemistry. Kinetic studies and DFT calculations validate the experimental findings and provide insights into the reaction mechanism. The method holds broad appeal due to its potential to access diverse compounds relevant to pharmaceuticals, agrochemicals, and materials science. By expanding the scope of accessible reactions, this exploration of experimental possibilities opens a new frontier in synthetic chemistry, enabling the exploration of previously inaccessible transformations. This study establishes a new direction for further innovations in organic synthesis, fostering advancements in both fundamental research and practical applications.

Digitization of molecular complexity is of key importance in chemistry and life sciences to develop structure–activity relationships in chemical behavior and biological activity. The complexity of a given molecule compared to others is largely based on intuitive perception and lacks a standardized numerical measure. Quantifying molecular complexity remains a fundamental challenge, with key implications currently remaining controversial. In this study, we introduce a novel machine learning-based framework employing a Learning to Rank (LTR) approach to quantify molecular complexity on the basis of labeled data. As a result, we developed a ranking model utilizing the dataset that comprizes approximately 300 000 data points across diverse chemical structures, leveraging human expertise to capture complex decision rules that researchers intuitively use. Applications of our model in mapping the current organic chemistry landscape, analyzing FDA-approved drugs, guiding lead optimization processes, and interpreting total synthesis approaches reveal key trends in increasing molecular complexity and synthetic strategy evolution. Our study advances the methodologies available for quantifying molecular complexity, changing it from an elusive property to a numerical characteristic. With machine learning, we managed to digitize human perception of molecular complexity. Moreover, a corresponding large labeled dataset was produced for future research in this area.

A novel series of bio-based cationic surfactants, synthesized from the platform chemical 5-(hydroxymethyl)furfural (5-HMF), fatty acids, and bio-based amines, has been developed, offering a sustainable alternative to conventional surfactants. These compounds, referred to as surface-active ionic liquids (SAILs), have critical micelle concentration (CMC) values lower compared to conventional quaternary ammonium cationic surfactants, indicating enhanced surface activity. The surface properties of the SAILs are predominantly influenced by the type of substitution in the cationic head group, with morpholinium-based surfactants having significantly lower CMC values than diethyl ammonium ones. The length of the alkyl chain also plays a significant role in determining the physicochemical and biological characteristics of these surfactants, which vary depending on the chain length. Surfactants with longer alkyl substituents demonstrate enhanced thermal stability and surface activity. The newly synthesized amphiphiles exhibit antimicrobial activity comparable to known quaternary ammonium cationic agents but with lower cytotoxicity. Importantly, these surfactants show controlled degradation under temperature-driven hydrolysis and basic conditions while maintaining stability in acidic environments. These findings highlight the potential of developed bio-based surfactants to deliver high performance with reduced environmental impact, positioning them as potential candidates for antimicrobial applications and industrial uses focusing on sustainability goal.

Isophthalonitrile derivatives (IPNs) have emerged as promising organic photocatalysts due to their efficiency and accessibility; however, their inherent lability under light-induced conditions poses significant challenges in monitoring their transformation pathways. Understanding these pathways is crucial for optimizing photocatalytic processes and enhancing reaction efficiency. In this study, we present a novel approach utilizing electrospray ionization mass spectrometry (ESI-MS) to visualize cyanoarene photocatalysts by taking advantage of their specific supramolecular interaction with bromide anions. Our findings reveal that bromide ions facilitate the detection of IPNs and their transformation products with high sensitivity and selectivity, even in complex reaction environments. The interaction predominantly occurs in the gas phase, minimizing interference in solution-based transformations. The developed anion-enhanced detection (AED-ESI-MS) not only provides real-time insights into photocatalyst behavior but also opens new possibilities for the detailed mechanistic investigation of light-driven reactions. The proposed AED-ESI-MS approach using other anions may offer broad applicability and may be worth studying further across various photocatalytic systems.

Adapting biological systems for nanoparticle synthesis opens an orthogonal Green direction in nanoscience by reducing the reliance on harsh chemicals and energy-intensive procedures. This study addresses the challenge of efficient catalyst preparation for organic synthesis, focusing on the rapid formation of palladium (Pd) nanoparticles using bacterial cells as a renewable and eco-friendly support. The preparation of catalytically active nanoparticles on the bacterium Paracoccus yeei represents a more suitable approach to increase the reaction efficiency due to its resistance to metal salts. We introduce an efficient method that significantly reduces the preparation time of Pd nanoparticles on Paracoccus yeei VKM B-3302 bacteria to only 7 min, greatly accelerating the process compared with traditional methods. Our findings reveal the major role of live bacterial cells in the formation and stabilization of Pd nanoparticles, which exhibit high catalytic activity in the Mizoroki–Heck reaction. This method not only ensures high yields of the desired product but also offers a greener and more sustainable alternative to conventional catalytic processes. The rapid preparation and high efficiency of this biohybrid catalyst opens new perspectives for the application of biosupported nanoparticles in organic synthesis and a transformative sustainable pathway for chemical production processes.

Palladium catalysts form a cornerstone of modern chemistry with upmost scientific and industrial impact. Bulk palladium metal itself is chemically inert, and a sequence of chemical transformations has to be utilized to convert the metal into Pd pre-catalyst covered by ligands. However, the "cocktail" of catalysis concept discovered recently has shown that Pd systems can efficiently operate in catalysis without the necessity of a complicated and expensive pre-installed ligand environment. Here, we point out on a green and sustainable process for Pd active species generation without the need of waste-abundant pre-catalyst-related chemistry. In this work, an electric current was used to generate an active Pd catalyst from a bulk metal in an ionic liquid medium for the efficient cross-coupling of aryl iodides/bromides and boronic acids. Synthetically important Suzuki cross-coupling was utilized as a representative test reaction to confirm the idea. It should be emphasized that electric current is used only at the Pd dissolution stage. Afterwards, the electrodes are removed from the reaction mixture and a standard reaction procedure can be followed. The reported catalyst preparation process via electrochemical dissolution is potentially compatible with a number of already existing catalytic methods.

Working with liquid/gas-phase systems in chemical laboratories is a fundamentally important but difficult operation, mainly due to the explosion risk associated with conventional laboratory equipment. Such systems, in the case of improper operation or destruction, may pose a significant threat to researchers. To address this challenge, our work explores the potential of additive technologies, particularly fused filament fabrication (FFF), for improving laboratory safety. We have successfully utilized FFF to produce compact safety modules, including integrated bursting discs, which can be easily made on demand and adapted to various types of reaction setups. Compared with traditional glassware, these modules, when integrated with laboratory reactors, significantly enhance operational safety. Our research highlights that in the event of excessive internal pressure, 3D-printed reactor parts undergo delamination and cracking of the wall, a mechanism that notably avoids the creation of hazardous fragments from the whole reaction vessel. This study demonstrated the efficiency and safety of additively manufactured reactors in organic synthesis using a variety of gases, including acetylene, carbon dioxide, and hydrogen. We systematically tested these reactors in vinylation and azide–alkyne cycloaddition reactions. Our findings confirm that 3D-printed reactors not only provide increased safety during pressurized operations but also maintain operational efficiency. The discussed approach offers a transformative solution for safer and more effective handling of gaseous reagents in laboratory settings, marking a significant advancement in flexible reactor design and chemical laboratory safety practices.