Targeted Energy Transfer Dynamics and Chemical Reactions.

Ultrafast reaction processes take place when resonant features of nonlinear model systems are taken into account. In the targeted energy or electron transfer dimer model this is accomplished through the implementation of nonlinear oscillators with opposing types of nonlinearities, one attractive while the second repulsive. In the present work, we show that this resonant behavior survives if we take into account the vibrational degrees of freedom as well. After giving a summary of the basic formalism of chemical reactions we show that resonant electron transfer can be assisted by vibrations. We find the condition for this efficient transfer and show that in the case of additional interaction with noise, a distinct non-Arrhenius behavior develops that is markedly different from the usual Kramers-like activated transfer.

Versatile and efficient protein association through chemically modified Sphingomyelin Nanosystems (SNs) for enhanced delivery.

Proteins are biological macromolecules well known to regulate many cellular signaling mechanisms. For instance, they are very appealing for their application as therapeutic agents, presenting high specificity and activity. Nonetheless, they suffer from unfolding, instability and low bioavailability making their administration through systemic and other routes very tough. To overcome these drawbacks, drug delivery systems and nanotechnology have arisen to deliver biomolecules in a sustained manner while, at the same time, increasing dose availability, protecting the cargo without compromising proteins' bioactivity, and enhancing intracellular delivery. In this work, we proposed the optimization of sphingomyelin nanosystems (SNs) for the delivery of a wide collection of proteins (ranging from 10-500 kDa and pI) using diverse chemical association strategies. We have further characterized SNs by varied analytical methodologies. We have also carried out in vitro experiments to validate the potential of the developed formulations. As the final goal, we aim to obtain evidence of the potential use of SNs for the development of protein therapeutics.

Delayed blow-up and enhanced diffusion by transport noise for systems of reaction-diffusion equations.

This paper is concerned with the problem of regularization by noise of systems of reaction-diffusion equations with mass control. It is known that strong solutions to such systems of PDEs may blow-up in finite time. Moreover, for many systems of practical interest, establishing whether the blow-up occurs or not is an open question. Here we prove that a suitable multiplicative noise of transport type has a regularizing effect. More precisely, for both a sufficiently noise intensity and a high spectrum, the blow-up of strong solutions is delayed up to an arbitrary large time. Global existence is shown for the case of exponentially decreasing mass. The proofs combine and extend recent developments in regularization by noise and in the L p ( L q ) -approach to stochastic PDEs, highlighting new connections between the two areas.

Implementation of a soft grading system for chemistry in a Moodle plugin: reaction handling.

Here, we present a new method for evaluating questions on chemical reactions in the context of remote education. This method can be used when binary grading is not sufficient as some tolerance may be acceptable. In order to determine a grade, the developed workflow uses the pairwise similarity assessment of two considered reactions, each encoded by a single molecular graph with the help of the Condensed Graph of Reaction (CGR) approach. This workflow is part of the ChemMoodle project and is implemented as a Moodle Plugin. It uses the Chemdoodle engine for reaction drawing and visualization and communicates with a REST server calculating the similarity score using ISIDA fragment descriptors. The plugin is open-source, accessible in GitHub ( https://github.com/Laboratoire-de-Chemoinformatique/moodle-qtype_reacsimilarity ) and on the Moodle plugin store ( https://moodle.org/plugins/qtype_reacsimilarity?lang=en ). Both similarity measures and fragmentation can be configured.Scientific contribution This work introduces an open-source method for evaluating chemical reaction questions within Moodle using the CGR approach. Our contribution provides a nuanced grading mechanism that accommodates acceptable tolerances in reaction assessments, enhancing the accuracy and flexibility of the grading process.

Mathematical analysis of synthesis chemical reactions for virus building block polymers in vivo.

For numerous viruses, their capsid assembly is composed of two steps. The first step is that virus structural protein monomers are polymerized to building blocks. Then, these building blocks are cumulative and efficiently assembled to virus capsid shell. These building block polymerization reactions in the first step are fundamental for virus assembly, and some drug targets were found in this step. In this work, we focused on the first step. Often, virus building blocks consisted of less than six monomers. That is, dimer, trimer, tetramer, pentamer, and hexamer. We presented mathematical models for polymerization chemical reactions of these five building blocks, respectively. Then, we proved the existence and uniqueness of the positive equilibrium solution for these mathematical models one by one. Subsequently, we also analyzed the stability of the equilibrium states, respectively. These results may provide further insight into property of virus building block polymerization chemical reactions in vivo.

Ualign: pushing the limit of template-free retrosynthesis prediction with unsupervised SMILES alignment.

MOTIVATION: Retrosynthesis planning poses a formidable challenge in the organic chemical industry, particularly in pharmaceuticals. Single-step retrosynthesis prediction, a crucial step in the planning process, has witnessed a surge in interest in recent years due to advancements in AI for science. Various deep learning-based methods have been proposed for this task in recent years, incorporating diverse levels of additional chemical knowledge dependency. RESULTS: This paper introduces UAlign, a template-free graph-to-sequence pipeline for retrosynthesis prediction. By combining graph neural networks and Transformers, our method can more effectively leverage the inherent graph structure of molecules. Based on the fact that the majority of molecule structures remain unchanged during a chemical reaction, we propose a simple yet effective SMILES alignment technique to facilitate the reuse of unchanged structures for reactant generation. Extensive experiments show that our method substantially outperforms state-of-the-art template-free and semi-template-based approaches. Importantly, our template-free method achieves effectiveness comparable to, or even surpasses, established powerful template-based methods. SCIENTIFIC CONTRIBUTION: We present a novel graph-to-sequence template-free retrosynthesis prediction pipeline that overcomes the limitations of Transformer-based methods in molecular representation learning and insufficient utilization of chemical information. We propose an unsupervised learning mechanism for establishing product-atom correspondence with reactant SMILES tokens, achieving even better results than supervised SMILES alignment methods. Extensive experiments demonstrate that UAlign significantly outperforms state-of-the-art template-free methods and rivals or surpasses template-based approaches, with up to 5% (top-5) and 5.4% (top-10) increased accuracy over the strongest baseline.

Dual-Stimuli Regulation of DNAzyme Cleavage Reaction by Coordination-Driven Nanoprobes for Cancer Cell Imaging.

Endowing current artificial chemical reactions (ACRs) with high specificity and intricate activation capabilities is crucial for expanding their applications in accurate bioimaging within living cells. However, most of the reported ACR-based evaluations relied on either single biomarker stimuli or dual activators without obvious biological relevance, still limiting their accuracy and fidelity. Herein, taking the metal-ion-dependent DNAzyme cleavage reaction as a model ACR, two regulators, glutathione (GSH) and telomerase (TE) activated DNAzyme cleavage reactions, were exploited for precise discrimination of cancerous cells from normal cells. DNA probe was self-assembled into the ZIF-90 nanoparticle framework to construct coordination-driven nanoprobes. This approach enhances the stability and specificity of tumor imaging by utilizing biomarkers associated with rapid tumor proliferation and those commonly overexpressed in tumors. In conclusion, the research not only paves the way for new perspectives in cell biology and pathology studies but also lays a solid foundation for the advancement of biomedical imaging and disease diagnostic technologies.

Machine Learning of Reactive Potentials.

In the past two decades, machine learning potentials (MLPs) have driven significant developments in chemical, biological, and material sciences. The construction and training of MLPs enable fast and accurate simulations and analysis of thermodynamic and kinetic properties. This review focuses on the application of MLPs to reaction systems with consideration of bond breaking and formation. We review the development of MLP models, primarily with neural network and kernel-based algorithms, and recent applications of reactive MLPs (RMLPs) to systems at different scales. We show how RMLPs are constructed, how they speed up the calculation of reactive dynamics, and how they facilitate the study of reaction trajectories, reaction rates, free energy calculations, and many other calculations. Different data sampling strategies applied in building RMLPs are also discussed with a focus on how to collect structures for rare events and how to further improve their performance with active learning.

Chemical and Enzyme-Mediated Chemical Reactions for Studying Nucleic Acids and Their Modifications.

Nucleic acids are genetic information-carrying molecules inside cells. Apart from basic nucleotide building blocks, there exist various naturally occurring chemical modifications on nucleobase and ribose moieties, which greatly increase the encoding complexity of nuclei acids, contribute to the alteration of nucleic acid structures, and play versatile regulation roles in gene expression. To study the functions of certain nucleic acids in various biological contexts, robust tools to specifically label and identify these macromolecules and their modifications, and to illuminate their structures are highly necessary. In this review, we summarize recent technique advances of using chemical and enzyme-mediated chemical reactions to study nucleic acids and their modifications and structures. By highlighting the chemical principles of these techniques, we aim to present a perspective on the advancement of the field as well as to offer insights into developing specific chemical reactions and precise enzyme catalysis utilized for nucleic acids and their modifications.

Correlation of protein binding pocket properties with hits' chemistries used in generation of ultra-large virtual libraries.

Although the size of virtual libraries of synthesizable compounds is growing rapidly, we are still enumerating only tiny fractions of the drug-like chemical universe. Our capability to mine these newly generated libraries also lags their growth. That is why fragment-based approaches that utilize on-demand virtual combinatorial libraries are gaining popularity in drug discovery. These à la carte libraries utilize synthetic blocks found to be effective binders in parts of target protein pockets and a variety of reliable chemistries to connect them. There is, however, no data on the potential impact of the chemistries used for making on-demand libraries on the hit rates during virtual screening. There are also no rules to guide in the selection of these synthetic methods for production of custom libraries. We have used the SAVI (Synthetically Accessible Virtual Inventory) library, constructed using 53 reliable reaction types (transforms), to evaluate the impact of these chemistries on docking hit rates for 40 well-characterized protein pockets. The data shows that the virtual hit rates differ significantly for different chemistries with cross coupling reactions such as Sonogashira, Suzuki-Miyaura, Hiyama and Liebeskind-Srogl coupling producing the highest hit rates. Virtual hit rates appear to depend not only on the property of the formed chemical bond but also on the diversity of available building blocks and the scope of the reaction. The data identifies reactions that deserve wider use through increasing the number of corresponding building blocks and suggests the reactions that are more effective for pockets with certain physical and hydrogen bond-forming properties.

Integrating density functional theory and chemical ionization mass spectrometry techniques to assess benzophenone derivatives in food packaging: implications for enhancing food quality and safety.

Benzophenone and related derivatives are widely used as photoinitiators for food packaging to cure inks or lacquers with ultraviolet (UV) light on cardboard and paper. However, there are concerns about the potential health risks of their migration into food. Knowing the physical and chemical properties of benzophenone and its derivatives could play a significant role in their quantification and analysis using chemical ionization mass spectrometry (CI-MS) methods. These parameters are evaluated using B3LYP/6-311++** density functional theory (DFT) implemented on Gaussian code. Ion-molecule chemistry through the selection of reagent ions, reaction energetics and kinetics, thermodynamic stability, and reactivity of molecules deemed to foster VOC identification and quantification via CI-MS techniques. The VOCs under study are expected to undergo exothermic reactions from H3 O+ , NH4 + , NO+ , and O2 + ions, except endothermic proton transfer from NH4 + to 2-hydroxy-4-methoxybenzophenone and 2,3,4-trihydroxy benzophenone. These compounds possess less proton affinities than NH3 and are least stable in their protonated forms. The DFT computed properties provide the basis for developing reliable and accurate methods to detect and measure the presence of benzophenone and its derivatives in packaging materials and food products.

Rxn-INSIGHT: fast chemical reaction analysis using bond-electron matrices.

The challenge of devising pathways for organic synthesis remains a central issue in the field of medicinal chemistry. Over the span of six decades, computer-aided synthesis planning has given rise to a plethora of potent tools for formulating synthetic routes. Nevertheless, a significant expert task still looms: determining the appropriate solvent, catalyst, and reagents when provided with a set of reactants to achieve and optimize the desired product for a specific step in the synthesis process. Typically, chemists identify key functional groups and rings that exert crucial influences at the reaction center, classify reactions into categories, and may assign them names. This research introduces Rxn-INSIGHT, an open-source algorithm based on the bond-electron matrix approach, with the purpose of automating this endeavor. Rxn-INSIGHT not only streamlines the process but also facilitates extensive querying of reaction databases, effectively replicating the thought processes of an organic chemist. The core functions of the algorithm encompass the classification and naming of reactions, extraction of functional groups, rings, and scaffolds from the involved chemical entities. The provision of reaction condition recommendations based on the similarity and prevalence of reactions eventually arises as a side application. The performance of our rule-based model has been rigorously assessed against a carefully curated benchmark dataset, exhibiting an accuracy rate exceeding 90% in reaction classification and surpassing 95% in reaction naming. Notably, it has been discerned that a pivotal factor in selecting analogous reactions lies in the analysis of ring structures participating in the reactions. An examination of ring structures within the USPTO chemical reaction database reveals that with just 35 unique rings, a remarkable 75% of all rings found in nearly 1 million products can be encompassed. Furthermore, Rxn-INSIGHT is proficient in suggesting appropriate choices for solvents, catalysts, and reagents in entirely novel reactions, all within the span of a second, utilizing nothing more than an everyday laptop.

Emerging synthetic strategies and pharmacological insights of 1,3,4-thiadiazole derivatives: a comprehensive review.

This review meticulously examines the synthesis techniques for 1,3,4-thiadiazole derivatives, focusing on cyclization, condensation reactions and functional group transformations. It enhances the understanding of these chemical methods that re crucial for tailoring derivative properties and functionalities. This study is considered to be vital for researchers, detailing established effects such as antioxidant, antimicrobial and anticancer activities, and revealing emerging pharmacological potentials such as neuroprotective, antiviral and antidiabetic properties. It also discusses the molecular mechanisms underlying these effects. In addition, this article covers structure-activity relationship studies and computational modelling that are essential for designing potent, selective 1,3,4-thiadiazole compounds. This work lays a foundation for future research and targeted therapeutic development.

Advances in machine learning with chemical language models in molecular property and reaction outcome predictions.

Molecular properties and reactions form the foundation of chemical space. Over the years, innumerable molecules have been synthesized, a smaller fraction of them found immediate applications, while a larger proportion served as a testimony to creative and empirical nature of the domain of chemical science. With increasing emphasis on sustainable practices, it is desirable that a target set of molecules are synthesized preferably through a fewer empirical attempts instead of a larger library, to realize an active candidate. In this front, predictive endeavors using machine learning (ML) models built on available data acquire high timely significance. Prediction of molecular property and reaction outcome remain one of the burgeoning applications of ML in chemical science. Among several methods of encoding molecular samples for ML models, the ones that employ language like representations are gaining steady popularity. Such representations would additionally help adopt well-developed natural language processing (NLP) models for chemical applications. Given this advantageous background, herein we describe several successful chemical applications of NLP focusing on molecular property and reaction outcome predictions. From relatively simpler recurrent neural networks (RNNs) to complex models like transformers, different network architecture have been leveraged for tasks such as de novo drug design, catalyst generation, forward and retro-synthesis predictions. The chemical language model (CLM) provides promising avenues toward a broad range of applications in a time and cost-effective manner. While we showcase an optimistic outlook of CLMs, attention is also placed on the persisting challenges in reaction domain, which would optimistically be addressed by advanced algorithms tailored to chemical language and with increased availability of high-quality datasets.

Impact of induced magnetic field on Darcy-Forchheimer nanofluid flows comprising carbon nanotubes with homogeneous-heterogeneous reactions.

The appealing traits of carbon nanotubes (CNTs) encompassing mechanical and chemical steadiness, exceptional electrical and thermal conductivities, lightweight, and physiochemical reliability make them desired materials in engineering gadgets. Considering such stimulating characteristics of carbon nanotubes, our goal in the current study is to scrutinize the comparative analysis of Darcy-Forchheimer nanofluid flows containing CNTs of both types of multi and single-wall carbon nanotubes (MWCNTs, SWCNTs) immersed into two different base fluids over a stretched surface. The originality of the model being presented is the implementation of the induced magnetic field that triggers the electric conductivity of carbon nanotubes. Moreover, the envisioned model is also analyzed with homogeneous-heterogeneous (h-h) chemical reactions and heat source/sink. The second-order slip constraint is assumed at the boundary of the surface. The transmuted high-nonlinearity ordinary differential equations (ODEs) are attained from the governing set of equations via similarity transformations. The bvp4c scheme is engaged to get the numerical results. The influence of different parameters is depicted via graphs. For both CNTs, the rate of heat flux and the surface drag coefficient are calculated using tables. It is highlighted that an increase in liquid velocity is witnessed for a varied counts volume fraction of nanoparticles. Also, Single-wall water-based carbon nanotube fluid has comparatively stronger effects on concentration than the multi-walled carbon nanotubes in water-based liquid. The analysis also indicates that the rate of heat flux and the surface drag coefficient are augmented for both SWCNTs and MWCNTs for different physical parameters. The said model is also validated by comparing it with a published result.

Charentaise distillation of cognac. Part I: Behavior of aroma compounds.

The Charentaise distillation plays an essential role in designing cognac aroma by extracting and selectively concentrating aroma compounds from the wine along with ethanol, in addition to promoting compound formation or degradation through different chemical reactions. This traditional mode of distillation still relies heavily on empirical knowledge and the impact of its different parameters on the composition of cognac is not fully elucidated. In this context, this study aimed to broaden the current knowledge on the behavior of aroma compounds throughout the two steps of the Charentaise distillation and to investigate the formation of aroma compounds during the operation, an aspect which is seldom considered. The concentration profiles of 62 aroma compounds were represented over time for a wine and a brouillis distillation in usual scale (25 hL) with recycling. A classification system was then proposed to group compounds based on their volatilities at different ethanol concentrations in the boiling liquid, their concentration profiles and their chemical properties. This could help identify how chemical characteristics of aroma compounds affect their volatilities in hydroalcoholic media during distillation. In addition, several compounds appear to be formed during distillation, most of which are terpenes, norisoprenoids and aldehydes. Finally, to highlight the importance of different compounds to the aroma of freshly distilled cognac, their odor activity values (OAV) in the heart fraction were estimated, revealing isobutanol and (E)-ß-damascenone to be the most odorant compounds. These results provided additional elements of understanding for different aspects of the Charentaise distillation for the production of cognac, several of which can be transposed, at least in part, to different modes of distillation pertaining to other distilled beverages.

Effect of turbulent diffusion in modeling anaerobic digestion.

In this study, the impact of turbulent diffusion on mixing of biochemical reaction models is explored by implementing and validating different models. An original codebase called CHAD (Coupled Hydrodynamics and Anaerobic Digestion) is extended to incorporate turbulent diffusion and validate it against results from OpenFOAM with 2D Rayleigh-Taylor Instability and lid-driven cavity simulations. The models are then tested for the applications with Anaerobic Digestion - a widely used wastewater treatment method. The findings demonstrate that the implemented models accurately capture turbulent diffusion when provided with an accurate flow field. Specifically, a minor effect of chemical turbulent diffusion on biochemical reactions within the anaerobic digestion tank is observed, while thermal turbulent diffusion significantly influences mixing. By successfully implementing turbulent diffusion models in CHAD, its capabilities for more accurate anaerobic digestion simulations are enhanced, aiding in optimizing the design and operation of anaerobic digestion reactors in real-world wastewater treatment applications.

Label-Free Imaging of Solid-Phase Peptide Synthesis Products and Their Modifications Tethered in Microspots Using Time-of-Flight Secondary Ion Mass Spectrometry.

Time-of-flight secondary ion mass spectrometry is used to analyze solid-phase synthesis products in 60 µm spots of high-density peptide arrays. As a result, a table of specific fragments for the individual detection of amino acids and their side chain protecting groups within peptides is compiled. The specific signal of an amino acid increases linearly as its number increases in the immobilized peptide. Mass-to-charge ratio values are identified that can distinguish between isomers such as leucine and isoleucine. The accessibility of the N-terminus of polyalanine will be studied depending on the number of its residues. The examples provided in the study demonstrate the significant potential of time-of-flight secondary ion mass spectrometry for high-throughput screening of functional groups and their accessibility to chemical reactions occurring simultaneously in hundreds of thousands of microreactors on a single microscope slide.

Freezing-Enhanced Photoreduction of Iodate by Fulvic Acid.

Iodate is a stable form of iodine species in the natural environment. This work found that the abiotic photosensitized reduction of iodate by fulvic acid (FA) is highly enhanced in frozen solution compared to that in aqueous solution. The freezing-induced removal of iodate by FA at an initial pH of 3.0 in 24 h was lower than 10% in the dark but enhanced under UV (77.7%) or visible light (31.6%) irradiation. This process was accompanied by the production of iodide, reactive iodine (RI), and organoiodine compounds (OICs). The photoreduction of iodate in ice increased with lowering pH (pH 3-7 range) or increasing FA concentration (1-10 mg/L range). It was also observed that coexisting iodide or chloride ions enhanced the photoreduction of iodate in ice. Fourier transform ion cyclotron resonance mass spectrometric analysis showed that 129 and 403 species of OICs (mainly highly unsaturated and phenolic compounds) were newly produced in frozen UV/iodate/FA and UV/iodate/FA/Cl- solution, respectively. In the frozen UV/iodate/FA/Cl- solution, approximately 97% of generated organochlorine compounds (98 species) were identified as typical chlorinated disinfection byproducts. These results call for further studies of the fate of iodate, especially in the presence of chloride, which may be overlooked in frozen environments.