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Di-fructofuranose 1,2′:2,3′ dianhydride (DFA-III) is a cyclic fructo-disaccharide, which is produced by the condensation of two fructose molecules via the caramelization or enzymatic reaction of inulin fructotransferase. A strain of Blautia producta was known to utilize DFA-III as a carbohydrate source; however, the mechanisms remain unclear. In this study, we characterized the glycoside hydrolase (GH) family 91 DFA-III hydrolase (DFA-IIIase) from B. parvula NBRC 113351. Recombinant BpDFA-IIIase catalyzed the reversible conversion of DFA-III to inulobiose, which is further degraded to fructose by the cooperative action of DFA-IIIase and GH32 β-d-fructofuranosidase. DFA-III was utilized in several Blautia species with a gene cluster for DFA-III degradation (e.g., B. parvula NBRC 113351, B. hydrogenotrophica JCM 14656, and B. wexlerae JCM 35486), but not by B. wexlerae JCM 31267, which does not possess the gene cluster. Furthermore, B. hansenii JCM 14655, which cannot metabolize fructose, could not utilize DFA-III; however, it could degrade DFA-III to fructose in the presence of DFA-III-degrading enzymes. Fecal fermentation tests showed that Blautia species are important gut microbe for degrading DFA-III. Key points: • BpDFA-IIIase is the first characterized DFA-IIIase in intestinal non-pathogenic bacteria. • DFA-IIIase is widely conserved in Blautia species. • DFA-III is degraded to d-fructose through inulobiose by the cooperative action of DFA-IIIase and β-d-fructofuranosidase. [ABSTRACT FROM AUTHOR]

Background: Positron emission tomography-magnetic resonance imaging (PET/MR) is a cutting-edge hybrid imaging technology with the potential to revolutionize medical diagnosis. This bibliometric study aims to map the research landscape of PET/MR by analyzing a curated set of Web of Science Core Collection documents from 2011 to 2023. Methods: We conducted a bibliometric analysis to map the research landscape of PET/MR, leveraging a curated dataset of 3,600 documents retrieved from the Web of Science Core Collection spanning the period from 2011 to 2023. We employed quantitative methods to assess the quantity and distribution of PET/MR studies, including patterns of change, research status, and directions. Additionally, we utilized VOSviewer software to conduct keyword co-occurrence analysis, explore collaborative networks among authors and institutions, and identify influential journals in the field. Results: Results: The analysis reveals several key insights: (1) a significant increase in the number of PET/MR publications over the past 12 years, highlighting the growing interest and activity in this field; (2) the United States and Germany as the leading countries in terms of research output and collaboration, with a growing presence of other countries such as China; (3) the Journal of Nuclear Medicine and the European Journal of Nuclear Medicine and Molecular Imaging as the most influential journals in the field; (4) a shift in research focus from imaging techniques to clinical applications, with an increasing emphasis on prostate cancer, PSMA, and FDG imaging. Conclusions: This study provides a comprehensive overview of PET/MR research, identifying prominent trends, key researchers, and influential works. Based on these findings, we propose recommendations for future research directions in this rapidly evolving field. [ABSTRACT FROM AUTHOR]

Alzheimer's disease (AD) is a degenerative neurological disorder that chronically and irreversibly affects memory, cognitive function, learning ability, and organizational skills. Numerous studies have demonstrated BACE1 as a critical therapeutic target for AD, emphasizing the need for specific inhibition of BACE1 to develop effective therapeutics. However, current BACE1 inhibitors have certain limitations. Therefore, the aim of this study was to identify potential novel candidates derived from natural products that can be utilized for the treatment of AD. To achieve this, 80,617 natural compounds from the ZINC database were subjected to virtual screening and subsequently filtered according to the rule of five (RO5), leading to the identification of 1,200 compounds. Subsequently, the 1,200 compounds underwent molecular docking studies against the BACE1 receptor, utilizing high-throughput virtual screening (HTVS), standard precision (SP), and extra precision (XP) techniques to identify high-affinity ligands. Of the 50 ligands that exhibited the highest G-Scores in HTVS, further analysis was conducted using SP docking and scoring methods. This analysis led to the identification of seven ligands with enhanced binding affinities, which were then subjected to additional screening via XP docking and scoring. Finally, the stability of the most promising ligand in relation to BACE1 was assessed through molecular dynamics (MD) simulations. The computational screening demonstrated that the docking energy values for seven ligands binding to target enzymes ranged between − 6.096 and − 7.626 kcal/mol. Among these, ligand 2 (L2) exhibited the best binding energy at -7.626 kcal/mol with BACE1. MD simulations further confirmed the stability of the BACE1-L2 complex, emphasizing the formation of a robust interaction between L2 and the target enzymes. Additionally, pharmacokinetic and drug-likeness evaluations indicated that L2 is non-carcinogenic and able to permeate the blood-brain barrier (BBB). The findings of this study will contribute to narrowing down the selection of candidates for subsequent in vitro and in vivo testing. [ABSTRACT FROM AUTHOR]

The paper presents the incorporation of in silico missenses and studies the effect of missenses to understand its effect on the ligand–protein interactions, of COVID-19 protein. In silico protein–ligand interaction, studies are being used to understand and investigate the drug-likeness of various molecules. 19 novel COVID-19 proteins are designed by inducing in silico missenses by mutating N691 amino acid residue in 7bv2 protein, the only residue forming H-bond with the ligand molecule in the parent protein. The work illustrates the effects of in silico-induced mutation on various interactions such as H-Bond, VDW, π-alkyl interactions, and changes in the number and type of surrounding amino acid residues. The results have suggested a common pattern of behaviour on mutation with T, V, W, and Y. Further, it is observed that the number and type of amino acid residues increase on mutation, suggesting the effect of mutation on the ligand–protein binding. [ABSTRACT FROM AUTHOR]

In this work hydrodeoxygenation of various lignin-derived model compounds and their mixture was studied over platinum catalysts based on porous aromatic frameworks (PAF) doped with Bronsted acid sites. Two catalysts, Pt-PAF-30-SO3H(5.0) and Pt-PAF-30-SO3H(7.5), with almost identical platinum loading of 5.1 and 4.7 wt. % but different sulfur concentration of 4.46 and 6.96 wt. % respectively, were synthesized using a method that allows to obtain highly uniform distribution of nanoparticles in the porous structure of the support. The HDO reaction pathways of lignin substrates with different structures were discussed, and the influence of the platinum source chosen for the synthesis of the catalyst on the activity and the selectivity of the latter was studied. We have demonstrated high stability of catalyst (for at least a 7 h reaction) and have confirmed the great activity of the Pt-PAF-30-SO3H(7.5) catalyst in HDO reactions with a large number of lignin bio-oil model compounds. Pt-PAF-30-SO3H(7.5) was also tested in the hydrodeoxygenation of a model mixture of aromatic substrates taken in proportions contained in lignin bio-oil. The selectivity to naphthenic hydrocarbons—products of the complete hydrodeoxygenation—was 54%. [ABSTRACT FROM AUTHOR]

The generation of antibody-drug conjugates with optimal functionality depends on many parameters. These include binder epitope, antibody format, linker composition, conjugation site(s), drug-to-antibody ratio, and conjugation method. The production of matrices that cover all possible parameters is a major challenge in identifying optimal antibody-drug conjugates. To address this bottleneck, we adapted our Format Chain Exchange technology (FORCE), originally established for bispecific antibodies, toward the generation of binder-format-payload matrices (pair-FORCE). Antibody derivatives with exchange-enabled Fc-heterodimers are combined with payload-conjugated Fc donors, and subsequent chain-exchange transfers payloads to antibody derivatives in different formats. The resulting binder-format-conjugate matrices can be generated with cytotoxic payloads, dyes, haptens, and large molecules, resulting in versatile tools for ADC screening campaigns. We show the relevance of pair-FORCE for identifying optimal HER2-targeting antibody-drug conjugates. Analysis of this matrix reveals that the notion of format-defines-function applies not only to bispecific antibodies, but also to antibody-drug conjugates. Designing optimal antibody-drug conjugates (ADCs) involves screening many complex parameters. Here, the authors present a payload-coupled chain-exchange technology for efficient ADC matrix production, demonstrating its power in designing ADCs targeting HER2. [ABSTRACT FROM AUTHOR]

To investigate the combustion characteristics of non-stick coal and the formation patterns of main combustion products and radicals, this study initially conducted a TG-DSC experiment of non-stick coal. Subsequently, non-stick coal was characterized and analyzed using XPS and 13C NMR experiments, and a molecular model with the formula C208H199O23N3S was constructed. Subsequently, the ReaxFF MD method was employed to simulate the combustion molecular dynamics of the non-stick coal periodic mixing model under varying oxygen content and temperature conditions. The analysis focused on elucidating the formation patterns of free and primary products during combustion. Experimental findings indicate that the combustion of non-stick coal progresses through stages, including water evaporation and desorption (30–145.65 °C), dynamic equilibrium (145.65–181.76 °C), oxygen weight gain (181.76–263.56 °C), thermal decomposition (263.56–379.14 °C), combustion (379.14–562.04 °C) and burnout (562.04–800 °C). The ignition temperature of non-stick coal was determined to be 379.14 °C. The simulation results indicate that an increase in temperature and oxygen content further enhances the relatively stable peak production of CO2. The peak increase of CO production is weaker than that of CO2. The rise in temperature also promotes the formation of H2O, while the increase in oxygen content initially enhances and subsequently inhibits the peak production of H2O. H radicals, O radicals, and OH radicals are primarily generated through the decomposition reactions of small molecular compounds or other free radicals. Free radical polymerization and the decomposition of small molecular compounds represent the primary pathways for the formation of CO, CO2, and H2O. [ABSTRACT FROM AUTHOR]

The difficulty of designing simple autocatalysts that grow exponentially in the absence of enzymes, external drives or ingenious internal mechanisms severely constrains scenarios for the emergence of evolution by natural selection in chemical and physical systems. Here, we systematically analyze these difficulties in the simplest and most generic autocatalyst: a dimeric molecule that duplicates by templated ligation. We show that despite its simplicity, such an autocatalyst can achieve exponential growth autonomously. We also show, however, that it is possible to design as simple sub-exponential autocatalysts that have an advantage over exponential autocatalysts when competing for a common resource. We reach these conclusions by developing a theoretical framework based on kinetic barrier diagrams. Besides challenging commonly accepted assumptions in the field of the origin of life, our results provide a blueprint for the experimental realization of elementary autocatalysts exhibiting a form of natural selection, whether on a molecular or colloidal scale. Autocatalysis plays an important role in the origin of life and molecular evolution, however, designing simple autocatalysts that grow exponentially remains challenging. Here, the authors computationally design simple autocatalysts-- dimeric molecules that duplicate by templated ligation, --and show that these autocatalysts can achieve exponential growth autonomously. [ABSTRACT FROM AUTHOR]

Achieving precise and controllable hierarchical self-assembly of functional nanoclusters within crystal lattices to create distinct architectures is of immense significance, yet it creates considerable challenges. Here we successfully synthesized a silver nanowheel Ag40, along with its optically pure enantiomers S-/R-Ag40. Each species possesses an internal nanospace and exhibits host-guest interactions. These structures are constructed from primary building blocks (Ag9). By manipulating the surface anions and guest molecules, the nanowheels function as secondary building blocks, spontaneously organizing into complex double- and triple-helical crystalline superstructures or one-dimensional chains {Ag41}n through conformational matching and diverse noncovalent interactions. Moreover, we demonstrate that the water-mediated complex specifically assembled with uridine monophosphate nucleotides, resulting in chiral assemblies of Ag40 that exhibit chiroptical activity for specific recognition. Our findings provide insights into the efficient construction of assemblies with hollow frameworks and propose strategies for superstructure engineering by manipulating surface motifs. Achieving precise and controllable hierarchical self-assembly of nanoclusters within crystal lattices is of immense significance yet remains challenging. Here the authors synthesize a silver nanowheel Ag40, along with its optically pure enantiomers S-/R-Ag40, and characterize its self-assembly into double- and triple-helical superstructures. [ABSTRACT FROM AUTHOR]

Despite recent dramatic improvements in the electronic characteristics of stretchable organic field-effect transistors (FETs), their low operational stability remains a bottleneck for their use in practical applications. Here, the operational stability, especially the bias-stress stability, of semiconducting polymer-based FETs under various tensile strains is investigated. Analyses on the structure of stretched semiconducting polymer films and spectroscopic quantification of trapped charges within them reveal the major cause of the strain-dependent bias-stress instability of the FETs. Devices with larger strains exhibit lower stability than those with smaller strains because of the increased water content, which is accompanied by the formation of cracks and nanoscale cavities in the semiconducting polymer film as results of the applied strain. The strain-dependence of bias-stress stability of stretchable OFETs can be eliminated by passivating the devices to avoid penetration of water molecules. This work provides new insights for the development of bias-stable stretchable OFETs. [ABSTRACT FROM AUTHOR]

The exploration of artificial metal-peptide assemblies (MPAs) is one of the most exciting fields because of their great potential for simulating the dynamics and functionality of natural proteins. However, unfavorable enthalpy changes make forming discrete complexes with large and adaptable cavities from flexible peptide ligands challenging. Here, we present a strategy integrating metal-cluster building blocks and peptides to create chiral metal-peptide assemblies and get a family of enantiopure [R-/S-Ni3L2]n (n = 2, 3, 6) MPAs, including the R-/S-Ni6L4 capsule, the S-Ni9L6 trigonal prism, and the R-/S-Ni18L12 octahedron cage. X-ray crystallography shows MPA formation reactions are highly solvent-condition-dependent, resulting in significant changes in ligand conformation and discrete cavity sizes. Moreover, we demonstrate that a structure transformation from Ni18L12 to Ni9L6 in the presence of benzopyrone molecules depends on the peptide conformational selection in crystallization. This work reveals that a metal-cluster building block approach enables facile bottom-up construction of artificial metal-peptide assemblies. The use of metal clusters to construct artificial protein-mimic structures with adaptable cavities has potential for simulating the dynamics and functionality of natural proteins. Here, the authors develop a family of chiral metal-peptide assemblies using {Ni3} clusters and flexible peptides, resulting in structures such as octahedral cages, trigonal prisms, and capsules. [ABSTRACT FROM AUTHOR]

Interpreting Raman and IR vibrational spectra in complex organic molecules lacking symmetries poses a formidable challenge. In this study, we propose an innovative approach for simulating vibrational spectra and attributing observed peaks to molecular motions, even when highly anharmonic, without the need for computationally expensive ab initio calculations. Our approach stems from the time-dependent stochastic self-consistent harmonic approximation to capture quantum nuclear fluctuations in atom dynamics while describing interatomic interaction through state-of-the-art reactive machine-learning force fields. Finally, we employ an isotropic charge model and a bond capacitor model trained on ab initio data to predict the intensity of IR and Raman signals. [ABSTRACT FROM AUTHOR]

This study explores open-shell biradical and polyradical molecular compounds based on extended multireference (MR) methods (MR-configuration interaction with singles and doubles (CISD) and MR-averaged quadratic coupled cluster (AQCC) approach) using the numbers of unpaired densities NU. These results were used to guide the analysis of the fractional occupation number weighted density (FOD) calculated within the finite temperature (FT) density functional theory (DFT) approach. As critical test examples, the dissociation of carbon–carbon (CC) single, double and triple bonds and a benchmark set of polycyclic aromatic hydrocarbons (PAHs) have been chosen. By examining single, double, and triple bond dissociations, we demonstrate the utility and accuracy but also limitations of the FOD analysis for describing these dissociation processes. In significant extension of previous work (Phys Chem Chem Phys 25: 27380–27393), the assessment of FOD applications for different classes of DFT functionals was performed examining the range-separated functionals ωB97XD, ωB97M-V, CAM-B3LYP, LC-ωPBE, and MN12-SX, the hybrid (M06-2X) functional and the double hybrid (B2P-LYP) functional. In all cases, strong correlations between NFOD and NU values are found. The major task was to develop a new linear regression formula for range-separated functionals allowing a convenient determination of the optimal electronic temperature Tel for the FT-DFT calculation. We also established an optimal temperature for the semiempirical extended tight-binding GFN2-xTB method. These findings significantly broaden the applicability of FOD analysis across various DFT functionals and semiempirical methods. [ABSTRACT FROM AUTHOR]

Context: Electrophilicity and nucleophilicity are two vastly important chemical concepts gauging the capability of atoms in molecules to accept and donate the maximal number of electrons. In our earlier studies, we proposed to simultaneously quantify them using the Kullback–Leibler divergence from the information-theoretic approach in density functional theory. However, several issues with this scheme remain to be clarified such as its general validity, predictability, and relationship with other information-theoretic quantities. In this work, we revisit the matter with bigger datasets and deeper theoretical insights. Five information-theoretic quantities including Kullback–Leibler divergence, Hirshfeld charge, Ghost-Berkowitz-Parr entropy, and second and third orders of relative Onicescu information energy are found to be reliable and robust descriptors of electrophilicity and nucleophilicity propensities. Employing these five descriptors, we design a list of new compounds and predict their electrophilicity and nucleophilicity scales. This work should markedly improve our confidence and capability in applying information-theoretic quantities to evaluate electrophilicity and nucleophilicity propensities and henceforth pave the route for more applications of these quantities from information-theoretic approach in density functional theory in the future. Methods: All structures were fully optimized at the M06-2X/6–311 + G(d) level of DFT functional using the Gaussian 16 package (version C01) with integration grids and tight self-consistent-field convergence. The solvent effect was taken into account by using the implicit solvent model (CPCM) in the CH2Cl2 solvent, and all 3D contour surfaces of Fukui function, local temperature, and ITA (information-theoretic approach) quantities were generated by GaussView. The Multiwfn 3.8 program was used to calculate the ITA indexes and atomic charges. [ABSTRACT FROM AUTHOR]

The biochemical composition of sapropels from Maloe Simaginskoe Lake, which were studied using modern methods of atomic emission spectrometry (ICP-AE) and high-performance liquid chromatography (HPLC), is presented. This source of organic matter is now the least in demand in the country; many deposits have been abandoned. However, such organic colloids and their humic extracts are not inferior in their spectrum of action to extracts from peat or coal. The predominance of the proportion of potassium and sodium in the gross composition of macroelements is shown. Among microelements, both in bulk and in mobile forms, iron and manganese ions dominated. Apparently, the Fe cation is bound in polyligand forms with carboxylic acids. The ratio of these two elements in the samples averaged 10 : 1—Fe : Mn. The content of heavy metals was within the limits acceptable for sapropel fertilizers according to GOST R 54000-2010. The results obtained during this study can be used further in the biotechnology of intensive crop production, when growing crops in a hydroponic environment while minimizing the use of synthetic fertilizers. In addition, since carbohydrates were found in all samples of the raw materials studied, the extracts can be used to develop modified nutrient media in agricultural microbiology to stabilize the titer of beneficial rhizobacteria. [ABSTRACT FROM AUTHOR]

The water content and solvent structure of six poly(vinyl alcohol)-based hydrogels possessing different proportions of borax have been studied as a function of cross-linker concentration. The differing proportions of borax in the matrices cause mesoscopic structural changes in the polymer hydrogel network which lead to differences in water content. The FTIR data show non-monotonic variations in the intensities of the various bands. For the Raman data, the region sensitive to the OH vibrations has been fitted with four Gaussian peaks corresponding to the stretching modes of liquid water. The wavenumber positions of the peaks are dependent on the water content. The non-monotonic behavior of the peak parameters suggests that the structure of entrapped water molecules is dependent on the local mesoscopic structure. From the measurements, cross-linker proportions are seen to affect, both, polymer network and the solvent. [ABSTRACT FROM AUTHOR]

This work is based on the productive idea of Mulliken about the alignment of electronegativities of atoms in the process of bond formation to their geometric mean value. The paper considers in detail the case of a binary molecule and obtains formulas for the dependence of the current values of the electronegativities of the two atoms forming the molecule on time, and finds a mathematical connection between the current and initial values of electronegativities. Also, in the work the theorem on the relation between the rates of alignment of electronegativities of atoms entering into chemical bonding is formulated and proved, and a special case of this theorem is considered. [ABSTRACT FROM AUTHOR]

Deep learning-based generative modeling demonstrates proven advantages as an effective approach in molecular discovery. This study introduces a generative-network based method called Inhibitor_Mol_VAE, which uses a variational autoencoder model to generate corrosion inhibitor molecules with targeted inhibition efficiency. We first evaluate the model's ability to reconstruct molecules. Then, we assess the model's ability to generate new inhibitor molecules using physiochemical properties (including MolWt, LogP, Vdw_volume, and Electronegativity). New molecules with high inhibition efficiencies at low concentrations, such as [ethoxy(methoxy)phosphoryl]-phenylmethanol and (alpha-methylamino-benzyl)-phosphonsaeure-monoaethylester are successfully discovered. [ABSTRACT FROM AUTHOR]

Purpose: The bioefficacy of accessible chemical structures has been established, leading to some optimization to address these constraints. Methods: This study aimed to identify the computational interactions of molecular docking, molecular simulations, and Network Pharmacology, which were used to predict the anticancer efficacy of Capsaicin and Quinidine complexes. Results: The molecular docking studies exhibited that the identified phytocompounds had excellent binding energy against all of these target receptors, with 6OP9 affinity binding energy at -8.62 kcal/mol and for 3VHE at -8.18 kcal/mol. Molecular dynamics simulation for 100 ns of MD studies with RMSD and RMSF plots indicated that the ligand is stable, as it establishes interactions within active site residues such as LYS868, ASP1046, PHE1447, LEU14035, and LEU1099, followed by hydrogen bond interactions with THR768, ASP833, PHE834, LEU726, SER728, VAL734, and MET801. Based on human intestinal absorption, bioavailability score, P-glycoprotein, and BBB penetrant data, these are positive physiochemical inhibitors of Capsaicin and Quinidine. In network pharmacology, the essential genes identified were associated with cancer development (HER-2, HER-3), not specific to those that play a role in the carcinogenesis of the breast as well discussed by the targeted therapy trials mentioned above, that is, VEGFR1-3 and FGFR2 versus identification of progression-related genes involved, including ESR1, SRC, and HSP. Conclusions: In vivo studies are needed to confirm the anticancer action of cancer regulatory proteins, and clinical trials are required to prove its safety and efficacy in other human subjects. [ABSTRACT FROM AUTHOR]

Microwave energy will soon be used in material manufacturing. Microwaves may generate a lot of heat. Through many interactions, microwaves convey electromagnetic energy straight to the material's molecules. Direct molecular interaction transfers electromagnetic energy to materials. Microwaves are increasingly used to process materials because to their time and energy economy, shorter process cycle times, enhanced mechanical characteristics, and reduced environmental dangers. Microwave processing transfers linearly equal electric and magnetic energy between molecules. Microwaves can process several materials. Metal matrix composites, fibre reinforced polymers (FRP), alloys, ceramics, metals, pulverised metallurgy, metal coatings, and metal cladding are examples. This article lists basic microwave properties and briefly discusses the mechanisms that regulate microwave-material interactions. The essay also discusses the systems that control interactions. Microwave heating basics have been covered here. Cladding, coating, and glazing are the main uses of microwave energy in surface engineering. [ABSTRACT FROM AUTHOR]

The composition of aerosols influenced by regional pollution sources during a post-monsoon haze event was studied including the isotopic, bulk, and molecular signatures. The air mass back trajectory and fire spot analysis revealed that the Delhi aerosols were influenced by the regional post-harvest crop (rice plant) residue-burning activities during the sampling period. To better understand the atmospheric processes during such an event, three samples of 4 h duration each (Period I: from 06:00–10:00, Period II: 10:00–14:00, and Period III: 14:00–18:00 h local time) were collected during the sampling period (8th -17th November, 2019) in the daytime. The average mass concentration of PM2.5, molecular compounds including the inorganic and carbonaceous components (dicarboxylic acid class compounds), along with the stable isotopes of C and N were observed to be elevated during Period I of the study. NH4+ and SO42− were found to be the most abundant inorganic ions during Period II and III with Cl− being the dominant ion during Period I. The OC/EC, WSOC/EC ratios indicated the influence of biomass burning on Delhi aerosols with little influence of local ageing processes evident from the minimal variation observed between the three periods of study during the day. High concentrations of dicarboxylic acids than previous studies are reported with oxalic and succinic acid being the most abundant diacids, a typical behaviour observed in biomass-burning influenced aerosols with an interesting observation of terephthalic acid to be found in an appreciable amount. The δ15 N of TN and δ13 C of TC signatures clearly indicated the influence of emissions from the burning of a C3 plant on the aerosols. The results strongly suggested that the aerosols were influenced by biomass-burning activities in the neighbouring regions and were aged during the atmospheric transport over to the city of Delhi with minimal effect of local ageing processes during the study period. [ABSTRACT FROM AUTHOR]

This work proposes a novel approach for drug molecule design using data-assisted techniques. This approach leverages a generation-based framework to expedite the drug discovery process, aiming to identify candidate molecules suitable for production while minimizing development timelines and regulatory hurdles. The core of the proposed method is a conditional variational autoencoder (CVAE) for molecule generation, employing NCSMILES string representation. The framework involves three key stages: (1) molecule generation using the CVAE, (2) filtering based on a scoring function, and (3) identification of the optimal molecule from the generated pool. To enhance the latent space representation, we incorporate molecule properties alongside conditional selection criteria. The performance of the proposed scheme is comprehensively evaluated on standard benchmark datasets using various metrics, including validity, diversity, usefulness, and novelty. The method demonstrates superior performance compared to existing state-of-the-art approaches, attributable to several key improvements, including intermediary optimizations and condition-based selection. [ABSTRACT FROM AUTHOR]

Actin filament turnover involves subunits binding to and dissociating from the filament ends, with the pointed end being the primary site of filament disassembly. Several molecules modulate filament turnover, but the underlying mechanisms remain incompletely understood. Here, we present three cryo-EM structures of the F-actin pointed end in the presence and absence of phalloidin or DNase I. The two terminal subunits at the undecorated pointed end adopt a twisted conformation. Phalloidin can still bind and bridge these subunits, inducing a conformational shift to a flattened, F-actin-like state. This explains how phalloidin prevents depolymerization at the pointed end. Interestingly, two DNase I molecules simultaneously bind to the phalloidin-stabilized pointed end. In the absence of phalloidin, DNase I binding would disrupt the terminal actin subunit packing, resulting in filament disassembly. Our findings uncover molecular principles of pointed end regulation and provide structural insights into the kinetic asymmetry between the actin filament ends. The pointed end of actin filaments is a dominant site of actin depolymerization. Here, the authors show how the actin modulators phalloidin and DNase I interact with the pointed end to either stabilize its arrangement or to promote its disassembly. [ABSTRACT FROM AUTHOR]

1,4-Naphthoquinone scaffold-derived compounds has shown considerable pharmacological properties against cancer, including acute myeloid leukemia (AML) However, its impact and mechanisms in AML are uncertain. In this study, the mechanisms of 1,4-naphthoquinone scaffold-derived compounds against AML were investigated via network pharmacology, molecular docking and molecular dynamics simulation. ASINEX database was used to collect the 1,4-naphthoquinone scaffold-derived compounds, and compounds were extracted from the software to evaluate their drug similarity and toxicity. The potential targets of compounds were retrieved from the SwissTargetPrediction Database and the Similarity Ensemble Approach Database, while the potential targets of AML were obtained from the GeneCards databases and Gene Expression Omnibus. The STRING database was used to construct a protein–protein interaction (PPI) network, topologically and Cyto Hubb plugin of Cytoscape screen the central targets. After selecting the potential key targets, the gene ontology (GO) function annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were performed for the intersection targets, and a network map of "compounds-potential targets-pathway-disease" were constructed. Molecular docking of the compounds with the core target was performed, and core target with the strongest binding force and 1,4-naphthoquinone scaffold-derived compounds was selected for further molecular dynamics simulation and further molecular mechanics/Poisson–Boltzmann surface area (MM/PBSA) approach verification. In addition, the Bloodspot database was applied to perform the overall survival of core targets. A total of 19 1,4-naphthoquinone scaffold-derived compounds were chosen out, and then 836 targets of compounds, 96 intersection targets of AML were screened. Core targets include STAT3, TLR4, HSP90AA1, JUN, MMP9, PTPRC, JAK2, PTGS2, KIT and CSF1R. GO functional enrichment analysis revealed that 90 biological processes, 10 cell components and 12 molecular functions were enriched while KEGG pathway enrichment analysis revealed 34 enriched signaling pathways. Analysis of KEGG enrichment hinted that these 10 core genes were located in the pathways in cancer, suggesting that 1,4-naphthoquinone scaffold-derived compounds had potential activity against AML. Molecular docking analysis revealed that the binding energies between 1,4-naphthoquinone scaffold-derived compounds and the core proteins were all higher than − 6 kcal/mol, indicating that the 10 core targets all had strong binding ability with compounds. Moreover, a good binding capacity was inferred from molecular dynamics simulations between compound 7 and MMP9. The total binding free energy calculated using the MM/GBSA approach revealed values of − 6356.865 kcal/mol for the MMP9-7 complex. In addition, Bloodspot database results exhibited that HSP90AA1, MMP9 and PTPRC were associated with overall survival. The findings provide foundations for future studies into the interaction underlying the anti-AML potential of compounds with 1,4-naphthoquinone-based scaffold structures. Compounds with 1,4-naphthoquinone-based scaffold structures exhibits considerable potential in mitigating and treating AML through multiple targets and pathways. [ABSTRACT FROM AUTHOR]

Trifluoromethyl arenes (Ar–CF3) are amongst the commonly encountered fluorinated substructures in pharmaceutical, agrochemical, and material sciences. However, predominant methods to access Ar–CF3 possess several limitations, including harsh conditions, lack of availability of substrates, and poor regioselectivity, which combined restrict access to desirable highly functionalized Ar–CF3-containing compounds. To expand the scope of accessible Ar–CF3-based molecules, we present an orthogonal deoxyfluoroalkylation/aromatization approach that exploits readily accessible and programable cyclohexan(en)one substrates, which undergo a reliable 1,2-addition reaction with the Ruppert-Prakash reagent (TMSCF3) followed by aromatization to deliver highly functionalized Ar–CF3 compounds in a one/two-pot sequence. This general strategy enables access to highly substituted Ar–CF3-containing molecules that are difficult, expensive, and/or impossible to access by current synthetic methods. Trifluoromethyl arenes are amongst the commonly encountered fluorinated substructures in pharmaceutical, agrochemical, and material sciences. Herein, the authors report a deoxytrifluoromethylation/aromatization strategy that converts readily available cyclohexan(en)one substrate into highly substituted trifluoromethyl arenes. [ABSTRACT FROM AUTHOR]

Huanglongbing (HLB), associated with the psyllid-vectored phloem-limited bacterium, Candidatus Liberibacter asiaticus (CLas), is a disease threat to all citrus production worldwide. Currently, there are no sustainable curative or prophylactic treatments available. In this study, we utilized mass spectrometry (MS)-based metabolomics in combination with 3D molecular mapping to visualize complex chemistries within plant tissues to explore how these chemistries change in vivo in HLB-infected trees. We demonstrate how spatial information from molecular maps of branches and single leaves yields insight into the biology not accessible otherwise. In particular, we found evidence that flavonoid biosynthesis is disrupted in HLB-infected trees, and an increase in the polyamine, feruloylputrescine, is highly correlated with an increase in disease severity. Based on mechanistic details revealed by these molecular maps, followed by metabolic modeling, we formulated and tested the hypothesis that CLas infection either directly or indirectly converts the precursor compound, ferulic acid, to feruloylputrescine to suppress the antimicrobial effects of ferulic acid and biosynthetically downstream flavonoids. Using in vitro bioassays, we demonstrated that ferulic acid and bioflavonoids are indeed highly bactericidal to CLas, with the activity on par with a reference antibiotic, oxytetracycline, recently approved for HLB management. We propose these compounds should be evaluated as therapeutics alternatives to the antibiotics for HLB treatment. Overall, the utilized 3D metabolic mapping approach provides a promising methodological framework to identify pathogen-specific inhibitory compounds in planta for potential prophylactic or therapeutic applications. [ABSTRACT FROM AUTHOR]

Target-aware drug discovery has greatly accelerated the drug discovery process to design small-molecule ligands with high binding affinity to disease-related protein targets. Conditioned on targeted proteins, previous works utilize various kinds of deep generative models and have shown great potential in generating molecules with strong protein-ligand binding interactions. However, beyond binding affinity, effective drug molecules must manifest other essential properties such as high drug-likeness, which are not explicitly addressed by current target-aware generative methods. In this article, aiming to bridge the gap of multi-objective target-aware molecule generation in the field of deep learning-based drug discovery, we propose ParetoDrug, a Pareto Monte Carlo Tree Search (MCTS) generation algorithm. ParetoDrug searches molecules on the Pareto Front in chemical space using MCTS to enable synchronous optimization of multiple properties. Specifically, ParetoDrug utilizes pretrained atom-by-atom autoregressive generative models for the exploration guidance to desired molecules during MCTS searching. Besides, when selecting the next atom symbol, a scheme named ParetoPUCT is proposed to balance exploration and exploitation. Benchmark experiments and case studies demonstrate that ParetoDrug is highly effective in traversing the large and complex chemical space to discover novel compounds with satisfactory binding affinities and drug-like properties for various multi-objective target-aware drug discovery tasks. A multi-objective molecule generation method with Pareto MCTS as searching procedure in chemical space and the pretrained molecule generative model conditioned on protein targets as searching guidance to synchronously optimize multiple properties. [ABSTRACT FROM AUTHOR]

Despite recent advancement in 3D molecule conformation generation driven by diffusion models, its high computational cost in iterative diffusion/denoising process limits its application. Here, an equivariant consistency model (EC-Conf) was proposed as a fast diffusion method for low-energy conformation generation. In EC-Conf, a modified SE (3)-equivariant transformer model was directly used to encode the Cartesian molecular conformations and a highly efficient consistency diffusion process was carried out to generate molecular conformations. It was demonstrated that, with only one sampling step, it can already achieve comparable quality to other diffusion-based models running with thousands denoising steps. Its performance can be further improved with a few more sampling iterations. The performance of EC-Conf is evaluated on both GEOM-QM9 and GEOM-Drugs sets. Our results demonstrate that the efficiency of EC-Conf for learning the distribution of low energy molecular conformation is at least two magnitudes higher than current SOTA diffusion models and could potentially become a useful tool for conformation generation and sampling. Scientific Contributions: In this work, we proposed an equivariant consistency model that significantly improves the efficiency of conformation generation in diffusion-based models while maintaining high structural quality. This method serves as a general framework and can be further extended to more complex structure generation and prediction tasks, including those involving proteins, in future steps. Key points: A novel ultra-fast equivariant diffusion model, EC-Conf, was proposed for low-energy conformation generation by construction of a consistency process. Compared with other SOTA diffusion models running with thousands denoising steps, EC-Conf can achieve comparable quality with only one sampling step and keep improving with a few more sampling iterations. The efficiency of EC-Conf is at least two magnitudes higher than current SOTA diffusion models. The EC-Conf is universal and can be easily extended to various conformation generation tasks such as protein–ligand docking pose. [ABSTRACT FROM AUTHOR]

The rest wavelengths of diffuse interstellar bands (DIBs) are parameters of fundamental importance owing to the lack of unambiguous identification for these mysterious features. Usually the wavelengths of DIBs are estimated using known interstellar atomic or molecular lines serving to shift the spectrum to the rest wavelengths velocity scale. However, the latter may not share in fact the same parts of a cloud which the carriers of diffuse bands occupy. Here we argue that the narrowest known diffuse interstellar band 6196 Å is the best reference feature for building the "interstellar" wavelength scale. Also, we offer the geometrical center of gravity (the effective wavelength) of the diffuse bands as a rest wavelength measurer for these generally asymmetric features. We assessed the magnitude of variation of the center of gravity of the diffuse bands at 5780, 5797, 6284, and 7224 Å measured in 41 lines of sight in the broad range of interstellar reddening ( varies in the range of 0.13–1.06 stellar magnitudes) with the lack of evident Doppler-split in the profiles of interstellar atomic/molecular lines. We demonstrated that diffuse bands show gradual broadening of their profile widths, accompanied with the red-shift of the center of gravity, i.e., the red wing of the profiles is their most variable part. To estimate the width of the diffuse bands, we offer to apply an "effective width" parameter , which is the relation of the equivalent width () to the depth of the feature. In contrast to habitual half width at half maximum (), the parameter originally introduced by us in 2008 (Galazutdinov, LoCurto, Krełowski) is not sensitive to the profile shape irregularities. provides lower measurement uncertainties than the does. The gradual increase of , accompanied with the red-shift of the center of gravity of the profile, may suggest populating of higher transitions of the P branch of the bands of molecules, assuming the latter are DIB carriers. It is also shown, that diffuse bands are broader although more shallow in the harsh conditions of -clouds, where atomic and molecular lines are weakened and/or totally absent. The difference of the effective width of DIBs in and clouds is discussed as well. [ABSTRACT FROM AUTHOR]

By reacting antimony(III) chloride with 2,4,6-trimethoxyphenyllithium, tris(2,4,6-trimethoxyphenyl)stibine was synthesized and the possibility of its alkylation with ethyliodoacetate was established to form {[2,4,6-(MeO)3C6H2]3SbCH2C(O)OEt}I·СHCl3·0.5Н2О. Antimony atoms in the Ar3Sb molecule and the [Ar3SbCH2C(O)OEt]+ cation, Ar = 2,4,6-(MeO)3C6H2 have distorted trigonal pyramidal [angle CSbC 101.97(4)°] and tetrahedral [angle CSbC 104.5(2)°‒113.6(2)°] coordination, respectively. The structure of the stibonium complex is additionally stabilized by intermolecular interactions, which involve water molecules (I···H 2.815, 2.881 Å) and chloroform C=O···Cl (3.141 Å), I···H (2.793 Å). [ABSTRACT FROM AUTHOR]

The literature data on the use of triphenylphosphine in determining peroxide functional groups and individual oxygen-containing compounds in the composition of the products of oxidation reactions of organic compounds with molecular oxygen and/or peroxides were systematized and discussed. The causes and mechanisms of distortions of the results of analytical determinations were identified and possible ways of their elimination were suggested. [ABSTRACT FROM AUTHOR]

Because the dust from beryllium (Be) is toxic to humans, ITER has recently decided to replace the first wall material of Be with tungsten boron (B). While there has been much discussion on the electron-impact ionization (EII) cross sections for Be-containing molecules, EII data is still limited for fusion-relevant molecules containing B. We report the EII cross section for the fusion-relevant diatomic molecules BH, BN, BO, BF3 and WB using the Binary-Encounter-Bethe (BEB) and Deutsch-Märk (DM) methods and evaluate its accuracy based on the available experimental data. The errors between our BEB and DM cross sections for these molecules are within 23%. [ABSTRACT FROM AUTHOR]

Accurate collisional rate coefficients for collisional transitions between 15 rotational levels of methanimine, colliding with p-H2 molecule, are available. Methanimine is a planar, asymmetric top molecule having electric dipole moment with components μa = 1.3396 Debye and μb = 1.4461 Debye, and thus, producing both the a and b type spectral lines of nearly equal intensities. Therefore, all the rotational levels need to be considered together. Between 15 rotational levels, 105 collisional transitions are considered in an investigation by others. We have discussed that each level is not connected with all others through the collisions, and therefore, there should be 77 instead of 105 collisional transitions between 15 levels of methanimine. With availability of accurate collisional rate coefficients, it is worth to perform the Sobolev analysis of methanimine. We have found six weak MASER transitions, 110-111, 211-212, 312-313, 41.3-41.4, 303-212 and 40.4-31.3, and one transition 111-202, showing anomalous absorption. These seven lines may play important role for the methanimine. [ABSTRACT FROM AUTHOR]

We have approached the construction of an artificial enzyme by employing a robust protein scaffold, lactococcal multidrug resistance regulator, LmrR, providing a structured secondary and outer coordination spheres around a molecular rhodium complex, [RhI(PEt2NglyPEt2)2]−. Previously, we demonstrated a 2–3 fold increase in activity for one Rh-LmrR construct by introducing positive charge in the secondary coordination sphere. In this study, a series of variants was made through site-directed mutagenesis where the negative charge is located in the secondary sphere or outer coordination sphere, with additional variants made with increasingly negative charge in the outer coordination sphere while keeping a positive charge in the secondary sphere. Placing a negative charge in the secondary or outer coordination sphere demonstrates decreased activity by a factor of two compared to the wild-type Rh-LmrR. Interestingly, addition of positive charge in the secondary sphere, with the negatively charged outer coordination sphere restores activity. Vibrational and NMR spectroscopy suggest minimal changes to the electronic density at the rhodium center, regardless of inclusion of a negative or positive charge in the secondary sphere, suggesting another mechanism is impacting catalytic activity, explored in the discussion. [ABSTRACT FROM AUTHOR]

With the advancements in technology, several theoretical and experimental methods are being developed to simplify the study of rotational spectra. Molecule-specific computational methods are promising as they enable vast studies and provide information in limited time frames. We have developed a Python program to calculate the quantized energies and populations of rotational levels for various linear diatomic molecules. Python is a simple and productive language. As its syntaxes are easy to understand, problems like these can be solved easily using Python. This study presents the program and discusses its use in providing variations in energy and population with changing J values for different molecules. [ABSTRACT FROM AUTHOR]

In the present manuscript, the compositional study of the six types of prevailing firecrackers is performed using laser-induced breakdown spectroscopy (LIBS) that works on the principle of atomic emission spectroscopy. The results reflect that these firecrackers contain higher amounts of toxic elements like Al, Cr, Ba, P, Sr, Cu, Mg, and Fe. LIBS coupled with chemometric techniques are used to classify their categories based on their composition and behavior. LIBS coupled with chemometric method is used to explain the crackling, sparkling and toxin effects of traditional firecrackers. The presence of spectral signature of electronic bands of diatomic molecules like AlO and BaO that directly indicate the corresponding elements (Al, Ba, etc.) are present in higher amounts in the crackers. The actual concentrations of the toxins are calculated to compare with the permissible limit in the crackers using calibration-free LIBS, and results are validated with inductively coupled plasma-optical emission spectroscopy. Both are in good agreement. Fourier transform infrared spectroscopy technique confirms the presence of functional groups (NO3−, SO4−, and charcoal) responsible for the emission of noise and fumes in the crackers. The UV–Visible (UV–Vis) technique is used to identify the compounds/molecules (AlO, FeO, KNO3, charcoal, and CaO) that confirm the presence of a knocking agent. Principal component analysis is applied to discriminate the conventional firecrackers into different groups/classes based on their amounts of constituents and behavior. [ABSTRACT FROM AUTHOR]

Molecular representation learning can preserve meaningful molecular structures as embedding vectors, which is a necessary prerequisite for molecular property prediction. Yet, learning how to accurately represent molecules remains challenging. Previous approaches to learning molecular representations in an end-to-end manner potentially suffered information loss while neglecting the utilization of molecular generative representations. To obtain rich molecular feature information, the pre-training molecular representation model utilized different molecular representations to reduce information loss caused by a single molecular representation. Therefore, we provide the MVGC, a unique multi-view generative contrastive learning pre-training model. Our pre-training framework specifically acquires knowledge of three fundamental feature representations of molecules and effectively integrates them to predict molecular properties on benchmark datasets. Comprehensive experiments on seven classification tasks and three regression tasks demonstrate that our proposed MVGC model surpasses the majority of state-of-the-art approaches. Moreover, we explore the potential of the MVGC model to learn the representation of molecules with chemical significance. [ABSTRACT FROM AUTHOR]

A topological index is a numerical property of a molecular graph that explains structural features of molecules. The potential of topological indices to discriminate between distinct structures is a significant topic to investigate. In this context, the exponential degree-based indices were put forward in the literature. The present work focuses on the exponential augmented Zagreb index (EAZ), which is defined for a graph G as E A Z (G) = ∑ v i v j ∈ E (G) e d i d j d i + d j - 2 3 , where d i represents the degree of the vertex v i and E(G) denotes the edge set of G. This work characterizes the maximal unicyclic graph for EAZ in terms of graph order, which was posed as an open problem in the recent article Cruz et al. (MATCH Commun Math Comput Chem 88:481-503, 2022). [ABSTRACT FROM AUTHOR]

Due to rapid advancements in deep learning techniques, the demand for large-volume high-quality datasets grows significantly in chemical research. We developed a quantum-chemistry database that includes 443,106 small organic molecules with sizes up to 10 heavy atoms including C, N, O, and F. Ground-state geometry optimizations and frequency calculations of all compounds were performed at the B3LYP/6-31G* level with the BJD3 dispersion correction, while the excited-state single-point calculations were conducted at the ωB97X-D/6-31G* level. Totally twenty-seven molecular properties, such as geometric, thermodynamic, electronic and energetic properties, were gathered from these calculations. Meanwhile, we also established a comprehensive protocol for the construction of a high-volume quantum-chemistry dataset. Our QCDGE (Quantum Chemistry Dataset with Ground- and Excited-State Properties) dataset contains a substantial volume of data, exhibits high chemical diversity, and most importantly includes excited-state information. This dataset, along with its construction protocol, is expected to have a significant impact on the broad applications of machine learning studies across different fields of chemistry, especially in the area of excited-state research. [ABSTRACT FROM AUTHOR]

Quantitative structure–property relationships have been extensively studied in the field of predicting retention times in liquid chromatography (LC). However, making transferable predictions is inherently complex because retention times are influenced by both the structure of the molecule and the chromatographic method used. Despite decades of development and numerous published machine learning models, the practical application of predicting small molecule retention time remains limited. The resulting models are typically limited to specific chromatographic conditions and the molecules used in their training and evaluation. Here, we have developed a comprehensive dataset comprising over 10,000 experimental retention times. These times were derived from 30 different reversed-phase liquid chromatography methods and pertain to a collection of 343 small molecules representing a wide range of chemical structures. These chromatographic methods encompass common LC setups for studying the retention behavior of small molecules. They offer a wide range of examples for modeling retention time with different LC setups. [ABSTRACT FROM AUTHOR]

Over the last decade, automatic chemical design frameworks for discovering molecules with drug-like properties have significantly progressed. Among them, the variational autoencoder (VAE) is a cutting-edge approach that models the tractable latent space of the molecular space. In particular, the usage of a VAE along with a property estimator has attracted considerable interest because it enables gradient-based optimization of a given molecule. However, although successful results have been achieved experimentally, the theoretical background and prerequisites for the correct operation of this method have not yet been clarified. In view of the above, we theoretically analyze and rigorously reconstruct the entire framework. From the perspective of parameterized distribution and the information theory, we first describe how the previous model overcomes the limitations of the beta VAE in discovering molecules with the desired properties. Furthermore, we describe the prerequisites for training the above model. Next, from the log-likelihood perspective of each term, we reformulate the objectives for exploring latent space to generate drug-like molecules. The distributional constraints are defined in this study, which will break away from the invalid molecular search. We demonstrated that our model could discover a novel chemical compound for targeting BCL-2 family proteins in de novo approach. Through the theoretical analysis and practical implementation, the importance of the aforementioned prerequisites and constraints to operate the model was verified. [ABSTRACT FROM AUTHOR]

Proteins are dynamic molecules whose movements result in different conformations with different functions. Neural networks such as AlphaFold2 can predict the structure of single-chain proteins with conformations most likely to exist in the PDB. However, almost all protein structures with multiple conformations represented in the PDB have been used while training these models. Therefore, it is unclear whether alternative protein conformations can be genuinely predicted using these networks, or if they are simply reproduced from memory. Here, we train a structure prediction network, Cfold, on a conformational split of the PDB to generate alternative conformations. Cfold enables efficient exploration of the conformational landscape of monomeric protein structures. Over 50% of experimentally known nonredundant alternative protein conformations evaluated here are predicted with high accuracy (TM-score > 0.8). Proteins have diverse functions due to their dynamic conformations. Here, authors introduce Cfold, a neural network that accurately predicts alternative protein structures in over 50% of known cases, addressing poor evaluations from previous methods due to biased data splits. [ABSTRACT FROM AUTHOR]

The Bernal-Fowler ice rules stipulate that each water molecule in an ice crystal should form four hydrogen bonds. However, in extreme or constrained conditions, the arrangement of water molecules deviates from conventional ice rules, resulting in properties significantly different from bulk water. In this study, we employ machine learning-driven first-principles simulations to identify a new stabilization mechanism in nanoconfined ice phases. Instead of forming four hydrogen bonds, nanoconfined crystalline ice can form a quasi-one-dimensional hydrogen-bonded structure that exhibits only two hydrogen bonds per water molecule. These structures consist of strongly hydrogen-bonded linear chains of water molecules that zig-zag along one dimension, stabilized by van der Waals interactions that stack these chains along the other dimension. The unusual interplay of hydrogen bonding and van der Waals interactions in nanoconfined ice results in atypical proton behavior such as potential ferroelectric behavior, low dielectric response, and long-range proton dynamics. Structural rules dictate that water molecules in bulk ice form four hydrogen bonds. Here, using atomistic simulations, the authors show that nanoconfined ice breaks these rules, and adopts a quasi-one-dimensional hydrogen-bonding network instead. [ABSTRACT FROM AUTHOR]

A regular method has been proposed to quantitatively describe the fluorescence of molecules randomly moving in a nanowell. Correlation functions of the fluorescence of single molecules depending on the geometry of the nanowell and the penetration depth of an exciting field into it have been determined. The obtained results can be used to quantitatively analyze the properties of molecules and to extract information on the parameters of the nanowell. [ABSTRACT FROM AUTHOR]

High-affinity and specific agents are widely applied in various areas, including diagnostics, scientific research, and disease therapy (as drugs and drug delivery systems). It takes significant time to develop them. For this reason, development of high-affinity agents extensively utilizes computer methods at various stages for the analysis and modeling of these molecules. The review describes the main affinity and specific agents, such as monoclonal antibodies and their fragments, antibody mimetics, aptamers, and molecularly imprinted polymers. The methods of their obtaining as well as their main advantages and disadvantages are briefly described, with special attention focused on the molecular modeling methods used for their analysis and development. [ABSTRACT FROM AUTHOR]

The molecular movement in single particle tracking (SPT) experiments shows a crucial role of diffusion in many biological processes such as signaling, cellular organization, transport mechanisms, and more. The SPT analysis detects not only classical Brownian motion but diffusion with other features. These include directed diffusion and confined motion. The behavior remains a challenging problem for several reasons. Due to the action of many physical processes, random trajectories of cellular molecules are segmented in different diffusive modes. Often their study requires sophisticated algorithms for the analysis of statistical properties. In this paper we consider the segment analysis for trajectories of G proteins and coupled-receptors in living cells. Their movement is often transient and switches among free diffusion, confined diffusion, directed diffusion, and immobility. Moreover, the confined segments can have both Gaussian and non-Gaussian statistics. The types of alternation of diffusive modes along the trajectories of G proteins and coupled-receptors are analyzed. [ABSTRACT FROM AUTHOR]

We present a methodology for the synthesis of inorganic-organic Janus-type molecules based on mono-T8 and difunctionalized double-decker silsesquioxanes (DDSQs) via hydrosilylation reactions, achieving exceptionally high yields and selectivities. The synthesized compounds were extensively characterized using various spectroscopic techniques, and their sizes and spatial arrangements were predicted through molecular modelling and density functional theory (DFT) calculations. Quantum chemical calculations were employed to examine the interactions among four molecules of the synthesized compounds. These computational results allowed us to determine the propensity for molecular aggregation, identify the functional groups involved in these interactions, and understand the changes in interatomic distances during aggregation. Understanding the aggregation behaviour of silsesquioxane molecules is crucial for tailoring their properties for specific applications, such as nanocomposites, surface coatings, drug delivery systems, and catalysts. Through a combination of experimental and computational approaches, this study provides valuable insights into the design and optimization of silsesquioxane-based Janus-type molecules for enhanced performance across various fields. [ABSTRACT FROM AUTHOR]

Utilizing medicinal plants and other natural resources to prevent different types of human cancers is the prime focus of attention. Cervical cancer in women ranks as the fourth most common type of malignancy. The current study used gas chromatography-mass spectrometry (GC–MS) to identify the active phytochemical constituents from Caladium lindenii leaf extracts using ethanol (ECL) and n-hexane (HCL) solvents. Plant extracts were tested for potential cytotoxic effects on HeLa and HEK-293 T cells using the MTT (3-(4,5-Dimethylthiazol-2-yl)-2,5-Diphenyltetrazolium Bromide) and the crystal violet assays. SYBR Green-based real-time PCR was performed to assess the mRNA expression profile of the apoptosis biomarkers (BCL-2 and TP53). The molecular interaction of the compounds with the targeted proteins (TP53, BCL2, EGFR, and HER2) was determined using molecular docking. GC–MS analysis revealed a total of 93 compounds in both extracts. The ECL extract significantly reduced the proliferation of HeLa cervical cancer cells, with an IC50 value of 40 µg/mL, while HEK-293 T cells showed less effect (IC50 = 226 µg/mL). The quantitative RT-PCR gene expression analysis demonstrated the ethanol extract regulated TP53 and BCL2 mRNA expressions in treated cancer cell samples. Heptanediamide, N,Nʹ-di-benzoyloxy-(− 10.1) is the best-docked ligand with a TP53 target found in the molecular docking study, whereas EGFR/Clionasterol had the second highest binding affinity (− 9.7), followed by EGFR/Cycloeucalenol (− 9.6). It is concluded that ECL extract has promising anti-cervical cancer potential and might be valued for developing new plant-derived anticancer agents after further investigations. [ABSTRACT FROM AUTHOR]

Designing compounds with a range of desirable properties is a fundamental challenge in drug discovery. In pre-clinical early drug discovery, novel compounds are often designed based on an already existing promising starting compound through structural modifications for further property optimization. Recently, transformer-based deep learning models have been explored for the task of molecular optimization by training on pairs of similar molecules. This provides a starting point for generating similar molecules to a given input molecule, but has limited flexibility regarding user-defined property profiles. Here, we evaluate the effect of reinforcement learning on transformer-based molecular generative models. The generative model can be considered as a pre-trained model with knowledge of the chemical space close to an input compound, while reinforcement learning can be viewed as a tuning phase, steering the model towards chemical space with user-specific desirable properties. The evaluation of two distinct tasks—molecular optimization and scaffold discovery—suggest that reinforcement learning could guide the transformer-based generative model towards the generation of more compounds of interest. Additionally, the impact of pre-trained models, learning steps and learning rates are investigated. Scientific contribution Our study investigates the effect of reinforcement learning on a transformer-based generative model initially trained for generating molecules similar to starting molecules. The reinforcement learning framework is applied to facilitate multiparameter optimisation of starting molecules. This approach allows for more flexibility for optimizing user-specific property profiles and helps finding more ideas of interest. [ABSTRACT FROM AUTHOR]

Iridium(III) complexes nowadays became rising stars in various health-related applications. Thus, there is a necessity to assess cytotoxicity of the synthesized molecules against cancer/normal cell lines. In this report, we present a dataset of 2694 experimental cytotoxicity values of 803 iridium complexes against 127 different cell lines. We specify the experimental conditions and provide representation of the complexes molecules in machine-readable format. The dataset provides a starting point for exploration of new iridium-based cellular probes and opens new possibilities for predictions of toxicities and data-driven generation of new organometallic anticancer agents. [ABSTRACT FROM AUTHOR]