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Accurate rovibrational molecular models are employed to gain insight in high-resolution into the collective effects and intermolecular processes arising when molecules in the gas phase interact with a resonant infrared (IR) radiation mode. An efficient theoretical approach is detailed, and numerical results are presented for the HCl, H2O, and CH4 molecules confined in an IR cavity. It is shown that by employing a rotationally resolved model for the molecules, revealing the various cavity-mediated interactions between the field-free molecular eigenstates, it is possible to obtain a detailed understanding of the physical processes governing the energy level structure, absorption spectra, and dynamic behavior of the confined systems. Collective effects, arising due to the cavity-mediated interaction between molecules, are identified in energy level shifts, in intensity borrowing effects in the absorption spectra, and in the intermolecular energy transfer occurring during Hermitian or non-Hermitian time propagation. [ABSTRACT FROM AUTHOR]

Machine learning (ML) potentials are a powerful tool in molecular modeling, enabling ab initio accuracy for comparably small computational costs. Nevertheless, all-atom simulations employing best-performing graph neural network architectures are still too expensive for applications requiring extensive sampling, such as free energy computations. Implicit solvent models could provide the necessary speed-up due to reduced degrees of freedom and faster dynamics. Here, we introduce a Solvation Free Energy Path Reweighting (ReSolv) framework to parameterize an implicit solvent ML potential for small organic molecules that accurately predicts the hydration free energy, an essential parameter in drug design and pollutant modeling. Learning on a combination of experimental hydration free energy data and ab initio data of molecules in vacuum, ReSolv bypasses the need for intractable ab initio data of molecules in an explicit bulk solvent and does not have to resort to less accurate data-generating models. On the FreeSolv dataset, ReSolv achieves a mean absolute error close to average experimental uncertainty, significantly outperforming standard explicit solvent force fields. Compared to the explicit solvent ML potential, ReSolv offers a computational speedup of four orders of magnitude and attains closer agreement with experiments. The presented framework paves the way for deep molecular models that are more accurate yet computationally more cost-effective than classical atomistic models. [ABSTRACT FROM AUTHOR]

The quadrupole moment of a non-fullerene acceptor (NFA) generated by the constituent electron donor (D) and acceptor (A) units is a significant factor that affects the charge separation (CS) and charge recombination (CR) processes in organic photovoltaics (OPVs). However, its impact on p-type polymer domains remains unclear. In this study, we synthesized p-type molecules, namely acceptor–donor–acceptor (ADA) and donor–acceptor–donor (DAD), which are components of the benchmark PM6 polymer (D: benzodithiophene and A: dioxobenzodithiophene). Planar heterojunction films, a model of bulk heterojunction, were prepared using ADA, DAD, and PM6 as the bottom p-type layers and Y6 NFA as the top n-type layer. Flash-photolysis time-resolved microwave conductivity, femtosecond transient absorption spectroscopy, and quantum mechanical calculations were employed to probe the charge carrier dynamics. Our findings reveal that while the subtle difference in quadrupole moment and energy gradient of the p-type materials has a minimal influence on CS, the molecular type (ADA or DAD) significantly affects the bulk CR. This study expands the understanding of how the p-type component and its conformation at the p/n interface impact the CS and CR in OPVs, highlighting the critical role of molecular donors in optimizing device performance. [ABSTRACT FROM AUTHOR]

Electronic friction and Langevin dynamics is a popular mixed quantum–classical method for simulating the nonadiabatic dynamics of molecules interacting with metal surfaces, as it can be computationally more efficient than fully quantum approaches. In this work, we extend the theory of electronic friction within the hierarchical equations of motion formalism to models with a position-dependent metal–molecule coupling. We show that the addition of a position-dependent metal–molecule coupling adds new contributions to the electronic friction and other forces, which are highly relevant for many physical processes. Our expressions for the electronic forces within the Langevin equation are valid both in and out of equilibrium and for molecular models containing strong interactions. We demonstrate the approach by applying it to different models of interest. [ABSTRACT FROM AUTHOR]

Coarse-grained (CG) molecular models greatly reduce the computational cost of simulations allowing for longer and larger simulations, but come with an artificially increased acceleration of the dynamics when compared to the parent atomistic (AA) simulation. This impedes their use for the quantitative study of dynamical properties. During coarse-graining, grouping several atoms into one CG bead not only reduces the number of degrees of freedom but also reduces the roughness on the molecular surfaces, leading to the acceleration of dynamics. The RoughMob approach [M. K. Meinel and F. Müller-Plathe, J. Phys. Chem. B 126(20), 3737–3747 (2022)] quantifies this change in geometry and correlates it to the acceleration by making use of four so-called roughness volumes. This method was developed using simple one-bead CG models of a set of hydrocarbon liquids. Potentials for pure components are derived by the structure-based iterative Boltzmann inversion. In this paper, we find that, for binary mixtures of simple hydrocarbons, it is sufficient to use simple averaging rules to calculate the roughness volumes in mixtures from the roughness volumes of pure components and add a correction term quadratic in the concentration without the need to perform any calculation on AA or CG trajectories of the mixtures themselves. The acceleration factors of binary diffusion coefficients and both self-diffusion coefficients show a large dependence on the overall acceleration of the system and can be predicted a priori without the need for any AA simulations within a percentage error margin, which is comparable to routine measurement accuracies. Only if a qualitatively accurate description of the concentration dependence of the binary diffusion coefficient is desired, very few additional simulations of the pure components and the equimolar mixture are required. [ABSTRACT FROM AUTHOR]

The Zeno line is the locus of points on the temperature–density plane where the compressibility factor of the fluid is equal to one. It has been observed to be straight for a broad variety of real fluids, although the underlying reasons for this are still unclear. In this work, a detailed study of the Zeno line and its relation to the vapor–liquid coexistence curve is performed for two simple model pair-potential fluids: attractive square-well fluids with varying well-widths λ and Mie n-6 fluids with different repulsive exponents n. Interestingly, the Zeno lines of these fluids are curved, regardless of the value of λ or n. We find that for square-well fluids, λ ≈ 1.8 presents a Zeno line, which is the most linear over the largest temperature range. For Mie n-6 fluids, we find that the straightest Zeno line occurs for n between 8 and 10. Additionally, the square-well and Mie fluids with the straightest Zeno line showed the closest quantitative agreement with the vapor–liquid coexistence curve for experimental fluids that follow the principle of corresponding states (e.g., argon, xenon, krypton, methane, nitrogen, and oxygen). These results suggest that the Zeno line can provide a useful additional feature, in complement to other properties, such as the phase envelope, to evaluate molecular models. [ABSTRACT FROM AUTHOR]

The relationship between saturate, aromatic, resin, and asphaltene (SARA) contents and asphalt properties remains unclear. This study aimed to propose a high‐throughput molecular dynamics simulation framework and demonstrate its application in rapidly building asphalt molecular models of various SARA ratios and predicting their properties, using density as an example. Based on the framework, 400 models with varying SARA ratios with different aging degrees were generated to calculate their densities and used to train machine learning algorithms. The ordinary least squares model achieved R2 values exceeding 80%, and quantitative formulas linking asphalt density to SARA ratios were derived. It was found that saturate content negatively correlates with asphalt density, while resin content positively correlates with asphalt density. Additionally, asphalt density and viscosity increase with aging, influenced simultaneously by the SARA ratio and aging degree. Overall, this paper creates a rapid, high‐throughput molecular simulation pathway to predict asphalt behavior. [ABSTRACT FROM AUTHOR]

The polarizability exaltation (increase) is typical for oligomers (C60)n. It comprises the superadditive polarizability growth with n. This phenomenon was previously studied only for the molecular models of the (C60)n oligomers. In the present work, we compare the DFT-computed polarizability, obtained for molecules (C60)n and periodic model of quasi-1D covalently-bonded fullerene chains. We found that the increment of the (C60)n mean polarizability growth (95.88 Å3 for n = 6) approaches the value, obtained for the periodic model, 97.49 Å3. According to the periodic computations, polymer (C60)n should be a semiconductor with the direct band gap at the Γ point (the computed energy gap value is 1.41 eV). [ABSTRACT FROM AUTHOR]

Aspergillus fumigatus is intrinsically resistant to the widely used antifungal fluconazole, and therapeutic failure can result from acquired resistance to voriconazole, the primary treatment for invasive aspergillosis. The molecular basis of substrate specificity and innate and acquired resistance of A. fumigatus to azole drugs were addressed using crystal structures, molecular models, and expression in Saccharomyces cerevisiae of the sterol 14α-demethylase isoforms AfCYP51A and AfCYP51B targeted by azole drugs, together with their cognate reductase AfCPRA2 and AfERG6 (sterol 24-C-methyltransferase). As predicted by molecular modelling, functional expression of CYP51A and B required eburicol and not lanosterol. A crowded conformationally sensitive region involving the BC-loop, helix I, and the heme makes AfCYP51A T289 primarily responsible for resistance to fluconazole, VT-1161, and the agrochemical difenoconazole. The Y121F T289A combination was required for higher level acquired resistance to fluconazole, VT-1161, difenoconazole, and voriconazole, and confirms posaconazole, isavuconazole and possibly ravuconazole as preferred treatments for target-based azole-resistant aspergillosis due to such a combination of mutations. [ABSTRACT FROM AUTHOR]

Simple Summary: This research focuses on finding more efficient and cost-effective methods for the development of therapeutic agents to treat malignant tumors. The study explores the design of special molecules called aptamers, which can bind to cancer cells and help in diagnosis and treatment. However, creating these molecules can be expensive and technically challenging. Using computer modeling, a way was found to simplify these molecules, reducing unnecessary parts while keeping their effectiveness intact. This method not only cuts down on production costs but also improves how quickly and precisely these molecules can work within the body. This approach promises to benefit researchers in developing targeted cancer therapies, even when detailed information about the cancer targets is lacking. Background: Oncological diseases are a major focus in medicine, with millions diagnosed each year, leading researchers to seek new diagnostic and treatment methods. One promising avenue is the development of targeted therapies and rapid diagnostic tests using recognition molecules. The pharmaceutical industry is increasingly exploring nucleic acid-based therapeutics. However, producing long oligonucleotides, especially aptamers, poses significant production challenges. Objectives: This study aims to demonstrate the efficacy of using molecular modeling, supported by experimental procedures, for altering aptamer nucleotide sequences while maintaining their binding capabilities. The focus is on reducing production costs and enhancing binding dynamics by removing nonfunctional regions and minimizing nonspecific binding. Methods: A molecular modeling approach was employed to elucidate the structure of a DNA aptamer, Gli-55, facilitating the truncation of nonessential regions in the Gli-55 aptamer, which selectively binds to glioblastoma (GBM). This process aimed to produce a truncated aptamer, Gli-35, capable of forming similar structural elements to the original sequence with reduced nonspecific binding. The efficiency of the truncation was proved by flow cytometry, fluorescence polarization (FP), and confocal microscopy. Results: The molecular design indicated that the new truncated Gli-35 aptamer retained the structural integrity of Gli-55. In vitro studies showed that Gli-35 had a binding affinity comparable to the initial long aptamer while the selectivity increased. Gli-35 internalized inside the cell faster than Gli-55 and crossed the blood–brain barrier (BBB), as demonstrated in an in vitro model. Conclusions: The success of this truncation approach suggests its potential applicability in scenarios where molecular target information is limited. The study highlights a strategic and resource-efficient methodology for aptamer development. By employing molecular modeling and truncation, researchers can reduce production costs and avoid trial and error in sequence selection. This approach is promising for enhancing the efficiency of therapeutic agent development, particularly in cases lacking detailed molecular target insights. [ABSTRACT FROM AUTHOR]

Recent proposals that the substrate of memory is molecular raise questions about where this molecular model stands in relation to the dominant synaptic model of memory. In this article, we address the perceived rivalry between these models and ask whether they can be integrated. We argue that addressing rivalry or integration requires delineating the explananda of synaptic and molecular models, as well as revisiting assumptions about how these models account for their explananda. The perceived rivalry between these models exemplifies epistemic costs that arise when we try to pit explanatory models as rivals or integrate them. [ABSTRACT FROM AUTHOR]

The asphalts were aged at different ageing levels to simulate oxidative ageing evolution. The changes in the four components and chemical functional groups of the asphalts at different ageing stages were analysed, and their corresponding asphalt molecular models were established. Then, the self-healing molecular models during ageing evolution were established based on the asphalt molecular models. The self-healing characteristics of the virgin, fast-aging, slow-aging and full-aging stages were studied using the density, radial distribution function (RDF), energy, relative concentration and diffusion coefficients. The results show that the characteristic peak values of RDF curves at the same distance increase with ageing. The bond energy increases less in the fast-ageing stage but greatly in the slow-ageing stage, due to limited sulphur atoms in the asphalt molecule, while the non-bond energy increases greatly in the fast-ageing stage, and less in the slow-ageing stage, due to the stronger polarity of sulphoxide. The relative concentration of saturate changes most, and asphaltene changes less, aromatic and resins change evenly during ageing. The decrease rate of the diffusion coefficient of saturate, aromatic, resin and asphaltene is 12.6%, 11.12%, 10.42% and 9.2% in the fast-ageing stage and 0.62%, 0.60%, 0.55% and 0.62% in the slow-ageing stage. [ABSTRACT FROM AUTHOR]

Asphalt oxidation kinetic aging effect on the adhesion behavior of asphalt-aggregate interfaces at atomistic scale was investigated in this study. The asphalts were aged at different aging durations. The sulfoxide and carbonyl functional group content as well as SARA fractions of the asphalts at different aging times were studied through the tests of Fourier Transform Infrared Spectroscopy (FTIR) and Saturate, Aromatic, Resin and Asphaltene (SARA) fractions. Asphalt molecular models of virgin, rapid-rate aged, slow-rate aged and full-aged were established based on SARA fractions and FTIR results, to construct asphalt quartz and asphalt-calcite interface systems at different aging stages. The simulation results showed that the nanostructure of asphalt is influenced by the aggregate type and asphalt oxidative aging. The growth rates of adhesion energy of asphalt-quartz and asphalt-calcite systems increase rapidly first and then slowly with aging time, but the growth rates are different. Meanwhile, the Van der Waals energy is the major energy source of adhesion energy, and the contribution of van der Waals and electrostatic energy to adhesion energy is different. The aging of asphaltene and resin promotes the adhesion ability to quartz or calcite, while the aging of aromatic components is not conducive and the saturate has almost no effect. [ABSTRACT FROM AUTHOR]

In this study, we constructed molecular friction models for polytetrafluoroethylene (PTFE)/graphene composites with various defect ratios to represent their different wear stages and simulated their friction processes under different load conditions. The friction and wear mechanisms were revealed by analyzing the variations in their molecular architectures and energies during friction. The results indicate that the friction coefficient decreased with increasing load. When the load was constant, the friction coefficients of the composites containing defections were resemble, indicating that they remained unchanged in the different wear stages. The composite wear continued to increase during friction. However, the wear increment decreased gradually. In addition, by analyzing the changes in MSD, wear can be divided into initial, stable, and accelerated wear stages. Highlights: Construct PTFE/Gr molecular models to represent their different wear stages.Simulate the dynamic changes of PTFE/Gr molecular structure during friction.The influence of PTFE/Gr dynamic structural changes was revealed.Reveal the normal force variation from an energy perspective. [ABSTRACT FROM AUTHOR]

Simple Summary: In this study, we applied translational informatics for intelligent medicine of acute myeloid leukemia, a type of cancer characterized by disease relapses even after seemingly successful treatments. Treatment failure is associated, at least in part, with the fact that targeting individual proteins often promotes rewiring of relevant networks and re-organization of interactions of, among others, non-targeted proteins to eventually evade single-target therapies. To develop efficient therapies, these dynamics should be taken into account and target whole network modules instead of singleton genes in order to prevent the establishment of compensating signaling circuits. Therefore, we integrated network-based methods, structural pharmacology, and molecular modeling to establish two complementary multitargeting strategies, one in the form of repurposable drug combinations and the other as a de novo synthesized triple-targeting agent. Of note, our study exploits, for the first time, a greedy algorithm to identify optimal combinations of drugs and therapeutic protein targets. Background/Objectives: Acute myeloid leukemia (AML) is characterized by therapeutic failure and long-term risk for disease relapses. As several therapeutic targets participate in networks, they can rewire to eventually evade single-target drugs. Hence, multi-targeting approaches are considered on the expectation that interference with many different components could synergistically hinder activation of alternative pathways and demolish the network one-off, leading to complete disease remission. Methods: Herein, we established a network-based, computer-aided approach for the rational design of drug combinations and de novo agents that interact with many AML network components simultaneously. Results: A reconstructed AML network guided the selection of suitable protein hubs and corresponding multi-targeting strategies. For proteins responsive to existing drugs, a greedy algorithm identified the minimum amount of compounds targeting the maximum number of hubs. We predicted permissible combinations of amiodarone, artenimol, fostamatinib, ponatinib, procaine, and vismodegib that interfere with 3–8 hubs, and we elucidated the pharmacological mode of action of procaine on DNMT3A. For proteins that do not respond to any approved drugs, namely cyclins A1, D2, and E1, we used structure-based de novo drug design to generate a novel triple-targeting compound of the chemical formula C15H15NO5, with favorable pharmacological and drug-like properties. Conclusions: Overall, by integrating network and structural pharmacology with molecular modeling, we determined two complementary strategies with the potential to annihilate the AML network, one in the form of repurposable drug combinations and the other as a de novo synthesized triple-targeting agent. These target–drug interactions could be prioritized for preclinical and clinical testing toward precision medicine for AML. [ABSTRACT FROM AUTHOR]

There is no doubt that when solid surfaces are modified, the functional groups and atoms directly bonded to solid atoms play a major role in adsorption interactions with molecules or resins. In this study, the adhesion and dissociation between epoxy resin and molecules containing Si atoms were analyzed. The analysis, conducted in contact with the solid surface of silicon, utilized quantum chemical calculations based on a molecular model. We compared some Si-containing molecular models to test quantum chemical calculations that contribute to the study of adhesion and dissociation between epoxy resins and solid surfaces somehow other than simple potential energy curve calculations. The AFIR (artificial force induced reaction) method, implemented in the GRRM (global reaction route mapping) program, was employed to separate an epoxy resin model molecule and three types of silicon compounds (Si(CH3)2(OH)2, Si(CH3)4, and (CH3)2SiF2) in three directions, determining their minimum dissociation energy when changing the applied energy by 2.5 kJ/mol. In systems with weak hydrogen bonds, such as Si(CH3)4 or (CH3)2SiF2, the energy required for dissociation was not large; however, in systems with strong hydrogen bonds, such as Si(CH3)2(OH)2, dissociation was more difficult in the vertical direction. Although anisotropy due to hydroxyl groups was calculated in the horizontal direction, dissociation remained relatively easy. [ABSTRACT FROM AUTHOR]

Feline panleukopenia virus (FPV), a globally pervasive and highly pathogenic pathogen, has garnered significant attention recently due to the cross-species transmission of its variants. Despite the vast body of research conducted on FPV, studies exploring its evolutionary history, dynamics, and the factors driving its evolution remain scarce. The pathogenicity of strains with the prevalent mutations (A91S and I101T) in the VP2 protein has also not been fully elucidated. This study conducted a comparative analysis of FPV VP2 sequences sourced from Xinjiang province in China, other provinces in China, and other countries. It was confirmed that the evolutionary rate of FPV approached that of RNA viruses, at approximately 1.13 × 10−4 substitutions/site/year. The study reconstructed molecular models of the VP2 protein with the A91S and I101T mutations and used viral strains carrying these mutations to perform the animal regression experiment. It was confirmed that isolates with the A91S and I101T mutations could cause typical leukopenia and acute enteritis symptoms, suggesting that the mutant strains still possess certain pathogenicity. This is the first study to report on the evolutionary dynamics of FPV in Xinjiang, China, and it emphasized the importance of continuously monitoring FPV evolutionary dynamics. [ABSTRACT FROM AUTHOR]

To enhance the photoluminescence (PL) of unconventional luminescent compounds, particularly their persistent room temperature phosphorescence (p‐RTP) performance, compressing the powder into tablets has been demonstrated as a viable approach. Nevertheless, the alterations in the emission capability of PL in compacted tablets have not been comprehensively investigated. In this study, four polyacrylamide (PAM) with controllable molecular weight (MW) are fabricated from powder to tablets, and their PL emission properties are thoroughly examined and compared with corresponding powders to elucidate the emission mechanism. As MW increases, both PL and p‐RTP emissions of the tablets gradually intensify, exhibiting significant enhancement compared to the corresponding powder while retaining the characteristic blue shift. Through small angle X‐ray scattering (SAXS), construction of molecular models for tablets, detailed analysis of molecular interactions, and theoretical calculations are conducted to reasonably explain these emission phenomena using clustering‐triggered emission (CTE) and average packing density promoted emission (PDE) mechanisms. These findings not only advance the understanding of nonconventional luminogens' emission mechanisms but also offer new insights for preparing nonconventional luminescent polymers with controllable p‐RTP emission performance. [ABSTRACT FROM AUTHOR]

• NMR is the gold standard for determining the solution structures of G-quadruplexes. • Higher-order G-quadruplexes (xG4s) are often not amenable to characterization by NMR. • We show how NMR can be utilized in an integrative structural biology (ISB) approach to study xG4s. • We illustrate the ISB method and provide resources and best practices for those new to the field. Guanine-rich nucleic acids can form intramolecularly folded four-stranded structures known as G-quadruplexes (G4s). Traditionally, G4 research has focused on short, highly modified DNA or RNA sequences that form well-defined homogeneous compact structures. However, the existence of longer sequences with multiple G4 repeats, from proto-oncogene promoters to telomeres, suggests the potential for more complex higher-order structures with multiple G4 units that might offer selective drug-targeting sites for therapeutic development. These larger structures present significant challenges for structural characterization by traditional high-resolution methods like multi-dimensional NMR and X-ray crystallography due to their molecular complexity. To address this current challenge, we have developed an integrated structural biology (ISB) platform, combining experimental and computational methods to determine self-consistent molecular models of higher-order G4s (xG4s). Here we outline our ISB method using two recent examples from our lab, an extended c-Myc promoter and long human telomere G4 repeats, that highlights the utility and generality of our approach to characterizing biologically relevant xG4s. [ABSTRACT FROM AUTHOR]

Cation conducting channelrhodopsins (ChRs) are a popular tool used in optogenetics to control the activity of excitable cells and tissues using light. ChRs with altered ion selectivity are in high demand for use in different cell types and for other specialized applications. However, a detailed mechanism of ion permeation in ChRs is not fully resolved. Here, we use complementary experimental and computational methods to uncover the mechanisms of cation transport and valence selectivity through the channelrhodopsin chimera, C1C2, in the high- and low-conducting open states. Electrophysiology measurements identified a single-residue substitution within the central gate, N297D, that increased Ca2+ permeability vs. Na+ by nearly two-fold at peak current, but less so at stationary current. We then developed molecular models of dimeric wild-type C1C2 and N297D mutant channels in both open states and calculated the PMF profiles for Na+ and Ca2+ permeation through each protein using well-tempered/multiple-walker metadynamics. Results of these studies agree well with experimental measurements and demonstrate that the pore entrance on the extracellular side differs from original predictions and is actually located in a gap between helices I and II. Cation transport occurs via a relay mechanism where cations are passed between flexible carboxylate sidechains lining the full length of the pore by sidechain swinging, like a monkey swinging on vines. In the mutant channel, residue D297 enhances Ca2+ permeability by mediating the handoff between the central and cytosolic binding sites via direct coordination and sidechain swinging. We also found that altered cation binding affinities at both the extracellular entrance and central binding sites underly the distinct transport properties of the low-conducting open state. This work significantly advances our understanding of ion selectivity and permeation in cation channelrhodopsins and provides the insights needed for successful development of new ion-selective optogenetic tools. [ABSTRACT FROM AUTHOR]

Context: Milk has nutrient-rich but thermal sensitive matrix that undergoes varying degrees of Maillard reaction (MR) at heating conditions. The MR mainly occurs between lysine residues (Lys) and lactose composed of glucose (Glc) and galactose (Gal), which are abundantly sourced from dairy products. In the present study, the MRs of Glc and Gal with Lys at the initial and intermediate stages have been investigated theoretically using density functional theory (DFT) to simulate the gaseous and aqueous phases. Reaction mechanisms have been proposed, and relative energy changes of different steps were calculated according to the total mass balance. The calculations reveal that both Nα- and Nε-amine groups of Lys can react with the carbonyl functional group of Glc and Gal with the similar potential energy profiles, and Gal is more reactive than Glc. However, the barrier in Nε-channel is lower than in Nα-channel, indicating a faster reaction rate through the former channel compared with the latter. The 5-hydroxymethyl-2-furfural (HMF) and derivative are formed under 3-deoxysone route in the intermediate stage. The calculation results are helpful for proposing a reasonable MR mechanism and suggesting possible control methods of the MRs. Methods: In this study, different levels of DFT calculations have been conducted to investigate the mechanisms and favorability of generating MR products in Glc-Lys and Gal-Lys models at initial and intermediate stages in the gaseous and aqueous conditions. In order to elucidate the molecular models from the perspectives of chemistry and geometry, DFT calculations were performed by the mean of B3LYP functional at basis sets of 6–311 + + G (d, p) and 6–311 + + G (2df, 2p) with optional solvation settings. To examine the solvation effect, the study further constructed models with solvent H2O and calculated in wB97XD functional with 6–31 + G (d) basis set. All computations were carried out Gaussian 09 suite of quantum chemistry software. [ABSTRACT FROM AUTHOR]

Aromatics constitute the highest proportion among the four asphalt components. The existing literature contains over 20 types of small molecules used to characterise asphalt aromatics, which presents a challenge in constructing asphalt molecular models. In this investigation, quantum chemical calculations were employed to determine the molecular morphology, molecular polarity, and infrared spectrum indicators of asphalt aromatics. Based on principal component analysis and hierarchical clustering, statistical analysis of the aforementioned indicators was conducted to present a recommended scheme for selecting representative molecules of aromatics, thereby reducing the difficulty of constructing asphalt molecular models. The results indicated that N2 was recommended as the representative molecule for aromatics with high molecular weight and long alkane chains, while N3 was recommended as the representative molecule for aromatics with low molecular weight. Based on the variations in substituents of aromatics, it was advisable to select one or more of N7, N10, N14 – N16, and N19 as representative molecules. The investigation guided the selection of aromatic representative molecules in the construction of asphalt molecular models. For major projects with test data, representative molecules could be adjusted according to the test data. [ABSTRACT FROM AUTHOR]

Enterovirus-D68 (EV68) has emerged as a global health concern over the last decade with severe symptomatic infections resulting in long-lasting neurological deficits and death. Unfortunately, there are currently no FDA-approved antiviral drugs for EV68 or any other non-polio enterovirus. One particularly attractive class of potential drugs are small molecules inhibitors, which can target the conserved active site of EV68-3C protease. For other viral proteases, we have demonstrated that the emergence of drug resistance can be minimized by designing inhibitors that leverage the evolutionary constraints of substrate specificity. However, the structural characterization of EV68-3C protease bound to its substrates has been lacking. Here, we have determined the substrate specificity of EV68-3C protease through molecular modeling, molecular dynamics (MD) simulations, and co-crystal structures. Molecular models enabled us to successfully characterize the conserved hydrogen-bond networks between EV68-3C protease and the peptides corresponding to the viral cleavage sites. In addition, co-crystal structures we determined have revealed substrate-induced conformational changes of the protease which involved new interactions, primarily surrounding the S1 pocket. We calculated the substrate envelope, the three-dimensional consensus volume occupied by the substrates within the active site. With the elucidation of the EV68-3C protease substrate envelope, we evaluated how 3C protease inhibitors, AG7088 and SG-85, fit within the active site to predict potential resistance mutations. [ABSTRACT FROM AUTHOR]

Chirality permeates every aspect of life's beauty. To foster a proper understanding of chirality among the public, this paper employs the differences between L- and D-tartaric acid enantiomers to design experiments for popularization of science: (1) Construction of molecular models of tartaric acid enantiomers; (2) Cultivation and observation of tartaric acid crystals; (3) Observation of the optical activity of tartaric acid solutions; (4) Recognition of chirality in tartaric acid molecules. These experiments range from basic to advanced, featuring simple operation, obvious phenomena, interactive communication, and have received positive feedback in the early stages of implementation. [ABSTRACT FROM AUTHOR]

We investigate the effect of pressure on the carbon dioxide (CO2) hydrate–water interfacial free energy along its dissociation line using advanced computer simulation techniques. In previous works, we have determined the interfacial energy of the hydrate at 400 bars using the TIP4P/Ice and TraPPE molecular models for water and CO2, respectively, in combination with two different extensions of the Mold Integration technique [J. Colloid Interface Sci. 623, 354 (2022) and J. Chem. Phys. 157, 134709 (2022)]. Results obtained from computer simulation, 29(2) and 30(2) mJ/m2, are found to be in excellent agreement with the only two measurements that exist in the literature, 28(6) mJ/m2 determined by Uchida et al. [J. Phys. Chem. B 106, 8202 (2002)] and 30(3) mJ/m2 determined by Anderson et al. [J. Phys. Chem. B 107, 3507 (2002)]. Since the experiments do not allow to obtain the variation of the interfacial energy along the dissociation line of the hydrate, we extend our previous studies to quantify the effect of pressure on the interfacial energy at different pressures. Our results suggest that there exists a correlation between the interfacial free energy values and the pressure, i.e., it decreases with the pressure between 100 and 1000 bars. We expect that the combination of reliable molecular models and advanced simulation techniques could help to improve our knowledge of the thermodynamic parameters that control the interfacial free energy of hydrates from a molecular perspective. [ABSTRACT FROM AUTHOR]

A set of molecular models for the alkali nitrates (LiNO3, NaNO3, KNO3, RbNO3, and CsNO3) in aqueous solutions is presented and used for predicting the thermophysical properties of these solutions with molecular dynamics simulations. The set of models is obtained from a combination of a model for the nitrate anion from the literature with a set of models for the alkali cations developed in previous works of our group. The water model is SPC/E and the Lorentz–Berthelot combining rules are used for describing the unlike interactions. This combination is shown to yield fair predictions of thermophysical and structural properties of the studied aqueous solutions, namely the density, the water activity and the mean ionic activity coefficient, the self-diffusion coefficients of the ions, and radial distribution functions, which were studied at 298 K and 1 bar; except for the density of the solutions of all five nitrates and the activity properties of solutions of NaNO3, which were also studied at 333 K. For calculating the water the activity and the mean ionic activity coefficient, the OPAS (osmotic pressure for the activity of selvents) method was applied. The new models extend an ion model family for the alkali halides developed in previous works of our group in a consistent way. [ABSTRACT FROM AUTHOR]

Entropy scaling relates dynamic and thermodynamic properties by reducing the viscosity to a function of only the residual entropy. Molecular simulations are used to investigate the entropy scaling of the viscosity of three models of sodium chloride and five monovalent salts. Even though the correlation between the potential energy and the virial is weak, entropy scaling applies at liquid densities for all models and salts investigated. At lower densities, entropy scaling breaks down due to the formation of ion pairs and chains. Entropy scaling can be used to develop more extendable correlations for the dynamic properties of molten salts. [ABSTRACT FROM AUTHOR]

In recent years computational methods for molecular modeling have become a prime focus of computational biology and cheminformatics. Many dedicated systems exist for modeling specific classes of molecules such as proteins or small drug-like ligands. These are often heavily tailored toward the automated generation of molecular structures based on some meta-input by the user and are not intended for expert-driven structure assembly. Dedicated manual or semi-automated assembly software tools exist for a variety of molecule classes but are limited in the scope of structures they can produce. In this work we present BuildAMol, a highly flexible and extendable, general-purpose fragment-based molecular assembly toolkit. Written in Python and featuring a well-documented, user-friendly API, BuildAMol empowers researchers with a framework for detailed manual or semi-automated construction of diverse molecular models. Unlike specialized software, BuildAMol caters to a broad range of applications. We demonstrate its versatility across various use cases, encompassing generating metal complexes or the modeling of dendrimers or integrated into a drug discovery pipeline. By providing a robust foundation for expert-driven model building, BuildAMol holds promise as a valuable tool for the continuous integration and advancement of powerful deep learning techniques. Scientific contribution BuildAMol introduces a cutting-edge framework for molecular modeling that seamlessly blends versatility with user-friendly accessibility. This innovative toolkit integrates modeling, modification, optimization, and visualization functions within a unified API, and facilitates collaboration with other cheminformatics libraries. BuildAMol, with its shallow learning curve, serves as a versatile tool for various molecular applications while also laying the groundwork for the development of specialized software tools, contributing to the progress of molecular research and innovation. [ABSTRACT FROM AUTHOR]

Aims: To date, nine classes of copper regulation mechanisms have been discovered in bacteria and CsoR regulator protein is the most recent among them. Only a few have been structurally and functionally characterized. The present study was aimed to isolate and characterize the sequence, structure and biochemical properties of CsoRGz-like hypothetical protein (HP) to be potentially used as a copper sensor protein. Methodology and results: A scan of the complete genome of a Geobacillus zalihae strain T1 revealed the presence of CsoR-like (CsoRGz) HP, which contains CsoR-like_DUF156 domain and highly conserved Cys-His-Cys residues essential for copper binding. It only shares moderate sequence identity with structurally characterized CsoR proteins. CsoRGz-like HP was subjected to sequence analyses to identify important domains, motifs and residues, while circular dichroism and X-ray crystallography were used to determine its secondary and tertiary structures. CsoRGz appears to be a dimer comprising mainly a-helices, with putative, conserved metal-binding ligands, Cys46-His71-Cys75, located on the a2 helix of the protein. Biophysical characterization of CsoRGz using UV/Vis and fluorescence spectrophotometry confirmed its interaction with Cu(I). Docking of Cu(I) to the dimeric structure of CsoRGz showed that Cu(I) could be coordinated by the above metal-binding residues, further stabilized by the hydrophobic core at the metal-binding site. Conclusion, significance and impact of study: The findings in this study suggest that CsoRGz-like HP might be a novel CsoR protein. This adds to the breadth and numbers of possible CsoR proteins, particularly uncharacterized ones, existing in the pool of hypothetical proteins, to be further characterized and compared across bacterial taxa. [ABSTRACT FROM AUTHOR]

This study employed molecular dynamics simulation to investigate the mechanism of action of graphene-modified asphalt. A series of molecular models of graphene-modified asphalt were constructed and validated using thermodynamic parameters. The impact of the graphene (PGR) size and number of layers on its interaction with asphalt components were examined, and the self-healing process and mechanism of action of PGR-modified asphalt were analyzed. The results demonstrated that the size and number of layers of PGR significantly influenced its interaction with asphalt components, with polar components demonstrating a stronger affinity for PGR. When the size and number of layers of PGR were held constant, the interfacial binding energy between it and ACR-modified asphalt was the highest, followed by SBS-modified asphalt, and 70# matrix asphalt exhibited the lowest interfacial binding strength. This interfacial binding strength is primarily attributed to intermolecular van der Waals interactions. Furthermore, the incorporation of multi-layer PGR can markedly enhance the mechanical properties of matrix asphalt, whereas small-sized PGR is more efficacious in improving the low-temperature performance of polymer-modified asphalt. PGR can act as a bridge between asphalt molecules through rapid heat transfer and π-π stacking with aromatic ring-containing substances, which markedly increases the free diffusion ability of asphalt molecules, shortens the healing time of asphalt, and enhances the collective self-healing performance of asphalt. This study provides an essential theoretical basis for understanding the mechanism and application of PGR in asphalt modification. [ABSTRACT FROM AUTHOR]

Circadian clocks are internal timing devices that have evolved as an adaption to the omnipresent natural 24 h rhythmicity of daylight intensity. Properties of the circadian system are photoperiod dependent. The phase of entrainment varies systematically with season. Plastic photoperiod-dependent re-arrangements in the mammalian circadian core pacemaker yield an internal representation of season. Output pathways of the circadian clock regulate photoperiodic responses such as flowering time in plants or hibernation in mammals. Here, we review the concepts of seasonal entrainment and photoperiodic encoding. We introduce conceptual phase oscillator models as their high level of abstraction, but, yet, intuitive interpretation of underlying parameters allows for a straightforward analysis of principles that determine entrainment characteristics. Results from this class of models are related and discussed in the context of more complex conceptual amplitude–phase oscillators as well as contextual molecular models that take into account organism, tissue, and cell-type-specific details. [ABSTRACT FROM AUTHOR]

Вуглецеві наноматеріали (ВН) зарекомендували себе надзвичайно ефективними для видалення важких металів із забрудненої води та навколишнього середовища завдяки своїм унікальним структурним і хімічним властивостям. Однак гідрофобна природа ВН і їхня схильність до агрегації в більшості розчинників створюють значні труднощі для їх практичного застосування. Функціоналізація вуглецевих наноматеріалів за допомогою білків є перспективним вирішенням цих проблем, що може призвести до створення систем з безпрецедентною ефективністю. Перед тим як створювати білково-ВН системи для виявлення та видалення важких металів, важливо оцінити афінність зв'язування металів та можливі взаємодії за допомогою комп'ютерних методів. У цьому дослідженні була використано метод молекулярного докінгу для вивчення взаємодій між солями важких металів (AsO4, Cd(NO3)2, Fe(NO3)3, NiSO4, PbSO4, PtCl4), вуглецевими наноматеріалами (фулерени С24 i C60, a також одностінні вуглецеві нанотрубки) і В-лактоглобуліном. Результати докінгу показали, що: 1) розмір, форма та поверхневі властивості вуглецевих матеріалів суттєво впливають на здатність комплексів В-лактоглобуліну з ВН взаємодіяти з різними важкими металами; 2) афінність солей важких металів до створених наносистем в значній мірі варіюється; 3) водневі зв'язки та гідрофобні взаємодії відіграють суттєву роль у комплексоутворенні солей важких металів В-лактоглобуліном та вуглецевими матеріалами. [ABSTRACT FROM AUTHOR]

Objective: Systemic lupus erythematosus (SLE), an autoimmune disease with incompletely understood etiology, has a strong genetic component. Although genome‐wide association studies (GWASs) have revealed multiple SLE susceptibility loci and associated single‐nucleotide polymorphisms (SNPs), the precise causal variants, target genes, cell types, tissues, and mechanisms of action remain largely unknown. Methods: Here, we report a comprehensive post‐GWAS analysis using extensive bioinformatics, molecular modeling, and integrative functional genomic and epigenomic analyses to optimize fine‐mapping. We compile and cross‐reference immune cell–specific expression quantitative trait loci (cis– and trans–expression quantitative trait loci) with promoter capture high‐throughput capture chromatin conformation (PCHi‐C), allele‐specific chromatin accessibility, and massively parallel reporter assay data to define predisposing variants and target genes. We experimentally validate a predicted locus using CRISPR/Cas9 genome editing, quantitative polymerase chain reaction, and Western blot. Results: Anchoring on 452 index SNPs, we selected 9,931 high linkage disequilibrium (r2 > 0.8) SNPs and defined 182 independent non‐human leukocyte antigen (HLA) SLE loci. The 3,746 SNPs from 143 loci were identified as regulating 564 unique genes. Target genes are enriched in lupus‐related tissues and associated with other autoimmune diseases. Of these, 329 SNPs (106 loci) showed significant allele‐specific chromatin accessibility and/or enhancer activity, indicating regulatory potential. Using CRISPR/Cas9, we validated reference SNP identifier 57668933 (rs57668933) as a functional variant regulating multiple targets, including SLE‐risk gene ELF1 in B cells. Conclusion: We demonstrate and validate post‐GWAS strategies for using multidimensional data to prioritize likely causal variants with cognate gene targets underlying SLE pathogenesis. Our results provide a catalog of significantly SLE‐associated SNPs and loci, target genes, and likely biochemical mechanisms to guide experimental characterization. [ABSTRACT FROM AUTHOR]

Possible molecular origin of ferroelectricity in the nematic phase is discussed in detail considering a number of models based on direct dipole–dipole interaction and electrostatic interaction between surface charge densities. A more model which combines dipole–dipole interaction and short-range orientational–translational correlations is also considered. In particular we derive a contribution to the total free energy of the long-range tail of the dipole–dipole interaction potential and show that this contribution depends on the sample shape and on the boundary conditions. As a result this shape-dependent contribution may strongly effect the transition into the ferroelectric phase depending on the actual boundary conditions maintained experimentally. We also consider two conjugated thermodynamic potentials which depend on the electric field in the medium and on the displacement field, respectively. It is shown that the actual polarization corresponds to the minimum of one of these potentials depending on the boundary conditions which may have significant experimental consequences. In the framework of the general Landau de Gennes theory, which employs both potentials, the ferroelectric properties of different nematic cells are considered including the ones with fixed applied voltage and free cells with disconnected electrodes. [ABSTRACT FROM AUTHOR]

The virial equation of state (VEOS) provides a rigorous bridge between molecular interactions and thermodynamic properties. The past decade has seen renewed interest in the VEOS due to advances in theory, algorithms, computing power, and quality of molecular models. Now, with the emergence of increasingly accurate first-principles computational chemistry methods, and machine-learning techniques to generate potential-energy surfaces from them, VEOS is poised to play a larger role in modeling and computing properties. Its scope of application is limited to where the density series converges, but this still admits a useful range of conditions and applications, and there is potential to expand this range further. Recent applications have shown that for simple molecules, VEOS can provide first-principles thermodynamic property data that are competitive in quality with experiment. Moreover, VEOS provides a focused and actionable test of molecular models and first-principles calculations via comparison to experiment. This Perspective presents an overview of recent advances and suggests areas of focus for further progress. [ABSTRACT FROM AUTHOR]

It is shown that the residual entropy (entropy minus that of the ideal gas at the same temperature and density) is mostly synonymous with the independent variable of density scaling, identifying a direct link between these two approaches. The residual entropy and the effective hardness of interaction (itself a derivative at constant residual entropy) are studied for the Lennard-Jones monomer and dimer as well as a range of rigid molecular models for carbon dioxide. It is observed that the density scaling exponent appears to be related to the two-body interactions in the dilute-gas limit. [ABSTRACT FROM AUTHOR]

We derive a systematic and general method for parameterizing coarse-grained molecular models consisting of anisotropic particles from fine-grained (e.g., all-atom) models for condensed-phase molecular dynamics simulations. The method, which we call anisotropic force-matching coarse-graining (AFM-CG), is based on rigorous statistical mechanical principles, enforcing consistency between the coarse-grained and fine-grained phase-space distributions to derive equations for the coarse-grained forces, torques, masses, and moments of inertia in terms of properties of a condensed-phase fine-grained system. We verify the accuracy and efficiency of the method by coarse-graining liquid-state systems of two different anisotropic organic molecules, benzene and perylene, and show that the parameterized coarse-grained models more accurately describe properties of these systems than previous anisotropic coarse-grained models parameterized using other methods that do not account for finite-temperature and many-body effects on the condensed-phase coarse-grained interactions. The AFM-CG method will be useful for developing accurate and efficient dynamical simulation models of condensed-phase systems of molecules consisting of large, rigid, anisotropic fragments, such as liquid crystals, organic semiconductors, and nucleic acids. [ABSTRACT FROM AUTHOR]

Chemical graph theory plays an essential role in modeling and designing any chemical structure or chemical network. For a (molecular) graph, the Zagreb indices and the Zagreb eccentricity indices are well-known topological indices to describe the structure of a molecule or graph and can be used to predict properties such as the size and number of rings in a molecule, as well as the thermodynamic stability and reactivity of compounds. In this paper, we introduce a class of molecular models, namely, the Vicsek polygon graphs, which extend the traditional Vicsek networks. We compute the Zagreb indices and the Zagreb eccentricity indices of Vicsek polygon graphs by self-similarity and the recurrence relation based on the construction of graphs. [ABSTRACT FROM AUTHOR]

Background: Polyalthia suberosa (Roxb.) Thwaites (Annonaceae) is a medicinal plant that has been reported for its various pharmacological potentials, such as its anti-inflammatory, analgesic, antioxidant, and neuropharmacological activities. This study aimed to analyze the leaf essential oils of P. suberosa (PSLO) collected in different seasons, to evaluate the acetylcholinesterase inhibitory activity, and to corroborate the obtained results via in-silico molecular docking studies. Methods: The leaf essential oils of P. suberosa collected in different seasons were analyzed separately by GC/MS. The acetylcholinesterase inhibitory activity of the leaves oil was assessed via colorimetric assay. In-silico molecular docking studies were elucidated by virtual docking of the main compounds identified in P. suberosa leaf essential oil to the active sites in human acetylcholinesterase crystal structure. Results: A total of 125 compounds were identified where D-limonene (0.07 − 24.7%), α-copaene (2.25 − 15.49%), E-β-caryophyllene (5.17 − 14.42%), 24-noroleana-3,12-diene (12.92%), β-pinene (0.14 − 8.59%), and α-humulene (2.49–6.9%) were the most abundant components. Results showed a noteworthy influence of the collection season on the chemical composition and yield of the volatile oils. The tested oil adequately inhibited acetylcholinesterase enzyme with an IC50 value of 91.94 µg/mL. Additionally, in-silico molecular docking unveiled that palmitic acid, phytol, p-cymene, and caryophyllene oxide demonstrated the highest fitting scores within the active sites of human acetylcholinesterase enzyme. Conclusions: From these findings, it is concluded that P. suberosa leaf oil should be evaluated as a food supplement for enhancing memory. [ABSTRACT FROM AUTHOR]

Introduction: Blood group antigens of the RH system (formerly known as "Rhesus") play an important role in transfusion medicine because of the severe haemolytic consequences of antibodies to these antigens. No crystal structure is available for RhD proteins with its partner RhAG, and the precise stoichiometry of the trimer complex remains unknown. Methods: To analyse their structural properties, the trimers formed by RhD and/ or RhAG subunits were generated by protein modelling and molecular dynamics simulations were performed. Results: No major differences in structural behaviour were found between trimers of different compositions. The conformation of the subunits is relatively constant during molecular dynamics simulations, except for three large disordered loops. Discussion: This work makes it possible to propose a reasonable stoichiometry and demonstrates the potential of studying the structural behaviour of these proteins to investigate the hundreds of genetic variants relevant to transfusion medicine. [ABSTRACT FROM AUTHOR]

This study presents an approach for precisely determining the stretching vibrational frequencies of the P-Cl bond in phosphorus trichloride (PCl3) using a vibrational Hamiltonian framework that maintains the C3v symmetry point group. Our methodology enables the accurate prediction of vibrational frequencies up to the fifth overtone. It identifies related combination bands, marking a significant advancement in vibrational spectroscopy and molecular modelling. By enhancing the accuracy and depth of our understanding of molecular vibrations, this research paves the way for developing more sophisticated computational models, thereby significantly improving the precision of chemical analyses, and contributing to the broader field of chemical physics. [ABSTRACT FROM AUTHOR]

Motivation Predicting molecular properties is a pivotal task in various scientific domains, including drug discovery, material science, and computational chemistry. This problem is often hindered by the lack of annotated data and imbalanced class distributions, which pose significant challenges in developing accurate and robust predictive models. Results This study tackles these issues by employing pretrained molecular models within a few-shot learning framework. A novel dynamic contrastive loss function is utilized to further improve model performance in the situation of class imbalance. The proposed MolFeSCue framework not only facilitates rapid generalization from minimal samples, but also employs a contrastive loss function to extract meaningful molecular representations from imbalanced datasets. Extensive evaluations and comparisons of MolFeSCue and state-of-the-art algorithms have been conducted on multiple benchmark datasets, and the experimental data demonstrate our algorithm's effectiveness in molecular representations and its broad applicability across various pretrained models. Our findings underscore MolFeSCues potential to accelerate advancements in drug discovery. Availability and implementation We have made all the source code utilized in this study publicly accessible via GitHub at http://www.healthinformaticslab.org/supp/ or https://github.com/zhangruochi/MolFeSCue. The code (MolFeSCue-v1-00) is also available as the supplementary file of this paper. [ABSTRACT FROM AUTHOR]

Potato virus Y (PVY) is one of the most dangerous agricultural pathogens that causes substantial harm to vegetative propagated crops, such as potatoes (Solanum tuberosum L.). A necessary condition for PVY infection is an interaction between the plant cap-binding translation initiation factors eIF4E and a viral protein VPg, which mimics the cap-structure. In this study, we identified the point mutations in potato eIF4E1 and eIF4E2 that disrupt VPg binding while preserving the functional activity. For the structural interpretation of the obtained results, molecular models of all the studied forms of eIF4E1 and eIF4E2 were constructed and analyzed via molecular dynamics. The results of molecular dynamics simulations corresponds to the biochemical results and suggests that the β1β2 loop plays a key role in the stabilization of both eIF4E–cap and eIF4E–VPg complexes. • S. tuberosum eIF4E1 and eIF4E2 are susceptible factors for the potato virus Y. • Conformation of eIF4E β1β2 loop defines an interaction with viral protein VPg. • Mutations that disrupt 4E-VPg binding but preserve factor functionality were found. [ABSTRACT FROM AUTHOR]

This article describes the synthesis and crystal structure of ten new Tc(V) complexes with pyrazine containing homologous alkoxy ligands. We carried out an analysis of the Hirshfeld surfaces for the described compounds and found out that with increasing length of the hydrocarbon substituent from CH3 to C10H21 the percentage of H⋯H interactions in crystals increases from 28% to 59%. The purpose of this article was to show the pattern of increase in interplanar distances in crystals depending on the length of the hydrocarbon substituent, and also to study the thermochemical properties of Tc(V) complexes. The mechanism of thermolysis does not depend on the length of the hydrocarbon substituent and differs only in the temperature range of the reactions. The absorption bands of the obtained complexes in IR and UV-vis spectra do not depend on the length of the hydrocarbon substituent. Using a complex with a propyl substituent as an example, the UV-vis spectral data of the mother solution are presented. Based on DFT calculations of spectra and molecular models, an indirect identification of the reaction intermediate is provided. Using a complex with a methyl substituent as an example, data on MALDI spectrometry are presented. [ABSTRACT FROM AUTHOR]

While untargeted analysis of biological tissues with ambient mass spectrometry analysis probes has been widely reported in the literature, there are currently no guidelines to standardize the workflows for the experimental design, creation, and validation of molecular models that are utilized in these methods to perform class predictions. By drawing parallels with hurdles that are faced in the field of food fraud detection with untargeted mass spectrometry, we provide a stepwise workflow for the creation, refinement, evaluation, and assessment of the robustness of molecular models, aimed at meaningful interpretation of mass spectrometry-based tissue classification results. We propose strategies to obtain a sufficient number of samples for the creation of molecular models and discuss the potential overfitting of data, emphasizing both the need for model validation using an independent cohort of test samples, as well as the use of a fully characterized feature-based approach that verifies the biological relevance of the features that are used to avoid false discoveries. We additionally highlight the need to treat molecular models as "dynamic" and "living" entities and to further refine them as new knowledge concerning disease pathways and classifier feature noise becomes apparent in large(r) population studies. Where appropriate, we have provided a discussion of the challenges that we faced in our development of a 10 s cancer classification method using picosecond infrared laser mass spectrometry (PIRL-MS) to facilitate clinical decision-making at the bedside. [ABSTRACT FROM AUTHOR]