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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]

Mild cognitive impairment (MCI) is characterized by a decline in cognitive functioning without significant interference in daily activities. Its high heterogeneity and elevated conversion rate to dementia pose challenges for accurate diagnosis and monitoring, highlighting the urgent need to identify methodologies focused on the early detection and intervention of MCI. Due to their biological characteristics, microRNAs (miRNAs) are potential candidates as non-invasive molecular markers for the identification and assessment of MCI progression. Therefore, in this study, we conducted a meta-analysis to identify the miRNAs commonly deregulated in MCI, focusing on expression profiles in plasma, serum, and extracellular vesicle samples. Our analysis identified eight upregulated miRNAs, including hsa-miR-149-3p, and four downregulated miRNAs, such as Let-7f-5p. Notably, hsa-miR-149-3p emerged as a central node in interaction networks, suggesting its crucial role in regulating cellular processes relevant to MCI. Additionally, pathway analysis revealed significant enrichment in biological processes associated with transcriptional regulation and neurodegeneration. Our results underscore the potential of circulating miRNAs as non-invasive molecular markers for MCI and open the possibility for new methodologies that enable more accurate diagnosis and monitoring of disease progression. Validating the expression of miRNAs such as hsa-miR-149-3p and Let-7f-5p, along with identifying their functional role in the specific context of MCI, is essential to establish their biological relevance. This work contributes to the understanding of the miRNA profile in mild cognitive impairment using easily accessible samples, which could be useful for the development of various strategies aimed at preventing or delaying MCI in individuals at risk of developing dementia, including Alzheimer's disease. [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]

• 3D molecular generation efficiently explores chemical space to promote drug design. • Common database, molecular representation and generation strategies are summarized. • Case studies of applying 3D molecular models to accelerate drug design are shown. • Future challenges in the development and application of 3D models are discussed. Drug discovery is essential in human diseases but faces challenges because of the vast chemical space. Molecular generation models have become powerful tools to accelerate drug design by efficiently exploring chemical space. 3D molecular generation has gained popularity for explicitly incorporating spatial structural information to generate rational molecules. Herein, we summarize and compare common data sets, molecular representations, and generative strategies in 3D molecular generation. We also present case studies utilizing generative modeling for ligand design and outline future challenges in developing and applying 3D models. This work provides a reference for drug design researchers interested in 3D generative modeling. [ABSTRACT FROM AUTHOR]