Search Results (339)

The design of materials with programmable degradation profiles presents a fundamental challenge in pattern recognition across molecular space, requiring the identification of complex structure–property relationships within an exponentially large chemical domain. This paper introduces a novel physics-informed deep learning framework that integrates multi-scale molecular sensing data with reinforcement learning algorithms to enable intelligent characterization and prediction of polymer degradation dynamics. Our method combines three key innovations: (1) a dual-channel sensing architecture that fuses spectroscopic signatures from Graph Isomorphism Networks with temporal degradation patterns captured by transformer-based models, enabling comprehensive molecular state detection across multiple scales; (2) a physics-constrained policy network that ensures sensor measurements adhere to thermodynamic principles while optimizing the exploration of degradation pathways; and (3) a hierarchical signal processing system that balances multiple sensing modalities through adaptive weighting schemes learned from experimental feedback. The framework employs curriculum-based training that progressively increases molecular complexity, enabling robust detection of degradation markers linking polymer architectures to enzymatic breakdown kinetics. Experimental validation through automated synthesis and in situ characterization of 847 novel polymers demonstrates the framework’s sensing capabilities, achieving a 73.2% synthesis success rate and identifying 42 structures with precisely monitored degradation profiles spanning 6 to 24 months. Learned molecular patterns reveal previously undetected correlations between specific spectroscopic signatures and degradation susceptibility, validated through accelerated aging studies with continuous sensor monitoring. Our results establish that physics-informed constraints significantly improve both the validity (94.7%) and diversity (0.82 Tanimoto distance) of generated molecular structures compared with unconstrained baselines. This work advances the convergence of intelligent sensing technologies and materials science, demonstrating how physics-informed machine learning can enhance real-time monitoring capabilities for next-generation sustainable materials. Full article

We present a theoretical model of the hydrogenation and amination of a primal carbon cluster of the tangled polycyclic type. Hydrogen atoms were introduced via H₂, while the nitrogen source was NH₃. The initial chemical processes were modeled using Born–Oppenheimer Molecular Dynamics. Metadynamics was employed to accelerate the saturation. The reactions were characterized in terms of barriers, topology, and intricate changes in the electronic structure. All transition states were identified. Multiple mechanisms for each type of reaction were discovered. Occasional unbiased changes in the carbon skeleton, induced by the guided processes, were observed. The initial addition reactions had no barriers due to the instability and high reactivity of the carbon structure. The final product of barrierless hydrogen saturation was C₂₅H₂₆. This molecule included multiple isolated double bonds, a medium-sized conjugated π system, and no triple bonds. Ammonia additions resulted in quaternary ammonium groups and primary amino groups. In the subsequent amination, a barrier appeared in fewer steps than in repetitive hydrogenation. The final product of barrierless saturation with NH₃ was C₂₅H₂(NH₃)₂NH₂. Further amination was characterized by a forward free-energy barrier of an order of magnitude larger than the reverse reaction, and the product was found to be unstable. Full article

Coronavirus disease 2019 (COVID-19) produced devastating health and economic impacts worldwide. While progress has been made in vaccine development, effective antiviral treatments remain limited, particularly those targeting the papain-like protease (PLpro) of SARS-CoV-2. PLpro plays a key role in viral replication and immune evasion, making it an attractive yet underexplored target for drug repurposing. In this study, we combined machine learning, molecular dynamics, and molecular docking to identify potential PLpro inhibitors in existing drugs. We performed long-timescale molecular dynamics simulations on PLpro–ligand complexes at two known binding sites, followed by structural clustering to capture representative structures. These were used for molecular docking, including a training set of 127 compounds and a library of 1107 FDA-approved drugs. A random forest model, trained on the docking scores of the representative conformations, yielded 76.4% accuracy via leave-one-out cross-validation. Applying the model to the drug library and filtering results based on prediction confidence and the applicability domain, we identified five drugs as promising candidates for repurposing for COVID-19 treatment. Our findings demonstrate the power of integrating computational modeling with machine learning to accelerate drug repurposing against emerging viral targets. Full article

Nanowear-resistant coatings are critical for extending the service life of mechanical components, yet their performance optimization remains challenging due to the complex interplay between atomic-scale defects and macroscopic wear behavior. While experimental characterization struggles to resolve transient interfacial phenomena, multiscale simulations, integrating ab initio calculations, molecular dynamics, and continuum mechanics, have emerged as a powerful tool to decode structure–property relationships. This review systematically compares mainstream computational methods and analyzes their coupling strategies. Through case studies on metal alloy nanocoatings, we demonstrate how machine learning-accelerated simulations enable the targeted design of layered architectures with 30% improved wear resistance. Finally, we propose a protocol combining high-throughput simulation and topology optimization to guide future coating development. Full article

Background/Objectives: PEGylated liposomes are widely recognized for their biocompatibility and capacity to extend systemic circulation via “stealth” properties. However, the PEG corona often limits tumor penetration and cellular internalization. Targeting matrix metalloproteinase-2 (MMP-2), frequently upregulated in breast cancer stroma, presents an opportunity to enhance tissue-specific drug delivery. In this study, we engineered MMP-2-responsive GPLGVRG peptide-modified cleavable PEGylated liposomes for targeted paclitaxel (PTX) delivery. Methods: Molecular docking simulations employed the MMP-2 crystal structure (PDB ID: 7XJO) to assess GPLGVRG peptide binding affinity. A cleavable, enzyme-sensitive peptide-PEG conjugate (Chol-PEG_2K-GPLGVRG-PEG_5K) was synthesized via small-molecule liquid-phase synthesis and characterized by ¹H NMR and MALDI-TOF MS. Liposomes incorporating this conjugate (S-Peps-PEG_5K) were formulated to evaluate whether MMP-2-mediated peptide degradation triggers detachment of long-chain PEG moieties, thereby enhancing internalization by 4T1 breast cancer cells. Additionally, the effects of tumor microenvironmental pH (~6.5) and MMP-2 concentration on drug release dynamics were investigated. Results: Molecular docking revealed robust GPLGVRG-MMP-2 interactions, yielding a binding energy of −7.1 kcal/mol. The peptide formed hydrogen bonds with MMP-2 residues Tyr A:23 and Arg A:53 (bond lengths: 2.4–2.5 Å) and engaged in hydrophobic contacts, confirming MMP-2 as the primary recognition site. Formulations containing 5 mol% Chol-PEG_2K-GPLGVRG-PEG_5K combined with 0.15 µg/mL MMP-2 (S-Peps-PEG_5K +MMP) exhibited superior internalization efficiency and significantly reduced clonogenic survival compared to controls. Notably, acidic pH (~6.5) induced MMP-2-mediated cleavage of the GPLGVRG peptide, accelerating S-Peps-PEG_5K dissociation and facilitating drug release. Conclusions: MMP-2-responsive, cleavable PEGylated liposomes markedly improve PTX accumulation and controlled release at tumor sites by dynamically modulating their stealth properties, offering a promising strategy to enhance chemotherapy efficacy in breast cancer. Full article

Grain boundary weakening in high-temperature environments significantly influences the fatigue crack growth mechanisms of nickel-based superalloys, introducing challenges in accurately predicting fatigue life. In this study, a dislocation-density-based crystal plasticity phase-field (CP–PF) model is developed to simulate the fatigue crack growth behavior of the GH4169 alloy under both room and elevated temperatures. Grain boundaries are explicitly modeled, enabling the competition between transgranular and intergranular cracking to be accurately captured. The grain boundary separation energy and surface energy, calculated via molecular dynamics simulations, are employed as failure criteria for grain boundary and intragranular material points, respectively. The simulation results reveal that under oxygen-free conditions, fatigue crack propagation at both room and high temperatures is governed by sustained shear slip, with crack advancement hindered by grains exhibiting low Schmid factors. When grain boundary oxidation is introduced, increasing oxidation levels progressively degrade grain boundary strength and reduce overall fatigue resistance. Specifically, at room temperature, oxidation shortens the duration of crack arrest near grain boundaries. At elevated service temperatures, intensified grain boundary degradation facilitates a transition in crack growth mode from transgranular to intergranular, thereby accelerating crack propagation and exacerbating fatigue damage. Full article

Background/Objectives: Human carbonic anhydrases (hCAs) are metalloenzymes involved in essential physiological processes, and their selective inhibition holds therapeutic potential across a wide range of disorders. However, the high degree of structural similarity among isoforms poses a significant challenge for the design of selective inhibitors. In this work, we present a machine learning (ML)-based platform for the isoform-specific prediction and profiling of small molecules targeting hCA I, II, IX, and XII. Methods: By integrating four molecular representations with four ML algorithms, we built 64 classification models, each extensively optimized and validated. The best-performing models for each isoform were applied in a virtual screening campaign for ~2 million compounds. Results: Following a multi-step refinement process, 12 candidates were identified, purchased, and experimentally tested. Several compounds showed potent inhibitory activity in the nanomolar to submicromolar range, with selectivity profiles across the isoforms. To gain mechanistic insights, SHAP-based feature importance analysis and molecular docking supported by molecular dynamics simulations were employed, highlighting the structural determinants of the predicted activity. Conclusions: This study demonstrates the effectiveness of integrating ML, cheminformatics, and experimental validation to accelerate the discovery of selective carbonic anhydrase inhibitors and provides a generalizable framework for activity profiling across enzyme isoforms. Full article

Quantum mechanics (QM) revolutionizes drug discovery by providing precise molecular insights unattainable with classical methods. This review explores QM’s role in computational drug design, detailing key methods like density functional theory (DFT), Hartree–Fock (HF), quantum mechanics/molecular mechanics (QM/MM), and fragment molecular orbital (FMO). These methods model electronic structures, binding affinities, and reaction mechanisms, enhancing structure-based and fragment-based drug design. This article highlights the applicability of QM to various drug classes, including small-molecule kinase inhibitors, metalloenzyme inhibitors, covalent inhibitors, and fragment-based leads. Quantum computing’s potential to accelerate quantum mechanical (QM) calculations is discussed alongside novel applications in biological drugs (e.g., gene therapies, monoclonal antibodies, biosimilars), protein–receptor dynamics, and new therapeutic indications. A molecular dynamics (MD) simulation exercise is included to teach QM/MM applications. Future projections for 2030–2035 emphasize QM’s transformative impact on personalized medicine and undruggable targets. The qualifications and tools required for researchers, including advanced degrees, programming skills, and software such as Gaussian and Qiskit, are outlined, along with sources for training and resources. Specific publications on quantum mechanics (QM) in drug discovery relevant to QM and molecular dynamics (MD) studies are incorporated. Challenges, such as computational cost and expertise requirements, are addressed, offering a roadmap for educators and researchers to leverage quantum mechanics (QM) and molecular dynamics (MD) in drug discovery. Full article

Activating mutations in the epidermal growth factor receptor (EGFR) are key oncogenic drivers across multiple cancers, yet the structural mechanisms by which these mutations promote persistent receptor activation remain elusive. Here, we investigate how three clinically relevant mutations—T790M, L858R, and the T790M_L858R double mutant—reshape EGFR’s conformational ensemble and regulatory network architecture. Using multiscale molecular simulations and kinetic modeling, we show that these mutations, particularly in combination, enhance flexibility in the αC-helix and A-loop, favoring activation-competent states. Markov state modeling reveals a shift in equilibrium toward active macrostates and accelerated transitions between metastable conformations. To resolve the underlying coordination mechanism, we apply neural relational inference to reconstruct time-dependent interaction networks, uncovering the mutation-induced rewiring of allosteric pathways linking distant regulatory regions. This coupling of conformational redistribution with network remodeling provides a mechanistic rationale for sustained EGFR activation and suggests new opportunities for targeting dynamically organized allosteric circuits in therapeutic design. Full article

Background/Objectives: Colorectal cancer (CRC) remains a major global health burden, marked by complex tumor–microenvironment interactions, genetic heterogeneity, and varied treatment responses. Effective preclinical models are essential for dissecting CRC biology and guiding personalized therapeutic strategies. This review aims to critically evaluate current experimental CRC models, assessing their translational relevance, limitations, and potential for integration into precision oncology. Methods: A systematic literature search was conducted across PubMed, Scopus, and Web of Science, focusing on studies employing defined in vitro, in vivo, and emerging integrative CRC models. Studies were included based on experimental rigor and relevance to therapeutic or mechanistic investigation. Models were compared based on molecular fidelity, tumorigenic capacity, immune interactions, and predictive utility. Results: CRC models were classified into in vitro (2D cell lines, spheroids, patient-derived organoids), in vivo (murine, zebrafish, porcine, canine), and integrative platforms (tumor-on-chip systems, humanized mice, AI-augmented simulations). Traditional models offer accessibility and mechanistic insight, while advanced systems better mimic human tumor complexity, immune landscapes, and treatment response. Tumor-on-chip and AI-driven models show promise in simulating dynamic tumor behavior and predicting clinical outcomes. Cross-platform integration enhances translational validity and enables iterative model refinement. Conclusions: Strategic deployment of complementary CRC models is critical for advancing translational research. This review provides a roadmap for aligning model capabilities with specific research goals, advocating for integrated, patient-relevant systems to improve therapeutic development. Enhancing model fidelity and interoperability is key to accelerating the bench-to-bedside translation in colorectal cancer care. Full article

Type 1 diabetes mellitus (T1D) is increasingly recognised not only as an autoimmune metabolic disorder but also as a condition associated with accelerated biological ageing. Among the hallmarks of ageing, telomere shortening has emerged as a key feature, driven by multiple molecular pathological pathways linked to T1D onset and progression. This review explores the molecular mechanisms contributing to telomere attrition in T1D, including cytokine-induced β-cell damage, ROS-mediated DNA damage, impaired mitochondrial dynamics, and dysregulated DNA damage response pathways. Empirical evidence supports a consistent association between shortened telomeres and T1D, vascular complications, nephropathy, and mortality in T1D patients. Furthermore, the bidirectional relationship between telomere biology and immune-metabolic stress suggests novel directions for intervention. Understanding these pathways may enhance the predictive value of telomere length as a biomarker and inform targeted therapeutic strategies aimed at mitigating premature ageing and disease progression in T1D. Full article

FeCrAl alloys have garnered considerable attention as candidate cladding materials for light water reactors due to their promising mechanical stability and irradiation resistance. However, the response characteristics of these alloys to irradiation and the associated mechanisms remain poorly understood. This study provides atomistic insights into irradiation-induced defect formation and microstructural evolution in polycrystalline FeCrAl. Using the LAMMPS molecular dynamics code, displacement cascades were simulated under irradiation doses ranging from 0.05 dpa to 0.5 dpa while evaluating the dependencies on temperature and grain size. The interaction between pre-existing defects and irradiation-induced microstructures (point defects, dislocations, clusters, etc.) was visualized and analyzed visually and quantitatively. The results indicate that the irradiation dose increases the number of surviving Frenkel pairs, whereas elevated temperatures reduce their stability. The cluster fraction of interstitials increases with both irradiation dose and temperature, while that of vacancies decreases at higher temperatures due to their lower stability. In the initial phase of the displacement cascade, the density and distribution of dislocations evolve continuously until the annealing stage. The dislocation density at the end of the annealing phase decreases with increasing dose and temperature. The thickness of grain boundaries increases with the irradiation dose, and the regions adjacent to grain boundaries transform into an amorphous state at higher dose levels. As both the irradiation dose and temperature increase, the amorphization process accelerates, and smaller grain size leads to a greater degree of amorphization. Full article

Soil degradation has been accelerating globally due to climate change, which threatens food production, biodiversity, and ecosystem balance. Traditional soil restoration strategies are often expensive, slow, or unsustainable in the long term. In this context, cyanobacteria have emerged as promising biotechnological alternatives, being the only prokaryotes capable of performing oxygenic photosynthesis. Moreover, they can capture atmospheric carbon and nitrogen, release exopolysaccharides (EPSs) that stabilize the soil, and facilitate the development of biological soil crusts (biocrusts). In recent years, the convergence of multi-omics tools, such as metagenomics, metatranscriptomics, and metabolomics, has advanced our understanding of cyanobacterial dynamics, their metabolic potential, and symbiotic interactions with microbial consortia, as exemplified by the cyanosphere of Microcoleus vaginatus. In addition, recent advances in bioinformatics have enabled high-resolution taxonomic and functional profiling of environmental samples, facilitating the identification and prediction of resilient microorganisms suited to challenging degraded soils. These tools also allow for the prediction of biosynthetic gene clusters and the detection of prophages or cyanophages within microbiomes, offering a novel approach to enhance carbon sequestration in dry and nutrient-poor soils. This review synthesizes the latest findings and proposes a roadmap for the translation of molecular-level knowledge into scalable biotechnological strategies for soil restoration. We discuss approaches ranging from the use of native biocrust strains to the exploration of cyanophages with the potential to enhance cyanobacterial photosynthetic activity. By bridging ecological functions with cutting-edge omics technologies, this study highlights the critical role of cyanobacteria as a nature-based solution for climate-smart soil management in degraded and arid ecosystems. Full article

The surface bonding of silica sol particles modified by methyltrimethoxysilane (MTMS) at different temperatures was investigated. Following modification, MTMS hydrolysis products react with silica hydroxyl groups on the surface of silica particles to create a -Si-O-Si-network structure. Additionally, the hydrolysis products formed hydrogen bonds with the silica hydroxyl groups in the silica sol, which strengthened the bonding strength between the silica particles in a synergistic manner. Increasing the modification temperature accelerated the hydrolysis rate of MTMS, promoted the formation of -Si-O-Si-, and enhanced its binding properties. A silica sol model of grafted MTMS was established using molecular dynamics methods at different modification temperatures to explore the effect of hydrogen bonding on the surface bonding of silica sol particles. Ultimately, it was confirmed experimentally that MTMS modification significantly enhanced the bonding strength on the surface of silica particles in silica sols. Full article

Chemiresistive gas sensors have gained significant attention and have been widely applied in various fields. However, the gap between experimental observations and fundamental sensing mechanisms hinders systematic optimization. Despite the critical role of modeling in explaining atomic-scale interactions and offering predictive insights beyond experiments, existing reviews on chemiresistive gas sensors remain predominantly experimental-centric, with a limited systematic exploration of the modeling approaches. Herein, we present a comprehensive overview of the modeling approaches for chemiresistive gas sensors, focusing on two critical processes: the reception and transduction stages. For the reception process, density functional theory (DFT), molecular dynamics (MD), ab initio molecular dynamics (AIMD), and Monte Carlo (MC) methods were analyzed. DFT quantifies atomic-scale charge transfer, and orbital hybridization, MD/AIMD captures dynamic adsorption kinetics, and MC simulates equilibrium/non-equilibrium behaviors based on statistical mechanics principles. For the transduction process, band-bending-based theoretical models and power-law models elucidate the resistance modulation mechanisms, although their generalizability remains limited. Notably, the finite element method (FEM) has emerged as a powerful tool for full-process modeling by integrating gas diffusion, adsorption, and electronic responses into a unified framework. Future directions highlight the use of multiscale models to bridge microscopic interactions with macroscopic behaviors and the integration of machine learning, accelerating the iterative design of next-generation sensors with superior performance. Full article