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33 articles for “Modeling molecular interactions”
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STRUCTURAL–EPIGENOMIC ATLAS: CNV/SV- DRIVEN PROGNOSTIC REFINEMENT ACROSS CANCERS
Abstract: Structural genomic alterations, including copy number variations (CNVs) and structural variants (SVs), play a central role in cancer initiation and progression. These alterations extend beyond gene dosage effects and interact dynamically with epigenomic mechanisms such as DNA methylation, histone modifications, and three-dimensional chromatin organization. Recent pan-cancer studies have demonstrated that CNV burden and SV signatures reflect key oncogenic processes including chromothripsis, homologous recombination deficiency, enhancer hijacking, and extrachromosomal DNA (ecDNA) …
Published in International Journal of Cell Biology and Cellular Functions · Vol. 4, Issue 1, 2026 Read article
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Modeling of ZnO Based Nano Sensor Device for Evaluating Electronic Interaction with NO2 Pollutant: Combining Multiphysics Simulation and DFT Study
Abstract: This study focuses on the designing and modeling of a sensor device employing zinc oxide (ZnO) nanowires (NWs), and the evaluation of the chemical response of the same in the presence of nitrogen dioxide (NO2), which is an acute harmful pollutant for human health and environment. Experimentally synthesized ZnO nanostructures were used as the basis for modeling of the ZnO NWs based sensor device along with a single ZnO NW, …
Published in Journal of Polymer & Composites · Vol. 13, Issue 2, 2025 · pp. 211–219 Read article
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InSilico Analysis and Homology Modeling of Tetrahydroprotoberberine Oxide involved in the Berberine Biosynthesis
Abstract: Objective: Berberine, a bioactive compound found in various plant species, exhibits diverse pharmacological properties with potential applications in pharmaceutical research. The biosynthesis of berberine involves several enzymatic steps, with (S)-tetrahydroprotoberberine oxidase playing a pivotal role. This study aimed to elucidate the structural and functional characteristics of (S)-tetrahydroprotoberberine oxidase to better understand its role in berberine biosynthesis and its potential biotechnological applications. Methods: Using bioinformatics tools and computational methods, the physicochemical …
Published in Emerging Trends in Metabolites · Vol. 1, Issue 1, 2024 · pp. 7–26 Read article
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The Characteristics of Square-Well Fluid Transport Coefficients
Abstract: The transport coefficients of the hard-sphere system were first computed by Alder, providing foundational insight into the microscopic origins of viscosity, diffusion, and thermal conductivity in simple fluids. Building on this framework, Evans derived a generalized Langevin equation to describe the time evolution of dynamical variables, incorporating memory effects and non-Markovian behavior in molecular motion. These theoretical developments established a bridge between microscopic interactions and macroscopic transport properties. When an …
Published in Research & Reviews : Journal of Physics · Vol. 14, Issue 3, 2025 · pp. 33–44 Read article
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Drug Induced Immune Mediated Nephritis: Molecular Mechanism , Pathways and Clinical Implications
Abstract: Drug-induced immune-mediated nephritis (DI-IMN) has become a more widely known cause of acute kidney injury (AKI), with the potential for development to chronic kidney disease if not detected and treated promptly. T-cell hypersensitivity to pharmaceuticals, such as antibiotics, proton pump inhibitors, nonsteroidal anti-inflammatory drugs, and immunological drugs, are the major causes for it. Beyond clinical burden, DI-IMN reflectsintricate molecular interactions that sustain interstitial inflammation and tubular injury. These interactions include …
Published in International Journal of Toxins and Toxics · Vol. 3, Issue 1, 2026 · pp. 13–29 Read article
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A Technical Survey on Nanotechnology in Nanorobots
Abstract: Nanotechnology, the manipulation of matter at the atomic and molecular scale, has given rise to a revolutionary frontier: nanorobots – microscopic machines capable of interacting with biological systems and environments with unprecedented precision. This study explores the design, functionality, and application of nanorobots in medicine, environmental remediation, and materials science. By integrating principles from materials engineering, biotechnology, and robotics, nanorobots are engineered to perform tasks such as targeted drug delivery, …
Published in Journal of Nanoscience, NanoEngineering & Applications · Vol. 16, Issue 1, 2026 · pp. 14–21 Read article
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Comparative Proteomics: From Cell Lines to Clinical Samples
Abstract: Comparative proteomics is a powerful tool for understanding the molecular differences between various biological samples. It entails identifying and measuring proteins in complex biological samples to assess their abundance, modifications, and interactions under various conditions. This approach plays a crucial role in advancing biomedical research, especially in disease understanding, biomarker discovery, and therapeutic development. While cell lines are widely used for proteomic studies due to their controlled environments and reproducibility, …
Published in Research and Reviews : Journal of Computational Biology · Vol. 13, Issue 3, 2024 · pp. 30–34 Read article
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Density Functional Theory (DFT): Understanding and Quantifying Molecular Structure of 2-D Materials
Abstract: Density Functional Theory (DFT) has emerged as a cornerstone in computational chemistry and materials science, offering a powerful framework for predicting electronic structures and properties of atoms, molecules, and solids. By focusing on electron density rather than wave functions, DFT simplifies the many-body problem through approximations like the local density approximation (LDA) and generalized-gradient approximations (GGAs). The Hohenberg-Kohn theorems establish the theoretical foundation, proving that ground-state properties are uniquely determined …
Published in Journal of Microelectronics and Solid State Devices · Vol. 12, Issue 2, 2025 · pp. 33–40 Read article
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Pathophysiology Reimagined: Integrating Systems Biology and AI for Disease Understanding
Abstract: Pathophysiology, the study of disease mechanisms at molecular, cellular, and systemic levels, has traditionally relied on reductionist approaches that often fail to capture the complex, dynamic, and interconnected nature of biological systems. Diseases such as cancer, neurodegenerative disorders, and infectious diseases arise from intricate interactions among genetic, epigenetic, metabolic, and environmental factors, necessitating integrative, data-driven methodologies for a deeper understanding. Systems biology has emerged as a powerful approach by leveraging …
Published in Research and Reviews: A Journal of Pharmaceutical Science · Vol. 16, Issue 2, 2025 · pp. 63–71 Read article
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Graph Theoretic Analysis of Cyclodextrin Polymers
Abstract: Topological indicators in chemical graph theory are essential tools in cheminformatics, providing valuable insights into molecular structure and properties to make more accurate predictions about the behavior and efficacy of novel compounds in drug design. The macro molecules are correlated with certain derivatives. The derivatives are growing structures which depends on the cyclic structures. The Cyclodextrin is one of the cyclic structures which depends on the carbon atoms. The polymer …
Published in Journal of Polymer & Composites · Vol. 14, Issue 1, 2026 · pp. 997–1006 Read article
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Role of Quantum Chemistry in Catalysis: A Comprehensive Review
Abstract: Catalysis plays a crucial role in modern chemical manufacturing, energy conversion, and environmental protection by enabling chemical reactions to occur more rapidly, selectively, and with reduced energy consumption. A fundamental understanding of catalytic processes at the atomic and electronic levels is essential for the rational design and optimization of catalysts. Quantum chemistry has emerged as a powerful theoretical and computational framework that enables detailed investigation of electronic structure, reaction energetics, …
Published in Journal of Catalyst & Catalysis · Vol. 13, Issue 1, 2026 · pp. 01–16 Read article
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Machine Learning-Based Quantification of Polymer Structure Property Relationships for Predictive Material Design
Abstract: Polymer structures exhibit complex, hierarchical arrangements that strongly influence macroscopic properties, yet consistent quantification remains challenging due to nonlinear interactions and limited unified modeling strategies. Existing approaches inadequately capture generalized structure–property mappings across diverse polymer systems. This research aims to establish a machine learning-based quantification model for polymer structure–property relationships to support predictive material design. A Polymer Structure Property Dataset of 5,000 polymer samples includes structural descriptors and experimentally measured …
Published in Journal of Polymer & Composites · Vol. 14, Issue 3, 2026 · pp. 737–754 Read article
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Genomic Selection for Grain Yield in Wheat Using Machine Learning on DArT Molecular Markers: A Comparative Evaluation Across Multi-Environment Trials
Abstract: Genomic selection (GS) predicts complex quantitative traits directly from genome-wide molecular markers, bypassing the need for extensive phenotypic trials and accelerating plant breeding cycles. We conducted a comparative evaluation of seven regression approaches — ridge regression (the machine-learning equivalent of RR-BLUP), Lasso, Elastic Net, Partial Least Squares, linear Support Vector Regression, Random Forest, and Gradient Boosting — for predicting grain yield from 1,279 Diversity Array Technology (DArT) molecular markers genotyped …
Published in Research and Reviews : Journal of Computational Biology · Vol. 15, Issue 2, 2026 Read article