Computational Insights Research Articles

Isorhapontigenin: exploring a promising resveratrol analog for disease management through diverse signaling pathways-a review with computational insights.

Plants are a meticulous sourceof biologically active substances that have positive health effects. Recent studies demonstrated the existence of Isorhapontigenin, a stilbene derivative,differs by its methoxylation pattern but shares a structure identical to resveratrol. Through a review of the literature and an in silico interpretation, the current review will be centeredon the anti-inflammatory, anti-cancer, anti-obesity, and other medicinal properties of Isorhapontigenin. The Gnetum genus has species that have a very significant amount of Isorhapontigenin, such as Gnetum parvifolium. Tropical zones of Africa, Asia, and South America are home to the majority of the Gnetaceae family. Several investigations have reported the compound's benefits for health. Isorhapontigenin is very effective against anti-inflammatory, anticancer, antibacterial, antifungal, neuroprotective, antiviral, and antioxidant effects andcould be a possible treatmentdrug. Using network pharmacology (ADME, Networking and Docking), the bioavailability of compound about disorders was examined. KeGG analysis and gene ontology (GO) demonstrated that Isorhapontigenin works via many immune system and cancer pathways. Human COX-1 receptor protein 6Y3C and receptor protein 1CX2 that had the best fit with the drug were used for docking analysis: Docking scores of - 8.2 and - 8.4 for Isorhapontigenin, respectively. In conclusion, Isorhapontigenin is a novel compound in the context of drug development.

Exploring Chalcone Derivatives as a Multifunctional Therapeutic Agent: Investigating Antioxidant Potential, Acetylcholinesterase Inhibition and Computational Insights

Background: The cholinergic hypothesis is an elementary approach employed for the research and drug discovery of novel anti-Alzheimer therapeutics. Method: In this context, the study focuses on synthesizing and evaluating a new series of chalcone derivatives (3a–3j) as multifunctional therapeutic agents, specifically investigating their antioxidant potential using the DPPH method with ascorbic acid as a standard. Ellman’s protocol for acetylcholinesterase inhibition assay was performed using donepezil as a standard, and computational insights were explored through molecular docking and ADME profiling. Results: Compounds 3a, 3d, 3e, 3f, and 3h exhibited excellent antioxidant activity compared to the standard. Most of the compounds exhibited moderate to good (3b, 3c, and 3h) AChE inhibitory activity. Molecular docking studies revealed conventional hydrogen bonding and π-π interactions with the enzyme’s active residues, facilitated by their electronegative groups and phenyl rings, respectively. In addition, a pharmacokinetic study was conducted using computational approach to assess druggability. The results demonstrated that compound 3b is an outstanding lead candidate with appreciable AChE inhibitory activity. Conclusions: The combined experimental and computational results of this study highlight the multifunctional nature of chalcone derivatives, suggesting their potential as promising therapeutic agents for the discovery of novel AChE inhibitors that could be employed in the management of Alzheimer’s disease and oxidative stress-related diseases.

Computational insight into the potential of tetraazapentacene as an active material in bulk-heterojunction solar cells

Computational insight into the potential of tetraazapentacene as an active material in bulk-heterojunction solar cells

Computational Insights into the Energetics of Single C2-C10 Aliphatic Moieties Adsorbed on the Hydrogenated Silicon (111) Surface.

Silicon's versatility as a semiconductor renders it indispensable across various domains, including electronics, sensors, and photovoltaics. Modifying hydrogen-terminated silicon surfaces with moiety adsorption offers a method to tailor the material's properties for specific applications. In this study, we employ ab initio density functional theory calculations to explore the energetics of single alkyl, 1-alkenyl, and 1-alkynyl moieties chemisorbed on the hydrogen-terminated silicon (111) surface. We analyze the interfacial dipole induced by Si-C bond formation that determines the Schottky barrier and examine the alignment of the frontier orbital energy levels with the silicon band structure to investigate charge transfer based on the tunneling mechanism. Our findings provide valuable insights into how aliphatic moiety functionalization affects interfacial electronic properties, offering clues for optimizing silicon-based devices.

Computational insight into synthetic alpha-glucosidase inhibitors: Homology modeling, docking, and molecular dynamics simulation

Diabetes mellitus is a metabolic disorder with high prevalence. As hyperglycemia is the main manifestation of diabetes, controlling postprandial hyperglycemia by inhibiting carbohydrate digestion is important to treat the disease. α-glucosidase is one of the carbohydrate hydrolyzing enzymes that breaks carbohydrates into monosaccharides and thus causes hyperglycemia. Therefore, α-glucosidase is an attractive target to decrease blood glucose level by suppressing carbohydrate digestion. There are clinically available α-glucosidase inhibitor drugs. However, these drugs are associated with adverse effects. Therefore, novel drugs with high efficacy and low adverse effects are needed. Heterocyclic compounds are under investigation to this end. In this study, active heterocyclic inhibitors were selected. The probable mode of action for these compounds was investigated through molecular docking and molecular dynamics (MD) simulation after the human α-glucosidase structure was built via homology modeling. The pharmacokinetic properties of the compounds were also assessed. The docking study showed that some of them have high binding potential to the α-glucosidase. However, the compounds with high binding potential gave enzyme-compound complexes with moderate stability. Compound 5 gave a complex with relatively higher stability. The computational pharmacokinetic study revealed that the compounds except compounds 12 and 13 would have good absorption or permeability for oral administration. Understanding the mechanism of action for the existing active compounds will be helpful to pursue the research for further applications and to design novel compounds with similar scaffolds. The findings of this study need further investigation through in vitro and in vivo methods.

Simulation and experiment on thermocatalytic conversions of SF6 by TiO2/SiC

Sulphur hexafluoride (SF6) is in urgent need of efficient degradation methods due to its widespread use in high-voltage electrical equipment and strict emission regulations. This study proposes a novel TiO2 /SiC catalyst synthesised via the sol–gel method for thermocatalytic SF6 conversion. Experimental evaluations revealed that TiO2/SiC achieves complete decomposition of 4% SF6 in a mixed gas (initial pressure: 0.14 MPa, temperature: 700°C) within 9 h, exhibiting a catalytic efficiency 3.73 times higher than pure TiO2. Key degradation products, including SOF4, SO2F2, SOF2, SO2, CF4, and CO2, were identified via GC-MS, with SO2 and SO2F2 as dominant sulphur-containing species. Density Functional Theory (DFT) simulations elucidated the catalytic mechanism: TiO2 facilitates charge transfer between Ti/O atoms and SF6, reducing the activation energy of the initial defluorination step. This energy reduction destabilises S–F bonds, accelerating SF6 breakdown into low-fluorine intermediates, which further oxidise to stable products. The SiC support enhances thermal stability by preventing TiO2 aggregation. TiO2/SiC combines superior efficiency, stability, and simpler synthesis, offering a practical solution for industrial SF6 waste treatment. This work bridges experimental and computational insights, advancing the design of high-performance catalysts for greenhouse gas mitigation. A novel and efficient catalyst, TiO2/SiC was successfully synthetised for the thermocatalytic conversion of SF6 waste gas by the sol–gel method. In addition, the catalytic defluorination mechanism was investigated via the density functional theory and transition state theory.

Enaminone‐Based Push–Pull Chromophores: Synthesis, Optical Properties, and Computational Insights

Chromophores incorporating enaminone units were efficiently synthesized using click‐type [2+2] cycloaddition‐retroelectrocyclization (CA‐RE) reactions. The preparation of electron‐rich alkynes necessary for these reactions initiated with the synthesis of alkynones via Sonogashira cross‐coupling, followed by nucleophilic conjugate addition to yield the target substrates. Subsequent reactions of these substrates with tetracyanoethylene (TCNE) through [2+2] CA‐RE achieved product yields of 85–93%, while employing the bulkier tetracyanoquinodimethane (TCNQ) produced the desired chromophores with yields ranging from 48% to 97%. These chromophores exhibited strong intramolecular charge transfer (ICT) absorption bands, with λmax values in between 460 and 695 nm as determined by UV/Vis studies. Furthermore, the chromophores demonstrated positive solvatochromism as an additional support for the ICT mechanism. Protonation‐neutralization experiments were performed to analyze the low‐energy absorption bands, verifying that they originate from ICT transitions. Computational techniques were applied to gain deeper insight into the ICT characteristics, and linear and nonlinear optical properties of the obtained compounds. The study involved geometry optimizations, TD‐DFT analyses, visualizations of frontier orbitals and electrostatic potential maps, as well as the calculation of parameters like, quinoid character, average polarizability, and first hyperpolarizability.

State of art in water jet machining: advances in modulated jet techniques and computational insights

State of art in water jet machining: advances in modulated jet techniques and computational insights

Deciphering Paraquat Remediation Pathways by Pseudomonas sp. FEN Through Computational Insights

Deciphering Paraquat Remediation Pathways by Pseudomonas sp. FEN Through Computational Insights

Computational insights into exploring the potential effects of environmental contaminants on human health

With rapid industrialization and urbanization, the increasing prevalence of air and water pollutants poses a significant threat to public health. Traditional research methods, such as epidemiological studies and in vitro/in vivo experiments, provide valuable biological insights but are often costly, time-consuming, and limited in scale. To address this gap, this study develops a machine learning-based approach to predict the carcinogenicity of pollutants. Using the dataset of carcinogenic and non-carcinogenic molecules that we collected, the pretrained KPGT model trained with molecular fingerprints and descriptors achieved an AUC of 0.83, surpassing traditional machine learning models. To validate this model, common pollutants from air and water sources were analyzed. Further clustering classified these pollutants into five distinct groups. Target prediction analysis identified key genes associated with representative pollutant molecules, such as MAPK1, MTOR, and PTPN11. GO and KEGG pathway analyses, along with survival analysis, revealed potential carcinogenic mechanisms and prognostic implications. Our findings contribute to improved pollution risk assessment and evidence-based environmental policy development, ultimately aiding in the mitigation of pollutant-related health risks.

Computational Insights into Structural, Optoelectronic, and Charge Transfer Properties of Monocarbonyl Curcumin Derivatives for Biomedical and Photonic Applications

Computational Insights into Structural, Optoelectronic, and Charge Transfer Properties of Monocarbonyl Curcumin Derivatives for Biomedical and Photonic Applications

Levofloxacin-Induced Insulin Aggregation: Biophysical and Computational Insights into Amyloidogenic Potential

Levofloxacin-Induced Insulin Aggregation: Biophysical and Computational Insights into Amyloidogenic Potential

Discovery of bis -chalcones and bis -pyrimidines as potential urease inhibitors: from synthesis to computational insights

Discovery of <i>bis</i> -chalcones and <i>bis</i> -pyrimidines as potential urease inhibitors: from synthesis to computational insights

Computational Insights into Aluminum and Cation Placement in Clinoptilolite: Optimizing DFT Parameters for Natural Zeolites, Part I

Several experimental studies have shown that clinoptilolite zeolite is a suitable candidate for the adsorption of pharmaceuticals and related compounds. However, there is a significant lack of detailed molecular-level insights regarding how the adsorbed species interact with the zeolite surface. In this work, we employ electronic structure calculations and propose a reliable set of input parameters within the CP2K code in the framework of dispersion-corrected density functional theory (DFT-D) to generate bulk models and study Al and cation distributions. We aim ultimately to investigate the adsorption of emerging contaminants at the clinoptilolite surfaces. Nine different exchange-correlation functionals were tested, and the results suggest that B97-D3 functional is the most robust for this system. Moreover, our results suggest that Na+ prefers the presence of Al at T2 and T3 sites but not at T1 sites and prefers being present in channel A and/or channel B. Ca2+ tends to favor being present in channel B and favors the presence of Al at T1, T2, and T3 sites. K+ prefers the smallest channel C and likes the presence of Al at T1 and T3 sites. Moreover, we found out that the optimization of the basis sets improves the coordination of extra-framework cations like Ca with the framework oxygens.

Experimental and Computational Insights into Polyurethane Plastic Waste Conversion to Microbial Bioplastic.

In this study, a seven-factor Hoke experimental design and the response surface methodology were used to optimize the fermentation conditions for the maximum polyhydroxyalkanoates (PHA) yield using polyurethane plastic waste (PUPW) as a source of carbon and energy for the microbial growth and biobased polyester production. The highest PHA yield (0.80g/L ± 0.01) was obtained under a pH of 8; a temperature of 35°C; a NaCl concentration of 5%; a PUPW concentration of 1%; an inoculum size of 15%, a monoculture of Pseudomonas rhizophila S211; and an incubation time of 6days. The response values predicted by the Hoke design model at each combination of factor levels aligned with the experimental results, and the analysis of variance demonstrated the predictability and accuracy of the postulated model. In addition to the experimental evidences, P. rhizophila genome was explored to predict the PUPW-degrading enzymes and the associated protein secretion systems. Moreover, physicochemical properties, phylogenetic analysis, and 3D structure of S211 LipA2 polyurethanase were elucidated through an in-silico approach. Taken all together, integrated experimental tests and computational modeling suggest that P. rhizophila S211 has the necessary enzymatic machinery to effectively convert the non-biodegradable PUPW into PHA bioplastics.

Decoding SP-D and glycan binding mechanisms using a novel computational workflow.

Decoding SP-D and glycan binding mechanisms using a novel computational workflow.

Enhancing drug delivery efficacy of antiretroviral drug Zalcitabine through silver/gold/platinum loaded silica nanocomposites: Surface modifications, molecular interactions, and computational insights.

Enhancing drug delivery efficacy of antiretroviral drug Zalcitabine through silver/gold/platinum loaded silica nanocomposites: Surface modifications, molecular interactions, and computational insights.

Unraveling the catalase dynamics: Biophysical and computational insights into co-solutes driven stabilization under extreme pH conditions.

Unraveling the catalase dynamics: Biophysical and computational insights into co-solutes driven stabilization under extreme pH conditions.

Structural and Computational Insights into Two Novel Organic Salts: XRD, Hirshfeld Analysis, DFT, and Molecular Docking Studies

Structural and Computational Insights into Two Novel Organic Salts: XRD, Hirshfeld Analysis, DFT, and Molecular Docking Studies

Exceptional CO2 and H2S adsorption by tuning micro/mesopore ratios with embedded graphene oxide/N-doped carbon quantum dots in MIL-101(Cr): Experimental and computational insights.

Exceptional CO2 and H2S adsorption by tuning micro/mesopore ratios with embedded graphene oxide/N-doped carbon quantum dots in MIL-101(Cr): Experimental and computational insights.