Search Results (8)

Forward osmosis (FO) membranes are commonly evaluated through macroscopic observables such as water flux and reverse solute flux. However, these quantities do not necessarily reveal whether water permeation and solute leakage remain governed by the same dominant transport pathways, particularly in heterogeneous nanostructured membranes where selective nanochannels and defect-mediated pores can contribute differently to solvent and solute transport. Here, we introduce a hierarchical diagnostic framework to assess transport coherence loss in heterogeneous FO membranes. The framework comprises a baseline model (BM), an extended model (EM) including chemistry–geometry coupling through accessibility loss, and a full model (FM) incorporating selective pore-size heterogeneity. The ratio of reverse solute flux to water flux $R_J=J_s/J_w$ is used as a regime-based diagnostic descriptor of transport organisation, while its normalised form maps coherence variations across the state-space defined by structural selectivity and nanochemical state. The results show that chemistry–geometry coupling produces the first clear reorganisation of the coherence landscape, whereas pore-size heterogeneity mainly broadens the response while preserving its dominant topology. Simulations based on both Monte Carlo and experimentally derived pore-size distributions show consistent trends. Overall, the BM–EM–FM hierarchy offers an interpretable framework for describing transport coherence loss and the emergence of leakage-prone regimes in heterogeneous FO membranes. Full article

Mild steel remains one of the most widely used structural materials in mechanical and industrial engineering due to its favorable mechanical performance and low cost. However, its high susceptibility to corrosion continues to cause significant operational and economic losses across engineering systems. This study presents a unified analytical framework for analyzing corrosion-related molecular and nanostructured systems using reverse degree-based topological descriptors, namely, the Reverse M-polynomial and Reverse NM-polynomial. The framework is demonstrated in two complementary stages relevant to corrosion engineering. First, an exploratory structure–property correlation analysis based on Quantitative Structure–Property Relationship (QSPR) principles is conducted for furan-based organic inhibitors reported in the literature, examining the relationship between reverse degree-based descriptors and inhibition efficiency on mild steel surfaces. The analysis reveals a strong statistical correlation within the analyzed dataset (r = 0.958), indicating the sensitivity of selected reverse topological descriptors to molecular structural variations. The statistical significance of the correlations was evaluated using p-values and F-statistics, confirming the reliability of the observed associations within the analyzed dataset. However, owing to the limited dataset size, no claims of external predictivity are made. Second, the framework is extended to advanced protective materials through the analytical formulation of reverse descriptors for nanoporous graphene nanoribbons containing 14-annulene pores, focusing exclusively on structural and topological characterization. These graphene structures are considered as potential physical barrier materials; however, in this study, the analysis is limited to structural descriptor characterization without modeling corrosion performance. This work provides analytical results for reverse degree-based descriptors of such graphene architectures. Overall, the findings establish a versatile analytical framework that supports exploratory structure–property investigations of organic inhibitors and provides descriptor-based structural benchmarks for graphene nanostructures, offering theoretical insights relevant to corrosion mitigation research. Full article

Background: Identifying potential targets for bioactive compounds is crucial for elucidating the mechanisms of action and drug development. Methods: This study presents ComTarget, a computational tool that integrates 3D molecular shape similarity analysis (based on combined 3D descriptors, C3DD) with reverse docking to predict protein targets for small molecules. ComTarget screens against a library of 4429 unique protein targets derived from 26,272 PDB complexes. Results: Validation on benchmark datasets (DEKOIS 2.0 and DUDE-Z) demonstrated that the C3DD molecular similarity calculation method effectively enriches active ligands by capturing critical 3D shape information not evident from chemical topology alone. It outperformed conventional 2D fingerprint methods and offered a favorable balance between shape sensitivity and computational efficiency, serving as a rapid pre-screening filter within the integrated workflow. For FDA-approved drugs (e.g., Imatinib, Aspirin) and natural products (e.g., Berberine). ComTarget identified targets consistent with reported therapeutic targets or putative off-targets in the literature, while also revealing potential targets aligned with the compounds’ pharmacological mechanisms. Conclusions: As a local program, ComTarget offers flexibility in computational resources customization and is freely available for polypharmacology studies, drug repurposing, and adverse reaction prediction. Full article

Granular deposition and grading strongly influence pore-space topology and hence hydraulic conductivity in natural and engineered porous media, yet quantitative links between deposition sequence, particle-scale morphology, pore-network descriptors, and permeability anisotropy remain incomplete. Here, we develop a process-based digital porous-media framework that couples discrete element method (DEM) deposition with pore-network characterization and Darcy-scale permeability evaluation. Two deposition sequences—normal grading (coarse-to-fine) and reverse grading (fine-to-coarse)—are simulated using bi-disperse particle sets with controlled size ratios. To further isolate the role of particle morphology, particle irregularity is parameterized by a Perlin-noise-based shape perturbation factor and incorporated into the DEM-generated packings. For each packing, pore networks are extracted and quantified in terms of pore/throat size distributions and connectivity, while pore-space complexity is measured via box-counting fractal dimension. Single-phase flow is solved under imposed pressure gradient, and intrinsic permeability is computed along three orthogonal directions to evaluate anisotropy. Results show that increasing size contrast reduces porosity, shifts pore and throat distributions toward smaller characteristic radii, increases pore-space fractal dimension, and yields a monotonic permeability reduction. For identical size ratios, reverse grading consistently yields higher permeability than normal grading, suggesting that deposition sequence exerts a strong control on the continuity and efficiency of effective flow pathways at the sample scale. Increasing particle irregularity decreases permeability and systematically modifies permeability anisotropy, transitioning from weak horizontal anisotropy toward near-isotropy and, at strong irregularity, toward preferential vertical permeability. The proposed framework provides a reproducible route to relate depositional history and particle morphology to pore-network structure and directional permeability, offering implications for filtration, packed-bed design, and sedimentary reservoir characterization. Full article

Over the past few years, the popularity of graphene as a potential 2D material has increased since graphene-based materials have applications in a variety of fields, including medicine, engineering, energy, and the environment. A large number of graphene sheets as well as an understanding of graphene’s structural hierarchy are critical to the development of graphene-based materials. For a variety of purposes, it is essential to understand the fundamental structural properties of graphene. Molecular descriptors were used in this study to investigate graphene sheets’ structural behaviour. Based on our findings, reverse degree-based molecular descriptors can significantly affect the exchange-correlation energy prediction. For the exchange-correlation energy of graphene sheets, a linear regression analysis was conducted using the reverse general inverse sum indeg descriptor, $R{G}_{IS{I}_{\left(p,q\right)}}$. From $R{G}_{IS{I}_{\left(p,q\right)}}$, a set of reverse topological descriptors can be obtained all at once as a special case, resulting in a model with a high correlation coefficient (R between 0.896 and 0.998). Used together, these reverse descriptors are graphed in relation to their response to graphene. Based on this study’s findings, it is possible to predict the exchange correlation energy as well as the geometric structures of graphene sheets with very little computational cost. Full article

The Quantitative Structure-Activity Relationship (QSAR) methodology was used to predict biological properties, i.e., the blood–brain distribution (log BB), fraction unbounded in the brain (f_u,brain), water-skin permeation (log K_p), binding to human plasma proteins (log K_a,HSA), and intestinal permeability (Caco-2), for three classes of fused azaisocytosine-containing congeners that were considered and tested as promising drug candidates. The compounds were characterized by lipophilic, structural, and electronic descriptors, i.e., chromatographic retention, topological polar surface area, polarizability, and molecular weight. Different reversed-phase liquid chromatography techniques were used to determine the chromatographic lipophilicity of the compounds that were tested, i.e., micellar liquid chromatography (MLC) with the ODS-2 column and polyoxyethylene lauryl ether (Brij 35) as the effluent component, an immobilized artificial membrane (IAM) chromatography with phosphatidylcholine column (IAM.PC.DD2) and chromatography with end-capped octadecylsilyl (ODS) column using aqueous solutions of acetonitrile as the mobile phases. Using multiple linear regression, we derived the statistically significant quantitative structure-activity relationships. All these QSAR equations were validated and were found to be very good. The investigations highlight the significance and possibilities of liquid chromatographic techniques with three different reversed-phase materials and QSARs methods in predicting the pharmacokinetic properties of our important organic compounds and reducing unethical animal testing. Full article

The proper pharmacological control of pain is a continuous challenge for patients and health care providers. Even the most widely used medications for pain treatment are still ineffective or unsafe for some patients, especially for those who suffer from chronic pain. Substances containing the chromone scaffold have shown a variety of biological activities, including analgesic effects. This work presents for the first time the centrally mediated antinociceptive activity of 5-O-methylcneorumchromone K (5-CK). Cold plate and tail flick tests in mice showed that the 5-CK-induced antinociception was dose-dependent, longer-lasting, and more efficacious than that induced by morphine. The 5-CK-induced antinociception was not reversed by the opioid antagonist naloxone. Topological descriptors (fingerprints) were employed to narrow the antagonist selection to further investigate 5-CK’s mechanism of action. Next, based on the results of fingerprints analysis, functional antagonist assays were conducted on nociceptive tests. The effect of 5-CK was completely reversed in both cold plate and tail-flick tests by GABA_A receptor antagonist bicuculline, but not by atropine or glibenclamide. Molecular docking studies suggest that 5-CK binds to the orthosteric binding site, with a similar binding profile to that observed for bicuculline and GABA. These results evidence that 5-CK has a centrally mediated antinociceptive effect, probably involving the activation of GABAergic pathways. Full article

In this study, 48 inhibitors were docked to 107 allosteric centers of human immunodeficiency virus 1 (HIV-1) reverse transcriptase from the Protein Data Bank (PDB). Based on the average binding scores, quantitative structure-activity relationship (QSAR) equations were constructed in order to elucidate directions of further development in the design of inhibitors. Such developments, informed by structural data, must have a focus on activity against mutated forms of the enzyme, which are the cause of the emergence of multidrug-resistant viral strains. Docking studies employed the HYDE scoring function. Two types of QSARs have been considered: One based on topological descriptors and the other on structural fragments of the inhibitors. Both methods gave similar results, indicating substructures favoring binding to mutated forms of the enzyme. Full article