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In structure-based drug design, accurately estimating the binding affinity between a candidate ligand and its protein receptor is a central challenge. Recent advances in artificial intelligence, particularly deep learning, have demonstrated superior performance over traditional empirical and physics-based methods for this task, enabled by the growing availability of structural and experimental affinity data. In this work, we introduce DeepGGL, a deep convolutional neural network that integrates residual connections and an attention mechanism within a geometric graph learning framework. By leveraging multiscale weighted colored bipartite subgraphs, DeepGGL effectively captures fine-grained atom-level interactions in protein-ligand complexes across multiple scales. We benchmarked DeepGGL against established models on CASF-2013 and CASF-2016, where it achieved state-of-the-art performance with significant improvements across diverse evaluation metrics. To further assess robustness and generalization, we tested the model on the CSAR-NRC-HiQ dataset and the PDBbind v2019 holdout set. DeepGGL consistently maintained high predictive accuracy, highlighting its adaptability and reliability for binding affinity prediction in structure-based drug discovery.

This study investigated the development and optimization of sol–gel synthesized Ni/ZrO2-Al2O3 catalysts, aiming to enhance the decomposition efficiency of CF4, a potent greenhouse gas. The research focused on improving catalytic performance at temperatures below 700 °C by incorporating zirconium and tungsten as co-catalysts. Comprehensive characterization techniques including XRD, BET, FTIR, and XPS were employed to elucidate the structural and chemical properties contributing to the catalyst’s activity and durability. Various synthesis ratios, heat treatment temperatures, and co-catalyst addition positions were explored to identify the optimal conditions for CF4 decomposition. The catalyst composition with 7.5 wt% ZrO2 and 3 wt% WO3 on Al2O3 (3W-S3) achieved over 99% CF4 decomposition efficiency at 550 °C. The study revealed that the appropriate incorporation of ZrO2 enhanced the specific surface area and prevented sintering, while the addition of tungsten further improved the distribution of active sites. These findings offer valuable insights into the design of more efficient catalysts for environmental applications, particularly in mitigating emissions from semiconductor manufacturing processes.

Tropomyosin kinase receptor B (TrkB) has been explored as a therapeutic target for neurological and psychiatric disorders. However, the development of TrkB agonists was hindered by our poor understanding of the TrkB agonist binding location and affinity (both affect the regulation of disorder types). This motivated us to develop a combined computational and experimental approach to study TrkB binders. First, we developed a docking method to simulate the binding affinity of TrkB and binders identified by our magnetic drug screening platform from Gotu kola extracts. The Fred Docking scores from the docking computation showed strong agreement with the experimental results. Subsequently, using this screening platform, we identified a list of compounds from the NIH clinical collection library and applied the same docking studies. From the Fred Docking scores, we selected two compounds for TrkB activation tests. Interestingly, the ability of the compounds to increase dendritic arborization in hippocampal neurons matched well with the computational results. Finally, we performed a detailed binding analysis of the top candidates and compared them with the best-characterized TrkB agonist, 7,8-dyhydroxyflavon. The screening platform directly identifies TrkB binders, and the computational approach allows for the quick selection of top candidates with potential biological activities based on the docking scores.

Accurate prediction of ligand-receptor binding affinity is crucial in structure-based drug design, significantly impacting the development of effective drugs. Recent advances in machine learning (ML)–based scoring functions have improved these predictions, yet challenges remain in modeling complex molecular interactions. This study introduces the AGL-EAT-Score, a scoring function that integrates extended atom-type multiscale weighted colored subgraphs with algebraic graph theory. This approach leverages the eigenvalues and eigenvectors of graph Laplacian and adjacency matrices to capture high-level details of specific atom pairwise interactions. Evaluated against benchmark datasets such as CASF-2016, CASF-2013, and the Cathepsin S dataset, the AGL-EAT-Score demonstrates notable accuracy, outperforming existing traditional and ML-based methods. The model’s strength lies in its comprehensive similarity analysis, examining protein sequence, ligand structure, and binding site similarities, thus ensuring minimal bias and over-representation in the training sets. The use of extended atom types in graph coloring enhances the model’s capability to capture the intricacies of protein-ligand interactions. The AGL-EAT-Score marks a significant advancement in drug design, offering a tool that could potentially refine and accelerate the drug discovery process. Scientific Contribution The AGL-EAT-Score presents an algebraic graph-based framework that predicts ligand-receptor binding affinity by constructing multiscale weighted colored subgraphs from the 3D structure of protein-ligand complexes. It improves prediction accuracy by modeling interactions between extended atom types, addressing challenges like dataset bias and over-representation. Benchmark evaluations demonstrate that AGL-EAT-Score outperforms existing methods, offering a robust and systematic tool for structure-based drug design.

The escalating environmental concerns regarding plastic waste necessitate innovative strategies for waste management and resource utilization. This study presents a novel approach for synthesizing high surface area activated carbon (AC) through a single-step process, using polyethylene terephthalate (PET) plastic waste as the precursor and potassium hydroxide (KOH) as the activating agent. The optimal activation conditions were established using a weight ratio of 1:1 of PET to KOH and annealing for 15 min at 700 °C. The conversion efficiency of PET plastic trash into AC exceeded 20%. The materials underwent thorough characterization using scanning electron microscopy (SEM), X-ray diffraction (XRD), Brunauer–Emmett–Teller (BET) analysis, and Fourier transform infrared spectroscopy (FTIR). The AC obtained has a mesoporous structure and a surface area of 1831.166 m 2 /g. The AC derived from plastic waste demonstrated excellent efficiency in removing organic dyes from aqueous solutions, achieving a maximum adsorption capacity of 131.58 mg/g. The successful transformation of plastic waste into a valuable resource underscores the importance of innovative approaches to mitigating environmental degradation. The synthesized AC’s efficacy in adsorption-based remediation strategies demonstrates its potential for addressing various pollution issues and opens new avenues for valuing plastic waste. Graphical Abstract Highlights PET plastic waste was converted into high-surface-area activated carbon using a single-step process. A surface area of 1831.166 m 2 /g was achieved, enhancing adsorption efficiency for pollutants. Activated carbon removed MB with a maximum adsorption capacity of 131.58 mg/g. SEM, XRD, BET, and FTIR were used to confirm the porous structure and chemical properties. A sustainable waste-to-resource approach was demonstrated for environmental remediation.

The use of nano-additives is widely recognized as a cheap and effective pathway to improve the performance of lubrication by minimizing the energy loss from friction and wear, especially in diesel engines. In this work, a simple and scalable protocol was proposed to fabricate a graphene additive to improve the engine lubricant oil. Graphene nanoplates (GNPs) were obtained by a one-step chemical exfoliation of natural graphite and were successfully modified with a surfactant and an organic compound to obtain a modified GNP additive, that can be facilely dispersed in lubricant oil. The GNPs and modified GNP additive were characterized using scanning electron microscopy, X-ray diffraction, atomic force microscopy, Raman spectroscopy, and Fourier-transform infrared spectroscopy. The prepared GNPs had wrinkled and crumpled structures with a diameter of 10–30 µm and a thickness of less than 15 nm. After modification, the GNP surfaces were uniformly covered with the organic compound. The addition of the modified GNP additive to the engine lubricant oil significantly enhanced the friction and antiwear performance. The highest reduction of 35% was determined for the wear scar diameter with a GNP additive concentration of approximately 0.05%. The mechanism for lubrication enhancement by graphene additives was also briefly discussed.

Purpose The solution plasma process (SPP) is a novel electrical discharge process for the green synthesis of nanomaterials, in which an atmospheric nonequilibrium plasma is generated at room temperature in a liquid environment such as water or an organic solvent; or a mixture of both. Methods In this study, SPP was employed as a green approach for synthesizing selenium nanoparticles (Se NPs) in an ethanol–water solution at room temperature. The prepared Se NPs were comprehensively characterized using ultraviolet–visible (UV–Vis) spectrophotometry, fourier transform infrared spectroscopy, X-ray diffraction, dynamic light scattering particle size analysis, scanning electron microscopy, and transmission electron microscopy. Results The results showed that the Se NPs in the ethanol–water solution were uniform flower-like nanostructures with diameters ranging from 50 to 100 nm. The as-prepared Se NPs were of high purity and underwent partial oxidation. Conclusion The synthesized Se NPs exhibited notable antimicrobial properties against the pathogenic Escherichia coli and Staphylococcus aureus bacteria, and Candida albicans yeast. Graphical Abstract

The growing presence of hazardous organic pollutants in wastewater poses severe environmental and health risks, necessitating sustainable and efficient treatment solutions. Traditional remediation methods have limitations, highlighting the need for innovative approaches. A green synthesis method was developed to produce TiO2-CeO2 nanocomposites using Cleistocalyx operculatus leaf extract. The photocatalytic efficiency of the synthesized nanocomposites was evaluated under simulated sunlight by degrading Methylene Blue (MB) dye. Various compositions were tested to determine the optimal performance. The 0.1% TiO2-CeO2 nanocomposite achieved the highest degradation efficiency (95.06% in 150 min) with a reaction rate constant (k) of 18.5 × 10−2 min−1, outperforming commercial TiO2 (P25, 74.85%, k ≈ 3.7 × 10−2 min−1). Additionally, the material maintained excellent stability over eight consecutive cycles with only a slight decrease in efficiency from 95.85% to 93.28%. The enhanced photocatalytic activity is attributed to the synergistic effects of CeO2 incorporation, which enhances charge separation, extends visible light absorption, and promotes reactive oxygen species (ROS) generation. These findings highlight the potential of green-synthesized TiO2-CeO2 nanocomposites as a cost-effective and sustainable solution for wastewater treatment.

Environmental pollution is increasingly negatively affecting our lives, requiring advanced methods and materials that are highly effective for pollutant treatment processes. This study proposes the synthesis of zero-valent iron nanoparticles (nZVIs) through a green chemistry approach, which were then encapsulated in calcium alginate (Alg) spheres for application in the treatment of Rhodamine B (RhB) and Methylene Blue (MB). The morphology and structure of the alginate particles encapsulating zero-valent iron nanoparticles (Alg-nZVIs) were characterized by scanning electron microscopy (SEM), energy-dispersive X-ray spectroscopy (EDS), Fourier transform infrared spectroscopy (FTIR), and X-ray diffraction (XRD). The analytical results indicate that the material consists of alginate polymer particles with an average diameter of 2.5 mm, containing nZVIs with an average size of 50 nm. Factors affecting the treatment of RhB and MB, including the proportion of components in the material, pH, solution concentration, and treatment time, were studied and evaluated by UV–Vis method. This material showed high removal efficiency for RhB and MB. 0.08 ml nZVIs in 1 g of Alg-nZVIs beads treated 100 mL of RhB 5 mg/L at pH 7 for 180 min with an efficiency of over 90%. The same amount of material effectively treated 100 mL of MB 5 g/L at pH 3 for 120 min with an efficiency of over 90%. The prepared Alg-nZVIs spheres were easy to collect and reuse for up to 6 cycles with a decrease in removal efficiency of less than 15%. Alginate-nZVIs spheres are derived from readily available and natural materials through a clean, cost-effective, and economically sustainable technique. Graphical Abstract

This study investigates the potential of a combined photocatalysis–adsorption approach to effectively degrade near wash yellow (NWY), a commonly used and highly persistent dye in the textile industry, notorious for its challenging treatment and removal from wastewater due to its colorfastness. A chitosan–glycerol (CTiG) film combined with titanium dioxide was examined in both batch and continuous-flow experiments under visible solar irradiation. The results show that this combination was more effective than a pure chitosan film (60%) or chitosan–glycerol film (63%), with up to 83% degradation of NWY achieved in just 60 min of visible solar irradiation. The kinetics of the film were evaluated using both pseudo-first-order and Langmuir–Hinshelwood kinetic models. The rate constant values (k, min−1) decreased with increasing NWY concentration from 20 to 80 mg/L, and k was found to be greater than twice as high under visible solar irradiation as it was in the dark. The Langmuir–Hinshelwood model’s KLH (reaction rate constant) and KL (adsorption coefficient) values were 0.029 mg/L·min and 0.019 L/mg, respectively. The optimal conditions for NWY degradation were found to be 4% TiO2 to chitosan ratio, glycerol/chitosan ratio of 40%, and a pH of 7. In the continuous-flow model, the CTiG film was submerged in an 8 L NWY solution (80 mg/L) and degraded at a rate of 22.6 mg NWY/g film under natural sunlight. This study contributes to the development of effective and sustainable methods for the degradation of dyes from textile industry wastewater.