Search Results (2,122)

Herpes simplex virus type 1 (HSV-1) remains a significant pathogen, particularly in immunocompromised patients. The emergence of drug-resistant strains necessitates alternative therapeutic agents. Lentinula edodes (LE), Hypsizygus marmoreus, and Pleurotus eryngii are edible mushrooms with recognized medicinal properties. However, their effects on drug-resistant HSV-1 remain unclear. This study characterized metabolites from high-temperature/high-pressure (121 °C) water extracts of fresh and dried fruiting bodies and evaluated anti-HSV-1 activities using in vitro and in silico approaches. Metabolic profiles were analyzed by electrospray ionization–quadrupole time-of-flight mass spectrometry. Antiviral activity against HSV-1 KOS (wild-type) and HSV-1 dxpiii (drug-resistant) strains was assessed by plaque assays and qPCR. Molecular docking and network pharmacology were performed on candidate compounds. LE extract from dried mushroom tended to show the highest levels of selected major bioactive constituents, along with greater antioxidant activities. All extracts significantly inhibited viral infection and gene expression in both strains. LE extract from dried mushroom modulated the expression of NFKB1 and IL6. Molecular docking analysis revealed that eritanidine showed a predicted binding affinity to HSV-1 DNA polymerase (−7.95 kcal/mol). Additionally, eritanidine, 5′-methylthioadenosine, and 3-indoleacrylic acid were predicted to interact with TNF and MAPK1. Several compounds also demonstrated favorable drug-likeness properties. Overall, these mushroom extracts are promising natural sources of antiviral agents against HSV-1, including drug-resistant variants. Full article

Background/Objectives: Cyclotides are plant-derived macrocyclic peptides distinguished by their head-to-tail cyclized backbone and cystine knot motif, which confer remarkable stability against thermal, enzymatic, and chemical degradation. These features, combined with a compact and rigid structure, position cyclotides as promising scaffolds for future antibacterial agents in response to the escalating threat of multidrug-resistant (MDR) pathogens and the stagnation of conventional antibiotic discovery pipelines. This review summarizes the structural features, antibacterial mechanisms, bioengineering strategies, and translational potential of cyclotides against MDR infections. Methods: A narrative review of the literature was conducted using recent original research articles and reviews on cyclotide structure, antibacterial activity, bioengineering, computational modeling, and pharmaceutical applications. Results: Cyclotides exhibit potent antimicrobial activity, primarily through membrane disruption mediated by amphipathic surfaces and affinity for anionic bacterial membranes. Some variants also demonstrate anti-virulence and antibiofilm properties, broadening their therapeutic relevance for difficult-to-treat infections. Bioengineering approaches, including epitope grafting and rational design, have improved selectivity and potency while reducing cytotoxicity. Advances in computational modeling, molecular dynamics, and artificial intelligence have accelerated the prediction and optimization of antimicrobial activity, toxicity, and pharmacokinetic properties. Conclusions: Innovations in synthesis, including recombinant expression and enzymatic ligation, are helping overcome translational barriers related to cost and scalability. Although challenges remain in oral bioavailability and systemic delivery, strategies such as lipidation and scaffold modification support the development of cyclotide-based therapeutics as adaptable platforms for peptide drug discovery. Full article

Background/Objectives: Microtubule-targeting agents represent a pillar of cancer chemotherapy; however, their clinical utility is constrained by significant toxicity, pharmacokinetic instability, and susceptibility to multidrug resistance transporters. This study aimed to explore the impact of replacing substituted phenyl rings with a naphthalene moiety in sulfonamide-based colchicine-site ligands, with the goal of identifying new antiproliferative candidates with improved profiles. Methods: We designed, synthesized, and evaluated a library of 35 naphthalene sulfonamides bearing varied aryl groups and sulfonamide nitrogen substituents. We assessed the antiproliferative activity against multiple cancer cell lines. Mechanistic studies, including fluorescence microscopy, cell cycle analysis, and cell death assays, were performed to evaluate the effect of these compounds on microtubule polymerization dynamics and cell fate. Molecular docking and in silico pharmacokinetic profiling were carried out to support the proposed binding mode at the colchicine site and to assess drug-likeness. Results: Exclusively, compounds bearing a trimethoxyphenyl group showed antiproliferative activity in the submicromolar range, thus identifying it as a structural requirement. The most potent compound (2) reached double-digit nanomolar IC₅₀ values (67–104 nM) across multiple cancer cell lines. Microscopy confirmed intracellular disruption of microtubule polymerization. Unlike colchicine, these compounds did not induce canonical mitotic arrest but instead triggered apoptotic cell death. In silico analyses supported binding at the colchicine site and revealed favorable predicted pharmacokinetic properties. Conclusions: The naphthalene sulfonamides described herein demonstrate potent antiproliferative activity through a distinct mechanism compared to colchicine, and their favorable in silico profiles position them as promising candidates for further development as antitumor agents. Full article

Shuanghuanglian oral liquid (SHL) is widely used in companion animals and poultry, but its molecular mechanism in pneumonia–myocarditis comorbidity and heart–lung inflammatory crosstalk remains largely unclear. This computational study investigated the conserved AKT1/HIF1A-mediated immunoregulatory mechanism of SHL and its cross-species translational potential in veterinary medicine. Network pharmacology was integrated with GO, KEGG, and Reactome enrichment analyses, protein–protein interaction network construction, ADMET evaluation, cross-species sequence homology analysis (human, dog, cattle, and pig), molecular docking, and molecular dynamics simulation. A total of 61 active compounds, 251 putative targets, and 52 common targets associated with pneumonia and myocarditis were identified. These targets were mainly enriched in inflammation- and immune-related pathways, including TNF, IL-17, AGE–RAGE, and PPAR signaling. AKT1 and HIF1A showed high sequence conservation across species (85–98%). Key compounds exhibited favorable binding affinity to AKT1, and molecular dynamics simulation suggested the stability of the Baicalein–AKT1 complex. ADMET analysis suggested favorable pharmacokinetic properties and low predicted toxicity. These findings suggest that SHL may potentially alleviate pneumonia and myocarditis through modulation of the conserved AKT1/HIF1A axis and support its potential as a complementary therapeutic approach for managing heart–lung inflammatory diseases in multiple livestock species. This entirely computational study highlights promising mechanisms that should be further validated in vivo. Full article

Marburg virus (MARV), a highly pathogenic member of the Filoviridae family, causes severe hemorrhagic fever with a high case fatality rate and currently lacks effective therapeutics. The viral entry process, mediated by the interaction between the MARV glycoprotein (GP) and host receptor C-type lectin domain family 4 member M (CLEC4M) (L-SIGN), represents a critical target for early-stage intervention. The active compounds from BindingDB and the decoy from DUDE were used. The RDKit was used for feature engineering. Machine learning models were trained on an initial dataset consisting of 56 active chemicals and 1232 decoys. Among the tested algorithms, the Random Forest model demonstrated superior performance, achieving the highest discriminative ability (AUC = 0.93, MCC = 0.88) on the test set. Virtual screening of 11,032 phytochemicals resulted in 120 predicted actives, of which 42 compounds satisfied drug-likeness criteria. Subsequent molecular docking identified three lead compounds (PubChem IDs: 42608095, 5281601, and 11243993) with moderate-to-promising binding affinities (−6.3 to −6.5 kcal/mol) toward the CLEC4M binding site. ADMET analysis revealed favorable pharmacokinetic and toxicity profiles for the selected lead compounds. DFT calculations of the three compounds highlighted their electronic stability and reactive nature, indicating that PubChem IDs 42608095 and 5281601 possess particularly stable electronic properties conducive to favorable target interactions. Combining machine learning models with molecular docking and Molecular Dynamics (MD) simulations worked well in finding promising phytochemical inhibitors. The MM/GBSA binding free energy calculations further confirmed binding affinities, with values of −10.83 and −11.08 kcal/mol, respectively, suggesting favorable complex stability. These findings provide a pathway for developing new antiviral agents against MARV, pending further experimental validation and optimization. Full article

Objective: Cerebral ischemia/reperfusion (I/R) injury triggers oxidative stress, neuroinflammation, neuronal degeneration, and white matter damage not only in directly affected cerebral regions but also in remote brain areas such as the cerebellum. Skimmianine, a naturally occurring furoquinoline alkaloid, has been reported to possess antioxidant and anti-inflammatory properties. This study investigated the protective effects of skimmianine pretreatment against secondary cerebellar injury following experimental cerebral I/R. Materials and Methods: Thirty-two female Wistar rats were randomly assigned to sham, Skimmianine, I/R, and I/R + Skimmianine groups (n = 8/group). Cerebral I/R was induced by transient middle cerebral artery occlusion for 60 min followed by 23 h reperfusion. Skimmianine (40 mg/kg/day, intraperitoneally) was administered for 14 days before ischemia induction. Oxidative stress markers, neuroinflammatory mediators, histopathological alterations, behavioral outcomes, and ultrastructural changes were evaluated. In addition, network pharmacology and molecular docking analyses were performed to explore potential molecular mechanisms. Results: Cerebral I/R significantly decreased TAS levels compared with sham (0.89 ± 0.15 vs. 1.52 ± 0.18 mmol Trolox Eq/L) and increased TOS (15.60 ± 3.03 vs. 6.80 ± 1.41 µmol H₂O₂ Eq/L), OSI (17.48 ± 0.50 vs. 4.43 ± 0.47), TNF-α (68.4 ± 10.2 vs. 18.6 ± 4.4 pg/mL), Iba1 (41.3 ± 9.7 vs. 11.7 ± 1.6 pg/mL), and GFAP levels (334.5 ± 12.5 vs. 87.7 ± 9.5 ng/mL; all p < 0.001). I/R also impaired motor performance, as shown by increased beam crossing time (11.7 ± 2.2 vs. 4.8 ± 0.7 s) and grid foot fault rate (18.6 ± 4.0% vs. 3.4 ± 1.1%). Skimmianine pretreatment significantly improved these alterations, increasing TAS to 1.29 ± 0.20 mmol Trolox Eq/L and reducing TOS, OSI, TNF-α, Iba1, and GFAP levels to 9.20 ± 2.04, 7.07 ± 0.47, 34.9 ± 7.4, 24.2 ± 6.9, and 237.0 ± 7.9, respectively, compared with the untreated I/R group. Histopathological scores for Purkinje cell loss, edema, vascular congestion, and TNF-α expression were also significantly reduced by skimmianine. Quantitative TEM analysis showed that I/R reduced myelin thickness (0.29 ± 0.05 vs. 0.53 ± 0.07 µm), increased G-ratio values (0.75 ± 0.05 vs. 0.63 ± 0.04), and increased vacuolized fibers (24.70 ± 4.20% vs. 3.20 ± 1.10%), whereas skimmianine partially restored myelin thickness (0.42 ± 0.07 µm), reduced the G-ratio (0.68 ± 0.05), and decreased vacuolized fibers (11.20 ± 2.80%; p < 0.05 vs. I/R). Molecular docking demonstrated favorable binding between skimmianine and TNF-α, with a predicted binding energy of −6.953 kcal/mol. Conclusions: These findings indicate that skimmianine exerts neuroprotective effects against secondary cerebellar injury following cerebral I/R through coordinated modulation of oxidative stress, systemic neuroinflammatory responses, astroglial injury-associated pathways, and inflammation-related mechanisms. Full article

This study investigates the influence of organic matter properties in surface waters on the efficiency of single- and two-stage coagulation processes in drinking water treatment plants. The research was conducted at three treatment plants supplied by different surface water sources over a 15-month monitoring period. The analyzed parameters included total and dissolved organic carbon (TOC and DOC), biodegradable dissolved organic carbon (BDOC), water color, UV absorbance, zeta potential, and molecular weight distribution of organic substances. The results showed that coagulation efficiency depends strongly on both the concentration and the molecular characteristics of organic matter. The highest removal efficiency was observed for high-molecular-weight fractions (>2.0 kDa), mainly humic substances, whereas low-molecular-weight compounds were removed less effectively. The study also demonstrated that surrogate spectrophotometric parameters, particularly UV₂₅₄ absorbance and color at 410 nm, can be used to monitor and optimize the coagulation process. Given the increasing frequency of extreme climate events and rapid shifts in raw water quality, optimizing single- and two-stage coagulation configurations has become an urgent operational necessity. This work provides a novel direct linkage between simple spectrophotometric indexes and precise chromatographic molecular ranges, delivering an immediate, high-impact predictive tool for real-time dosage optimization in water treatment engineering. Full article

Background/Objectives: Poly (ADP-ribose) polymerase (PARP1) has emerged as a promising therapeutic target in human breast cancer particularly in BRCA1/2 mutation carriers where a synthetic lethal interaction leads to massive tumor cell death upon specific inhibitors’ administration. Current clinically approved PARP inhibitors (Talazoparib and Olaparib) show outstanding therapeutic capabilities but suffer from severe side effects. Most importantly, some of them can cause life-threatening cardiotoxicity through hERG off-target effects. Here, we performed an extensive study to identify lead compounds with improved binding modes and favorable predicted pharmacokinetics using an integrated computational strategy. Methods: An artificial intelligence-driven drug design (AIDDISON™ v2023) workflow was employed to search ultra-large chemical space libraries for active compounds, which were then optimized via computer-aided methods to form a PARP-Tailored Database (PTD). This database was then analyzed through a virtual screening workflow, molecular docking studies, molecular dynamics (MD) simulations, MM/GBSA binding free energy calculations, DFT analysis and ADME/Tox predictions using the Schrödinger suite (v2023-2), MobaXterm v25.2, Gaussian 16.0, ProTox-3 and Pred-hERG v5.0 respectively. Results: Three compounds (1a–1c) were identified as promising candidates. Among them 1a appeared to be the most active compound with a favorable docking score (−9.488 kcal/mol) that is not only higher than 1b and 1c but also higher than that of Talazoparib (−6.778 kcal/mol). MD simulations of 1a–1c in the active site revealed an average RMSD of ~2.5–3.6 Å which is better compared to the parent Talazoparib (5.6 Å). Interestingly, on the 250 ns extended MD study, 1a exhibited a slightly reduced RMSD between 2.4 and 3.2 Å, whereas Talazoparib retained higher fluctuations of ~5 Å to 6 Å. MM/GBSA binding energy analysis indicated 1a to have better predicted binding affinity (−67.820 kcal/mol), which is also better than Talazoparib (−63.734 kcal/mol). DFT calculations showed good electronic properties and in silico ADMET studies also indicated 1a to have good drug-likeness and lower predicted hepatotoxicity and cardiotoxicity risk. Conclusions: These findings identify compound 1a as a promising lead, while compounds 1b and 1c remain viable candidates for further optimization. However, experimental validation is critical to confirm the predicted biological activity and safety profiles. Full article

Background/Objectives: Coagulation factor XIa (FXIa) has emerged as a promising therapeutic target for the development of safer anticoagulant therapies with reduced bleeding risk. This study aimed to develop an interpretable machine learning-driven quantitative structure–activity relationship (QSAR) framework for predicting the inhibitory activity of FXIa inhibitors and supporting virtual screening applications. Methods: A total of 3026 curated compounds retrieved from the ChEMBL database were used for regression modeling, whereas 2119 compounds were retained for classification modeling after excluding intermediate-activity molecules. Molecular descriptors were generated using RDKit, Mordred, and Morgan fingerprint representations. Following preprocessing and feature selection, multiple machine learning algorithms were systematically benchmarked. Model robustness and reliability were further evaluated using 5-fold cross-validation, scaffold-aware validation, applicability domain analysis, and Y-randomization testing. Results: Nonlinear ensemble learning approaches consistently outperformed conventional linear algorithms. The optimized HistGradientBoostingRegressor achieved the best regression performance, with an independent test-set R² value of 0.711 and an RMSE value of 0.759, whereas the optimized classification model achieved accuracies approaching 95%. SHAP analysis identified lipophilicity-related descriptors, aromatic scaffold organization, electrostatic surface properties, and molecular topology as major contributors to FXIa inhibitory activity prediction. In addition, a proof-of-concept virtual screening workflow successfully identified several candidate compounds exhibiting high predicted pKi values and elevated active-class probabilities. Conclusions: The proposed framework provides a robust, interpretable, and reproducible machine learning-driven QSAR strategy for FXIa inhibitor discovery and may facilitate future virtual screening campaigns and medicinal chemistry optimization studies targeting FXIa-associated anticoagulant drug discovery. Full article

Water-soluble viscosity-modifying admixtures (VMAs) were initially introduced into cementitious materials to enhance cohesion, stability and resistance to bleeding and segregation. With the development of self-compacting concrete, underwater concrete, grouting materials and 3D-printed cementitious materials, VMAs have become increasingly important for regulating rheological behavior, workability retention, shape retention and construction processability. Recent studies further indicate that VMAs can affect not only fresh-state properties, but also hydration kinetics, early-age microstructure evolution, mechanical performance, transport behavior and long-term durability. This review systematically summarizes the types, action mechanisms, and performance effects of water-soluble VMAs in cementitious materials. Particular emphasis is placed on the relationships among the molecular structure, liquid phase viscosity enhancement, particle adsorption and bridging, polymer-chain entanglement, ion-responsiveness, admixture compatibility, and microstructure evolution. The review shows that the effects of VMAs are not governed solely by admixture type or dosage, but depend strongly on molecular mass, functional groups, substituent composition, charge characteristics, binder chemistry, and the pore solution environment. Finally, current research gaps and future directions are discussed, including quantitative structure–mechanism–performance relationships, applicability in low-carbon binders, service-life prediction, and application-oriented VMA design. Full article

Background/Objectives: Antimicrobial peptides (AMPs) are promising candidates against multidrug-resistant bacteria, although their clinical translation is frequently limited by cytotoxicity. In this study, we investigated whether combinations of structurally related oligomeric analogs could cooperatively enhance bacterial membrane targeting while redistributing the associated cytotoxic burden. Methods: Monomeric, dimeric, and tetrameric AMPs were evaluated through antimicrobial susceptibility testing, checkerboard interaction assays, RAW 264.7 macrophage cytotoxicity assays, and all-atom molecular dynamics simulations, including biased membrane insertion and umbrella sampling analyses. In addition, we introduced the Combinatorial Therapeutic Index (CTI) as an exploratory metric to estimate the predicted reduction in cytotoxic burden associated with peptide combinations. Results: Cytotoxicity varied substantially among oligomeric forms, with larger and more hydrophobic peptides, particularly tetramers, exhibiting the highest cytotoxicity. Additive effects were observed in checkerboard assays involving linear, dimeric, and tetrameric forms, supporting the redistribution of the toxic burden and enabling the beneficial membrane-interaction properties of hydrophobic linear peptides to be leveraged at lower cytotoxic cost. Predicted therapeutic improvement ranged from approximately twofold for the SET-M33:L33 combination to nearly ninefold for the SET-M33:DIM-33:L8 triple combination. Molecular dynamics simulations revealed non-redundant membrane interaction behaviors, with smaller peptides exhibiting deeper membrane insertion and the dimeric form favoring interfacial membrane engagement. Conclusions: These findings support a cooperative formulation strategy in which structurally related SET-M33 oligomers contribute complementary antibacterial functions while reducing the predicted cytotoxic burden. Further experimental validation using direct cytotoxicity assays of complete peptide mixtures will be necessary to confirm the therapeutic potential of these formulations. Full article

Tenebrio molitor (Coleoptera: Tenebrionidae) is a suitable candidate for use as a biomass resource, recognized for its large-scale breeding and the high nutritional value of its protein content. Feeding obese Zucker rats the cuticle of T. molitor enhances fatty liver metabolism through the mediation of gut microorganisms. Cuticular proteins are demonstrated to be pivotal in the formation of the insect cuticle throughout the developmental stage. The endocuticle structural glycoprotein (Abd) belongs to the RR-1 subclass, a major group of structural cuticular proteins characterized by the conserved Rebers–Riddiford (RR) motif. Nevertheless, there remains a paucity of research into the molecular properties and functions of SgAbd (endocuticle structural glycoprotein) in Coleoptera. In this study, we successfully identified and described the gene TmAbd5 in T. molitor. The coding sequence of TmAbd5 is 306 bp, corresponding to a 101-aa protein. The functional domain predicted that TmAbd5 consists of a signal peptide and a chitin-binding domain 4 (ChtBD4). Motif prediction analysis indicated that TmAbd5 belongs to the CPR (cuticular proteins with Rebers–Riddiford consensus) family with the RR-1 motif. Expression analysis revealed that TmAbd5 is upregulated in the integument, particularly during the first three days of development in the 13th instar stage. Although the RNAi-mediated silencing of TmAbd5 did not cause apparent phenotypic abnormalities and the insects successfully molted into pupae, histological examination revealed a substantial thickening of the endocuticle at 72 h post-pupation, along with a notable increase in lamellar spacing and a disrupted pore canal. In summary, TmAbd5 contributes to the formation and structural organization of the endocuticle, which provides a theoretical basis for the screening of target genes for cuticle development and for the effective utilization of cuticle resources in T. molitor. Full article

The recycling of acrylonitrile-butadiene-styrene (ABS) is crucial for a circular plastics economy, but repeated extrusion induces degradation that limits its reuse. This study establishes a comprehensive structure-property evolution mechanism for ABS 757K over five extrusion cycles and develops a novel image-recognition model for aging degree prediction. Multi-faceted characterization revealed that chain scission, oxidation of the polybutadiene (PB) phase, and the formation of chromophores led to progressive embrittlement, yellowing, and reduced thermal-oxidative stability. A key finding from Energy Dispersive Spectroscopy (EDS) was the stability and homogeneous distribution of sulfur-based antioxidants, which underpin the material’s superior resistance to degradation by effectively scavenging free radicals, which function as effective free radical scavengers. This mechanism underpins the material’s superior resistance to thermo-oxidative degradation. Consequently, significant molecular weight reduction and property deterioration were delayed until later extrusion cycles. Furthermore, a deep learning model based on the DeepLabV3+ architecture was trained to predict extrusion history directly from scanning electron microscopy (SEM) images of impact-fractured surfaces. The model achieved an average prediction accuracy exceeding 96.5%. Remarkably, it demonstrated excellent generalizability, maintaining high accuracy on two unseen commercial ABS grades. This indicates that the micro-morphological evolution pathway is a universal fingerprint of thermo-mechanical aging in ABS. This work not only elucidates the multi-scale degradation mechanism of recycled ABS but also provides a rapid, non-destructive tool for intelligent quality assessment in plastic recycling streams, bridging advanced machine learning with practical sustainability challenges. Full article

Harmful algal blooms pose a persistent threat to the integrity of freshwater ecosystems and public health. However, there are no selective chemical control agents available to suppress cyanobacterial growth without damaging beneficial phytoplankton. In this study, ten structurally diverse monoterpenes were assessed in vitro for their differential activity against the potent toxin-producing cyanobacterium Microcystis aeruginosa and the ecologically valuable microalga Chlorella sorokiniana using disc diffusion (DDM) and minimum inhibitory concentration (MIC) assays. Inhibition zones against M. aeruginosa ranged from 6.9 to 43.6 mm, with thymol recording the largest zone (43.6 mm). MIC values ranged from 0.25 to >1 mg/mL for both organisms, and selectivity indices identified camphor and carvone as the most cyanobacterium-preferential compounds, while carene and α-pinene showed the inverse selectivity pattern. Molecular docking against six AlphaFold2-predicted target proteins, photosynthetic complexes, Adenosine Triphosphate (ATP) synthase subunits, and superoxide dismutase (SOD) from both organisms, revealed binding affinities between −3.9 and −6.2 kcal/mol. Phenolic monoterpenes consistently engaged active-site glutamate and aspartate residues via hydrogen bonds and Pi–Anion interactions, most strikingly in the M. aeruginosa ATP synthase, whereas the M. aeruginosa SOD represented the least amenable target for all compounds. Computational ADMET profiling confirmed favorable pharmacokinetic properties and low predicted toxicity for the full panel. Full article

Molecular property prediction is a fundamental task in drug discovery and materials design. While graph neural networks (GNNs) and SMILES-based Transformers have made significant strides, the former are often limited by local message-passing bottlenecks such as over-squashing, while the latter frequently lack explicit topological constraints and suffer from severe vocabulary imbalance. In this work, we revisit the granularity of molecular modeling and propose a representation learning framework built upon bond-level sequences. Our framework models molecules as sequences of directed bond tokens and introduces a structure-aware hybrid attention mechanism. By imposing hard topological constraints on a subset of attention heads to reinforce local connectivity while preserving global receptive fields in the remaining heads, the design is intended to separate short-range chemical bonding from long-range contextual dependencies. For pre-training, we implemented a multi-scale consistency learning paradigm, which utilizes an atom-centric group masking strategy to induce a hierarchical loss of local structural information and employs contrastive and triplet losses to ensure identity consistency across varying scales of structural degradation. Furthermore, by incorporating macro-scale physicochemical descriptors (e.g., LogP, TPSA) as global anchors, we examined how the inclusion of global attribute bias can provide weak physicochemical priors during pre-training, while its effect during downstream fine-tuning remains task-dependent. Experimental results demonstrate that our lightweight model, with approximately 3.5 million parameters, exhibits a dataset-dependent performance profile across MoleculeNet benchmarks and shows promising behavior on selected topology-sensitive tasks, particularly MUV. Ablation studies further analyze the contribution of bond-level connectivity, the stage-dependent dynamics of global attribute bias, structured masking, and pre-training configurations. Ultimately, this work provides an alternative representation design for molecular modeling, offering a parameter-efficient option for future molecular learning systems alongside traditional SMILES-based and graph-based formulations. Full article