Search Results (115)

Background: Mitogen-activated protein kinase 3 (MAPK3/ERK1) plays a central role in cellular proliferation, inflammation, apoptosis, and survival signalling and has been implicated in oral squamous cell carcinoma (OSCC), periodontitis, oral lichen planus, and other chronic oral inflammatory diseases. The present study employed an integrated computational workflow combining machine learning (ML)-based quantitative structure–activity relationship (QSAR) modelling, molecular docking, density functional theory (DFT), molecular dynamics (MD) simulation, and MM-GBSA analysis to identify and characterise potent MAPK3 inhibitors. Methods: A curated dataset of 907 experimentally validated MAPK3 inhibitors was retrieved from the ChEMBL database and processed using molecular descriptors and Morgan fingerprints. Multiple ML algorithms were evaluated under scaffold-based validation, with Light Gradient Boosting Machine (LightGBM) demonstrating the best predictive performance. Results: The final model achieved strong classification capability with ROC-AUC values of 0.898 and 0.926. Feature importance analysis revealed that local structural motifs captured by fingerprint descriptors played dominant roles in MAPK3 inhibitory activity. The top-ranked compounds were subjected to molecular docking, where compounds 58324148 and 137531515 exhibited strong binding affinities of −11.9 and −11.0 kcal/mol, respectively. DFT calculations demonstrated favourable electronic properties with low HOMO–LUMO energy gaps, while MD simulations confirmed stable receptor–ligand interactions throughout 200 ns trajectories. MM-GBSA analysis further supported strong binding stability dominated by van der Waals interactions. Conclusions: Overall, the integrated computational framework successfully identified promising MAPK3 inhibitor candidates with potential therapeutic relevance for oral inflammatory and proliferative diseases. Full article

Triple-negative breast cancer (TNBC) is the most clinically aggressive subtype of breast cancer, characterized by the absence of targetable hormone receptors and HER2 amplification, significantly constraining treatment choices. Estrogen Receptor Beta (ERβ) has emerged as a biologically relevant yet underutilized target in TNBC, with its re-expression linked to tumor suppression and improved prognosis, prompting the development of selective ERβ modulators as a precision therapeutic approach. We introduce ERβ-Score, an interpretable machine learning scoring system developed using a curated dataset of 1699 ERβ bioactive chemicals obtained from ChEMBL, characterized by 39 physicochemical and three-dimensional molecular descriptors. After implementing scaffold-disjoint train/test partitioning to avert structural data leakage, a Gradient Boosting Classifier, fine-tuned through Bayesian hyperparameter optimization, attained in five-fold cross-validation a Precision–Recall AUC (Area Under the Curve) of 0.891, a ROC-AUC (Receiver Operating Characteristic) of 0.888, a Matthews Correlation Coefficient of 0.664, an F1-score of 0.838, and a balanced accuracy of 0.831; on the scaffold-disjoint hold-out test set it attained a Precision–Recall AUC of 0.905, a ROC-AUC of 0.864, and a Matthews Correlation Coefficient of 0.578, indicating strong and balanced discrimination between active and inactive ERβ modulators. We note explicitly that this scaffold-disjoint hold-out constitutes internal validation, since it derives from the same curated ChEMBL workflow used for model development, and it is therefore reported throughout as scaffold-disjoint internal validation rather than as independent external validation. The applicability domain boundaries were established using a k-nearest-neighbor Tanimoto-similarity method with ECFP4 (Extended-Connectivity Fingerprint with a Diameter of 4) fingerprints, offering a quantitative confidence metric that identifies structurally new molecules beyond the model’s reliable prediction range. External validation against independent Tox21 ERβ bioassay data confirmed genuine, statistically significant predictive signal (ROC-AUC = 0.71) while revealing reduced sensitivity for structurally novel active compounds. The model was subsequently used for extensive virtual screening of natural product and drug-like compound libraries, with prioritized candidates undergoing structure-based molecular docking against the ERβ co-crystal structure (PDB: 7XWQ) using Smina, facilitating a comprehensive evaluation of hits based on both ligand and structural properties. To enhance accessibility, the complete pipeline was implemented as an open-access interactive web application utilizing Streamlit, enabling researchers to input any SMILES string and obtain, in real time, an activity prediction with a probability score, applicability domain classification, Lipinski drug-likeness assessment, interactive three-dimensional visualization of protein–ligand interactions, and on-demand docking within the ERβ active site. Full article

Federated learning enables privacy-preserving distributed training but suffers from client drift and slow convergence under statistical data heterogeneity. Most existing federated optimization methods address client drift via parameter-space constraints or aggregation-level corrections, while fewer works directly shape the gradient covariance spectral structure of the optimization landscape. This paper analyzes the convergence problem from a spectral perspective, revealing that non-IID data causes spectral diffusion in the gradient covariance matrix and degrades convergence. Guided by random matrix theory, we propose federated spectral regularization (Fed-SR), a computationally efficient method that indirectly constrains spectral spread via gradient norm regularization. Although computing the regularizer gradient requires Hessian vector products, our optimized auto-differentiation implementation avoids storing full Hessian matrices and restricts extra computational overhead to a negligible level. Experiments on CIFAR-10, CIFAR-100, and other benchmarks show that Fed-SR outperforms baselines including FedAvg, FedProx, and SCAFFOLD in non-IID scenarios, reducing communication rounds and improving accuracy and stability. Ablation studies, spectral analysis, and controlled spectral feature manipulation experiments provide consistent empirical evidence showing a strong empirical association between the “spectral concentration” effect and performance gains, offering mechanistic interpretability consistent with our proposed theoretical framework within the tested experimental settings. Full article

Hollow glass microspheres, as demonstrated in recent studies, have shown significant importance in the field of composite materials and have emerged as transformative materials in biomedical applications. This is necessitated by their ability to provide a physicochemical gradient, a desirable tool for complex tissues and biological interfaces, through the spatiotemporal release of bioactive factors and nanophase ceramics. HGMs are structures with diameters ranging from 1 to 1000 µm that can be used as support for cell growth, either in the form of a scaffold or a drug delivery system. In this review, we describe the various methods for HGM fabrications, synthesis (e.g., flame spraying, sol-gel processes, spray drying, etc.), structural characterizations, and chemical and physical properties (e.g., densities ranging from 0.1 to 0.6 g/cm³ and compressive strength ranging from 10 MPa to 30 MPa for low and high densities, respectively), highlighting how these methods influence their drug delivery, tissue engineering, bone implants, and nanocarrier abilities. Furthermore, a comprehensive list of other materials and their various biomedical uses is reported. Some of the limitations of existing techniques and future investigations into how HGM can perform as a biomedical material are discussed. Full article

Background/Objectives: Antibiotic-resistant Pseudomonas aeruginosa and limited experimental validation capacity motivate efficient prioritization of antibacterial candidates from large chemical libraries. Quantitative structure–activity relationship (QSAR) benchmarks often binarize disk diffusion inhibition-zone (IZ) measurements, obscuring activity gradients and imposing threshold dependence. We examined whether continuous-IZ modeling provides complementary retrospective prioritization relative to calibrated binary classification in a highly imbalanced dataset. Methods: In this retrospective matched-data evaluation, we revisited a published ChEMBL-derived P. aeruginosa disk diffusion dataset using preserved training and locked external validation partitions. A calibrated support vector classifier using Molecular ACCess System keys (SVC/MACCS) provided conservative binary active calls. RegressionStack combined source-descriptor extreme gradient boosting (XGBoost) and ElasticNet regressors, Morgan-fingerprint random forest and gradient-boosting regressors, and a MACCS-key XGBoost regressor through an XGBoost meta-regressor to predict continuous IZ. Results: On the locked external set (n = 1130; 87 actives), SVC/MACCS achieved a positive predictive value (PPV) = 0.619, receiver operating characteristic area under the curve (ROC-AUC) = 0.857, precision–recall area under the curve (PR-AUC) = 0.479, and enrichment factor at 1% (EF@1%) = 7.58. RegressionStack achieved a mean absolute error (MAE) = 3.20 mm, ROC-AUC = 0.896, PR-AUC = 0.545, and EF@1% = 9.74. Neither paired permutation tests (ROC-AUC, p = 0.501; PR-AUC, p = 0.442) nor paired bootstrap confidence intervals resolved these differences. The y-randomization analyses supported non-random signals; scaffold-grouped validation retained early enrichment but showed reduced broader performance. The consensus-positive tier contained 27 actives among 35 nominations (PPV = 0.771). At measured IZ ≥ 30 mm, MAE increased to 9.46 mm, and all 30 compounds were underpredicted. Conclusions: Continuous-target modeling retained the IZ scale during training and generated a threshold-flexible predicted-IZ prioritization coordinate complementary to, but not statistically superior to, calibrated binary classification. The methodological contribution is a matched-data evaluation of complete workflows and their nomination behavior under the same partitions, yielding a retrospective compound-tiering scheme. Because the pipelines differed in architecture and molecular representation, their differences cannot be attributed solely to endpoint formulation. The workflows were not prospectively evaluated on compounds lacking pre-existing IZ measurements, and whether retrospective enrichment improves experimental hit discovery or reduces screening workload remains to be established. Full article

Chemical artificial intelligence increasingly relies on molecular property prediction models trained from proprietary compound libraries, bioassay records, and reaction-screening data. However, these data often contain commercially sensitive structures, confidential activity labels, and privacy-relevant experimental metadata, making direct centralization impractical. This paper proposes PrivMol, a privacy-preserving computational chemistry framework for federated molecular representation learning. PrivMol introduces two novel algorithms: Secure Substructure-Aware Federated Optimization and Differentially Private Molecular Gradient Calibration. The first algorithm decomposes molecular graphs into privacy-sensitive and task-relevant substructure regions, enabling local clients to train graph neural networks while transmitting only securely aggregated model updates. The second algorithm adaptively calibrates clipping and perturbation according to atom- and substructure-level contribution scores, reducing unnecessary utility loss on chemically informative fragments while retaining formal differential privacy guarantees. To improve robustness under heterogeneous chemical spaces, PrivMol incorporates local contrastive molecular alignment without exposing raw molecules, labels, scaffolds, substructure masks, or embeddings. Experimental evaluation on widely used public molecular benchmarks, including ESOL, FreeSolv, Lipophilicity, BBBP, BACE, HIV, and Tox21, demonstrates that PrivMol provides a favorable trade-off among prediction accuracy, communication efficiency, empirical leakage resistance, and privacy protection. The study offers a practical route toward secure collaborative chemical intelligence for computer-aided drug discovery, toxicology prediction, and materials informatics. Full article

This study presents a numerical investigation into the hydrodynamic and biomechanical performance of bone-repair scaffolds based on Triply Periodic Minimal Surfaces (TPMSs). Focusing on Gyroid and Diamond architectures, scaffolds with uniform (40–70%) and functionally graded porosities were developed. Computational Fluid Dynamics (CFD) simulations were employed to evaluate permeability, pressure drop, and Wall Shear Stress (WSS) distributions. Results indicate distinct topological advantages: the Gyroid structure demonstrates superior permeability and uniform WSS distribution due to its isotropic fluid channels, whereas the Diamond structure maintains better flow velocity stability. Crucially, the introduction of a porosity gradient (40–60%) successfully mitigates localized pressure surges and optimizes the bioactive WSS window for cell differentiation. Notably, increasing porosity to 70% in Gyroid scaffolds yielded a 277% enhancement in permeability. These findings establish a theoretical basis for designing functionally graded TPMS scaffolds that balance fluid transport efficiency with a favorable cellular microenvironment. Full article

The extracellular matrix (ECM) provides a dynamic microenvironment that regulates cell proliferation, migration, and tissue remodeling during wound healing. However, replicating the structural and functional complexity and ECM heterogeneity of native skin ECM remains challenging with conventional single-material hydrogels. Recent advances in multimaterial 3D bioprinting have enabled the spatial integration of diverse biomaterials within a single construct. Lignocellulose has attracted increasing attention as a promising biomaterial for recreating key structural features of the native ECM because of its fibrous architecture, mechanical strength, and biocompatibility. This review offers a comprehensive and integrated perspective on the use of lignocellulose-based multimaterial printing to recreate ECM-mimicking architectures, an underexplored area at the intersection of biomaterials and biofabrication. The roles of cellulose, hemicellulose, and lignin in printability, scaffold stability, porosity, bioactivity, and wound-healing performance are discussed. Representative studies have demonstrated that lignocellulose-based multimaterial bioinks provide porous architectures that support cell adhesion, proliferation, and tissue regeneration. These benefits are accompanied by improved mechanical performance, as cellulose nanofibers exhibit elastic moduli exceeding 100 GPa, and lignin-containing hydrogels have achieved compressive moduli of up to 135 kPa. Such mechanical advantages make lignocellulosic materials particularly attractive for fabricating ECM-mimicking scaffolds that require long-term structural integrity. Finally, key design considerations and current limitations associated with lignocellulose-based multimaterial bioprinting are critically discussed. A framework for the rational design of lignocellulose-based multimaterial bioinks is presented, together with future directions toward gradient and adaptive scaffolds, smart wound dressings, and advanced wound-healing applications. 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

Background/Objectives: Supramolecular hydrogels formed by low-molecular-weight gelators present a chemically heterogeneous transport environment whose molecular-scale dynamics remain poorly understood. This study aimed to investigate how drug physicochemistry governs transport within a liquid-crystalline C18ADPA hydrogel at the molecular scale. Methods: Pulsed-field gradient NMR spectroscopy was used to measure self-diffusion coefficients of five model drugs (5-fluorouracil, acetylcholine, paracetamol, prednisolone, and amphotericin B) spanning a broad range of size, polarity, and charge state, in both free solution and the hydrogel matrix at pH 5.37. Results: Observed drug diffusion coefficients deviated substantially from classical obstruction theory predictions, demonstrating that transport is governed by host–guest chemical affinity rather than molecular size. The three water-soluble drugs exhibited bimodal diffusion, with relative amplitudes providing a direct estimate of bound and free drug fractions. Prednisolone co-diffused with the gelator scaffold, consistent with hydrophobic bilayer partitioning, while amphotericin B diffused at rates consistent with the structured interfacial water layer. The gel pH (5.37) emerged as an active determinant of transport: drug charge states at this pH from permanent cation (acetylcholine) to near-zwitterion (amphotericin B) correlated directly with the observed transport behavior. The near-zwitterionic character of amphotericin B at pH 5.37, arising from its carboxyl pKa (~5.5), suggests a previously unreported electrostatic interfacial trapping mechanism. Conclusions: The liquid-crystalline bilayer architecture creates chemically distinct microdomains that selectively recruit drugs based on hydrophobicity, hydrogen-bonding capacity, and pH-dependent charge state, providing a molecular-scale framework for rational formulation design in supramolecular drug delivery. Full article

Aberrant glycosylation is a recognized hallmark of cancer, establishing Golgi α-mannosidase II (GMII) as strategic therapeutic target. While the natural alkaloid swainsonine demonstrated potent anticancer activity, its clinical use is hampered by toxicity from off-target inhibition of the lysosomal α-mannosidase (LMan). This review surveys computational methodologies advancing inhibitor development from empirical observations to precision structural optimization. We examine the evolution from Molecular Docking to advanced Quantum Mechanics (QM) and Molecular Dynamics (MD), highlighting their combined role in modeling metalloenzyme flexibility and energetics. Analysis reveals that selectivity relies on exploiting peripheral structural divergences, organelle-specific pH gradients, and distinct substrate conformational itineraries. In this context, electronic structure calculations and pKa predictions prove critical for designing “electrostatic switches”, inhibitors binding neutrally at Golgi pH while incurring lysosomal repulsion. Structurally, targeting the non-conserved “anchor site”, mimicking specific transition-state ring distortions and utilizing conformationally restricted scaffolds represent the most effective strategies. Integrating dynamic sampling with rigorous energetic profiling is therefore crucial for developing the next generation of safe, selective GMII inhibitors. Full article

Liver cancer remains a significant global health burden, requiring the development of precise nucleic acid delivery systems. Lipid nanoparticles (LNPs) are leading candidates; however, their efficiency is governed by the pK_a of ionizable lipids, which dictates nanoparticle stability and endosomal escape. In this study, we employed a machine learning–driven quantitative structure–activity relationship framework to predict the pK_a of ionizable lipids derived from the DLin–KC2–DMA scaffold. Utilizing a dataset of 56 compounds, we compared Random Forest, Artificial Neural Network, and Extreme Gradient Boosting (XGB) models integrated with Permutation Importance (PI) for feature selection. The optimized PI–XGB model exhibited exceptional predictive accuracy (R² = 0.970, R²_CV = 0.901, RMSE_test = 0.115) and robust generalization confirmed via external validation (RMSE_ext. = 0.313). Mechanistic insights derived from SHapley Additive exPlanation analysis identified charge distribution, molecular topology, and polarity as critical determinants of lipid ionization. These results demonstrate the power of interpretable machine learning in elucidating molecular structure–property relationships, offering a robust computational strategy for the rational design of next–generation ionizable lipids to optimize LNP–mediated gene therapy for liver cancer. Full article

Bone defects remain a significant challenge in bone tissue engineering, driving an urgent need for advanced materials with enhanced therapeutic properties. Additive manufacturing highlights a unique capacity for customization, which enables the precise realization of complex and personalized composite scaffolds. This study innovatively integrates the superior mechanical properties of polycaprolactone (PCL) with the antibacterial characteristics of S53P4 bioactive glass. Utilizing thermal melt extrusion processing and fused deposition modeling (FDM) technology, we fabricated gradient-structured S53P4@PCL composite three-dimensional porous scaffolds with varying doping ratios (5 wt%, 10 wt%, 20 wt%). To further improve the antibacterial efficacy of the scaffold, exosomes (EXO) derived from grouper eggs were functionalized with bacteria-targeting aptamers (APTs), a type of functional DNA capable of binding to bacterial peptidoglycan, and EXO-APT-20%S53P4@PCL was fabricated. The resulting EXO-APT-20%S53P4@PCL scaffold was able to facilitate the targeted capture and subsequent eradication of bacteria. This study pioneers the synergistic integration of aptamer-modified exosomes into 3D composite scaffolds. Our analysis confirmed that the incorporation of APTs enabled targeted bacterial capture, and antibacterial EXO further enhanced the overall bacterial killing capability of the S53P4@PCL scaffolds. The fabrication of porous S53P4@PCL scaffolds through an innovative composite-molding strategy, combined with EXO-APT functionalization, establishes a new paradigm for customized bone repair. Full article

This study investigates the flow behavior of gyroid scaffolds using computational fluid dynamics (CFD) and three rheological models, Newtonian, Power-law, and Carreau, to assess the influence of pore size, inlet velocity, and scaffold size on wall shear stress (WSS) and permeability. The results show that non-Newtonian models yield substantially higher and broader WSS distributions than the Newtonian model, reflecting the importance of shear-dependent viscosity for physiologically realistic simulations. Larger pore size reduces the WSS and increases the permeability. Nevertheless, localized high-shear regions persist, particularly for the non-Newtonian fluids. Higher inlet velocities produce an increase in both WSS and permeability. However, this effect is lees remarkable for the Newtonian model. Comparisons between small and large scaffolds show lower wall shear stress levels in the larger geometry due to reduced local velocity gradients and a more evenly distributed flow field. Overall, rheological models influence the magnitude and heterogeneity of WSS. These findings highlight the need to incorporate non-Newtonian models when evaluating the scaffold performance in tissue engineering applications. Full article

Laceration is one of the most common meniscus injuries, which can cause knee joint dysfunction. The treatment of meniscus injuries remains one of the greatest challenges in orthopedics. In this study, a three-dimensional sponge-like Poly(lactic acid)/Poly(3-hydroxybutyrate-co-4-hydroxybutyrate) (PLA/P(3HB-co-4HB)) scaffold with oriented microtubules was fabricated using an improved gradient thermal phase separation technique. The scaffold surface was modified by adsorbing gelatin. The surface-modified scaffolds and the unmodified scaffolds were divided into two groups. All preparation parameters were adjusted to meet tissue engineering requirements. The prepared scaffolds were tested for porosity, compression modulus, hydrophilicity, and degradability. Following scaffold preparation, induced differentiated rabbit bone marrow mesenchymal stem cells (BMSCs) were seeded to evaluate scaffold cytocompatibility. Cell proliferation was observed in the two scaffold groups, and cell viability was analyzed using CCK-8 assay, scanning electron microscopy (SEM), and confocal microscopy. Histological staining was performed to comparatively study cell synthetic function. Subsequently, tissue reconstruction and regeneration were evaluated following subcutaneous implantation of gelatin/PLA/P(3HB-co-4HB) scaffolds loaded with induced differentiated BMSCs in the dorsal regions of athymic nude mice. Results demonstrated that the gelatin/PLA/P(3HB-co-4HB) scaffold exhibited good cell compatibility, providing a suitable microenvironment for cell proliferation and differentiation. Furthermore, the scaffold supported the growth of seeded induced differentiated rabbit MSCs in vivo, maintaining meniscus cell phenotyping and function. The cell-laden scaffold has the potential to generate meniscus fibrocartilage. Full article