Search Results (596)

Background: The dysregulation of the CDK1/Cyclin B1 complex drives tumor progression in breast cancer (BC). The natural flavonoid luteolin (LT) shows anti-cancer potential, but its mechanism targeting CDK1/CCNB1 remains unclear. Methods: CDK1, CCNB1, and CCNB2 expression were profiled in normal and BC cell lines. An engineered HEK293T GST-CDK1/CCNB1 cell model was used to evaluate LT’s effects on proliferation, ROS levels, and target gene transcription. Computational approaches (molecular docking, dynamics simulations, pharmacophore modeling, MM/GBSA, ADMET, and network pharmacology) assessed LT and its analogues. Results: CDK1/CCNB1 expression was lower in MCF7 BC cells than in normal cells, suggesting the loss of a growth barrier. In engineered HEK293T cells, LT suppressed CCNB1 transcription with minimal effect on CDK1 levels, correlating with anti-proliferative and ROS-modulating effects. Computational analyses confirmed stable LT binding to the CDK1/CCNB1 complex. Designed LT analogues showed improved binding and favorable ADMET profiles. Network pharmacology identified cell cycle regulation, particularly in BC stem cells, as the primary pathway targeted. Conclusions: LT and its analogues inhibit the CDK1/Cyclin B1 complex, revealing a dual mechanism that suppresses both tumor growth and BC stemness. Full article

Tuberculosis therapy is prolonged partly because dormant subpopulations of Mycobacterium tuberculosis show reduced susceptibility to first-line drugs. Therefore, agents active against both replicating and non-replicating mycobacteria remain important to explore. Here, we investigated secondary metabolites from the Indonesian marine-derived fungus Aspergillus ostianus for activity against Mycobacterium smegmatis, a BSL-1 mycobacterial model, under aerobic and hypoxia-induced non-replicating conditions, and examined the underlying mechanism. Bioassay-guided fractionation and spectroscopic analysis identified three hydroxamate pyrazinones: neohydroxyaspergillic acid (NHAA), hydroxyaspergillic acid (HAA), and neoaspergillic acid (NAA). The MIC values were 1.56 µg/mL for NHAA and 3.13 µg/mL for HAA and NAA under both aerobic and hypoxic atmospheres. Time-kill kinetics showed ≥3-log10 CFU reductions within 24–72 h at 4–8× MIC under aerobic conditions and within 48–96 h at 4–8× MIC under hypoxia, with no regrowth at the final sampling point. Scanning electron microscopy and release of UV-absorbing intracellular material at OD₂₆₀/OD₂₈₀ were consistent with envelope disruption in both atmospheres. Antimycobacterial activity was attenuated in a concentration-dependent manner by exogenous Fe³⁺ and was reversed at 100 µM FeCl₃, whereas isoniazid activity was unaffected, supporting iron-reversible and pyrazinone-specific killing. Together with the established Fe³⁺-binding hydroxamate pharmacophore shared by this compound class, these findings support iron sequestration as a plausible mechanism and identify fungal hydroxamate pyrazinones as scaffolds that retain bactericidal activity against hypoxia-adapted non-replicating mycobacteria, warranting further evaluation in M. tuberculosis models. Full article

Natural coumarins are a structurally privileged group of bioactive benzopyranone lactones widely spread across the Apiaceae, Rutaceae, and Leguminosae families, and hold significant potential as inhibitors of pathological protein aggregation in Alzheimer’s disease, Parkinson’s disease, and type 2 diabetes mellitus. The fully planar, rigid bicyclic structure of the coumarin nucleus (~3.4–3.5 Å thickness) is geometrically compatible with intercalative π–π stacking with aggregation-nucleating aromatic residues, including Phe19 of Aβ(1–42), providing a mechanistically coherent pharmacophoric basis for anti-aggregation activity according to computational and indirect biophysical evidence. This review critically evaluates the peer-reviewed literature on naturally occurring coumarins and their synthetic derivatives as candidate β-sheet intercalators, with analysis of SAR at C-3 to C-8 positions; multi-target-directed ligand designs with dual activities of inhibiting AChE, BACE-1, GSK-3β, and MAO-B, and as blood–brain barrier-penetrating neuroprotective agents validated in cellular and rodent models. The critical analysis identifies the translational gap between in vitro IC₅₀ values and attainable brain drug concentrations as the primary pharmacological obstacle. It identifies the absence of systematic investigation of coumarin against IAPP, a directly relevant amyloid target in metabolic neurodegeneration, as the most significant unmet research priority in the field. Full article

The primary control mechanism for synaptic uptake of GABA is through γ-aminobutyric acid transporter 1 (GAT-1, SLC6A1), a known target for anti-epileptic drugs. Although there is a clinically used GAT-1 inhibitor, tiagabine, the development of a new ligand with an advanced pharmacological profile is desirable. For this purpose, a multi-tiered virtual approach to screening has been created, involving pharmacophore-based search; application of the Informational Spectrum Method for Small Molecules, followed by EIIP/AQVN filtering (ISM-SM); molecular docking using an ensemble of several experimentally obtained structures of GAT-1; and ADMET predictions. Pharmacophore-based screening of the ZINC database of natural products, combined with ISM-SM/EIIP filtering, yielded 237 candidate compounds. Structural separation analysis discriminated between the positives and negatives, enabling enrichment-based prioritization. The use of a composite normalized rank score based on docking affinity and structural similarity allowed for the identification of the top candidates: ZINC03643214 and ZINC67840571. Collectively, these refinements establish a more sophisticated computational model for identifying novel GAT-1 inhibitors and highlight promising candidates for future experimental evaluation. Full article

Methyl methacrylate (MMA)-based materials are widely used in temporary and permanent prosthetic dentistry; the prolonged presence of these materials in the oral cavity and potential residual monomer release can affect local biological responses. This study aimed to evaluate the biocompatibility and toxicity profiles of MMA, the monomeric unit of polymethyl methacrylate (PMMA), a key component of dental materials used in temporary prosthetic restorations. Molecular docking simulations were performed using CB-Dock2 and Autodock vina, while protein–protein interaction (PPI) analysis was performed using STRING and Cytoscape. In addition, Swiss ADME Target Prediction, toxicity prediction, and enrichment analyses were used to characterize the biological significance of selected targets in more detail. Molecular docking studies revealed promising interactions of MMA with valuable biomolecular targets relevant to biocompatibility. The toxicity profile revealed aspects of MMA that could be improved. Pharmacophore modeling, highlighting the importance of carbonyl and hydroxyl groups as pharmacophoric properties, revealed compounds with suitable biocompatibility profiles. Consequently, it emphasizes the interactions of MMA with biomolecules and safety considerations. It can guide the design and optimization of biocompatible materials as an exploratory avenue for future developments in dental biomaterials. Full article

NAD+ plays crucial roles in various biological processe and its aberrant regulation has been suggested to be critical in the pathogenesis of diverse diseases. Intracellular NAD+ is synthesized largely from nicotinamide mononucleotide (NMN), which is the product of reaction catalyzed by nicotinamide phosphoribosyltransferase (NAMPT). Thus, the development of specific inhibitors targeting NAMPT has been suggested as a promising treatment strategy. In this study, we developed a pharmacophore-based QSAR model to discover novel NAMPT inhibitors based on diverse structural features. By virtual screening using the conformation model, we could identify eight novel active analogs having distinct pharmacophores. The biological activity of these candidates on cell viability were further examined. Our study proves the efficiency of our novel screening model and demonstrates its usefulness in the application of drug discovery process. Full article

Pharmacophore hybridization is a well-established strategy for developing novel anticancer agents with improved biological profiles. In this study, a new series of (E)-4-(4-acrylamidophenoxy)-N-methylpicolinamide derivatives has been rationally designed by hybridizing key structural features of sorafenib with cinnamide pharmacophores and subsequently synthesized. The antiproliferative activities of the synthesized compounds were evaluated against a panel of human cancer cell lines, including A549 (lung), DU-145 (prostate), SKOV3 (ovarian), and HepG2 (liver), along with non-cancerous Hek293T cells. In comparison with the standard drug sorafenib, most of the (E)-4-(4-acrylamidophenoxy)-N-methylpicolinamides demonstrated significant antiproliferative activity, with specificity toward the HepG2 (liver cancer) cell line, and no effect on the noncancerous cells (Hek293T). Among them, compound 5f, the derivative containing a trifluoromethyl-substituted cinnamoyl moiety was identified as the lead candidate, exhibiting an IC₅₀ of 5.3 µM towards HepG2 (liver) cancer cells, comparable to the reference drug sorafenib. Enzyme inhibition studies showed that compound 5f inhibited both b-Raf and VEGFR-2 with IC₅₀ values of 1.45 and 0.37 µM, respectively. Furthermore, compound 5f suppressed angiogenesis in vitro and in vivo, as evidenced by the tube formation assay using HUVECs and in transgenic zebrafish Tg(fli1a:EGFP) models, respectively. Mechanistic studies indicated that compound 5f induced apoptosis in HepG2 cells through mitochondrial membrane depolarization and increased ROS generation. Molecular docking studies supported experimental findings and showed that 5f can interact with catalytically active residues via hydrogen-bonding interactions. Overall, these results highlight the potential of compound 5f as a promising dual target therapeutic lead with dual direct anticancer and antiangiogenic properties. Full article

This study investigated the application of six machine learning regression algorithms such as Random Forest, CatBoost, K-Nearest Neighbours, XGBoost, LightGBM, and Gradient Boosting, paired with Molecular ACCess System (MACCS) key fingerprints for the quantitative prediction of aromatase (CYP19A1) inhibitory potency, expressed as pIC₅₀. A dataset of 187 compounds was assembled from the ChEMBL database (version 33, Target ID: CHEMBL1978) following by systematic data curation workflow encompassing duplicate removal, pIC₅₀ transformation, and activity-based filtering. Model performance was rigorously evaluated using an 80/20 stratified train/test split, 5-fold cross-validation, and Y-randomisation testing to ensure unbiased assessment of predictive generalisation. Feature selection via CatBoost permutation importance on the held-out test set identified the top 20 predictive MACCS keys from an initial 166-bit space, substantially reducing dimensionality and improving generalisation across all models. Among the algorithms evaluated, CatBoost trained on the top 20 features achieved the strongest test-set performance (R² = 0.693, RMSE = 0.794, MAE = 0.659) with the most stable cross-validation R² (0.062 ± 0.304), outperforming all other algorithms. Y-randomisation testing returned an empirical p-value of <0.01, confirming that model performance reflects genuine structure–activity relationships rather than statistical chance. Permutation importance and SHAP analysis identified nitrogen-containing heterocyclic fragments (MACCS_41, MACCS_145) and halide-bearing substructures (MACCS_109) as the primary structural determinants of aromatase inhibitory potency, consistent with established CYP19A1 pharmacophoric requirements. Application of the model to ten representative plasticizers demonstrated that the refined applicability domain (h* = 0.423) accommodated eight of the ten compounds, enabling reliable potency predictions across phthalate esters and bisphenol analogues. These findings establish a transparent and reproducible QSAR framework for first-tier endocrine disruption risk screening of plasticizers and highlight the importance of permutation-based feature selection and applicability domain assessment in QSAR model development. Full article

Phosphodiesterase-1 (PDE1) represents an attractive target for the treatment of idiopathic pulmonary fibrosis (IPF). However, the limited chemical diversity of current PDE1 inhibitors has hindered the development of potential anti-IPF drugs, primarily due to an ambiguous understanding of interactions between inhibitors and PDE1. Herein, we report an integrated virtual screening strategy containing pharmacophore modeling, molecular docking, and molecular dynamics simulations, which markedly accelerated the discovery of novel PDE1 inhibitors. Enzymatic assays identified eleven active compounds with moderate inhibition from twenty-six purchased candidates, encompassing nine distinct scaffold types. Notably, 6484-0008 and 6484-0032 exhibited more than 50% inhibition at a concentration of 1 μM. Hydrogen bond analysis and residue-based energy decompositions revealed key recognition mechanisms involving crucial residues Gln421, His373, and Phe424, as well as the unique Thr271 in the flexible H-loop region, providing insights for the rational design of inhibitors with enhanced potency. Full article

Compound repurposing is an efficient method to save both time and costs by redirecting previously synthesized small molecules towards new biological targets. In this research, we employ computational methodologies to investigate and assess target engagement of small molecules as tyrosine kinase inhibitors (TKIs). Therefore, compounds TKI.2a, TKI.2b, TKI.6, TKI.16, TKI.19, and TKI.21b identified from our earlier research, undergo assessments of molecular similarity, docking studies, and pharmacophore modeling along with those discovered through database searches. Compounds TKI.2a, TKI.2b, TKI.6, and TKI.19 appear to exhibit multi-target tyrosine kinase inhibitory activities against VEGFR-2 (Vascular Endothelial Growth Factor Receptor), RET (proto-oncogene tyrosine–protein kinase receptor), PDGFRα (Platelet-Derived Growth Factor Receptor alpha), EGFR (Epidermal Growth Factor Receptor), and HER2 (Human Epidermal Receptor) receptors. Pharmacophore models were applied for ligand-based virtual screening using defined parameters to discover candidate compounds that exhibit drug-likeness with FDA (Food and Drug Administration)-approved tyrosine kinase inhibitors. Molecular docking studies identified lead compounds for each biological target based on their overall affinity values and established interactions. Compound ChEMBL2170947 was found to be the most promising candidate for the VEGFR-2 receptor, ChEMBL5019511 for PDGFRα, ChEMBL2216869 for EGFR, and ChEMBL3355044 for HER2. Full article

Cancer represents a major global health challenge, contributing to an estimated 19 million new cases annually. While conventional chemotherapeutic approaches continue to advance, target-based therapeutic strategies are increasingly recognized as effective pathways in modern drug development. A prominent biological target in current anticancer research is the selective inhibition of Topoisomerase II alpha (TOP2A). TOP2A, a crucial DNA topoisomerase, is vital for maintaining genomic integrity by mediating the cleavage and re-ligation of double-stranded DNA during essential cellular processes, such as DNA replication and transcription. Inhibiting TOP2A effectively disrupts these processes, leading to cell death. This study employed computer-aided drug design approaches to virtually screen a library of 3000 xanthone derivatives against the TOP2A target, and the results were preliminarily validated through cytotoxicity assays on the A549 and HepG2 cancer cell lines. The computational methods utilized included molecular docking, pharmacological modeling, molecular dynamics simulations, and steered molecular dynamics simulations. The virtual screening identified two highly promising HIT compounds, CID162372098 and CID156619937, that exhibited the most favorable interactions and stability profiles in relation to the TOP2A active site. The experimental results demonstrated that both hit compounds effectively exhibited significant anti-proliferative activities against the HepG2 cell line, with IC50 values of 9.54 ± 0.26 µg mL⁻¹ (CID162372098) and 10.03 ± 0.36 12.69 ± 0.31 µg mL⁻¹ (CID156619937), respectively. Collectively, these findings demonstrate the potential of xanthone-based scaffolds as inhibitors of TOP2A and provide a rational framework for the screening and development of novel anticancer agents. Full article

Computational chemistry has played a central role in early-stage drug discovery by accelerating target selection, hit identification, and lead optimization. This review summarizes recent developments in molecular docking, pharmacophore modeling, molecular dynamics (MD), and virtual screening (VS), with a focus on their application in practical drug discovery workflows. Advances in docking protocols, including consensus scoring, physics-based rescoring, and ensemble approaches, addressed the challenges of receptor flexibility. Both ligand-based and structure-based pharmacophore models facilitated scaffold hopping and guided library prioritization. MD simulations were used to assess binding pose stability, identify cryptic binding pockets, and characterize solvent interactions. These simulations also supported free-energy calculations using endpoint and alchemical methods. Large-scale VS campaigns employed curated compound libraries, often composed of make-on-demand molecules, and relied on high-performance computing or cloud infrastructure to screen up to 10⁹ compounds. Hits were validated using orthogonal biophysical assays and filtered by absorption, distribution, metabolism, excretion, and toxicity (ADMET) predictions. Integrated pipelines combining pharmacophore modeling, docking, MD, and free-energy calculations improved enrichment rates and reduced the number of compounds requiring synthesis. Several case studies demonstrated the identification of nanomolar-affinity leads from ultra-large screening campaigns. The review also addressed ongoing challenges, such as inconsistent scoring of binding affinity, protonation, and tautomeric errors, dataset bias, and reproducibility issues. Strategies to mitigate these limitations included standardized library preparation, adherence to FAIR (Findable, Accessible, Interoperable, and Reusable) data principles, and the use of prospective benchmarking protocols. The review discussed emerging trends, including the use of quantum chemistry for electronic structure refinement, ensemble docking guided by cryo-electron microscopy (cryo-EM) data, and the integration of computational tools with automated synthesis and high-throughput screening in closed-loop discovery systems. These approaches have the potential to accelerate the design–make–test cycle, increase hit novelty, and improve decision-making in early drug development programs. Full article

Background/Objectives: Cyclooxygenase-2 (COX-2) is a well-established target in the development of anti-inflammatory drugs due to its central role in mediating inflammation. The identification of novel COX-2 inhibitors remains a key focus in pharmaceutical research. This study aimed to develop a robust and interpretable machine learning framework to predict COX-2 inhibitory activity and support virtual screening efforts. Methods: A curated dataset of 2052 compounds was obtained from the ChEMBL database. Molecular structures were encoded using Morgan fingerprints derived from SMILES representations. Several machine learning algorithms were trained and evaluated, including ensemble-based methods. Model performance was assessed using internal validation and external test sets. Robustness was further evaluated through Y-randomization tests. Model interpretability was investigated using SHAP (SHapley Additive exPlanations) analysis to identify key structural features contributing to activity. Results: Among the evaluated models, ensemble methods demonstrated superior predictive performance, with the Random Forest algorithm providing the most consistent and reliable results across validation and external datasets. Y-randomization confirmed that the model predictions were not due to chance correlations. SHAP analysis revealed that the most influential features corresponded to chemically meaningful substructures aligned with known COX-2 pharmacophore characteristics. The final optimized model was successfully deployed as a publicly accessible web application for real-time prediction using SMILES input. Conclusions: This study demonstrates the effectiveness of explainable machine learning approaches in predicting COX-2 inhibitory activity. The developed framework provides a reliable and interpretable tool for accelerating COX-2 inhibitor discovery and facilitating virtual screening in drug development. Full article

Alzheimer’s disease (AD) is a multifactorial neurodegenerative disorder characterized by cholinergic dysfunction, amyloid-β aggregation, mitochondrial stress, and aberrant kinase activity. Carotenoids, naturally occurring pigments with antioxidant and neuroprotective properties, have emerged as promising candidates for AD intervention. In this study, we performed a systematic stepwise computational screening of a large carotenoid library (n = 1191) to identify multitarget candidates against AD–related proteins. The workflow consisted of predefined ADMET filtering (oral absorption > 90%, Caco-2 > 0.9, logBB > −1, and absence of major CYP inhibition and toxicity alerts), reducing the dataset to 61 compounds, followed by multi-target molecular docking against AChE, BChE, BACE-1, MAO-B, and GSK3-β. Compounds were ranked using an aggregated mean docking score across all five targets, and the top-performing candidate was subjected to detailed mechanistic analyses. Hopkinsiaxanthin emerged as the highest-ranked multitarget carotenoid and was further evaluated using frontier molecular orbital (FMO) analysis, pharmacophore modeling, 100 ns molecular dynamics (MD) simulations, MM/PBSA binding free energy calculations, and per-residue decomposition. Docking predicted favorable estimated binding affinities toward all targets. MD simulations confirmed stable receptor–ligand complexes with low RMSD values (0.278–0.285 nm). MM/PBSA analysis indicated favorable binding free energies, particularly for GSK3-β (−22.73 kcal/mol) and AChE (−21.50 kcal/mol). Per-residue decomposition identified key hotspot residues driving stabilization. Overall, this structured computational framework identifies Hopkinsiaxanthin as a promising multitarget scaffold and supports its prioritization for experimental validation in AD models. Full article

Pharmacological inhibition of the nucleoside transporter hENT1 is a promising therapeutic target across a range of diseases, including cardiovascular disorders, neurodegenerative conditions, and cancer. However, current inhibitors lack drug-like properties, necessitating the development of new inhibitors with improved pharmacological profiles. We employed a dual-pharmacophore virtual screening protocol to identify putative hENT1 inhibitors from a library of over 2 million compounds, followed by structure-based molecular docking. To validate the inhibition effect of the lead compounds, we established a functional assay using gemcitabine (GEM)-induced cytotoxicity as a readout of hENT transport activity using eight cancer cell lines. H292 was the optimal cancer cell line for the validation assay based on its high GEM sensitivity (IC50 = 28 nM) and the concentration-dependent cytotoxicity inhibition of the reference inhibitor NBTI, a hENT1 inhibitor. Of the 19 candidate compounds, two leads (compounds 2 and 3) demonstrated potency comparable to NBTI, increasing GEM IC50 values by 2.2- and 2.9-fold at 5 µM, respectively. Both compounds were non-cytotoxic to normal fibroblasts, exhibited favorable ADME properties, displayed superior docking scores of −12.63 and −12.49 kcal/mol compared to NBTI (−9.06 kcal/mol), and displayed a novel vertical binding orientation within the hENT1 binding pocket distinct from NBTI’s horizontal mode. This study established a validated non-radioactive, gemcitabine-based functional assay for hENT inhibitor discovery and identified two putative inhibitors with therapeutic potential for cancer chemosensitization, pain management, and cardio- and neuroprotection. The non-radioactive functional assay overcomes the limitations of traditional radiolabeled methods, enabling scalable, broader screening applications. Full article