Search Results (501)

Polycystic Ovary Syndrome (PCOS) is a prevalent endocrine disorder in women of reproductive age, characterized by hyperandrogenism, insulin resistance, and ovarian dysfunction. Current therapies are often associated with adverse effects, highlighting the need for safer therapeutic alternatives. Peganum harmala (P. harmala), a medicinal plant rich in bioactive metabolites, was investigated through in silico approaches to identify compounds with predicted binding affinity for the androgen receptor (AR), steroid 17α-hydroxylase/17,20-lyase (CYP17A1), and glycogen synthase kinase-3 beta (GSK-3β). GC-MS analysis of P. harmala leaf essential oil collected in Riyadh, Saudi Arabia, identified 109 compounds, with terpenoids as the dominant class (21.89%). The major constituents were cis-chrysanthenyl acetate (3.48%), cis-β-damascenone (3.06%), farnesylacetone (1.44%), β-calacorene (1.36%), dihydroedulan II (1.04%), and trans-calamenene (0.46%). In silico ADMET evaluation indicated that most compounds complied with Lipinski’s rule of five and showed favorable predicted pharmacokinetic properties. Safety profiling suggested an overall acceptable toxicity profile, with minimal predicted CYP450 inhibition, except for L11, which showed broader inhibitory potential. Molecular docking showed that L15 (trans-calamenene), L14 (dihydroedulan II), L6 (β-calacorene), L3 (farnesylacetone), and L8 exhibited higher predicted binding affinity toward the androgen receptor; L3, L10 (cis-β-damascenone), and L16 (cis-chrysanthenyl acetate) interacted with CYP17A1, while L3, L9, and L6 exhibited higher affinity toward GSK-3β. Overall, these findings provide hypothesis-generating in silico predictions of ligand–target binding affinities and drug-likeness profiles. These computational findings highlight the importance of future experimental investigations to substantiate the biological activity, pharmacokinetic behavior, and safety profile of P. harmala constituents. Full article

Chagas disease affects millions of people worldwide, including those in Latin America. The only drugs available for its treatment are benznidazole and nifurtimox. However, these drugs present high toxicity and limited efficacy. Therefore, the search for new treatments continues. In this regard, computer-assisted drug design has been implemented in scientific research for drug repurposing, allowing for reduced costs and time. Therefore, the objective of this work was to search for analogs of FDA-approved drugs with activity against Trypanosoma cruzi through ligand-based virtual screening and their biological evaluation against blood trypomastigotes. The compound TD-095 (LC₅₀ = 48.60 and 13.75 µM), a ketanserin analogue, TS-936 (LC₅₀ = 71.55 and 37.54 µM), a terfenadine analogue, and TD-831 (LC₅₀ = 75.94 and 26.17 µM), a sulfasalazine analogue, were considered as potential trans-sialidase inhibitors; TIM-967 (LC₅₀ = 69.70 and 39.69 µM) and LK-284 (LC₅₀ = 116.7 and 82.29 µM), two sulfonylurea analogues, were considered as potential triosephosphate isomerase inhibitors, showing better trypanocidal activity against NINOA and INC-5 strains, respectively, than the reference drugs. Molecular dynamics simulations predicted the stability of the compounds in complex with their respective proteins. Finally, the ADMET predictive analysis showed favorable properties for the compounds. These results support continued research into new agents against Trypanosoma cruzi, using structures of drugs already approved by the FDA. Full article

Wound healing involves the coordinated regulation of inflammation, angiogenesis, and extracellular matrix remodeling, processes modulated by natural bioactives. In this context, Chelidonium majus L. (C. majus), a plant rich in alkaloids and flavonoids, remains mechanistically underexplored. This study, therefore, investigates its metabolites using an integrated computational–experimental approach and evaluates their applicability in sericin-based wound-healing systems. A curated database of 83 C. majus bioactive compounds was analyzed using cheminformatics and molecular docking against key wound-healing targets (iNOS, VEGF, MMP-3, and tyrosinase), followed by ADMET and toxicity prediction (StopTox). Selected plant–sericin formulations were subsequently evaluated for wound-healing activity using an in vitro fibroblast scratch assay. Docking revealed strong binding affinities for several metabolites, particularly protopine, kaempferol-3-rutinoside, cynaroside, hesperidin, quercetin-3-rhamnosylrutinoside, and vitexin, indicating multi-target modulation across inflammatory, proliferative, and remodeling phases of tissue repair. ADMET and toxicity analyses predicted favorable dermal safety and pharmacokinetic profiles for most compounds. Consistently, in vitro assays demonstrated that C. majus–sericin systems had fibroblast migration and wound closure in a concentration- and ratio-dependent manner, with improved healing kinetics observed at 150 µg/mL and for formulations containing higher relative proportions of both components. The experimental outcomes supported the pro-angiogenic and matrix-stabilizing mechanisms predicted in silico. Overall, C. majus metabolites exhibit polypharmacological wound-healing activity, supporting their integration into sericin-based systems as a promising strategy for topical therapies. Full article

Natural products (NPs) have historically provided the foundational scaffolds for drug development, yet traditional bioprospecting faces critical limitations: high rediscovery rates, laborious isolation workflows, and substantial attrition during clinical translation. The emergence of big data technologies is fundamentally transforming this landscape, enabling a shift from serendipity-based discovery toward systematic, data-driven approaches. This review examines how the integration of artificial intelligence (AI), machine learning (ML), and multi-omics datasets is accelerating natural product research across three key domains: (1) genome mining for biosynthetic gene cluster identification using platforms such as antiSMASH, (2) cheminformatics-driven prediction of structure–activity relationships and ADMET properties, and (3) metabolomics-guided dereplication to prioritize novel bioactive scaffolds. We evaluate the convergence of genomics, metabolomics, and computational chemistry in enabling in silico lead optimization and the discovery of cryptic metabolites from previously inaccessible microbial taxa. While challenges in data standardization and scalability persist, the synergy between big data and NP research is accelerating clinical translation. Despite persistent challenges in data standardization, scalability, and equitable benefit-sharing, the convergence of big data and NP research is poised to redefine drug development. These advances position computational NP research as a cornerstone of next-generation 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

This study aimed to evaluate the physicochemical characterization and antibacterial activity of the essential oil (EO) extracted from the leaves of Mentha rotundifolia (L.) Huds. Molecular interactions between bioactive ligand compounds, target bacterial proteins and DNA gyrase subunit B (GyrB), as well as an in silico ADMET prediction study, were also conducted. The EO was obtained by hydrodistillation of the plant leaves. The Gas Chromatography–Tandem Mass Spectrometry (GC-MS/MS) analysis revealed Rotundifolone (27.95%) and carvacrol (19.48%) as the major constituents. Other components identified included Piperitenone (6.09%), Cinerolon (4.73%), and Pulegone (4.47%). Antibacterial activity was assessed against six bacterial strains: Enterococcus faecalis CIP 103214, Salmonella Typhi CIP 5535, Staphylococcus aureus ATCC 9144, Bacillus cereus ATCC 33019, Streptococcus agalactiae IPM 24842, and Providencia alcalifaciens CIP 82.90T. The disk diffusion assay showed a strong inhibitory effect against E. faecalis (inhibition zone: 19.66 ± 0.3 mm), while the lowest minimum inhibitory concentration (MIC) was observed for B. cereus (0.58 ± 0.01 µL/mL). The time-kill kinetics assay showed a progressive inactivation of all tested bacterial strains after their exposure to EO for 8 h at MICs. Furthermore, Molecular docking showed remarkable affinities between EO components, target proteins and DNA gyrase subunit B (GyrB). Moreover, the in silico ADMET predictions provided preliminary insights into the safety-related properties of the major EO components. In addition, EO compounds have the potential to interact with bacterial structures. These findings highlight the in vitro antibacterial potential of the M. rotundifolia EO and suggest its promise as a natural source of bioactive compounds. Full article

Background/Objectives: Paracetamol is a widely analgesic and antipyretic drug; however, its limited anti-inflammatory efficacy and safety concerns motivate the search for novel non-opioid alternatives. In this study, a series of 4-hydroxyphenylamino-naphthoquinones were designed as paracetamol-inspired analogs and synthesized via a solvent-free, silica-assisted Michael addition, providing a sustainable and efficient synthetic route. Methods: The compounds were evaluated using an integrated strategy combining in silico prediction, density functional theory calculations, molecular docking, ADMET profiling, and in vivo phenotypic pharmacological assays. Results: In vivo evaluation revealed pronounced peripheral antinociceptive activity in the acetic acid-induced writhing model and robust anti-inflammatory effects in carrageenan-induced paw edema, comparable to those of naproxen. These findings suggest a predominantly peripheral mechanism consistent with anti-inflammatory and antinociceptive profiles linked to cyclooxygenase inhibition. A normalization-based multi-criteria analysis integrating peripheral, anti-inflammatory, central, and antipyretic endpoints enabled transparent phenotypic prioritization within the series. Under this framework, compound 7 emerged as the most balanced peripheral–anti-inflammatory candidate, whereas compound 8, evaluated experimentally as a regioisomeric mixture, showed comparatively stronger central antinociceptive activity in the hot plate test. Antipyretic activity in an LPS-induced fever model was limited and not sustained. Conclusions: Overall, these findings indicated that the 4-hydroxyphenylamino-naphthoquinone scaffold emerges as a promising non-opioid platform for peripheral inflammatory pain, supporting further investigation of its pharmacological and mechanistic properties. Full article

Nepetalactone (NL) is a volatile iridoid monoterpene widely used in biopesticidal and repellent applications, yet its toxicokinetic behavior and metabolic fate as a pure compound remain poorly characterized. This study aimed to provide an integrated toxicokinetic evaluation of NL by combining in silico absorption, distribution, metabolism, excretion and toxicity (ADMET) modeling with in vitro metabolism assays using rat and human liver microsomes, supported by UHPLC–MS/MS analysis for metabolite identification. The in silico biotransformation predicted extensive phase I oxidation followed by phase II conjugation, while ADMET predictions indicated low systemic persistence and limited toxicological concern for most metabolites. The performed in vitro microsomal assays confirmed the in silico prediction by a rapid and time-dependent NL metabolism via both oxidative (86% reduction in NL concentration after 120 min) and conjugative (89% reduction in NL concentration after 120 min) pathways in rat and human systems, with comparable depletion kinetics between species. UHPLC–MS/MS enabled the identification of multiple phase I and phase II metabolites, pointing to pronounced interspecies differences in conjugative metabolism. In this sense, while oxidoreduction and hydrolysis reactions were consistent with previously reported iridoid metabolism. This study suggests the possible formation of previously unreported amino acid-related derivatives, although these require further confirmation. Overall, these findings advance the understanding of NL biotransformation, propose a new, previously unknown, metabolic pathway for iridoids, and provide relevant data to support human health and environmental risk assessment frameworks. Full article

The development of novel antiepileptic agents requires early identification of pharmacokinetic limitations to mitigate risks at later stages. This study aimed to perform in silico profiling of a library containing 448 novel 2H,5H-chromeno[4’,3’:4,5]thiopyrano[2,3-d]thiazol-2-one derivatives to select lead compounds with an optimal balance of safety and efficacy. The study was conducted using the ADMET-AI platform, based on a graph neural network, to predict physicochemical, pharmacokinetic, and toxicological properties. The methodology involved calculating drug-likeness descriptors for primary screening and a comparative statistical analysis of the top 20 selected structures against 16 approved antiepileptic drugs and four reference compounds. Based on drug-likeness descriptors and predicted ADMET (absorption, distribution, metabolism, excretion, toxicity) related parameters, 20 structures were prioritized for further analysis. Their predicted profiles suggested high intestinal absorption and blood–brain barrier (BBB) permeability, which may be relevant for central nervous system (CNS) directed agents. In comparison with the reference thiazolidinones, the prioritized compounds showed comparatively more favorable predicted mutagenicity and carcinogenicity profiles. Elevated predicted risks of hepatotoxicity and cardiotoxicity were observed for several structures, indicating the need for further structural optimization. The results suggest that the thiopyranothiazolidinone scaffold merits further anticonvulsant-oriented investigation at the stage of early compound prioritization. Experimental validation will be required to confirm the actual pharmacokinetic, toxicological, and anticonvulsant properties of the prioritized compounds. Full article

Background: Cysteinyl leukotrienes are components of slow-reacting substances of anaphylactic shock (SRS-A) and play a key role in asthma and inflammatory responses. Although chromone-2-carboxylic acids and substituted (aryloxy)alkanoic acids have the potential to be SRS-A antagonists, their comprehensive structure–activity relationships and pharmacokinetic characteristics remain understudied. Objective: This study integrated computational and experimental approaches, including QSAR modeling, molecular docking, ADMET analysis, molecular dynamics (MD) simulations, and antioxidant evaluation to identify and prioritize bifunctional compounds with anti-inflammatory and free radical-scavenging properties. Methods: A set of 68 compounds was analyzed using 2D and 3D quantitative structure–activity relationships (QSAR) (MLR, MNLR, SVR, ANN, and atom-based partial least squares). Molecular docking and 100 ns MD simulations were performed against the CysLT₁ receptor (PDB ID: 6RZ5). ADMET and drug-like properties of the compounds were predicted using ADMETlab 2.0 and SwissADME, and the in vitro antioxidant activity of the top-ranked compounds was evaluated using the DPPH method. Results: The atom-based 3D-QSAR model showed strong predictive power (R² = 0.9524, Q² = 0.5382). Compounds 25, 41, and 47 stood out with the most significant binding energies: −9.5 kcal/mol for 25, −10.0 kcal/mol for 41, and −9.4 kcal/mol for 47. MD simulations confirmed the structural stability and consistent interactions of the protein-compound 47 complex. ADMET analysis showed that compounds 25 and 41 had good pharmacokinetic properties, and in vitro antioxidant assays verified their free radical-scavenging efficacy. Conclusion: Our results highlight the utility of an integrated computational–experimental strategy for the discovery of dual-acting SRS-A antagonists. Compound 25 is highlighted as a promising lead compound for further preclinical development, which effectively combines leukotriene receptor antagonism and antioxidant activity. This framework provides an effective strategy for prioritizing lead compounds in anti-inflammatory drug development. Full article

Background/Objectives: Neurodegenerative diseases like Alzheimer’s, Parkinson’s, and Amyotrophic lateral sclerosis (ALS) share common molecular pathways, including neuroinflammation and oxidative stress, which complicate the effectiveness of single-target treatments. Garcinia mangostana L. (mangosteen) has shown neuroprotective properties, but previous studies focused on lipophilic xanthones, which have poor bioavailability and uncertain blood–brain barrier permeability. Methods: In the current study, polar metabolites from G. mangostana peel aqueous extract (GMPE) were assessed for potential multi-target interactions via UHPLC-QTOF-MS-based metabolomics, systems pharmacology, and molecular docking analysis. Further, in silico ADMET screening and network-based analyses assessed for overlap between GMPE compounds and genes associated with neurodegeneration (AD, PD, ALS). Results: Analysis of genes linked to AD, PD, and ALS revealed 121 common molecular targets influenced by GMPE metabolites. Network and enrichment analyses indicated that the compounds derived from GMPE may be involved in common pathways related to oxidative stress, neuroinflammation, and neuronal survival. Molecular docking analyses suggest that selected metabolites are likely to exhibit moderate binding affinities to their respective protein targets. Conclusions: The results presented in this study provide evidence that GMPE may possess potential multi-target interactions within common neurodegenerative pathways. However, since the data are based on computational and predictive approaches, these results should be considered hypothesis-generating and warrant further experimental validation. Full article

Traditional drug discovery processes are typically expensive, time-consuming, and have a very high failure rate. Artificial intelligence (AI) is currently reshaping this field in unprecedented ways, promising to significantly improve the efficiency and success rate of drug development. This article systematically compares and analyzes the application of AI for two major drug types: small molecule vs. peptide drugs. It explores their applications in several key stages of drug development, including virtual screening, lead compound optimization, de novo drug design, ADMET (absorption, distribution, metabolism, excretion, and toxicity) property prediction, and chemical synthesis planning. While both drug types benefit from AI-driven approaches, fundamental differences exist in molecular representation, data availability, key challenges, and model adaptability. For small molecule drugs, AI focuses on drug efficacy, synthetic feasibility, and accurate structure–activity relationship prediction. In contrast, for peptide drugs, AI faces more unique biological challenges, such as inherent flexibility, complex biological functions, stability, and immunogenicity. Finally, this article provides a forward-looking perspective on the future of AI-driven drug discovery, highlighting the immense potential of basic models, multimodal integrated systems, and autonomous discovery platforms, which will collectively drive the next wave of precision drug development. Full article

The presence of an unbalanced gut microbiome and the dysregulation of bile acid signalling are considered pivotal causes of various inflammation-based diseases. The Takeda G protein-coupled receptor (TGR5), TGR5 is a bile acid-responsive receptor that modulates inflammatory signalling pathways, making it an enticing molecular target for the discovery of novel anti-inflammatory agents. Herein, a comprehensive in silico approach was employed to identify potential TGR5 agonists from sterol-rich phytocompounds present in Triphala, a traditional polyherbal formulation. Using in silico computational methods, such as molecular docking and molecular dynamics simulations (MDS), we screened the putative agonistic potential of 10 phytocompounds obtained from Terminalia chebula, Terminalia bellirica, and Phyllanthus emblica against the crystal structure of human TGR5 (PDB ID: 7XTQ). Based on binding energy and molecular interactions, ergosterol (−12.34 ± 0.17 kcal/mol) and stigmasterol (−10.35 ± 0.04 kcal/mol) were predicted to be the top and best compounds. Furthermore, the stability of these two compounds in the docked complex was analysed using MDS for 200 ns. The mean Cα RMSD values were 0.22 ± 0.02 nm for both ergosterol- and stigmasterol-bound complexes, compared to 0.21 ± 0.02 nm for the unbound apo protein. Further, the molecular mechanics/Poisson–Boltzmann surface area (MMPBSA) analysis revealed that ergosterol exhibited binding free energy (−139.868 ± 12.318 kJ/mol) comparable to that of the co-crystallised ligand R399 −93.424 ± 8.919 kJ/mol. In silico ADMET predictions indicated acceptable drug-like properties and low toxicity for both compounds. Collectively, these computational findings suggest that ergosterol is a promising putative TGR5 agonist, warranting further experimental validation of its potential role in modulating inflammation-related pathways. Full article

Melanoma remains a highly aggressive malignancy, particularly in advanced metastatic stages where therapeutic options are limited. Natural compounds provide a structural basis for discovering novel anticancer agents. In this study, we employed an integrated in silico approach to evaluate the pharmacokinetic properties, toxicity profiles, and molecular targets of key alkaloids from Chelidonium majus, including berberine, sanguinarine, chelerythrine, chelidonine, protopine, umbelliferone and coptisine. ADME/T predictions (SwissADME and DeepPK) revealed favorable drug-likeness and oral bioavailability for most compounds, with berberine exhibiting the most balanced safety and absorption profile. All compounds demonstrated high intestinal absorption (>99%) and implicated key melanoma targets, including APE1/Ref-1, CXCR4, CCR2, TLR8, galectin-3, and VEGFR2. These molecules represent valuable templates for the development of melanoma therapies. Among the tested compounds, chelidonine emerged as a potential APE1 inhibitor, exhibiting the highest binding affinity and forming specific interactions within the enzyme’s catalytic site, suggesting its potential as a DNA repair-targeted agent in melanoma. These findings support the further exploration of natural alkaloids, including structural optimization or advanced formulation strategies, to enhance safety, bioavailability, and therapeutic efficacy in melanoma. Full article

Background: Oral squamous cell carcinoma (OSCC) is an aggressive cancer closely associated with smoking and alcohol consumption, with a higher incidence in men. Despite changes in treatment strategies, poor survival persists in most patients, highlighting the need for novel and improved therapeutic options. Naphthoquinone analogs are being investigated because of their active redox structure and broad pharmacological profile; they demonstrate cytotoxic antitumor activity, making them potential candidates for new drug agents. Objective: This study investigated new benzyl-naphthoquinone compounds as potential anticancer agents for various genotypes of oral squamous cell carcinoma (OSCC) and other cancer cells. Methods: This study reports the synthesis and evaluation of a series of eight benzyl-naphthoquinone compounds against oral squamous cell carcinoma. Results: Four compounds 1–4 showed the best cytotoxic profiles, with a selectivity index ≥ 3 for all OSCC cell lines tested. Compound 1 was the most selective compound in all OSCC models, showing a higher selectivity index than both carboplatin and shikonin. Furthermore, compound 1 induced DNA fragmentation, cell-cycle arrest, and caspase-3/7 activation, changes consistent with apoptosis, and time-lapse imaging corroborated the apoptotic phenotype. Hemolysis assays showed minimal toxicity in human erythrocytes, and acute in vivo evaluation in mice revealed no evident adverse effects under the conditions tested, indicating low acute toxicity, although more detailed histopathological and biochemical studies will be required to fully establish the safety profile. Molecular modeling suggested that compound 1 may interact with topoisomerase II, RSK2, and PKM2, which could contribute to the activation of apoptotic pathways, although these interactions remain predictive and require biochemical validation. Finally, in silico analysis of physicochemical and ADMET parameters indicated properties compatible with oral absorption and systemic exposure, together with predicted low toxicity; however, these results are model-based and should be confirmed experimentally. Conclusions: Based on these findings, compound 1 emerges as a promising lead candidate for the development of a novel chemotherapeutic agent against OSCC, with potential therapeutic efficacy against other cancer types. Full article