Introduction: Tuberculosis (TB) is one of the most serious global health issues, with the increasing number of multidrug-resistant TB cases emphasizing the need for new therapeutic approaches. Phytochemicals, with their diverse structures and favorable safety profiles, are a largely unexplored area for anti-TB drug development. Methods: An ethnobotanical study and literature analysis identified 310 medicinal plants traditionally used to treat respiratory infections, which produced 4,087 phytochemicals. Their structures were obtained from PubChem or drawn using ChemDraw, and pharmacokinetic properties were analyzed using QikProp. Enoyl-acyl carrier protein reductase (InhA, PDB ID: 4TRO), an important enzyme involved in mycolic acid biosynthesis, was selected as the target protein. Molecular docking was performed using Glide, followed by MMGBSA calculations, and the best hits were validated by 300 ns molecular dynamics simulations using the GROMACS pipeline. Results: Binding affinities showed that four phytochemicals, namely Patuletin, skimmin, flavonol- 3-O-D-glycoside, and salicin, had significantly higher binding affinity scores (-10.06 to -8.82 kcal/mol) than first-line anti-TB drugs isoniazid (-6.35 kcal/mol) and pyrazinamide (-4.22 kcal/mol). Patuletin and skimmin had MM-GBSA binding affinity scores of -74.89 and -71.76 kcal/mol, respectively. MD simulations of the top 3 compounds and control showed that the protein-ligand complexes were stable, as indicated by the RMSD, RMSF, Radius of gyration, SASA, and HBONDS. Discussion: Patuletin and Skimmin demonstrated strong binding affinity and structural stability against the InhA enzyme (PDB: 4TRO), indicating their potential as promising lead compounds for anti-tuberculosis effects. Derived from ethnomedicinal plants, these phytochemicals not only target key mycobacterial pathways but may also offer hepatoprotective and immunomodulatory benefits. The findings support the exploration of plant-derived compounds as safer and effective alternatives or adjuncts to conventional anti-TB therapies. Conclusion: The combination of virtual screening, ADME studies, and MD simulations enabled the identification of phytochemicals with promising interactions toward InhA, an established anti-TB target. The findings are based solely on computational analysis and should be interpreted as preliminary evidence of target engagement rather than confirmed inhibitory activity or therapeutic efficacy. These natural products may serve as potential lead compounds for further anti-tubercular drug discovery, warranting subsequent biochemical, cellular, and in vivo validation to establish their inhibitory potential, safety, and pharmacological effectiveness.
OBJECTIVE:Shao Kuiling Decoction (SKD) is used in the long-term management of Ulcerative Colitis (UC), but its molecular basis remains unclear. Because SKD is a multicomponent formula, a computational approach is useful for identifying candidate compounds, targets, and pathways for further validation. METHODS:Active compounds in SKD were screened from TCMSP using oral bioavailability and drug-likeness criteria. Putative targets were predicted and intersected with UC-related targets. Shared targets were analyzed by protein-protein interaction network construction, GO/KEGG enrichment, molecular docking, and 100 ns molecular dynamics simulations. RESULTS:A total of 145 active compounds and 94 shared SKD-UC targets were identified. AKT1, TNF, and TP53 were the main hub targets, and the PI3K-Akt and MAPK pathways were the most enriched. Molecular docking showed favorable binding of the major compounds to core targets, with kaempferol‑TNF showing the strongest binding energy of -8.9 kcal/mol and β‑sitosterol‑AKT1 showing -8.4 kcal/mol. Molecular dynamics simulations revealed that the kaempferol‑TNF and β‑sitosterol‑AKT1 complexes remained stable over 100 ns, with RMSD values plateauing at 0.20-0.25 nm and 0.15-0.20 nm, respectively, and maintained consistent hydrogen bonding. In contrast, the acacetin‑TP53 complex showed greater fluctuations, indicating weaker stability. DISCUSSION:These findings suggest that SKD may exert therapeutic effects in UC through coordinated modulation of multiple targets and pathways involved in inflammation and epithelial repair. In a broader context, this study provides a systems-level basis for understanding the potential mechanism of SKD in UC and offers focused directions for future experimental validation. CONCLUSION:SKD may exert anti-UC effects through a multi-component, multi-target mechanism involving AKT1/TNF/TP53-associated networks and PI3K-Akt/MAPK signaling.
BACKGROUND:Rosacea is a chronic inflammatory skin disorder with limited therapeutic options. Puhuaiyin (PHY), a traditional Chinese medicinal formula, shows clinical efficacy, but its multi-component mechanisms remain unclear. METHODS:Chemical constituents of PHY were identified by UPLC-Q-TOF-MS. Network pharmacology was used to predict potential targets, which were intersected with rosacea-associated genes. Bioinformatics analyses (differential expression, WGCNA, and machine learning) were applied to the GEO dataset GSE65914 to refine core targets. Molecular docking and molecular dynamics simulations were conducted to validate the binding modes and stability between key active constituents and the core targets. RESULTS:A total of 59 chemical constituents were identified in PHY, with five key active components subsequently screened: quercetin, emodin, kushenol N, physcion, and palmitic acid. Network pharmacology analysis revealed 44 intersecting targets, which were significantly enriched in inflammation-related pathways, such as MAPK, NF-κB, and JAK-STAT signaling. Integrated bioinformatics and machine learning approaches identified MMP9 and IL1B as core targets, both of which were markedly upregulated in rosacea lesions and demonstrated prominent diagnostic value (AUC = 0.999 for MMP9, 0.964 for IL1B). Molecular docking indicated strong binding affinity between the core components and MMP9/IL1B. Molecular dynamics simulations confirmed stable complex conformations over 200 ns, with MM/PBSA binding free energies of -15.54 Kcal/mol (quercetin-MMP9) and -15.57 Kcal/mol (quercetin- IL1B). DISCUSSION:This study, through a multidisciplinary approach, systematically elucidates the "multi-component, multi-target, and multi-pathway" mode of action of PHY in the treatment of rosacea. However, the computational predictions remain to be further validated by in vivo and in vitro experiments. Future research should focus on verifying its therapeutic efficacy in animal or cellular models, as well as elucidating the regulatory effects of key active components on the MMP9 and IL1B targets. CONCLUSION:These computational predictions suggest that PHY may exert therapeutic effects against rosacea via quercetin and other components targeting MMP9 and IL1B, thereby modulating MAPK, NF-κB, and JAK-STAT pathways. The proposed mechanisms include inhibition of inflammation, regulation of the immune microenvironment, attenuation of vascular dilation, and promotion of skin barrier recovery. These findings provide a theoretical basis for future experimental validation.
Background: Securidaca inappendiculata is a traditional medicinal plant used for the treatment of Rheumatoid Arthritis (RA) and related inflammatory conditions, and it possesses significant anti-RA activity. However, the precise molecular mechanisms underlying its therapeutic effects remain to be fully elucidated. Aim: This study aimed to elucidate the therapeutic mechanisms of a Xanthone-enriched Fraction (XRF) from S. inappendiculata against RA by integrating network pharmacology with experimental validation. Methods: Using both an Adjuvant-Induced Arthritis (AIA) rat model and an LPS/IFN-γ- stimulated THP-1 macrophage model, an integrated strategy was implemented. Potential targets and pathways for the xanthones were predicted via network pharmacology and molecular docking. Thereafter, the anti-arthritic efficacy of the xanthone fraction was evaluated in vivo, while its pro-apoptotic effect and regulation of key signaling pathways were investigated in vitro. Results: XRF significantly alleviated joint swelling and synovial inflammation in AIA rats. Mechanistically, it inhibited the PI3K/AKT/mTOR/HIF-1α signaling cascade and reduced phosphorylation of GSK-3β at Ser9, thereby promoting the apoptosis of pro-inflammatory M1 macrophages. Discussion: This work elucidated the anti-RA mechanism of S. inappendiculata: eight xanthones acted on the PI3K/AKT/mTOR/HIF-1α/GSK-3β axis to induce M1 macrophage apoptosis, validating the integrated approach and laying groundwork for RA treatment and natural product research. Conclusion: The present study provides a scientific foundation for further elucidating the mechanisms of S. inappendiculata against RA.
BACKGROUND:Dyslipidemia is a major risk factor for the development and progression of coronary artery disease. It results from an abnormal increase in low-density lipoprotein cholesterol, cholesteryl esters, or triglyceride levels in the blood. Cholesteryl ester transfer protein (CETP) is a protein that promotes the bidirectional allocation of triglycerides and cholesteryl esters between lipoproteins in the blood. CETP inhibition reduced dyslipidemia. OBJECTIVE:This work describes the synthesis, computational characterization, and CETP inhibitory activity of ten para-fluorinated phenoxymethyl benzenesulfonamides (6a-6j). METHODS:Compounds 6a-6j were characterized using various spectroscopic techniques and subsequently evaluated using computational analysis and biological assays. RESULTS:The compounds showed in vitro CETP % inhibition ranging from 4.2% to 100% at 10 μM. Among them, compound 6g (para-chloro substituted) demonstrated complete CETP inhibition (100 ± 1.2%), the highest pharmacophore fit value (7.72), and strong docking affinity (LibDock = 144.35; -CDocker = 0.24). Compounds 6e and 6f (ortho- and meta-chloro analogs) also exhibited high inhibitory activity (67% and 72%, respectively) with good docking scores (LibDock: 135.16 and 148.65; fit values: 2.02 and 6.84, respectively). Conversely, the unsubstituted derivative 6a exhibited minimal inhibition (4.2%) and fit/docking scores (LibDock = 140.21; fit = 2.09). DISCUSSION:Cheminformatics, PCA, and descriptor-loading analyses provided insight into the chemical space defined by the descriptors and the structural diversity of the synthesized compounds. Overall, compounds with higher CETP inhibitory activity generally exhibited more favorable docking interactions and higher pharmacophore fit values. Electron-withdrawing chloro substituents may promote favorable interactions within the hydrophobic CETP binding pocket. CONCLUSION:These findings suggest that electronic effects, substituent orientation, and hydrophobic properties contribute to CETP inhibitory activity, providing a foundation for future optimization and biological evaluation of sulfonamide-based CETP inhibitors.
Drug discovery is frequently limited by high attrition rates, and poor absorption, distribution, metabolism, excretion, and toxicity (ADMET) profiles are a major cause of late-stage failure. Therefore, precise ADMET property prediction is necessary to develop safe and effective drug candidates. Traditional experimental assays and rule-based computational procedures are limited by their poor predictive power, cost, and time, despite providing valuable insights. Innovative strategies to deal with these issues have been introduced by developments in artificial intelligence (AI), such as machine learning (ML), deep learning (DL), graph neural networks (GNNs), generative models, and multi-task learning (MTL). AI techniques can better generalize scaffolds, capture interdependencies between pharmacokinetic and toxicological endpoints, and model complex nonlinear relationships by leveraging large, diverse datasets. Explainable AI (XAI) enhances transparency by detecting biological and structural characteristics that are relevant to predictions, even if integrated pipelines combine predictive modeling with molecular creation and optimization. AI-driven ADMET prediction is becoming a vital tool in lowering attrition, speeding up candidate prioritization, and influencing the direction of rational drug development, despite persistent issues with data quality, regulatory acceptance, and synthetic viability.
Introduction: Yifei Capsule (YFC), a Traditional Chinese Medicine (TCM) formulation, has shown clinical benefit in patients with stable Chronic Obstructive Pulmonary Disease (COPD). However, its molecular mechanisms remain insufficiently understood. This study aimed to systematically investigate the active compounds, key targets, and signaling pathways underlying the therapeutic effects of YFC in stable COPD by integrating computational pharmacology with single-cell transcriptomic validation. Methods: An integrated workflow combining network pharmacology, molecular docking, and 100 ns Molecular Dynamics (MD) simulations was used to identify bioactive compounds, candidate therapeutic targets, and major signaling pathways of YFC. To externally validate the in silico findings, an independent public single-cell RNA sequencing (scRNA-seq) dataset (GSE249584) containing 54,821 cells from 15 human samples (7 controls and 8 COPD patients) was analyzed to characterize the cell-type-specific expression patterns of hub genes in COPD. Results: A total of 167 bioactive compounds in YFC and 977 putative targets were identified, of which 222 overlapped with COPD-related targets. Network topology analysis identified SRC, PIK3R1, and STAT3 as major hub genes, and KEGG enrichment analysis indicated that these targets were primarily enriched in the PI3K-Akt signaling pathway (hsa04151, p = 1.23E-15). Molecular docking revealed strong binding affinities between representative compounds and core targets, while MD simulations confirmed the dynamic stability of key complexes, particularly SRC-Korseveriline. scRNA-seq analysis further demonstrated cell-type-specific dysregulation of hub genes in COPD: STAT3 was significantly upregulated in monocyte/macrophage populations (adjusted p = 6.59E-08, log2FC = 0.32), PIK3R1 was downregulated in T cells (adjusted p = 2.05E-20, log2FC = -0.41), and SRC showed bimodal expression, with upregulation in endothelial cells (adjusted p = 0.002, log2FC = 0.38) but downregulation in T cells (adjusted p = 0.03, log2FC = -0.27). Discussion: These findings suggest that YFC may exert therapeutic effects in stable COPD through multi-component, multi-target regulation of the PI3K-Akt pathway and related inflammatory signaling networks. The cell-type-specific dysregulation of SRC, PIK3R1, and STAT3 supports a nuanced immunomodulatory and vascular-regulatory mechanism, highlighting the value of integrating computational prediction with single-cell transcriptomic evidence. Conclusion: This multi-scale study provides a mechanistic framework for understanding the action of YFC in stable COPD and identifies SRC, PIK3R1, and STAT3 as plausible core targets. The results support the utility of combining network pharmacology, molecular simulation, and single-cell transcriptomics to dissect the mechanisms of complex herbal medicines. Nevertheless, the present findings remain correlative and require further experimental validation to establish causality.
Introduction: Chronic Atrophic Gastritis (CAG) and Intestinal Metaplasia (IM) are critical precancerous lesions of gastric cancer. E-Lian granule (ELKL), a traditional Chinese medicine formula, has demonstrated favorable clinical efficacy in treating precancerous gastric lesions; however, its underlying molecular mechanisms remain unclear. Methods: A comprehensive approach integrating network pharmacology, molecular docking, and experimental validation was employed to investigate the pharmacological mechanisms of ELKL against CAG and IM. Potential active compounds, therapeutic targets, and signaling pathways were identified through database analysis, Protein-Protein Interaction (PPI) networks, and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis. Molecular docking was utilized to evaluate compound-target interactions. In vitro experiments were performed using MNNG-induced human Gastric Epithelial Cells (GES-1) treated with magnoflorine, columbamine, or 3-O-acetyl-glycyrrhetinic acid, and in vivo validation was conducted in a CAG mouse model. Results: A total of 178 overlapping therapeutic targets were identified. KEGG analysis revealed that the TNF, MAPK, and PI3K/Akt signaling pathways were closely associated with the therapeutic effects of ELKL. AKT1, MAPK1, and NFKB1 were identified as key hub targets. Molecular docking demonstrated strong and stable binding affinities of magnoflorine, columbamine, and 3-O-acetyl-glycyrrhetinic acid toward these targets. In vitro and in vivo assays comprehensively confirmed that these compounds significantly attenuated inflammatory cytokine secretion and inhibited the expression of AKT1, MAPK1, and NFKB1. Discussion: These findings indicate that ELKL exerts significant anti-inflammatory effects through the multi-component and multi-target regulation of inflammatory signaling pathways, thereby supporting its therapeutic potential in preventing the progression of CAG and IM. Conclusion: The representative active compounds identified from ELKL may ameliorate CAG and IM by regulating the TNF, MAPK, and PI3K/Akt signaling pathways.
INTRODUCTION:Amyloid-β accumulation, aberrant tau protein, neuroinflammation, and oxidative stress are some of the main pathogenic characteristics of Alzheimer's disease (AD), a degenerative illness characterized by cognitive deterioration. The majority of current AD therapies provide symptomatic alleviation with significant adverse effects, highlighting the urgent need for novel therapeutic approaches. OBJECTIVE:This study examines nefopam, a centrally acting analgesic with NMDA antagonist and monoaminergic properties, as a potential treatment for AD using in silico methods like Network Pharmacology, Docking, and Molecular Dynamics Simulation studies. RESULTS:Using network pharmacology, 90 molecular targets shared by the AD and nefopam pathways were identified. Following the selection of important hub proteins for further analysis, eight proteins with accessible 3D structures were put through molecular docking and MMGBSA computations. Nefopam demonstrated significant binding affinities, especially to 5HTR2A, GRIN1, 5HTR2C, SLC6A4, SLC6A3, and MAOB, whereas OPRM1 displayed weaker interactions, consistent with its lower MM-GBSA value (-37.04 kcal/mol) and docking score (-2.963). Molecular dynamics simulations of particular complexes over 100 ns revealed stable contacts and minimal structural changes for SLC6A3, SLC6A4, and 5HTR2A, suggesting strong and long-lasting binding. DISCUSSION:The findings suggest that nefopam exhibits significant multi-target interactions with several proteins involved in AD pathogenesis, particularly those associated with neurotransmission, neuroprotection, and neuroinflammatory pathways. Its stable binding behavior and favorable interaction profiles support its potential role in modulating disease progression beyond symptomatic management. CONCLUSION:Overall, this computational analysis confirms that nefopam can target multiple proteins linked to AD. These results show that more experimental research is necessary to validate nefopam's therapeutic potential and provide positive support for its repositioning in AD treatment.
Background: Colorectal Cancer (CRC) is one of the most common cancers worldwide. There is an urgent need to identify new drugs for CRC treatment. The apoptotic factors Bad, Bid, Bik, Bim, Bax, and Bak play a crucial role in activating the apoptotic cascade pathways. Inhibition of these factors is a main strategy of cancer therapy. This study investigates the effects of omeprazole on pro-apoptotic factors (Bak, Bad, Bim, Bik, Bid, and Bax) involved in the apoptosis induction in CRC using both in-silico and in-vitro techniques. Methods: The 3D structure of omeprazole was docked to pro-apoptotic factors obtained from the Protein Data Bank (PDB) using Autodock 4.2.6 software. Molecular Dynamics (MD) simulations were also carried out with GROMACS 2022 to validate the stability of the ligandreceptor complexes. Additionally, an in-vitro experiment was performed to evaluate the cytotoxicity of omeprazole on the HT-29 cell line using the MTT assay. Results: The strong binding affinity between omeprazole and pro-apoptotic factors was observed in molecular docking, with binding energies less than -5.0 kcal/mol. The stability of the omeprazole-receptor complexes was confirmed through MD simulations. Moreover, in vitro results indicated that omeprazole can inhibit the growth and proliferation of colorectal cancer cells with an IC50 value of 400 μM. Discussion: In silico studies presented that omeprazole could affect pro-apoptotic factors responsible for inducing apoptotic pathways. The in vitro studies assume that omeprazole has significant cytotoxic activity against colorectal cancer cell lines. Conclusion: These results demonstrated the therapeutic effects of omeprazole by inducing apoptosis in colorectal cancer through the influence of pro-apoptotic factors, and it appears that omeprazole could be a promising drug repurposing candidate for the treatment of colorectal cancer.
INTRODUCTION:Protein Kinase C alpha (PKCα), a key regulator of cellular signaling, is frequently dysregulated in breast cancer. Despite its clinical relevance, there is a dearth of selective and safe PKCα inhibitors. This study aims to identify and characterize novel PKCα inhibitors using computational and experimental methodologies. METHODS:A structure-based virtual screening of the Enamine Hinge Binders Library against the PKCα receptor (PDB ID: 3IW4) was performed using the Glide module of Schrödinger Suite. Analysis of docking scores, protein-ligand interactions, binding free energies, and ADMET profiles helped identify lead candidates. The stability of protein-ligand complexes was confirmed using MD simulations. The functional activity of identified candidates was validated using a kinase assay, and cell death was analyzed using an MTT assay on breast cancer cell lines. RESULTS:Compounds Z3077775938 (P1) and Z22177390 (P2) exhibited strong binding and stability within the hinge-binding region of PKCα and showed favorable drug-likeness profiles. Structural analysis revealed P1 and P2 as novel scaffolds. The kinase assay revealed dosedependent inhibition of PKCα's ATP-binding activity with IC50 values of P1 and P2 as 0.92 nM and 15.27 nM, respectively. Both compounds induced dose-dependent cytotoxicity in breast cancer cells. DISCUSSION:This study identified two novel, structurally diverse PKCα inhibitors. Their low IC50 values and dose-dependent cytotoxicity, as observed in both ER+ and triple-negative breast cancer cell lines, validate their functional efficacy and highlight therapeutic potential for breast cancer treatment. CONCLUSION:Overall, this study identified two PKCα inhibitors that provide a foundation for further optimization as selective therapeutic agents in PKCα-driven malignancies.
INTRODUCTION:Constipation, prevalent globally (16-35%), is particularly common in the elderly, reducing quality of life and causing complications like intestinal obstruction. Current treatments (e.g., methylcellulose, polyethylene glycol, bisacodyl, prucalopride) provide temporary relief but have side effects, such as electrolyte disturbances and drug dependence. It is worth noting that the traditional Chinese herbal drug Taohe Chengqi Decoction (TCD) has demonstrated good laxative efficacy and safety advantages in long-term clinical practice. However, its systematic mechanism of action involving multiple components, multiple targets, and multiple pathways remains unclear. METHODS:This study innovatively integrates network pharmacology prediction, high-throughput blind molecular docking assisted by machine learning (DiffDock), and molecular dynamics simulation to construct a multidimensional mechanism model for TCD in treating constipation. RESULTS:A total of 115 active molecules were identified from TCD through high-throughput blind docking and network pharmacology, targeting 153 constipation-related proteins. After an 80 ns molecular dynamics simulation, six core components demonstrated stable binding with key targets like AKT1 and MAPK3. DISCUSSIONS:TCD exerts its therapeutic effects by intervening in key signaling pathways such as PI3K-Akt, IL-17 inflammation, T cell receptor, MAPK, and cAMP. It regulates the dynamic balance of intestinal epithelial cell proliferation/apoptosis, inhibits inflammatory cascades, and enhances intestinal motility and secretory functions to relieve constipation. CONCLUSION:The innovative integration of machine learning-driven high-throughput blind docking technology with network pharmacology establishes a novel paradigm for traditional Chinese medicine research, significantly enhancing target prediction reliability through advanced structural validation.
BACKGROUND:Ribonucleotide Reductase (RR) is a pivotal enzyme in DNA synthesis and repair, making it a vital target in cancer therapy. While synthetic RR inhibitors such as gemcitabine and hydroxyurea are used clinically, their long-term efficacy is hampered by drug resistance and systemic toxicity. This has prompted growing interest in natural compounds with potential anticancer properties and reduced toxicity. This study aims to evaluate the molecular interactions and pharmacokinetic profiles of two naturally derived compounds traditionally used, genistein (Chinese name: , derived from Glycine max) and hematoxylin (Chinese name: , extracted from Caesalpinia sappan), against human RR using molecular docking and in silico pharmacological analyses. METHODS:The three-dimensional structure of human RR (PDB ID: 6L7L) was retrieved and subjected to docking simulations using AutoDock Vina and Chimera. Genistein and hematoxylin were docked into the RR active site, and their interactions were analyzed in comparison to gemcitabine. Molecular dynamics simulations over 100 ns further confirmed the structural stability and binding persistence of both compounds within the RR active site. Pharmacokinetic and drug-likeness properties were evaluated via SwissADME and Molinspiration platforms. RESULTS:Both genistein and hematoxylin exhibited strong binding affinities (-8.2 kcal/mol, -8.8 kcal/mol), respectively, to the RR active site, interacting with key catalytic residues in a manner comparable to the standard drug gemcitabine (-7.3 kcal/mol). Molecular dynamics simulations over 100 ns further confirmed the structural stability and binding persistence of both compounds within the RR active site. In silico pharmacokinetic predictions indicated good oral bioavailability, compliance with drug-likeness criteria, and low predicted toxicity. DISCUSSION:Genistein emerges as a good candidate owing to its strong RRM1 binding, favorable physicochemical attributes, clean medicinal chemistry profile, and low predicted toxicity. Hematoxylin offers superior binding affinity but requires refinement to address solubility and PAINS-related concerns. Gemcitabine, while efficacious clinically, suffers from limited oral bioavailability and potential toxicities. CONCLUSION:Genistein and hematoxylin demonstrate promising molecular interactions and pharmacological profiles as potential natural RR inhibitors. Their dual role in traditional healing and modern molecular pharmacology supports further preclinical development as anticancer agents.
INTRODUCTION:Endometriosis, a chronic gynecological disorder, significantly impacts fertility and quality of life. Identifying effective therapeutic targets is critical for their management. OBJECTIVES:The objective of this study was to explore the therapeutic potential of Chuanxiong Rhizoma and Angelicae Sinensis Radix (CXDG) in treating endometriosis by identifying active compounds and key molecular targets, with a particular emphasis on ESR1 as a potential regulatory hub involved in disease progression. METHODS:A bioinformatics approach identified active compounds from CXDG and their therapeutic targets. WGCNA identified key gene modules related to endometriosis, while machine learning models identified critical genes associated with the disease. Molecular docking and assays (CCK-8, EdU, Transwell) evaluated compound effects on cell proliferation, invasion, and migration. RESULT:Machine learning models identified ESR1, HMGCR, and NTRK2 as key molecular targets. Molecular docking showed Coniferyl Ferulate binds ESR1. Coniferyl Ferulate inhibited the proliferation, invasion, and migration of endometriosis cells. Immune infiltration analysis revealed differences in immune cell composition linked to ESR1 expression. DISCUSSION:Our findings suggest that ESR1 modulates immune responses in endometriosis, with Coniferyl Ferulate showing potential to inhibit cell proliferation and migration. Future studies, including animal models and clinical trials, are necessary to confirm its efficacy and explore combination therapies for enhanced treatment outcomes. CONCLUSION:Coniferyl Ferulate exhibits therapeutic potential against endometriosis by inhibiting cell proliferation, migration, and invasion. ESR1 acts as a central regulatory hub linking hormonal and immune pathways, suggesting that CXDG may exert multi-target effects by modulating ESR1-mediated signaling.
INTRODUCTION/OBJECTIVE:Autism spectrum disorder (ASD) aetiology is a multifactorial concept, involving genetic susceptibility as well as environmental and neuroinflammatory mechanisms. In this context, the present study aimed to identify neuroprotective and antiinflammatory phytoconstituents of indigenous medicinal plants using molecular docking and molecular dynamics (MD) studies against key proteins involved in the pathophysiology of ASD. METHODS:A panel of ASD-associated proteins, including brain-derived neurotrophic factor (BDNF), interleukin-17 alpha (IL-17α), and indoleamine 2,3-dioxygenase-1 (IDO-1), was virtually screened. The most promising protein-ligand complexes, selected based on docking scores, were further assessed using Nanoscale Molecular Dynamics (NAMD) and Visual Molecular Dynamics (VMD) to evaluate their conformational stability. Using SwissADME and additional ADMET tools, the pharmacokinetic properties and possible toxicity of the lead compounds were predicted. RESULTS:Piperine was found to possess the highest affinity to BDNF (-8.62 kcal mol⁸¹), IDO-1 (-8.75 kcal mol⁸¹), and IL-17α (-9.18 kcal mol⁸¹). The MD simulations indicated no unstable interactions, and the BDNF-piperine complex (PDB ID: 1BND) had the lowest root-mean-square deviation (RMSD) of 0.306 ± 0.203 nm. ADMET and SwissADME models indicated that piperine's bioavailability was favourable and its predicted toxicity was low. DISCUSSION:The strong binding affinities and stable MD trajectories indicate that piperine may inhibit neuroinflammatory signaling pathways involved in ASD. Its pharmacokinetic properties also solidify its lead compound candidature. However, such computational understanding requires further investigation to support therapeutic efficacy and safety. CONCLUSION:The findings suggested that piperine may be a promising natural phytochemicalbased therapeutic in treating neuropsychiatric disorders, including ASD, by targeting neuroinflammatory pathways.
INTRODUCTION:Drug repositioning identifies new therapeutic applications for existing drugs, particularly for diseases lacking effective treatments. This approach can significantly reduce the time and cost of drug discovery. Recently, Machine Learning (ML) and Deep Learning (DL) have become pivotal tools in this field, facilitating the analysis of large-scale datasets and enhancing the accuracy, efficiency, and speed of identifying novel drug indications. METHODS:This systematic review examines recent advances in machine learning and deep learning approaches for drug repositioning. A comprehensive search of multiple databases yielded 24 relevant studies published between 2015 and 2025. The review analyzes model architectures, methodologies, and evaluation metrics, while also investigating data types and key findings related to the repositioned drugs. RESULTS:Results show that deep learning architectures, such as Graph Convolutional Networks (GCN), Deep Neural Networks (DNN), alongside machine learning models like Random Forest (RF), and Support Vector Machines (SVM), are highly prevalent. Across all reviewed studies, predictive models consistently demonstrate strong performance, with most accuracy-based studies reporting values above 90%, while studies using other evaluation metrics also show competitive results. DrugBank, PubChem, and ChEMBL emerged as the primary benchmarked databases, particularly in studies aimed at predicting Drug-Target Interactions (DTIs). While research remains concentrated on oncology and neurodegenerative diseases, the field is rapidly expanding toward other conditions. DISCUSSION:Our analysis demonstrates a clear move toward advanced deep learning frameworks, which have become the modern pillars for drug repurposing. However, a critical translational gap remains between successful in silico predictions and actual clinical outcomes, primarily due to challenges in model interpretability and data integrity. CONCLUSION:AI-driven drug repositioning offers a cost-effective strategy to accelerate drug discovery. To achieve real-world medical impact, future research must prioritize interpretable architectures, advanced deep learning frameworks, and robust hybrid models, particularly for treating rare diseases, to bridge the gap between computational success and clinical implementation effectively.
INTRODUCTION/OBJECTIVE:The brain has a semipermeable interface, the blood-brain barrier (BBB), which protects the brain but greatly limits the absorption of most small-molecule drugs, with only ~ 24% able to cross. METHODS:This study involved 5,412 drug-like molecules from three public BBB datasets and calculated 200 physicochemical descriptors with RDKit and Mordred. With ChemBERTa-2, SMILES strings were tokenized using the ChemBERTa-2 tokenizer and embedded into a 768-dimensional chemical language space. A label-aware SMILES mask-and-replace augmentation expanded the corpus fivefold while retaining chemical validity. Using Bemis-Murcko scaffolds and 5-fold cross-validation on a 4-layer MLP, descriptor and embedding vectors were combined into a 968-dimensional hybrid representation. Statistical reliability was assessed using 2,000-sample bootstrap confidence intervals. RESULTS:The hybrid model (accuracy, 95%) and an AUROC of 0.96 showed an improvement of 7-11% above the descriptors-only (accuracy 0.88; AUROC 0.90) or embedding-only (accuracy 0.91; AUROC 0.93) baselines. Statistically significant (p < 0.001) gains were supported by saliency analyses of descriptors and embeddings, which made unique and orthogonal contributions. DISCUSSION:The findings indicate that integrating traditional descriptors and chemical-language embeddings yields two distinct types of information, enabling strong, understandable predictions. Augmentation strategies increased performance more quickly without causing syntactic invalidity. CONCLUSION:A hybrid AI pipeline shows much better BBB penetration forecasting than single- baseline unimodal baselines. This work facilitates reproducibility by releasing cleaned data, trained weights, and open-source code, and by providing a viable resource for ongoing CNS drug discovery.
INTRODUCTION:The aim is to study the effect of Fuzheng Xiezhuo Decoction (FZXZD) on renal fibrosis (a marker of chronic kidney disease). METHODS:Network pharmacology and molecular docking are used to explore the active components and targets of FZXZD, and then to explore the key pathways or mechanisms of its anti-renal fibrosis. Verification is carried out with an adenine-induced renal fibrosis rat model. RESULTS:Network pharmacology and molecular docking studies have found that the NLRP3/TNF-α/IL-6 axis is the key target of FZXZD in anti-renal fibrosis. Animal experiments show that in adenine-induced nephropathy rats, FZXZD treatment improves renal function parameters and alleviates renal injury. H&E and Masson staining were performed for histopathological examination, and it was found that FZXZD could effectively reduce renal tissue damage and slow down the progression of fibrosis. Immunohistochemical analysis further confirmed that it could inhibit the expression of fibrosis markers αSMA and collagen I. Western blot results showed that FZXZD down-regulated the protein levels of fibrosis mediators (TGF-β and Smad2/3) and inflammatory cytokines (NLRP3, TNF-α, and IL-6). DISCUSSION:FZXZD exerts anti-fibrosis through a dual mechanism, inhibiting the NLRP3/TNF-α/IL-6 inflammatory axis and inhibiting the TGF-β/Smad fibrosis pathway. These findings establish NLRP3/TNF-α/IL-6 as new targets for the treatment of renal fibrosis. CONCLUSION:FZXZD inhibits the TGF-β pathway and regulates the NLRP3 pathway to reduce the release of inflammatory cytokines, thereby alleviating renal fibrosis (RF) and then relieving inflammation and renal fibrosis. This study shows that the NLRP3/TNF-α/IL-6 signaling axis is a promising mechanism of action for FZXZD in the treatment of renal fibro.
BACKGROUND:Colorectal cancer (CRC) is a leading cause of cancer-related death worldwide. Despite advances in current therapeutic strategies, treatment outcomes remain limited and are associated with adverse effects. Consequently, there is growing interest in bioactive compounds such as Luteolin, which has been linked to anticancer properties. OBJECTIVE:Explore the therapeutic potential of Luteolin in CRC using a network pharmacological approach, molecular docking, and molecular dynamics. METHODS:CRC targets were obtained from the MalaCards and DisGeNET databases. Luteolin targets were obtained from the Comparative Toxicogenomics and SwissTargetPrediction databases. Eighteen common potential targets were analyzed in DAVID to understand the gene ontology. Subsequently, a protein-protein interaction network was constructed in Cytoscape, and six hub genes were identified. These hub genes were analyzed for immune cell infiltration using the TISIDB database. Subsequently, molecular docking of the proteins with Luteolin was performed in Autodock Vina, and the three complexes with the lowest ΔG values were selected for molecular dynamics analysis using GROMACS. RESULTS:Gene ontology results showed that Luteolin activates apoptosis mechanisms, affects transcription and translation processes, affects the cell nucleus, and dysregulates protein kinase activity. The affected KEGG pathways were estrogen signaling and microRNAs. Notably, TOP1 is associated with B-cell and macrophage infiltration. Finally, molecular docking and molecular dynamics confirm stable interactions between Luteolin and the hub genes. DISCUSSION:Integrative analysis suggests that Luteolin exerts multi-target anticancer effects in CRC by modulating key signaling pathways and forming stable interactions with target proteins. Molecular docking and dynamics simulations support the stability and affinity of the binding. CONCLUSION:These findings suggest that Luteolin holds significant anticancer potential and may serve as a promising candidate for further experimental investigations in CRC therapy.
INTRODUCTION:Diabetes mellitus is a chronic metabolic disease characterized by longterm hyperglycaemia. Prolonged illness may lead to serious complications in vital organs, including the kidneys, nerves, and cardiovascular system. Therefore, early prevention of diabetes is of paramount importance. METHODS:In this study, we propose a novel integrated learning method (FT-TRF) that combines the capabilities of FT-Transformer and stochastic Senri for efficiently identifying potential antidiabetic compounds. The proposed method combines the global feature interaction capabilities of FT-Transformer with the local feature dependency modeling of Random Forest (RF). It incorporates feature engineering and selection to identify important features, enhancing the data representation capability of the FT-Transformer model. The predictive probabilities from both models are integrated through a dynamic linear weighting mechanism, enhancing the model's generalization ability. RESULTS:We validated the proposed model through experiments using the most recent diabetesrelated compounds dataset. The results demonstrate that our hybrid model outperforms traditional classifiers across multiple metrics, including the Area Under the Curve (AUC), sensitivity, specificity, Kappa coefficient, Matthews correlation coefficient (MCC), F1 Score, Precision- Recall (PR) curve, and Receiver Operating Characteristic (ROC) curve. In addition, our model correctly screened compounds related to diabetes on other datasets. DISCUSSION:Our method outperforms others in identifying diabetes-related compounds, showing a robust F1 Score and AUC. To enhance the statistical rigor of these findings, future studies will apply multiple-comparison corrections, such as Bonferroni or false discovery rate control. CONCLUSION:The findings are summarized primarily through AUC and F1 Score, which serve as comprehensive comparison measures. These results confirm the superior performance of our integrated method for identifying diabetes-related compounds.
