Current Computer - Aided Drug Design - Online First

Mycobacterium abscessus (MAB) is severely resistant to available antibacterial agents. The current study aimed to find natural inhibitors against MAB to fight the resistant isolates.

Ten lead compounds were selected against MAB VapC5 for Molecular Dynamics (MD) simulations for 200 ns. Root Mean Square Fluctuation (RMSF), Root Mean Square Deviation (RMSD), Radius of Gyration (Rg), and Dynamic Cross Correlation Matrix (DCCM) of apo and VapC5-ligand complexes were analyzed.

Among the ten lead compounds, eight compounds (deoxy artemisinin, glaucocalyxin A, (1R,4E,9E,11S)-4,12,12-trimethyl-8-oxobicyclo[9.1.0]dodeca-4,9-dien-2-yl acetate, isorhamnetin, Kissoone C, piperlongumine, tectorigenin, and wogonin) showed a good potential against MAB VapC5. The apo-VapC5 exhibits a stable RMSD of 0.154 nm and RMSF of 0.088 nm ± 0.14. At the same time, ligands including Deoxy Artemisinin, Ftaxilide, Glaucocalyxin-A, and others range in RMSF from 0.097 nm to 0.147 nm, with standard deviations varying between 0.12 and 0.22. The highest RMSF was observed with Kissoone C (0.147 nm ± 0.15), and the lowest with Tectorigenin (0.097 nm ± 0.12). The Apo-VapC5 exhibited an Rg of 3.064 nm, whereas in complexes with ligands, the Rg values ranged from 0.097 nm to 0.147 nm. The DCCM analysis of VapC5-ligand complexes also reveals a more pronounced negative correlated motion.

The simulation outcomes indicate that ligand binding enhanced the structural stability of VapC5 compared to the apo form. Among the tested compounds, deoxy artemisinin, glaucocalyxin A, and tectorigenin showed the most stable interactions, highlighting their potential as promising VapC5 inhibitors.

The selected compounds exhibit good binding affinity and residue interaction patterns. The ligand binding influenced VapC5 flexibility and conformational changes observed in complexes with MABVapC5, which could be useful inhibitors after experimental validation.

There has been increasing interest in neuroimaging studies in recent years, and computer-aided approaches have gained prominence in improving diagnostic accuracy. Attention Deficit Hyperactivity Disorder (ADHD) is a prevalent neurodevelopmental disorder characterized by inattention, impulsivity, and hyperactivity. Traditional diagnostic approaches often rely on subjective assessments, highlighting the need for more objective, data-driven methods. This study aims to classify ADHD subtypes and assess medication effects by converting resting-state fMRI images into one-dimensional (1D) signals and extracting statistical features using Variational Mode Decomposition (VMD).

Resting-state fMRI data from the ADHD-200 dataset, including 41 healthy controls (HC), 41 medicated ADHD-Combined (ADHD-C) individuals, and 41 non-medicated ADHD-C individuals, were analyzed. The 1D fMRI signals were decomposed into nine sub-bands using VMD. Statistical features were extracted from each sub-band and classified using Support Vector Machines (SVM), Linear Discriminant Analysis (LDA), and Artificial Neural Networks (ANN).

VMD-derived features substantially improved classification performance. The highest binary classification accuracy was achieved by LDA: 96.34% distinguishing non-medicated ADHD from controls and 88.41% for medicated ADHD versus controls. The classification between medicated and non-medicated ADHD yielded 79.63% accuracy. Ternary classification across all groups reached 69.51% accuracy.

These findings show that the VMD-based approach improves the classification of ADHD subtypes and helps evaluate medication effects. However, the lower performance in multi-class classification reflects the complexity of ADHD neuroimaging data.

The VMD-based approach improves classification accuracy, especially in distinguishing ADHD subtypes and medication effects, supporting its potential as an objective tool for diagnosis and treatment planning.

Qigesan (QGS) is a traditional Chinese herbal medicine used for the treatment of esophageal carcinoma (EC) and possesses anti-cancer properties. However, the mechanism of QGS in the treatment of EC remains unclear.

This study aimed to investigate the molecular basis of QGS in the treatment of EC and establish a scientific foundation for its application.

This study employed a multifaceted approach-including network pharmacology, molecular docking, and molecular dynamics simulations-to investigate the therapeutic mechanisms of QGS in EC. By leveraging a comprehensive array of databases such as TCMSP, HERB, TTD, OMIM, GeneCards, and DrugBank, we systematically identified potential bioactive components and their corresponding targets related to QGS, as well as targets associated with EC.

271 overlapping targets of QGS and EC were obtained. Network pharmacology analysis identified eight hub targets (TP53, AKT1, IL6, STAT3, TNF, IL1B, EGFR, and CTNNB1) mediating the effects of QGS through dysregulated pathways, including PI3K-Akt signaling, apoptosis regulation, AGE-RAGE, and IL-17 signaling. Molecular docking revealed that three QGS-derived compounds-peimisine, salvianolic acid J, and songbeinone- exhibited high binding affinities for multiple hub targets. These compounds concomitantly inhibit the MAPK/NF-κB pathways while activating cell cycle regulation, DNA repair, and apoptosis, suggesting a multi-target therapeutic mechanism against esophageal carcinoma.

QGS, a TCM formulation, has been extensively applied in the clinical treatment of EC for a long time and has been demonstrated to relieve esophageal obstruction. Nevertheless, the exact active components within QGS and their underlying molecular mechanisms remain elusive. In this study, network pharmacology, molecular docking, and MD simulation were employed to investigate the potential molecular mechanisms by which QGS exerts its therapeutic effects in the treatment of EC.

These findings provide a comprehensive elucidation of the multi-component, multi-target therapeutic strategy employed by QGS in the treatment of EC, laying a solid theoretical foundation for subsequent pharmacological development and clinical validation.

Breast cancer is a leading cause of cancer-related mortality in women. Although the traditional Chinese medicine Codonopsis Pilosula (CP) is empirically used in its treatment the underlying mechanisms of action remain elusive. This study aimed to apply a novel integrative network pharmacology and machine learning approach to identify bioactive compounds in CP and elucidate their anti-breast cancer mechanisms.

The analysis utilized a comprehensive and innovative workflow that combined network pharmacology machine learning-based target prediction bioinformatics analyses and molecular docking and molecular dynamics simulations. Publicly available datasets were mined for CP constituents and putative targets and integrated with breast cancer-associated gene profiles. Key compound-target interactions were prioritized via machine learning algorithms.

Machine learning highlighted EGFR and PTGS2 as primary targets. Molecular docking and dynamics demonstrated stable binding of Taraxerol and Stigmasterol to these proteins with EGFR–Taraxerol EGFR–Spinasterol PTGS2–Stigmasterol and PTGS2–Taraxerol complexes exhibiting robust affinity and stability.

The findings are significant as they reveal previously unreported interactions between CP’s bioactive compounds and critical breast cancer targets. This provides a molecular-level explanation for the traditional use of CP bridging the gap between TCM and modern pharmacology. These results offer a solid foundation for further experimental validation.

This multidisciplinary predictive strategy successfully identified key bioactive compounds in CP and their molecular targets in breast cancer. The study provides crucial mechanistic evidence for CP’s therapeutic potential and highlights the power of this integrated approach for drug discovery from TCM (Traditional Chinese Medicine).

The aim of the study was to investigate the mechanism of Qi Zhu Formula (QZF) against Metabolic-Associated Fatty Liver Disease (MAFLD) via network pharmacology and experimental validation.

Network pharmacology identified QZF components, targets, and pathways for MAFLD. Key predicted AMPK pathway targets (SREBP1C, FASN, ACC1) were validated. MAFLD was induced in rats with a 16-week high-fat/high-sugar diet. Low/medium/high QZF doses and positive control (YSF) were administered for 8 weeks. Serum parameters (liver function, lipids, glucose, cytokines, oxidative stress markers), liver histopathology (HE, Oil Red O), and hepatic mRNA/protein levels (SREBP1C, FASN, ACC1, p-AMPK) were assessed. In vitro, lipid accumulation and protein expression (p-AMPK, SREBP1C, FASN, ACC1) were measured in fatty AML12 cells treated with control/model/normal serum/QZF serum/AMPK inhibitor/QZF serum + inhibitor.

Network pharmacology identified 36 QZF components, 236 targets, and 138 intersecting MAFLD targets, enriching the AMPK pathway. QZF significantly reduced liver steatosis, inflammation, necrosis, serum liver enzymes, lipids, glucose, IL-6, IL-1β, TNF-α, FFA, MDA, and increased SOD in MAFLD rats. QZF upregulated hepatic p-AMPK protein and downregulated SREBP1C, FASN, and ACC1 mRNA/protein. QZF serum reduced lipid droplets in cells, most effectively at 24h, increasing p-AMPK and decreasing SREBP1C/FASN/ACC1 protein. AMPK inhibitor abolished QZF serum's effects.

QZF's AMPK-mediated lipid suppression advances TCM mechanism validation, though unexamined pathways and compound synergies require exploration.

QZF ameliorates MAFLD by improving serum profiles, inhibiting lipid synthesis (via AMPK activation, suppressing SREBP1C/FASN/ACC1), reducing inflammation, and attenuating liver injury. Its “multi-target-multi-pathway” action supports its potential as a novel MAFLD treatment.

Protein-Protein Interactions (PPI) are crucial for cellular functions. Computational prediction of protein complexes from PPI networks is essential, yet traditional methods relying solely on network topology often lack biological features. Integrating topological and biological features can enhance prediction accuracy.

We proposed TOP-BIOCom, a machine learning-based approach that integrates feature fusion of novel topological, structural, and sequence-based features with the Embedding Lookup technique. The benchmark dataset was CYC2008, while the PPI network datasets were DIP and BioGrid. The performance evaluation measures precision, recall, and F-1 score were carried out to assess the efficiency of the TOP-BIOcom model and compared with the reported models.

Our result with a novel feature fusion approach, demonstrated that the BioGrid PPI network dataset with Random Forest yielded an accuracy of 0.99, precision of 0.96, recall of 0.97, and an F1-score of 0.96. The model's validation accuracy was 0.99 and completed the task in 3.85 seconds. DIP dataset with LightGBM model achieved an accuracy of 0.95, with a precision of 0.88, a recall of 0.91, and an F1-score of 0.89. The validation accuracy matched the accuracy at 0.95.

These results highlight the robustness of the proposed TOP-BIOcom model in predicting protein complexes from PPI networks with higher accuracy and faster execution. The proposed approach demonstrates superiority over existing methods, showing its effectiveness across different datasets and machine learning models.

These findings suggest that integrating topological and biological features can provide a holistic view of protein complexes enhancing prediction accuracy and aiding in drug discovery and understanding cellular mechanisms.

Diabetes mellitus is an endocrine disorder characterized by metabolic abnormalities and chronic hyperglycemia, caused by insulin deficiency (Type I) or resistance (Type II). It affects various tissues differently, and its complications extend beyond classical targets, such as the kidneys and eyes, to lesser-studied organs, including the lungs. Understanding tissue-specific damage is crucial for effective disease management and the prevention of complications.

This study aims to evaluate the histopathological and immunohistochemical effects of diabetic lung fibrosis using a streptozotocin (STZ)-induced diabetes model. Additionally, it seeks to develop a high-performance image classification system based on deep neural networks to accurately classify tissue damage in diabetic models.

Lung tissue samples were collected from the STZ-induced diabetes model and analyzed through histopathological and immunohistochemical techniques. Image data were further processed using convolutional neural networks (CNNs), including pre-trained models, such as ResNet50, VGG16, and SqueezeNet. Classification was conducted in multiple color spaces (RGB, Grayscale, and HSV) and evaluated using performance metrics, including confusion matrix, precision, recall, F1 score, and accuracy.

The use of color significantly enhanced image patch classification performance. Among the models tested, SqueezeNet in the RGB color space demonstrated the highest accuracy, achieving an F1 score of 93.49% ± 0.04 and an accuracy of 93.77% ± 0.04. These results indicated the efficacy of CNN-based classification in detecting lung damage associated with diabetes.

Our findings confirmed that diabetes induces histopathological changes in lung tissue, contributing to fibrosis and potential pulmonary complications. Deep learning-based classification methods, particularly when utilizing color space variations and advanced preprocessing techniques, provide a powerful tool for analyzing diabetic tissue damage and may aid in the development of diagnostic support systems.

The study aims to explore selective potential inhibitors for the homologous BD1/BD2 domains of bromodomain-containing protein 4 (BRD4) and uncover the binding mechanisms between these inhibitors and BD1/BD2. Given BRD4's role as an epigenetic regulator and its potential in treating triple-negative breast cancer (TNBC), overcoming the challenge of domain-specific inhibition due to the structural similarity of BD1 and BD2 is crucial.

For comparison with experimental research, FL-411 was selected as a novel inhibitor for BD1/BD2. The AutoDock vina method was employed to screen potential lead compounds of BD1/BD2 from Traditional Chinese herbal medicines (TCMs) for nervous diseases. Molecular dynamics (MD) simulations were conducted to investigate the interaction mechanisms between BD1/BD2 and potential inhibitors (miltirone/FL-411).

The analysis shows that the inhibitors stabilize the conformation of BD1/BD2 and enhance their hydrophobic and salt-bridge interactions. Notably, atomic interaction studies reveal that the oxygen atom of FL-411 binds with E85 of BD1, while the 1,1-Dimethylcyclohexane group of miltirone binds with H437 of BD2, indicating the selective characteristics of these potential inhibitors.

The study reveals key structural determinants for BD1/BD2 selectivity, addressing a major challenge in BRD4-targeted drug design. MD simulations support the experimental data, validating the screening approach.

Based on conformational characters of FL-411/miltirone and atomic interaction mechanism of BD1/BD2 and inhibitors, the potential inhibitors with a new skeleton and lower binding energy were generated with artificial intelligence drug discovery (AIDD) methods.

Banxia Xiexin Decoction (BXD) has been shown to exert therapeutic effects on Functional dyspepsia (FD). This study aims to investigate the therapeutic mechanisms of BXD in treating FD.

Network pharmacology was employed to explore the potential targets of BXD in the treatment of FD. Immunoinfiltration analysis assessed immune activation in FD, with the XGBoost machine learning algorithm used to predict the feature importance of key targets. Deep learning and molecular docking were employed to assess the interactions between active compounds and key targets. Finally, an FD mouse model was established, and Western blotting, immunofluorescence, immunohistochemistry, and Enzyme-linked immunosorbent assay were conducted to validate the findings.

Through network pharmacology analysis and machine learning predictions, three key active compounds were identified. GO enrichment analysis indicated that the mechanism of BXD primarily involves biological processes related to inflammatory responses. Immunoinfiltration analysis suggested that immune activation in FD may be associated with increased mast cell presence. Seven hub genes were identified through PPI analysis, with STAT3 identified as a key feature in XGBoost predictions of FD. In vivo experiments showed that BXD inhibited p-STAT3, alleviated mast cell infiltration and mucosal barrier damage, and enhanced gastrointestinal motility.

BXD may alleviate mast cell infiltration and mucosal barrier damage in FD by inhibiting the expression of p-STAT3, thereby exerting its therapeutic effects.

Microalgae, with their high photosynthetic efficiency and sustainability, hold promise to produce bioactive compounds, chemicals, cosmetics, and biofuels. This study aims to understand the molecular mechanisms of bioactive compounds from microalgae using integrative bioinformatics approaches to identify their potential therapeutic applications.

Gene expression profiles from the GSE113144 and GSE115827 datasets were retrieved from the GEO database using keywords such as liver disease, microalgae, and bioactive compounds. Different expressed genes (DEGs) were identified using the GEO2R tool. Subsequently, a PPI network was constructed to identify hub genes and key regulatory elements. The findings were further cross-validated using a range of bioinformatics tools, databases, and literature to explore their potential applications in drug development, nutraceuticals, and disease modulation.

Following oxo-fatty acid treatment, 2051 differentially expressed genes (DEGs) were identified, while 399 DEGs were detected after sea spray aerosol treatment, with 39 genes shared between the two treatments. These DEGs were primarily enriched in immune and metabolic processes. Protein-protein interaction analysis revealed ten key hub genes: PBK, CENPA, ASPM, DLGAP5, DEPDC1, SPC25, CDCA3, HJURP, ERCC6L, and KIF18B, which are involved in immune and metabolic responses. Functional enrichment highlighted roles in cholesterol and fatty-acyl-CoA binding, peptidoglycan recognition, metal ion binding, and protease activity. Notably, PBK and CDCA3 are associated with approved drugs, suggesting potential for therapeutic repurposing.

The molecular functions enriched among hub genes, such as cholesterol binding, fatty-acyl-CoA binding, peptidoglycan receptor activity, and metal ion binding, suggest actionable pathways that could be pharmacologically modulated. These targets are highly relevant to diseases such as NAFLD and chronic inflammation. The identification of druggable hub genes and enriched immune-metabolic functions provides a foundation for further preclinical and translational research.

This study offers valuable insights into the molecular mechanisms underlying human immune and metabolic responses to sea spray aerosols and oxo-fatty acids, identifying cellular pathways and processes that are often regulated in human immune and metabolic responses to various microalgae. Overall, this study enhances our understanding of the potential therapeutic applications of microalgae-derived bioactive compounds, offering potential breakthroughs in drug discovery and nutraceutical development.