Computational Investigation of Cinnamon Phytochemicals Targeting Key Cancer Signaling Pathways: Molecular Docking, ADMET and Molecular Dynamics Simulations Analysis

In silico Computational Study of Cinnamon ( Cinnamomum verum ) Bioactive Compounds as Cancer-Immunotherapy Drug Candidate Targeting RAS-JAK-ERK Signaling Pathway to Inhibit the Programmed Death Protein Ligand 1 (PD-L1) Expression

Exploration of Compounds Identified from Amomum cardamomum Seed Extract: LC-MS Profiling, Network Pharmacology, and Computational Analyses for Innovative Multi-Targeted Breast Cancer Therapy

Background Breast cancer remains one of the leading causes of cancer-related mortality worldwide, and the emergence of drug resistance, systemic toxicity, and limited efficacy of current therapies highlight the need for safer and more effective treatment. Natural products have emerged as promising sources of multi-target anticancer agents. A. cardamomum has demonstrated preliminary anticancer potential, yet the bioactive constituents and their molecular mechanisms in breast cancer remain poorly elucidated. Methods This study integrated in silico approaches to investigate the therapeutic potential of A. cardamomum seed extract against breast cancer. LC–MS analysis identified phytochemical compounds, followed by network pharmacology to determine their potential targets and molecular pathways. Pharmacokinetic and toxicity predictions were assessed through ADMET and Lipinski’s rule of five analyses to evaluate drug-likeness and safety. Molecular docking and molecular dynamics (MD) simulations were conducted to evaluate binding affinity and structural stability of compounds with key oncogenic proteins. Results LC-MS profiling identified 22 distinct compounds in A. cardamomum seeds. ADMET and Lipinski analyses demonstrated that most compounds possessed high gastrointestinal absorption, favorable oral bioavailability, and low toxicity risk. Network pharmacology highlighting SRC, TNF-α, Caspase-3, and EGFR as central nodes in the protein-protein interaction network. Molecular docking identified compounds C17 and C20 as the most promising bioactives, showing strong binding affinities and interactions similar to control ligands. MD simulations confirmed their stable complexes, indicating conformational stability and robust ligand–protein interactions. Conclusion This study highlights the promising multi-target anticancer potential of A. cardamomum seeds. Compounds C17 and C20 were identified as lead candidates with strong and stable interactions with key breast cancer-related proteins and favorable pharmacokinetic properties. These results suggest that A. cardamomum could serve as a potential source for developing new plant-based therapies against breast cancer. Further in vitro and in vivo investigations are warranted to validate their efficacy and safety.

Network pharmacology, molecular docking, and dynamics simulation reveal multi-target mechanisms of Mesua ferrea linn phytochemicals against cervical cancer.

Cervical cancer represents the second most prevalent malignancy among women in India and is etiologically linked to persistent infection with high-risk human papillomavirus (HPV) strains. Mesua ferrea Linn, a medicinal tree species distributed across the Eastern Himalayan region, Eastern Indian states, and the Western Ghats, has demonstrated anticancer potential in several preclinical investigations; however, its mechanistic basis in cervical cancer remains insufficiently elucidated. In this study, a network pharmacology-based workflow was employed to identify the phytochemicals of Mesua ferrea, predict key targets, analyse protein-protein interactions and construct a compound-target pathway network along with network enrichment analysis. ADMET analysis, molecular docking, and molecular dynamics simulation were used to assess the stability of key compounds relative to potential targets. 16 phytochemicals, based on drug-likeness criteria, were identified, yielding 60 common targets, including AKT1, KDR, PIK3CA, MTOR, and EGFR, which are involved in Ras, PI3K-Akt, Jak-Stat, and HPV-associated pathways. Molecular docking and molecular dynamics simulations verified the stable binding of key phytochemicals to core cervical cancer-associated proteins. Collectively, these findings indicate that Mesua ferrea Linn exerts multi-target, multi-pathway regulatory effects in cervical cancer, supporting its potential as a source of phytotherapeutic agents. The study provides a mechanistic foundation for future in vitro and in vivo validation, contributing to the rational development of plant-derived interventions for cervical cancer management.

Elucidating the Therapeutic Mechanism of Puhuaiyin in Rosacea: An Integrated Study Based on Network Pharmacology, Bioinformatics, Molecular Docking, and Molecular Dynamics Simulations.

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.

Computational Predicted Mechanism of Tiliroside Compound Against Triple-Negative Breast Cancer Using Network Pharmacology and Induced-Fit Docking Approach

BACKGROUND: Triple-negative breast cancer (TNBC) progression is driven by dysregulation of multiple interconnected signaling pathways rather than a single molecular abnormality, but current treatment strategies TNBC primarily rely on single-target therapy, highlighting the need for therapeutic candidates that may target multiple signaling pathway of TBNC. Tiliroside, a naturally occurring flavonoid glycoside, has demonstrated promising anticancer activity; however, its predicted effects on TNBC-associated oncogenic signaling networks remain poorly understood. Therefore, this study aimed to investigate the predicted molecular mechanisms by which tiliroside may modulate TNBC-associated oncogenic signaling networks.METHODS: Integrated computational approach combining network pharmacology, comparative molecular docking, and induced-fit docking was employed. Potential targets of tiliroside were predicted using SwissTargetPrediction and intersected with TNBC-associated genes retrieved from GeneCards. Protein–protein interaction and KEGG pathway enrichment analyses were performed to identify key molecular targets and signaling pathways. Comparative molecular docking was subsequently conducted using 19 structurally related flavonoids with reported anticancer activities against the identified hub proteins, followed by induced-fit docking to characterize the binding mechanism of tiliroside.RESULTS: Fifteen overlapping targets were identified between tiliroside and TNBC. Network analysis highlighted Akt serine/threonine kinase 1 (AKT1), sarcoma (SRC), and epidermal growth factor receptor (EGFR) as the principal hub genes, while the KEGG enrichment revealed significant involvement of phosphoinositide 3-kinase (PI3K)–AKT, erythroblastic leukemia viral oncogene B (ErbB), EGFR tyrosine kinase inhibitor resistance, focal adhesion, and vascular endothelial growth factor (VEGF) signaling pathways. Tiliroside consistently exhibited one of the most favorable binding profiles among the evaluated flavonoids, with binding energies of −9.41, −8.62, and −8.25 kcal/mol toward AKT1, SRC, and EGFR, respectively. Induced-fit docking further confirmed stable hydrogen-bond and hydrophobic interactions within the active sites of these proteins.CONCLUSION: Tiliroside exerts potential anti-TNBC activity through multitarget modulation of interconnected oncogenic signaling pathways, suggesting that tiliroside might be a promising lead compound for TNBC.KEYWORDS: tiliroside, triple-negative breast cancer, network pharmacology, induced-fit docking

Deciphering the multi-target anticancer potential of β-sitosterol in breast cancer through integrated computational approaches

Breast cancer is a complex disease comprising multiple deregulated signaling pathways, oxidative stress, metabolic rewiring, and resistance to therapy. The multi-target therapeutic efficacy of β-sitosterol against breast cancer was studied using an integrated approach that combined network pharmacology, molecular docking, molecular dynamics simulations, and ADMET. Out of which 98 common targets were identified between β-sitosterol and breast cancer, wherein PPARG, TNF, ABL kinase, HIF1A, ESR1, PGR, PPARA, MAPK8, AR, and ESR2 are the key hub genes. The enrichment analysis showed that β-Sitosterol had strong binding affinities towards ABL kinase (-9.7 kcal/mol), PPARA (-9.5 kcal/mol), MAPK8 (-8.7 kcal/mol), and PPARG (-8.6 kcal/mol). Indeed, molecular dynamics simulations were performed for 1000 ns at the molecular level, and the progesterone receptor (PGR) proved to be the most dynamically stable target, as the β-sitosterol-PGR complex remained stable throughout the simulation. The predicted ADMET profile was good. The results suggest that β-sitosterol acts on multiple targets in breast cancer and identify PGR, a target not strongly favored by docking alone, as an important therapeutic target revealed through extended molecular dynamics simulation.