CORTEXA
← Browse
crossrefInternational Journal of Molecular Sciences2023-11-03Cited by 12

Network Pharmacology Combined with Machine Learning to Reveal the Action Mechanism of Licochalcone Intervention in Liver Cancer

Fangfang Guo, Xiaotang Yang, Chengxiang Hu, Wannan Li, Weiwei Han

There are reports indicating that licochalcones can inhibit the proliferation, migration, and invasion of cancer cells by promoting the expression of autophagy-related proteins, inhibiting the expression of cell cycle proteins and angiogenic factors, and regulating autophagy and apoptosis. This study aims to reveal the potential mechanisms of licochalcone A (LCA), licochalcone B (LCB), licochalcone C (LCC), licochalcone D (LCD), licochalcone E (LCE), licochalcone F (LCF), and licochalcone G (LCG) inhibition in liver cancer through computer-aided screening strategies. By using machine learning clustering analysis to search for other structurally similar components in licorice, quantitative calculations were conducted to collect the structural commonalities of these components related to liver cancer and to identify key residues involved in the interactions between small molecules and key target proteins. Our research results show that the seven licochalcones molecules interfere with the cancer signaling pathway via the NF-κB signaling pathway, PDL1 expression and PD1 checkpoint pathway in cancer, and others. Glypallichalcone, Echinatin, and 3,4,3′,4′-Tetrahydroxy-2-methoxychalcone in licorice also have similar structures to the seven licochalcones, which may indicate their similar effects. We also identified the key residues (including ASN364, GLY365, TRP366, and TYR485) involved in the interactions between ten flavonoids and the key target protein (nitric oxide synthase 2). In summary, we provide valuable insights into the molecular mechanisms of the anticancer effects of licorice flavonoids, providing new ideas for the design of small molecules for liver cancer drugs.

View free PDFSource page

Related papers

crossrefInternational Journal of Molecular Sciences2025-12-12

Computational Insights into the Molecular Mechanisms of Coptis chinensis Franch. in Treating Chronic Atrophic Gastritis: An Integrated Network Pharmacology, Machine Learning, and Molecular Dynamics Study

Chengxiang Hu, Yang Liu, Yiyao Ding, Yue Jin, Weiwei Han

Chronic atrophic gastritis (CAG) is a precancerous gastric condition with limited therapeutic interventions, and the mechanisms underlying the benefits of Coptis chinensis Franch. (CCF) remain insufficiently defined. This study employed an integrated computational strategy to cla…

View free PDFSource page
crossrefInternational Journal of Molecular Sciences2025-11-26

Multi-Pathway Mechanisms of Engeletin in Ischemic Stroke: A Comprehensive Study Based on Network Pharmacology, Machine Learning, and Immune Infiltration Analysis

Huiming Xue, Yuchen Wen, Jiahui Yang, Yue Zhang, Chang Jin, Bing Li, et al.

Ischemic stroke (IS) is a leading cause of mortality and long-term disability, underpinned by complex molecular mechanisms, such as oxidative stress, neuroinflammation, and apoptosis. The flavonoid Engeletin exhibits promising neuroprotective properties, but its mechanism of acti…

View free PDFSource page
crossrefInternational Journal of Molecular Sciences2026-05-10

An Integrated Machine-Learning and Reverse Network-Pharmacology Pipeline Reveals JUN/C3 Candidate Biomarkers and an Anti-Fibrotic Mechanism of Resveratrol via MAPK/JNK Signaling in Chronic Kidney Disease

Yuan Cai, Xiaolong Feng, Xinru Tao, Penghui Li, Jiaqin Liu, Ping’an Liu, et al.

Chronic kidney disease (CKD) lacks highly specific early diagnostic biomarkers and safe, effective therapeutic options. To address this, we integrated multi-cohort transcriptomics, bioinformatics, and machine learning with reverse network pharmacology, molecular docking, molecula…

View free PDFSource page
crossrefInternational Journal of Molecular Sciences2026-07-04

Potential Molecular Associations Between Triphenyl Phosphate Exposure and Thyroid Cancer: Integration of Network Toxicology and Machine Learning for Core Target Identification with Molecular Docking

Yongling Pei, Junxi Liu, Zixin Liu, Meng Xiao, Bohou Xia, Yamei Li

Triphenyl phosphate (TPhP) is a ubiquitous environmental contaminant and endocrine disruptor potentially associated with an increased risk of thyroid cancer (TC). However, whether TPhP directly contributes to TC remains unclear. This study integrated network toxicology and machin…

View free PDFSource page
crossrefInternational Journal of Molecular Sciences2025-12-25

Integrating Network Pharmacology, Machine Learning, and Experimental Validation to Elucidate the Mechanism of Cardamonin in Treating Idiopathic Pulmonary Fibrosis

Wenyue Zhang, Yi Guo, Qiushi Wang, Kai Wang, Huning Zhang, Sirong Chang, et al.

Idiopathic pulmonary fibrosis (IPF) is a chronic and irreversible interstitial lung disease characterized by progressive scarring of the lungs. The available therapeutic strategies are limited and primarily focus on slowing disease progression rather than achieving fibrosis rever…

View free PDFSource page
crossrefInternational Journal of Molecular Sciences2025-11-21Cited by 1

Elucidating the Mechanisms of Chrysanthemum Action on Atopic Dermatitis via Network Pharmacology and Machine Learning

Shiying Li, Yongxin Jiang, Chengxiang Hu, Yiyao Ding, Xueqi Fu, Shu Xing, et al.

Chrysanthemum (Chrysanthemum morifolium Ramat.) has been recognized as both a food and medicinal substance in China since 2002 and possesses antioxidant, anti-inflammatory, antibacterial, and immunomodulatory activities. Previous studies suggest that Chrysanthemum may alleviate s…

View free PDFSource page