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Network Pharmacology: An Emerging Field in Drug Discovery

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A special issue of International Journal of Molecular Sciences (ISSN 1422-0067). This special issue belongs to the section "Molecular Pharmacology".

Deadline for manuscript submissions: 20 July 2025 | Viewed by 10622

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Special Issue Editor

Prof. Dr. Weiwei Han

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Guest Editor

Key Laboratory for Molecular Enzymology and Engineering of Ministry of Education, Jilin University, Changchun 130012, China
Interests: the relationship between enzyme structure and function; computer-aided drug design; computational structural biology; machine learning
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Special Issue Information

Dear Colleagues,

Network pharmacology is an emerging interdisciplinary field that integrates systems biology, network analysis, and pharmacology to explore the complex interactions between drugs, targets, and diseases. By analyzing the intricate networks of biological systems, network pharmacology aims to provide a holistic understanding of drug actions and their effects on the body, thereby addressing the multifaceted nature of diseases. This approach has revolutionized traditional drug discovery by shifting the focus from single-target to multi-target strategies, enabling the identification of novel therapeutic agents and the optimization of existing ones.

The purpose of this Special Issue is to provide a comprehensive overview of the state of the art in network pharmacology methodologies and their applications in drug discovery and development. We welcome original research articles, review articles, and short communications on one or more of the following topics:

Development, implementation, and application of network pharmacology databases;
Development and application of new network analysis tools and algorithms;
Integration of multi-omics data in network pharmacology studies;
Construction, visualization, and analysis of drug–target interaction networks;
Identification of novel drug targets and pathways through network pharmacology;
Application of network pharmacology in understanding complex diseases and multi-target drug discovery;
Development and application of computational models for predicting drug efficacy and safety;
Case studies on the successful application of network pharmacology in drug discovery and repurposing.

We hope that this Special Issue will serve as an entry point for newcomers into the exciting world of network pharmacology as well as a valuable reference for more experienced practitioners in the field.

Prof. Dr. Weiwei Han
Guest Editor

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Keywords

network pharmacology
machine learning
databases
drug discovery
computer-aided drug design (CADD)
data mining

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Published Papers (6 papers)

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Research

22 pages, 5195 KiB

Open AccessArticle

Therapeutic Mechanisms of Medicine Food Homology Plants in Alzheimer’s Disease: Insights from Network Pharmacology, Machine Learning, and Molecular Docking

by Shuran Wen, Ye Han, You Li and Dongling Zhan

Int. J. Mol. Sci. 2025, 26(5), 2121; https://doi.org/10.3390/ijms26052121 - 27 Feb 2025

Viewed by 717

Abstract

Alzheimer’s disease (AD) is a progressive neurodegenerative disorder characterized by a gradual decline in cognitive function. Currently, there are no effective treatments for this condition. Medicine food homology plants have gained increasing attention as potential natural treatments for AD because of their nutritional value and therapeutic benefits. In this work, we aimed to provide a deeper understanding of how medicine food homology plants may help alleviate or potentially treat AD by identifying key targets, pathways, and small molecule compounds from 10 medicine food homology plants that play an important role in this process. Using network pharmacology, we identified 623 common targets between AD and the compounds from the selected 10 plants, including crucial proteins such as STAT3, IL6, TNF, and IL1B. Additionally, the small molecules from the selected plants were grouped into four clusters using hierarchical clustering. The ConPlex algorithm was then applied to predict the binding capabilities of these small molecules to the key protein targets. Cluster 3 showed superior predicted binding capabilities to STAT3, TNF, and IL1B, which was further validated by molecular docking. Scaffold analysis of small molecules in Cluster 3 revealed that those with a steroid-like core—comprising three fused six-membered rings and one five-membered ring with a carbon–carbon double bond—exhibited better predicted binding affinities and were potential triple-target inhibitors. Among them, MOL005439, MOL000953, and MOL005438 were identified as the top-performing compounds. This study highlights the potential of medicine food homology plants as a source of active compounds that could be developed into new drugs for AD treatment. However, further pharmacokinetic studies are essential to assess their efficacy and minimize side effects. Full article

(This article belongs to the Special Issue Network Pharmacology: An Emerging Field in Drug Discovery)

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18 pages, 61277 KiB

Open AccessArticle

Network Pharmacology and Bioinformatics Study of Six Medicinal Food Homologous Plants Against Colorectal Cancer

by Xinyue Zhao, Jian Xiu, Hengzheng Yang, Weiwei Han and Yue Jin

Int. J. Mol. Sci. 2025, 26(3), 930; https://doi.org/10.3390/ijms26030930 - 23 Jan 2025

Viewed by 1793

Abstract

Integrating network pharmacological analysis and bioinformatic techniques, this study systematically investigated the molecular mechanisms of six medicinal food homologous plants (Astragalus membranaceus, Ganoderma lucidum, Dioscorea opposite, Curcuma longa, Glycyrrhiza uralensis, and Pueraria lobata) against colorectal cancer. Through screening the TCMSP database, 303 active compounds and 453 drug targets were identified. By integrating differential expression gene analysis with WGCNA on the GSE41258 dataset from the GEO database, 49 potential therapeutic targets were identified. GO and KEGG enrichment analyses demonstrated that these targets are primarily involved in drug response, fatty acid metabolism, and key cancer-related pathways. Cross-validation using three machine learning algorithms—LASSO regression, SVM-RFE, and Random Forest—pinpointed four critical target genes: CA1, CCND1, CXCL2, and EIF6. Further, CIBERSORT immune infiltration analysis revealed strong associations between these core genes and the tumor immune microenvironment in colorectal cancer patients, notably in modulating M0 macrophage infiltration and mast cell activity. Molecular docking analyses confirmed robust binding interactions between active compounds and core target proteins. This study systematically elucidated the molecular mechanisms of six medicinal food homologous plants against colorectal cancer, providing scientific evidence for their rational clinical application. Full article

(This article belongs to the Special Issue Network Pharmacology: An Emerging Field in Drug Discovery)

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20 pages, 15648 KiB

Open AccessArticle

Unveiling the Anti-Obesity Potential of Thunder God Vine: Network Pharmacology and Computational Insights into Celastrol-like Molecules

by Siyun Zheng, Hengzheng Yang, Jingxian Zheng, Yidan Wang, Bo Jia and Wannan Li

Int. J. Mol. Sci. 2024, 25(23), 12501; https://doi.org/10.3390/ijms252312501 - 21 Nov 2024

Cited by 1 | Viewed by 1215

Abstract

Obesity, characterized by abnormal or excessive fat accumulation, has become a chronic degenerative health condition that poses significant threats to overall well-being. Pharmacological intervention stands at the forefront of strategies to combat this issue. Recent studies, notably by Umut Ozcan’s team, have uncovered the remarkable potential of Celastrol, a small-molecule compound derived from the traditional Chinese herb thunder god vine (Tripterygium wilfordii) as an anti-obesity agent. In this research, computational chemical analysis was employed, incorporating the “TriDimensional Hierarchical Fingerprint Clustering with Tanimoto Representative Selection (3DHFC-TRS)” algorithm to systematically explore 139 active small molecules from thunder god vine. These compounds were classified into six categories, with a particular focus on Category 1 molecules for their exceptional binding affinity to obesity-related targets, offering new avenues for therapeutic development. Using advanced molecular docking techniques and Cytoscape prediction models, six representative Celastrol-like molecules were identified, namely 3-Epikatonic Acid, Hederagenin, Triptonide, Triptotriterpenic Acid B, Triptotriterpenic Acid C, and Ursolic Acid. These compounds demonstrated superior binding affinity and specificity toward two key obesity targets, PPARG and PTGS2, suggesting their potential to regulate fat metabolism and mitigate inflammatory responses. To further substantiate these findings, molecular dynamics simulations and MM-PBSA free-energy calculations were applied to analyze the dynamic interactions between these small molecules and the enzymatic active sites of their targets. The results provide robust theoretical evidence that support the feasibility of these molecules as promising candidates for anti-obesity therapies. This study underscores the power of the 3DHFC-TRS algorithm in uncovering bioactive compounds from natural sources, such as thunder god vine, and highlights the therapeutic promise of PPARG and PTGS2 as novel obesity-related targets. Furthermore, it emphasizes the essential role of computational science in expediting drug discovery, paving the way for personalized and precision-based treatments for obesity and heralding a future of more effective healthcare solutions. Full article

(This article belongs to the Special Issue Network Pharmacology: An Emerging Field in Drug Discovery)

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22 pages, 7373 KiB

Open AccessArticle

Insights into the Therapeutic Potential of Active Ingredients of Citri Reticulatae Pericarpium in Combatting Sarcopenia: An In Silico Approach

by Amin Ullah, Yacong Bo, Jiangtao Li, Jinjie Li, Pipasha Khatun, Quanjun Lyu and Guangning Kou

Int. J. Mol. Sci. 2024, 25(21), 11451; https://doi.org/10.3390/ijms252111451 - 25 Oct 2024

Viewed by 1453

Abstract

Sarcopenia is a systemic medical disorder characterized by a gradual decline in muscular strength, function, and skeletal muscle mass. Currently, there is no medication specifically approved for the treatment of this condition. Therefore, the identification of new pharmacological targets may offer opportunities for the development of novel therapeutic strategies. The current in silico study investigated the active ingredients and the mode of action of Citri Reticulatae Pericarpium (CRP) in addressing sarcopenia. The active ingredients of CRP and the potential targets of CRP and sarcopenia were determined using various databases. The STRING platform was utilized to construct a protein–protein interaction network, and the key intersecting targets were enriched through the Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) analyses. Molecular docking was used to determine the binding interactions of the active ingredients with the hub targets. The binding affinities obtained from molecular docking were subsequently validated through molecular dynamics simulation analyses. Five active ingredients and 45 key intersecting targets between CRP and sarcopenia were identified. AKT1, IL6, TP53, MMP9, ESR1, NFKB1, MTOR, IGF1R, ALB, and NFE2L2 were identified as the hub targets with the highest degree node in the protein–protein interaction network. The results indicated that the targets were mainly enriched in PIK3-AKT, HIF-1, and longevity-regulating pathways. The active ingredients showed a greater interaction affinity with the hub targets, as indicated by the results of molecular docking and molecular dynamics simulations. Our findings suggest that the active ingredients of Citri Reticulatae Pericarpium, particularly Sitosterol and Hesperetin, have the potential to improve sarcopenia by interacting with AKT1 and MTOR proteins through the PI3K-AKT signaling pathway. Full article

(This article belongs to the Special Issue Network Pharmacology: An Emerging Field in Drug Discovery)

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16 pages, 4414 KiB

Open AccessArticle

Network Pharmacology Revealing the Therapeutic Potential of Bioactive Components of Triphala and Their Molecular Mechanisms against Obesity

by Ratchanon Inpan, Chotiwit Sakuludomkan, Mingkwan Na Takuathung and Nut Koonrungsesomboon

Int. J. Mol. Sci. 2024, 25(19), 10755; https://doi.org/10.3390/ijms251910755 - 6 Oct 2024

Cited by 1 | Viewed by 2175

Abstract

Obesity, characterized by the excessive accumulation of fat, is a prevalent metabolic disorder that poses a significant global health concern. Triphala, an herbal combination consisting of Phyllanthus emblica Linn, Terminalia chebula Retz, and Terminalia bellerica (Gaertn) Roxb, has emerged as a potential solution for addressing concerns related to obesity. This study aimed to investigate the network pharmacology and molecular docking of Triphala to identify its bioactive ingredients and their interactions with pathways associated with obesity. The bioactive compounds present in Triphala and genes linked to obesity were identified, followed by an analysis of the protein-protein interaction networks. Enrichment analysis, including Gene Ontology analysis and Kyoto Encyclopedia of Genes and Genomes pathway analysis, was conducted. Prominent genes and compounds were selected for further investigation through molecular docking studies. The study revealed a close correlation between obesity and the AKT1 and PPARG genes. The observed binding energy between beta-sitosterol, 7-dehydrosigmasterol, peraksine, α-amyrin, luteolin, quercetin, kaempferol, ellagic acid, and phyllanthin with AKT1 and PPARG indicated a favorable binding affinity. In conclusion, nine compounds showed promise in regulating these genes for obesity prevention and management. Further research is required to validate their specific effects. Full article

(This article belongs to the Special Issue Network Pharmacology: An Emerging Field in Drug Discovery)

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11 pages, 3207 KiB

Open AccessArticle

Systemic Immune Factors and Risk of Allergic Contact Dermatitis: A Bidirectional Mendelian Randomization Study

by Yingxin Long, Wenzhang Dai, Kexin Cai, Yuan Xiao, Anqi Luo, Ziwei Lai, Junlin Wang, Lipeng Xu and Hong Nie

Int. J. Mol. Sci. 2024, 25(19), 10436; https://doi.org/10.3390/ijms251910436 - 27 Sep 2024

Viewed by 1706

Abstract

Skin inflammation and immune regulation have been suggested to be associated with allergic contact dermatitis (ACD) progression, but whether the system’s immune regulation is a cause or a potential mechanism is still unknown. This study aims to assess the upstream and downstream of systemic immune factors on ACD within a bidirectional Mendelian-randomization design. A bidirectional two-sample MR analysis was employed to implement the results from genome-wide association studies for 52 system immune factors and ACD. Genetic associations with systemic immune factors and ACD were obtained from the IEU Open GWAS project database. The inverse-variance weighted (IVW) method was adopted as the primary MR analysis, MR-Egger, weighted median, MR-pleiotropy residual sum, and outlier (MR-PRESSO) was also used as the sensitivity analyses. Only Tumor necrosis factor ligand superfamily member 11 (TNFS11) from among 52 systemic immune factors was associated with a protective effect of ACD. However, ACD was associated with a decrease in Interleukin-9 (IL9) and an increase in C-X-C motif chemokine 1 (GROα), Tumor necrosis factor ligand superfamily member 10 (TRAIL), C4, and complement factor B of the assessed systemic immune factors. This study identified TNFS11 as the upstream regulator and IL9, GROα, TRAIL, C4, and complement factor B as the downstream regulator of ACD, providing opportunities for new therapeutic exploitation of ACD. Nonetheless, these associations of systemic immune factors need to be verified in vivo. Full article

(This article belongs to the Special Issue Network Pharmacology: An Emerging Field in Drug Discovery)

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