Exploring the Anticancer and Mechanisms and Therapeutic Targets of Neolinderatin through Network Pharmacology and Molecular Docking

Cancer remains one of the most significant global health challenges and is a leading cause of morbidity and mortality worldwide^1,2. Despite substantial advances in chemotherapy, radiotherapy, targeted therapy, and immunotherapy, the effectiveness of current treatment strategies is often limited by drug resistance, severe adverse effects, and tumor recurrence^25,26. Consequently, the search for safer and more effective therapeutic agents continues to be a major focus of cancer research.

Natural products have long served as valuable sources of anticancer drugs and lead compounds due to their structural diversity and broad spectrum of biological activities³. Numerous plant-derived compounds have demonstrated promising anticancer potential by modulating multiple molecular targets involved in tumor initiation, progression, and metastasis³. Neolinderatin, a naturally occurring phytochemical isolated from medicinal plants, has attracted considerable attention because of its reported pharmacological activities, including antioxidant and anti-inflammatory effects. However, the molecular mechanisms underlying its potential anticancer activity remain poorly understood.

Network pharmacology has emerged as a powerful systems-based approach for elucidating the complex interactions among bioactive compounds, target proteins, genes, and disease pathways^4,5. Unlike the traditional one-drug–one-target paradigm, network pharmacology emphasizes multitarget and multipathway regulation, making it particularly suitable for investigating natural compounds with diverse biological effects. By integrating bioinformatics databases and computational analyses, network pharmacology enables the identification of key therapeutic targets and signaling pathways involved in disease treatment.

Molecular docking is widely employed in computer-aided drug discovery to predict the binding interactions and affinities between small molecules and target proteins^16,29,30. In addition, in silico absorption, distribution, metabolism, excretion, and toxicity (ADMET) analyses provide valuable insights into the pharmacokinetic and safety profiles of candidate compounds^18,19. The integration of network pharmacology, molecular docking, and ADMET evaluation offers a comprehensive strategy for exploring the therapeutic potential of bioactive molecules and accelerating the drug discovery process.

Therefore, the present study aimed to systematically investigate the anticancer mechanisms of Neolinderatin using an integrated computational approach involving network pharmacology, protein–protein interaction (PPI) network construction, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses, molecular docking, and in silico ADMET evaluation^16,18,19. The findings of this study may provide valuable insights into the molecular basis of Neolinderatin's anticancer activity and offer a theoretical foundation for its future development as a potential anticancer agent.

2. MATERIALS AND METHODS

2.1 Identification of Neolinderatin Targets

The chemical structure of Neolinderatin was obtained from the PubChem database. Potential targets of Neolinderatin were predicted using Swiss Target Prediction and related target prediction databases⁶.

2.2 Collection of Cancer-Associated Targets

Cancer-related genes were collected from Gene Cards, OMIM, and DisGeNET databases using the keyword "Cancer"^7,8.

2.3 Identification of Common Targets

The overlapping targets between Neolinderatin-associated genes and cancer-associated genes were identified using Venny/GeneVenn.

2.4 Protein–Protein Interaction Network Construction

The common targets were imported into STRING database to generate a PPI network⁹. The network was visualized and analysed using Cytoscape software¹⁰.

2.5 Hub Gene Analysis

CytoHubba plugin was used to identify the top hub genes according to degree centrality¹¹.

2.6 GO and Pathway Enrichment Analysis

Enricher was employed to perform Gene Ontology (GO) and pathway enrichment analyses¹². GO annotation was performed according to the Gene Ontology database^14,15. While pathway enrichment analysis was conducted based on the KEGG database¹³.

2.7 Molecular Docking Studies

Protein structures were obtained from the Protein Data Bank.

Docking studies were performed using AutoDock Vina¹⁶, and interaction analyses were conducted using Discovery Studio Visualizer¹⁷.

2.8 ADMET Analysis

Swiss ADME and pkCSM servers were used to evaluate physicochemical properties, pharmacokinetics, and toxicity profiles of Neolinderatin^18,19.

3. RESULTS AND DISCUSSION

3.1 Common Target Identification

A total of 110 common targets were identified between Neolinderatin and cancer-associated genes. These overlapping targets suggest the multitarget therapeutic potential of Neolinderatin.

Figure 1. Study workflow of the network pharmacology and molecular docking   approach.

Figure 2. Venn diagram showing 110 overlapping targets between Neolinderatin targets and cancer-related genes.

Figure 3. STRING PPI network of 110 common targets.

Figure 4. Top 10 hub genes identified by CytoHubba.

Figure 5. Gene Ontology (GO) enrichment analysis of the identified hub genes, showing the top enriched Biological Process (BP), Cellular Component (CC), and Molecular Function (MF) categories.

Figure 6. KEGG pathway enrichment analysis of the identified hub genes, highlighting the top significantly enriched signaling and cancer-related pathways.

Figure 7. 2D interaction diagram of Neolinderatin with PTGS2 (5IKR).

Figure 8. 2D interaction diagram of Neolinderatin with MAPK14 (1A9U).

Figure 9. 2D interaction diagram of Neolinderatin with MMP9 (1GKC).

Figure 10. 2D interaction diagram of Neolinderatin with CASP3 (1PAU).

(A) PTGS	(B) MAPK14
(C) HSP90AA	(D) CASP3

Figure 11. Representative 3D receptor–ligand binding poses of Neolinderatin with (A) PTGS2, (B) MAPK14, (C) HSP90AA1, and (D) CASP3.

Figure 12. Swiss ADME bioavailability radar of Neolinderatin.

Table 3. Swiss ADME physicochemical and drug-likeness properties.

Table 4. pkCSM-predicted ADMET properties of Neolinderatin.

These findings indicate an acceptable safety profile despite limitations in solubility and bioavailability.

4. CONCLUSION

This study systematically explored the anticancer potential and underlying molecular mechanisms of Neolinderatin through an integrated network pharmacology, molecular docking, and in silico ADMET approach. A total of 110 common targets associated with Neolinderatin and cancer were identified, and protein–protein interaction network analysis revealed CASP3, PTGS2, MMP9, MAPK14, ERBB2, CDK2, CDK4, CASP9, HSP90AA1, and MMP2 as key hub genes. GO and KEGG enrichment analyses demonstrated that these targets are primarily involved in apoptosis, p53 signaling, IL-17 signaling, inflammatory responses, and cancer-related pathways.

Molecular docking analysis showed strong binding affinities of Neolinderatin toward major hub proteins, particularly PTGS2, MMP9, MAPK14, and CASP3, supporting its potential therapeutic relevance in cancer treatment. Furthermore, Swiss ADME and pkCSM predictions indicated favorable drug-likeness, pharmacokinetic behavior, and acceptable toxicity profiles.

Collectively, these findings suggest that Neolinderatin exerts anticancer effects through multitarget and multipathway modulation and may serve as a promising lead compound for anticancer drug development. Nevertheless, further in vitro and in vivo studies are required to validate the predicted targets, biological activities, and therapeutic efficacy of Neolinderatin

ACKNOWLEDGMENTS

The authors acknowledge the use of Swiss Target Prediction, STRING, Cytoscape, Enrichr, Auto Dock Vina, Discovery Studio Visualizer, SwissADME, and pkCSM databases and software tools.

CONFLICT OF INTEREST

The authors declare no conflict of interest.

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