FAQs
1. Is network pharmacology a type of bioinformatics?
Network pharmacology is an interdisciplinary field that uses bioinformatics, but it’s not a subfield of it. Bioinformatics provides the computational tools and methods for analyzing biological data and building networks. Network pharmacology uses these tools, along with principles from pharmacology and systems biology, and applies that network-based knowledge to drug discovery and therapeutic strategies.
2. How does network pharmacology help reduce drug side effects?
Network pharmacology allows researchers to map a drug’s interactions across a whole network. This enables them to identify potential unintended targets of the drug that might cause adverse effects. Researchers can then either modify the drug or choose a different one with a more favorable network profile.
3. How can network pharmacology be used to study drug resistance?
Network pharmacology can be used to model the redundancy and alternative pathways within a biological system. This reveals how disease networks adapt to evade drugs, which helps to identify potential multi-target treatments or combinations to prevent or overcome resistance.
Reference
1. Hopkins, A. L. (2008). Network pharmacology: the next paradigm in drug discovery. Nature chemical biology, 4(11), 682-690.
2. Reddy, A. S., & Zhang, S. (2013). Polypharmacology: drug discovery for the future. Expert review of clinical pharmacology, 6(1), 41-47.
3. Grennan, K. S., Chen, C., Gershon, E. S., et al. (2014). Molecular network analysis enhances understanding of the biology of mental disorders. Bioessays, 36(6), 606-616.
4. Hossain, M. S., & Hussain, M. H. (2025). Multi‐Target Drug Design in Alzheimer’s Disease Treatment: Emerging Technologies, Advantages, Challenges, and Limitations. Pharmacology Research & Perspectives, 13(4), e70131.
5. Lehár, J., Krueger, A. S., Avery, W., et al. (2009). Synergistic drug combinations tend to improve therapeutically relevant selectivity. Nature biotechnology, 27(7), 659-666.
6. Parvathaneni, V., Kulkarni, N. S., Muth, A., et al. (2019). Drug repurposing: a promising tool to accelerate the drug discovery process. Drug discovery today, 24(10), 2076-2085.
7. Li, L., Yang, L., Yang, L., et al. (2023). Network pharmacology: a bright guiding light on the way to explore the personalized precise medication of traditional Chinese medicine. Chinese medicine, 18(1), 146.
8. Koh, G. C., Porras, P., Aranda, B., et al. (2012). Analyzing protein–protein interaction networks. Journal of proteome research, 11(4), 2014-2031.
9. Stitt, M., Sulpice, R., & Keurentjes, J. (2010). Metabolic Networks: How to Identify Key Components in the Regulation of Metabolism and Growth. Plant Physiology, 152(2), 428.
10. Levine, M., & Davidson, E. H. (2005). Gene regulatory networks for development. Proceedings of the National Academy of Sciences, 102(14), 4936-4942.
11. Lei, X., Zhang, M., Guan, B., et al. (2021). Identification of hub genes associated with prognosis, diagnosis, immune infiltration and therapeutic drug in liver cancer by integrated analysis. Human genomics, 15(1), 39.
12. Nogales, C., Mamdouh, Z. M., List, M., et al. (2022). Network pharmacology: curing causal mechanisms instead of treating symptoms. Trends in pharmacological sciences, 43(2), 136-150.
13. Liu, Q., Chen, Z., Wang, B., et al. (2025). Leveraging Network Target Theory for Efficient Prediction of Drug‐Disease Interactions: A Transfer Learning Approach. Advanced Science, 12(11), 2409130.
14. Chen, S., Jiang, H., Cao, Y., et al. (2016). Drug target identification using network analysis: taking active components in Sini decoction as an example. Scientific reports, 6(1), 24245.
15. Huang, W., Liu, C., Xie, L., et al. (2019). Integrated network pharmacology and targeted metabolomics to reveal the mechanism of nephrotoxicity of triptolide. Toxicology research, 8(6), 850-861.
16. Chandran, U., Mehendale, N., Patil, S., et al. (2016). Network pharmacology. Innovative approaches in drug discovery, 127.
17. Lin, C. Y., Chin, C. H., Wu, H. H., et al. (2008). Hubba: hub objects analyzer—a framework of interactome hubs identification for network biology. Nucleic acids research, 36(suppl_2), W438-W443.
18. Yan, C., Zhang, Z., Bao, S., et al. (2020). Computational methods and applications for identifying disease-associated lncRNAs as potential biomarkers and therapeutic targets. Molecular Therapy Nucleic Acids, 21, 156-171.
19. Serrano, D. R., Luciano, F. C., Anaya, B. J., et al. (2024). Artificial Intelligence (AI) Applications in Drug Discovery and Drug Delivery: Revolutionizing Personalized Medicine. Pharmaceutics, 16(10), 1328.
20. Alexeyenko, A., Lee, W., Pernemalm, M., et al. (2012). Network enrichment analysis: extension of gene-set enrichment analysis to gene networks. BMC bioinformatics, 13(1), 226.
21. Wu, Z., Li, W., Liu, G., et al. (2018). Network-based methods for prediction of drug-target interactions. Frontiers in pharmacology, 9, 1134.
22. Li, H., Zhao, L., Zhang, B., et al. (2014). A network pharmacology approach to determine active compounds and action mechanisms of ge‐gen‐qin‐lian decoction for treatment of type 2 diabetes. Evidence‐based Complementary and Alternative Medicine, 2014(1), 495840.
23. Morgan, S. J., Elangbam, C. S., Berens, S., et al. (2013). Use of animal models of human disease for nonclinical safety assessment of novel pharmaceuticals. Toxicologic pathology, 41(3), 508-518.
24. Brody, T. (2016). Clinical trials: study design, endpoints and biomarkers, drug safety, and FDA and ICH guidelines. Academic press.
25. Tang, J., & Aittokallio, T. (2014). Network Pharmacology Strategies Toward Multi-Target Anticancer Therapies: From Computational Models to Experimental Design Principles. Current Pharmaceutical Design, 20(1), 20.
26. Chen, H., Zhu, Z., Zhu, Y., et al. (2015). Pathway mapping and development of disease‐specific biomarkers: protein‐based network biomarkers. Journal of Cellular and Molecular medicine, 19(2), 297-314.
27. Zhang, W., Chen, Y., Jiang, H., et al. (2020). Integrated strategy for accurately screening biomarkers based on metabolomics coupled with network pharmacology. Talanta, 211, 120710.
28. Kidd, B. A., Readhead, B. P., Eden, C., et al. (2015). Integrative network modeling approaches to personalized cancer medicine. Personalized medicine, 12(3), 245-257.
29. Li, L., Yang, L., Yang, L., et al. (2023). Network pharmacology: a bright guiding light on the way to explore the personalized precise medication of traditional Chinese medicine. Chinese medicine, 18(1), 146.
30. Gokulakrishnan, D., & Venkataraman, S. (2024). Ensuring data integrity: Best practices and strategies in pharmaceutical industry, Intelligent Pharmacy, 3(4), 296-303.
31. Bloomingdale, P., Nguyen, V. A., Niu, J., et al. (2018). Boolean network modeling in systems pharmacology. Journal of pharmacokinetics and pharmacodynamics, 45(1), 159-180.
32. Patel, A., Parab, S., Gupta, S., et al. (2025). Pharmaceutical Inhalation Compounds Development by Using In Silico Modeling Tools. In Applications of Computational Tools in Drug Design and Development (pp. 279-309). Singapore: Springer Nature Singapore.
33. Khozyainova, A. A., Valyaeva, A. A., Arbatsky, M. S., et al. (2023). Complex analysis of single-cell RNA sequencing data. Biochemistry (Moscow), 88(2), 231-252.
34. Duran-Frigola, M., Mosca, R., & Aloy, P. (2013). Structural systems pharmacology: the role of 3D structures in next-generation drug development. Chemistry & biology, 20(5), 674-684.
35. Proietti, M., Ragno, A., Rosa, B. L., et al. (2024). Explainable AI in drug discovery: self-interpretable graph neural network for molecular property prediction using concept whitening. Machine Learning, 113(4), 2013-2044.
36. Madabushi, R., Seo, P., Zhao, L., et al. (2022). Role of model-informed drug development approaches in the lifecycle of drug development and regulatory decision-making. Pharmaceutical Research, 39(8), 1669.