Network pharmacology is an emerging field that applies systems biology and bioinformatics approaches to understand the complex interactions between drugs and biological systems. This presentation provides an overview of network pharmacology, its principles, and applications in drug discovery and development. Key areas:- 1. Introduction to Network Pharmacology: Definition, history, and significance. 2. Systems Biology and Bioinformatics: Tools and techniques used in network pharmacology. 3. Network Construction and Analysis: Methods for building and analyzing drug-target networks. 4. Applications in Drug Discovery: Identification of new targets, prediction of drug efficacy and toxicity. 5. Case Studies: Examples of successful applications of network pharmacology. Network pharmacology offers a holistic understanding of drug action, enabling the identification of potential side effects and new therapeutic applications. The integration of omics data and computational models facilitates the prediction of drug response and toxicity. Network pharmacology has the potential to revolutionize the field of drug discovery and development by providing a systems-level understanding of drug action. This presentation highlights the key concepts, tools, and applications of network pharmacology, demonstrating its value in modern pharmacology research.

1. From Concept to Application:
A Comprehensive Overview on
Network Pharmacology
Presented by:
Anas Ahmed Mohammed
M.Pharm (Pharmacology)
Roll no: 24451S0101

2. LIST OF CONTENTS
• Introduction
• History and Evolution
• Conceptual Framework
• Methodological Framework
• Databases in Network Pharmacology
• Tools & Computational Techniques
• Research Approaches
• Applications
• Limitations
• Futures Perspectives
• Conclusion
• References

3. INTRODUCTION
• Network pharmacology integrates pharmacology, systems
biology and bioinformatics
• Bridges modern pharmacology with traditional medicine (e.g.,
TCM, Ayurveda)
• Traditional model: “one drug - one target - one disease”
• In contrast: “multi drug – multiple targets – disease networks”
• Complex diseases involve multiple genes, proteins and
pathways
• Supports multi-target therapies, drug repurposing and precision
medicine .
• Need for a systems-level approach to drug discovery

4. HISTORY AND EVOLUTION
• 1999: Traditional
medicine syndromes
linked to molecular
networks
• 2002-07: Integration of
herbal medicine with
network approaches
• 2008-11: Emergence of
network pharmacology
as a drug discovery
paradigm
Fig. 1: Timeline and origin of network
pharmacology

5. CONCEPTUAL FRAMEWORK
1. Multi-Target
Paradigm
• Drugs act on
multiple targets
simultaneously
• Reflects the
complexity of
biological systems
and multifactorial
diseases
2. Biological
Network
Perspective
• Considers the body
as a network of
interactions:
 Drug-target
 Protein –protein
 Gene-disease
• Useful for pathway
mapping
3. Data-Driven
Integration
• Uses
bioinformatics
tools,databases,
andcomputation-
almodels.
• Integrates
experimentaland
computational
approaches

6. METHODOLOGICAL FRAMEWORK
Define Research Scope
Data Collection & Curation
Target Prediction &
Network Construction
Network Analysis
Validation
Functional & Pathway
Enrichment

7. Fig. 3: Compound Target network for YCHD
‑
Fig. 2: The workflow for the network
pharmacology approach

8. Fig 5: Compound Pathway
‑
network for YCHD
Fig 4: Gene Disease network for
‑
YCHD

9. Databases in Network Pharmacology
WEBSITE DESCRIPTION DATABASE
http://tcmspw.com/tcmsp.php
Provides information on TCM herbs,
active compounds, PK (OB, DL) and
predicted targets
TCMSP (Traditional Chinese
Medicine Systems Pharmacology
Database)
https://www.genome.jp/kegg
/
Pathway mapping and analysis of
genes, proteins and metabolites
.
KEGG (Kyoto Encyclopedia of Genes
and Genomes)
http://stitch.embl.de
/ Integrates known and predicted
chemical -protein interactions
STITCH (Search Tool for Interactions
of Chemicals)
http ://string-db.org
Protein - protein interaction (PPI)
database for functional relationship
STRING (Search tool for the
Retrieval of Interacting
genes/proteins)
http://pubchem.ncbi.nlm.nih.gov
/
Open-access chemistry database
with structures, properties, &
bioactivity data
PubChem
http://www
.
ebi.ac.uk/chembl
/
Bioactivity data on small molecules
& drug like compounds for drug
discovery
ChEMBL

10. Tools & Computational Techniques
Network Construction &
Visualization – Cytoscape & Gephi
Computational Modelling –
Molecular Docking (AutoDock)
Systems-level Analysis – Pathway &
Enrichment: KEGG, GO
Advanced Approaches – Machine
learning & Multi-omics integration (Drug-
target predictions)

11. Fig. 6: Study of workflow comprising a network pharmacology stage and a validation
and prediction stage, aimed at elucidating the mechanisms underlying the anti-
diabetic effects of fenugreek.

12. Research Approaches
Target –
Based
Approach
Computational
& AI-Driven
Approach
Pathway -
Based
Approach
Multi –
Omics
Integration
Network –
Based
Approach
Focuses
on specific
proteins or
genes
Combines
genomics
&
proteomics
Identifies
affected
biological
pathways
Used in
drug
repurposing
& prediction
Builds drug
target
disease
networks

13. Applications
• Cancer Research→ Identifies multi-target drugs; explains tumor
signaling pathways and drug resistance mechanisms.
• Neurodegenerative Diseases (Alzheimer’s, Parkinson’s)→
Reveals complex gene–protein interactions; supports drug
repurposing strategies.
• Cardiovascular Disorders→ Maps drug effects on interconnected
metabolic and signaling pathways.
• Metabolic Disorders (Diabetes, Obesity)→ Explains synergistic
effects of multi-component herbal medicines.
• Immunological & Inflammatory Diseases→ Studies immune
regulation, cytokine networks, and novel therapeutic targets.
• Drug Repurposing & Precision Medicine → Facilitates discovery
of new uses for existing drugs and personalized therapies.

14. Limitations
• Data Quality Issues→ Incomplete, inconsistent, or biased datasets
reduce reliability.
• Complexity of Biological Systems→ Difficult to model multi-level
interactions (genes, proteins, metabolites).
• Computational Challenges→ Requires advanced algorithms and
high computing power.
• Experimental Validation Gap→ Many predictions lack in vitro / in
vivo confirmation.
• Integration Limitations→ Omics data integration across platforms
remains a major hurdle.
• Standardization Issues→ Lack of universal guidelines for
databases, models, and validation.

15. Future Perspectives
• Integration with Multi-Omics→ Combine genomics,
transcriptomics, proteomics, and metabolomics for holistic
insights.
• Artificial Intelligence & Machine Learning→ Enhance prediction
accuracy of drug–target interactions and disease networks.
• Big Data & Cloud Computing→ Handle large-scale biological data
with faster processing and accessibility.
• Precision Medicine→ Develop patient-specific drug networks for
personalized therapies.
• Drug Repurposing & Polypharmacology→ Explore new
therapeutic uses of existing drugs.
• Enhanced Validation Models→ Incorporate organ-on-chip, 3D
cultures, and advanced in vivo models for better translation.

16. Conclusion
• Network pharmacology has evolved from traditional single-target
models to multi-target, systems-based approaches.
• It effectively integrates databases, computational tools, and
biological networks to unravel drug–disease mechanisms.
• Applications span across cancer, neurodegeneration, metabolic,
cardiovascular, and immune disorders, bridging modern and
traditional medicine.
• Despite challenges of data quality, validation gaps, and
standardization, ongoing progress in AI, big data, and multi-omics
integration promises significant advancements.
• Ultimately, network pharmacology serves as a transformative
paradigm in drug discovery, paving the way for precision and
personalized medicine.

17. References
1. Hopkins AL. Network pharmacology. Nat Biotechnol. 2007;25(10):1110–1111.
2. Hopkins AL. Network pharmacology: the next paradigm in drug discovery. Nat Chem
Biol. 2008;4(11):682–690.
3. Li S, Zhang B. Traditional Chinese medicine network pharmacology: theory, methodology
and application. Chin J Nat Med. 2013;11(2):110–120.
4. Boezio B, Audouze K, Ducrot P, Taboureau O. Network-based approaches in
pharmacology. Mol Inform. 2017;36(10):1700048.
5. Zhang R, Zhu X, Bai H, Ning K. Network pharmacology databases for traditional
Chinese medicine: review and assessment. Front Pharmacol. 2019;10:123.
6. Nogales C, Mamdouh ZM, List M, et al. Network pharmacology: curing causal
mechanisms instead of treating symptoms. Trends Pharmacol Sci. 2022;43(2):136–150.
7. Fang J, Liu C, Wang Q, Lin P, Cheng F. In silico polypharmacology of natural products.
Brief Bioinform. 2018;19(6):1153–1171.
8. Yu G, Wang W, Wang X, et al. Network pharmacology-based strategy to investigate
pharmacological mechanisms of Zuojinwan. BMC Complement Altern Med.
2018;18(1):289.
9. Luo TT, Lu Y, Yan SK, et al. Network pharmacology in research of Chinese medicine
formula. Chin J Integr Med. 2020;26(1):72–80.
10. Zhang F, Zhai Y, Zhou J, et al. Network pharmacology: a crucial approach in traditional
Chinese medicine research. Chin Med. 2024;19(1):129.

18. THANK YOU