Master Network Pharmacology

Mastering Network Pharmacology: From Target Identification to Data Visualization with Python and Cytoscape

What you'll learn

Shift from "one drug, one target" to systems-based drug discovery.

Understand the importance of network pharmacology in modern research.

Retrieve drug and disease targets using PubChem and SwissTargetPrediction.

Identify common genes and interactions with Venn diagrams and Cytoscape.

Discover hub genes using STRINGdb and CytoHubba.

Analyze gene ontologies (GO) and pathways with DAVID.

Visualize data with Python libraries like Matplotlib, Seaborn, and Pandas.

Build and analyze molecular networks using Cytoscape.

Work on real-world case studies, such as diabetes and the drug acarbose.

Gain hands-on experience with downloadable Python scripts and a PDF guide.

Explore multi-target drug design and drug repurposing strategies.

Apply knowledge to solve complex problems in drug discovery and systems biology.

Requirements

No prior experience needed! This course is designed for absolute beginners.

No programming or pharmacology background is required—you’ll learn everything from scratch.

A computer with internet access is all you need to get started.

Description

Unlock the power of Network Pharmacology with this comprehensive course designed for researchers, pharmacologists, and students. Move beyond the traditional "one drug, one target" approach and embrace a systems-based strategy for drug discovery. Learn key concepts like target identification, hub gene analysis, gene ontologies, and pathway mapping using cutting-edge tools such as Cytoscape, STRINGdb, and DAVID. Gain hands-on experience with Python for data analysis and visualization, including creating bubble plots and processing biological data. This course equips you with the skills to tackle real-world challenges in drug discovery and systems biology. By the end, you’ll have access to a complete PDF guide and Python scripts for future reference, ensuring you can apply these techniques confidently in your research or projects. Whether you’re a beginner or an experienced professional, this course offers the knowledge and tools to advance your career and contribute to the future of pharmacology. Join now and transform your understanding of drug discovery with the power of network pharmacology. Explore real-world case studies, master advanced tools, and gain the confidence to innovate in your field. With step-by-step guidance, practical examples, and downloadable resources, this course is your gateway to mastering the complexities of modern pharmacology. Enroll today and take the first step toward becoming an expert in network pharmacology.

Overview

Section 1: Foundations of Network Pharmacology

Lecture 1 Introduction to Network Pharmacology: A Systems-Based Approach to Drug Discovery

Section 2: Essential Tools for Network Pharmacology

Lecture 2 Tools for Network Pharmacology: Cytoscape, Python, and Data Visualization

Section 3: Practical Workflow in Network Pharmacology

Lecture 3 From Data to Networks: Drug-Disease Interaction Analysis

Section 4: Target Identification in Network Pharmacology

Lecture 4 Drug and Disease Target Analysis: A Case Study on Diabetes

Section 5: Analyzing Drug-Disease Interactions

Lecture 5 Identifying Common Genes and Interactions Using Venn Diagrams and Cytoscape

Section 6: Network Analysis in Pharmacology

Lecture 6 Identifying Hub Genes: Key Players in Biological Networks

Section 7: Functional Analysis in Network Pharmacology

Lecture 7 Gene Ontologies, Pathways, and Network Development

Section 8: Comprehensive Overview of Network Pharmacology

Lecture 8 Summary of Network Pharmacology: From Theory to Practice

Section 9: Practical Coding for Network Pharmacology

Lecture 9 Python Script for Bubble Plots: Visualizing Gene Ontologies and Pathways

Researchers and scientists looking to explore network pharmacology.,Students in pharmacology, bioinformatics, or related fields.,Professionals in drug discovery or systems biology seeking to upgrade their skills.,Beginners curious about drug discovery and data analysis.