Bioinformatics for Drug Discovery: Metabolic Pathway Analysis

Understanding how pathogens interact with host cells is key to developing effective medical treatments. Computational pathway bioinformatics allows researchers to map these complex biological networks and identify vulnerability points for new drugs without relying solely on slow, expensive laboratory trials. In this text-based course, you will transition from a curious beginner to understanding how metabolic pathway analysis is used to discover drug targets for viruses like SARS-CoV-2. You will learn to navigate biological databases, analyze metabolic networks, and evaluate potential therapeutic targets through structured written explanations and step-by-step analytical walkthroughs. What you'll learn: Understand the foundational principles of pathway bioinformatics and metabolic network modeling; Explore major biological databases such as KEGG and Reactome to retrieve pathway data; Analyze host-pathogen interactions to identify critical metabolic bottlenecks; Apply computational methods to evaluate and rank potential drug targets; Practice interpreting genomic and metabolic data using modern data analysis concepts. The course begins with essential terminology, defining metabolic pathways and the basics of viral replication. You will then progress to practical computational techniques, exploring network analysis and database querying, before concluding with a written step-by-step guide to finding targets for viral pathogens. This course is designed for beginners in bioinformatics, life science students, or programming enthusiasts interested in computational biology, with no prior advanced biology or coding experience required. Start reading today to unlock the fundamentals of computational drug discovery.