Introduction to Bioinformatics in Drug Discovery

Course Overview

Bioinformatics, in the context of genetics and genomics, is a branch of science that leverages computing technology to gather, store, analyze, and share biological data and information, such as sequences of DNA and proteins, along with annotations related to those sequences.
Bioinformatics utilizes knowledge from biology, chemistry, physics, computer science, data science, programming, information engineering, mathematics, and statistics to evaluate and make sense of biological data.

Syllabus

• Course Outline
• 🗓 Module 1: Introduction to Bioinformatics
• What is Bioinformatics?
• Role of Bioinformatics in Pharmacy
• Introduction to Biological Databases (NCBI, PubChem, DrugBank, UniProt)
• FASTA format and sequence types
• ✅ Learning Outcome: Understand the basics of bioinformatics and how to retrieve data from key biological databases.
• 🗓 Module 2: Sequence Alignment & Database Searching
• Introduction to BLAST (Basic Local Alignment Search Tool)
• Types of BLAST (blastn, blastp)
• Sequence alignment (pairwise vs multiple)
• Use cases in pharmacogenomics
• ✅ Learning Outcome: Learn how to use BLAST and interpret sequence similarity results for genes and proteins.
• 🗓 Module 3: Drug Discovery & Molecular Modeling
• Introduction to drug discovery pipeline
• Target identification using bioinformatics
• Ligand-based and structure-based drug design basics
• Introduction to molecular docking tools (PyMOL, AutoDock overview)
• ✅ Learning Outcome: Understand how bioinformatics assists in target identification and drug design.
• 🗓 Module 4: Genomics, Proteomics & Applications
• Introduction to Genomics & Proteomics
• Pharmacogenomics: role in personalized medicine
• Applications of bioinformatics in pharmaceutical industry
• Future of bioinformatics in pharma
• ✅ Learning Outcome: Grasp the real-world pharmaceutical applications of bioinformatics tools.

Prerequisites

Bioinformatics utilizes knowledge from biology, chemistry, physics, computer science, data science, programming, information engineering, mathematics, and statistics to evaluate and make sense of biological data.